A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

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  • Published: 09 June 2023
  • Volume 21 , pages 527–570, ( 2023 )

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  • Miguel Alves Gomes   ORCID: orcid.org/0000-0003-3664-0360 1 &
  • Tobias Meisen   ORCID: orcid.org/0000-0002-1969-559X 1  

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The importance of customer-oriented marketing has increased for companies in recent decades. With the advent of one-customer strategies, especially in e-commerce, traditional mass marketing in this area is becoming increasingly obsolete as customer-specific targeting becomes realizable. Such a strategy makes it essential to develop an underlying understanding of the interests and motivations of the individual customer. One method frequently used for this purpose is segmentation, which has evolved steadily in recent years. The aim of this paper is to provide a structured overview of the different segmentation methods and their current state of the art. For this purpose, we conducted an extensive literature search in which 105 publications between the years 2000 and 2022 were identified that deal with the analysis of customer behavior using segmentation methods. Based on this paper corpus, we provide a comprehensive review of the used methods. In addition, we examine the applied methods for temporal trends and for their applicability to different data set dimensionalities. Based on this paper corpus, we identified a four-phase process consisting of information (data) collection, customer representation, customer analysis via segmentation and customer targeting. With respect to customer representation and customer analysis by segmentation, we provide a comprehensive overview of the methods used in these process steps. We also take a look at temporal trends and the applicability to different dataset dimensionalities. In summary, customer representation is mainly solved by manual feature selection or RFM analysis. The most commonly used segmentation method is k-means, regardless of the use case and the amount of data. It is interesting to note that it has been widely used in recent years.

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1 Introduction

“ As the Internet emerges as a new marketing channel, analyzing and understanding the needs and expectations of their online users or customers are considered as prerequisites to activate the consumer-oriented electronic commerce. Thus, the mass marketing strategy cannot satisfy the needs and expectations of online customers. On the other hand, it is easier to extract knowledge out of the shopping process under the Internet environment. Market segmentation is one of the ways in which such knowledge can be represented and make it new business opportunities. ” (Kim and Ahn 2004 ). Already in 2004, Kim and Ahn ( 2004 ) described an essential paradigm shift that online marketing was encountering in a time in which the world wide web was rising. The statement focused on the limitation of mass marketing in a period where data-driven technological possibilities arose to analyze web-users footprints and enable personalized-oriented marketing. About two decades later personalized-oriented marketing is still a key challenge that many researchers conduct in their work (Chen et al. 2018 ; Apichottanakul et al. 2021 ; de Marco et al. 2021 ; Nguyen 2021 ; Sokol and Holy 2021 ). Not only has it been shown that personalized customer targeting is more profitable for companies, but also that knowledge about customer behavior is a decisive factor for success and failure (Mulhern 1999 ; Zeithaml et al. 2001 ; Kumar et al. 2008 ). In this respect, it is essential to understand the customers and their needs, and to be aware of their behavioral changes over time (Liu et al. 2009 ; Ding et al. 2019 ; Griva et al. 2021 ; Apichottanakul et al. 2021 ). In addition to technological changes and increasing functional requirements, legal regulations are also subject to constant change. This results in further non-functional requirements, as these regulations firstly describe local conditions and secondly can counteract the functional objectives (Burri and Schär 2016 ; European-Parliament 2016 ). From a functional perspective, companies that want to analyze customer behavior need (1) the capacity to record customer data, (2) an algorithm to characterize similar user behavior, and (3) strategies or processes that use the extracted information to achieve the business goal.

Regarding the first requirement, it is necessary to collect data that enable algorithm-based characterization of user behavior. Thereby, we distinguish between customer behavior data that is collected explicitly and implicitly. As the names suggest, explicit data collection is intentional to collect customers’ information. In implicit data collection, the main purpose is not to collect information about customers, but to collect information about the process in which the customer appears as the interactant, such as purchase information for accounting purposes. Explicitly collected data such as demographic information, on the other hand, is difficult to collect and maintain for several reasons. Not all customers are willing to share demographic data or they browse anonymously on the web. In addition, information collected in this way is subject to change over time and, accordingly, is always subject to uncertainty that is difficult to quantify (Chan et al. 2011 ; Chen et al. 2018 ). Accordingly, implicitly gathered data is easier to collect. This data can be tracked with every user interaction. E.g. information about products that are purchased together or the amount of money spent for a purchase. Nonetheless, data collected in such an implicit manner requires deeper analytical skills to exploit.

For the second requirement, the gathered interaction data is used. A frequently used approach for managing different customers with diverse preferences is segmentation (Hong and Kim 2012 ; Hsieh 2004 ; Chen et al. 2018 ). Customer segmentation is an unsupervised-learning process and utilizes different clustering approaches which have the goal to separate aforementioned customer data based on similarity. Hereby, similarity is measured by an objective function such as euclidean distance. It should be noted that customer behavior is a continuous process, with customer needs, wants and satisfaction changing over time. Accordingly, the processes and underlying procedures implemented in companies must be flexible in order to accommodate this high level of dynamism (Liu et al. 2009 ; Ding et al. 2019 ; Griva et al. 2021 ).

The last requirement is to utilize the analyzed customer information. Domain experts like marketers can tailor appropriate marketing strategies for individual customer groups based on segmentation. As Birtolo et al. ( 2013 ) already stated and showed, instead of domain experts, more and more automated methods to extract and to learn underlying patterns in customer behavior allow to target customers in advance.

The aforementioned dynamics are not only reflected in the respective target market but also can be observed in the underlying segmentation methods. Therefore, the goal of our survey is to provide an overview of digital and autonomous customer targeting processes for customer relationship management (CRM) based on historical data. The main objective of the literature research lies in the customer segmentation process for different e-commerce related use cases like retailing or services in the banking sector. Our study is structured by three guiding questions, to which we provide answers in this work.

Which clustering processes and methods are frequently used to understand customer behavior and targeting afterward?

Are there methodological limits with regard to data dimensionality?

Do methodological trends exist that can be observed over a period of two decades?

The main difference between our survey and former ones is that we focus on the process of customer targeting and behavior analysis in the e-commerce domain. The most recent literature review with a related topic is from 2016 (Sari et al. 2016 ). However, six years have passed since then, which makes an updated view necessary. Besides that in our study, we conduct a more extensive literature review that leads to a different classification of segmentation methods and more use case examples. In addition, we recognized a more extensive e-commerce process for customer targeting. Our contribution and main finding are:

We provide an overview with examples from the literature of how customer behavior analysis is used.

We determine a customer targeting process with four phases.

We could not identify a consensus in metrics to evaluate and compare the quality of the segmentation algorithm and therefore it cannot be said which of the methods is “best”.

Based on the frequency in publications and ability to handle large amounts of data, we recommend a process that uses RFM-analysis as a feature representation and k-means for segmentation.

We identify open questions and possible research gaps regarding embeddings for customer representation and deep learning-based segmentation for customer analysis and customer targeting strategies

Our study is structured as follows: In Sect.  2 , we present and explain our research methodology. In Sect.  3 , we present a literature overview of the identified works. Hence, in this section, we address the first guiding question accordingly and provide an answer. Moreover, we present the survey literature more in-depth. Based on the identified process, we notice that feature selection (be it manual or computerized) is an essential preprocessing step of customer behavior segmentation. Therefore, we explain the different segmentation and feature selection methods that are used. Additionally, the methods in the surveyed literature are described regarding the applied use cases for customer targeting and data volume. Section  3 ends with an overview of the publications’ evaluation metrics for customer segmentation. We analyze and discuss our findings in Sect.  4 which is further divided into two subsections. The first subsection is about the feature selection. In terms of feature selection methods, we present an answer to guiding question two and three. Similarly, in the second subsection we analyzes, discusses, and answers guiding questions two and three regarding the reviewed segmentation methods. In each subsection, we state open research questions that are not covered by our survey but have future potential. Finally, we conclude the survey in Sect.  5 with a brief summary of the findings and new open research questions and potential.

2 Literature research methodology

As already encouraged in the introduction we want to scientifically investigate which processes exist for personalized customer marketing approaches. Especially, to get an overview of commonly used customer segmentation methods in the context of CRM in e-commerce, we have conducted an extensive literature review. Thereby, Vom Brocke et al. ( 2015 ) published a recommendation on how to conduct such a search in an effective and highly qualified way. Hence, we followed their recommendation for the most part. Figure  1 illustrates our review process. We started our literature research by reading survey papers to derive an integrated and consolidated understanding of the conceptualization of the subject. Thereafter, we started the literature search. Therefore, we defined our search scope. Vom Brocke et al. ( 2015 ) refer to Cooper ( 1988 ) which states four steps on how to define a search scope: (1) process, (2) sources, (3) coverage, and (4) technique. Leaning on these four steps we choose a sequential search process. As a publication source, we used the Web of Science Footnote 1 (WoS) online research tool as it is one of the leading scientific citation and analytical platforms and provides scientific publications across a wide amount of knowledge domains (Li et al. 2018 ). To keep the focus on the customer segmentation methods we used the following search term:

“Customer segmentation” or “customer clustering” or “user segmentation” or “user clustering”

Herein, we chose to use the word “user” as a synonym for “customer” and “clustering” for “segmentation”. We wanted the search to be as less restrictive as possible to not miss relevant publications. Therefore, we expected works that are not relevant to our research. After having a corpus of hundreds of publications, we started reading the title, abstract, and keywords of the publications. We filtered out all publications that did not deal with customer behavior in commerce, especially in the context of e-commerce. The next step was to read all remaining papers and excluded all publications that did not deal with customer segmentation in an e-commerce use case and it became apparent that customers were segmented based on their information and actions. We extracted all wanted information from publications we classified as relevant. Specifically, we retrieved bibliometric information, information about the use case, the used methods, information about the used data, and the results.

figure 1

Flow chart of the literature research process

3 Literature overview

As aforementioned in Sect.  2 , we started our literature review with reading related surveys. Plenty of research surveys in the field of segmentation prioritize the underlying methodology or class of methods but not their usage in specific domain (Gennari 1989 ; Rokach 2010 ; Hiziroglu 2013 ; Ben Ayed et al. 2014 ; Firdaus and Uddin 2015 ; Reddy and Vinzamuri 2018 ; Shi and Pun-Cheng 2019 ). For example, Shi and Pun-Cheng ( 2019 ) review clustering methods for spatiotemporal data which are collected in diverse domains like social media, human mobility, or transportation analysis. Another survey example is brought by Hiziroglu ( 2013 ). The author reviews segmentation approaches for applications of soft computing techniques. Other surveys or studies focus on specific methods like k-means or RFM-analysis (Sarvari et al. 2016 ; Deng and Gao 2020 ). The most related literature review we found in our literature search is from Sari et al. ( 2016 ) which reviews customer and marketing segmentation methods and the necessary data. They identify different segmentation approaches and e-commerce process which coincides in some parts with our outcomes. However, as already mentioned before, six years have already passed and their paper corpus consist of less than 20 publications. From this, we deduce the need for an up-to-date and more detailed review in the area of customer segmentation in e-commerce.

The WoS search from 2023/01/01 led to 852 publications, of which not all were related to our research as assumed. As described we excluded all publication that did not deal with customer behavior in e-commerce. The major domain that was not related to our research objective dealt with user segmentation in non-orthogonal multiple access (NOMA) techniques. Over half (66%) of the publications were not related to our research topic and we had 289 publications left that were somehow e-commerce related. From the 289 publications, we classified 149 publications as “not relevant” and 140 publications as “relevant” based on the title, abstract and keywords with the aforementioned criteria.

figure 2

Process of customer targeting based on behavioral information gathered from data

Reading the remaining literature (140 publications), we paid particular attention to recurring processes. We identified a process that is constantly used to determine customer behavior with segmentation approaches. Figure  2 illustrates the identified process that depicts the answer to our first question. It illustrates the customer targeting process and it can be divided into four steps: (1) customer information, (2) customer representation, (3) customer analysis, and (4) customer targeting.

In the first step, the customer information is stored in form of data and is made available for further processing. In the literature, this step is usually given by provided datasets. Nevertheless, in some publications, the information is collected by the researcher. Especially, when data is collected explicitly which is for example done by Hong and Kim ( 2012 ), Nakano and Kondo ( 2018 ), and Wu ( 2011 ).

Based on the collected information a customer representation is built as the second step. The customers are represented by their features which are selected manually or with a feature selection method. In nearly half of the cases (47.6%), features are selected manually and in the other half (52.4%) feature selection methods are used. Both feature selection approaches have their advantages and disadvantages. For example, feature selection methods are utilized to eliminate features with less information content or to aggregate and extract additional knowledge out of the customer data. The most used method in our literature review is the Recency, Frequency, Monetary (RFM) analysis that aggregates additional information about the customers’ behavior and value to a company (Hughes 1994 ) which we show in Sect.  4 . Manual feature selection usually is performed by extracting information like item view or click events, purchased items, and item information such as the associated category. In some other cases, mostly for recommendation, the authors additionally use ratings and reviews for the behavior analysis. Otherwise, demographic data is collected through membership or similar programs. Another approach to get demographic or psychographic information is by user surveys.

The third step of the found process is customer analysis which is the key component of the process and is done by applying segmentation methods. Customers are split into more homogeneous groups of similar behavior. This is done by different segmentation approaches, like methods that compare the similarity between the customer representation or other methods that partition the customers by given thresholds. In Sect.  3.4 , we further explain the interaction of customer representation with feature selection methods and the customer analysis on found case studies.

The fourth and last step, customer targeting, uses the behavioral information from the customer analysis to target the right user with the right CRM decision. In the literature, we identified different targeting approaches which includes recommendation, marketing campaigns, and pricing strategies. The main difference in the literature is that recommenders are evaluated against others with evaluation metrics like hit-rate, accuracy, etc, and marketing campaigns or pricing strategies focus on the plausibility of the customer segmentation and try to explain the outcomes over the performance.

We decided to consider only literature that mostly adheres to this characteristic process because it fulfills all necessary conditions for personalized customer marketing which is our defined investigation scope. The work of Coussement et al. ( 2014 ) is an example of a scientific publication which we did not consider in our work because it is not in our scope. In their research, they investigated the impact of data quality on different segmentation methods and showed which methods are more robust to inaccuracies.

figure 3

Distribution of surveyed publications from 2000 until 2022

Based on this aforementioned method, we further filtered our corpus to obtain a final corpus of 105 scientific papers. The literature is distributed between the years 2000 and 2022 over different use cases and journals. The reviewed publications are not equally distributed over the years. Figure  3 illustrates the distribution of the paper’s publication year. We see that there are more publications over time in the field of e-commerce considering customer analysis with segmentation methods. Before 2010, we usually find one publication per year. In the period from 2001 to 2003, however, there is no publication in the paper corpus at all. In total, there are 16 publications in the period from 2000 to 2010. After 2010, there are at least three publications per year with an increasing tendency. 43 out of 105 publications (about 41%) are published in 2020, 2021 and 2022.

Table 4 gives an overview of the 105 publications containing title, author, and year. Footnote 2

3.1 In-depth feature selection methods for customer representation

figure 4

Distribution of the surveyed feature selection methods over the years

We identify customer representation as a fundamental step in the customer targeting process. Therefore, before applying segmentation methods for customer analysis an appropriated customer representation is needed. As mentioned earlier, this is achieved by applying feature selection methods. In the following, we will refer to manual feature selection as “none” feature selection method. Figure  4 displays the distribution of the used feature selection methods over the years as well as the total amount in percentage. In 50 publications, the authors decide to use handcrafted features to represent the customers.

The RFM-analysis is by far the most popular feature selection method with 44 (80%) of 55 publications that use feature selection methods and 41.9% in total. In the RFM-analysis three features are extracted from customer data. The features are recency, frequency, and monetary. Recency relates to the time of the last user activity, like a purchase. Frequency describes how often a customer interacts in a given period and monetary measures how much money a customer spends in that period (Hughes 1994 ). In some works, e.g., Stormi et al. ( 2020 ), Chang and Tsai ( 2011 ) the RFM-analysis is extended by additional features.

Principal component analysis (PCA) is applied in four publications. In 2015 and 2022 once and in 2020 twice. PCA is a dimensionality-reduction method in which the information content of the features is determined and features with low information content can be removed (Pearson 1901 ; Hotelling 1933 ).

Purchase Tree are used in two publications and were proposed by Chen et al. ( 2018 ). The fundamental idea is to represent purchased products by a tree in which products are the leafs and the product category the nodes of the tree.

The remaining five feature selection methods are each only used once. Chi-square Automatic Interaction Detectors (CHAID) is based on decision trees to handle categorical variables (Kass 1980 ). Customer Lifetime Value is a popular economic key performance indicators which describes the profit of the customer for the entire lifetime. Discrete wavelet transform captures location and frequency information. In Graph representation, the customer interaction is encoded in such. Multiple correspondence analysis (MCA) allows the representation of categorical features in lower-dimension.

3.2 In-depth customer segmentation methods

figure 5

Distribution of the surveyed clustering methods over the years of publication

The authors of the reviewed publications utilize different customer segmentation methods for the customer targeting process. Figure  5 shows the distribution of segmentation methods among all publications and over the years.

K-means is the most frequently used customer segmentation method in our surveyed literature (41 of 105). The goal of the k-means algorithm is to partition a set of data points into k segments which minimize the distance between the data. Usually, the euclidean distance is used. Solving the underlying optimization problem is NP-hard and therefore, various approximation algorithms are used (MacQueen 1967 ; Lloyd 1982 ). The usage of k-medoids and other k-means variations is included in our k-means classification.

The second most used segmentation algorithms are Hybrid approaches that are used twelve times (11.4%), followed by Other approaches that are used ten times (9.5%). Hybrid clustering refers to the application of two or more clustering approaches to segment the customers. As “other” clustering, we define the clustering methods which don’t fit the previous cluster definitions. For example, Abbasimehr and Shabani ( 2021 ) propose a time series segmentation approach to get knowledge from customer behavior or Chen et al. ( 2018 ) proposed an segmentation an algorithm which they call PurTreeClust. Hsu and Chen, Y.-g.C. ( 2007 ) propose an algorithm to cluster mixed data which is named CAVE and An et al. ( 2018 ) proposes a segmentation algorithm based on non-negative matrix factorization.

Nine publications use Rule-based clustering to segment their customers into different behavioral groups. In rule-based approaches, data points are assigned to predefined segments by value thresholds.

In our surveyed literature, five publications utilize a Fuzzy C-Means (FCM) approach. In a fuzzy clustering algorithm, data points can be assigned to different clusters at the same time. The fuzzy c-means (FCM) clustering algorithm is a fuzzy version of the k-means algorithm (Dunn 1973 ; Bezdek et al. 1984 ).

Latent class models are used for the latent class analysis to classify discrete variables (Lazarsfeld 1950 ). This segmentation approach is used six times in the surveyed literature.

Evolutionary Algorithm (EA) are inspired by the biological evolution of living things. EAs are a class of optimization methods to find an approximate solution to a problem which also includes clustering. Simplified, the algorithm can be described as follows. In the first step, a random solution is initialized. The second step is to determine the quality of the solution using a fitness function. In the third step, the best solutions are selected and these are randomly changed, which is also referred to as mutation in this context. This process is repeated until a stopping criterion is met (Eiben and Smith 2003 ; De Jong 2016 ). Genetic algorithms (GA) like particle swarm optimization (PSO) (Kennedy and Eberhart 1995 ) or chaotic ant swarms (CAS) (Zhu et al. 2007 ) also belong to the family of EAs. Our survey contains five publications that utilize EAs.

Hierarchical clustering is utilized five times by the authors of the surveyed litterateur. The basic idea of hierarchical clustering is to bring similar data points close to each other regardless of the distribution. There are two approaches, known as the agglomerative and divisive approaches. In the agglomerative approach, the algorithm starts with each data point being in its own cluster. At each iteration step, the most similar clusters are merged until a distance criterion is met. The divisive approach works similarly, except that it starts with one cluster and splits in each iteration (Maimon and Rokach 2005 ).

Self-organizing map (SOM) is also used five times in our paper corpus and are based on neural networks. Neural networks are mainly used for supervised learning tasks. However, it is also possible to use neural networks in an unsupervised manner for clustering by pushing fully connected neurons towards the data points that are closest to them (Kohonen 1982 ).

The Rough Set Theory was introduced by Pawlak ( 1982 ) and is a data mining method to extract knowledge of databases. Besides the use for segmentation, the rough set theory can also be used for feature selection, data reduction, and other applications. In our research, we found three publication utilizing rougth sets to segment the customers.

Deep learning (DL)-based clustering , spectral clustering , and clustering via expectation-maximization are only used once. Similar to SOMs, deep learning-based clustering methods are based on neural networks. Nguyen ( 2021 ) presents a deep learning-based clustering approach named Deep Embedding Clustering that combines a deep neural network and a self-supervised probabilistic clustering technique. They state that their approach produces explainable customer segments. In the first step, they determine the optimal number of clusters with a spectral clustering approach and the elbow method. Then they encode their manually selected variables and apply the deep embedding clustering which is a deep autoencoder that is trained with the mean squared error (MSE) loss. The expectation-maximization (EM) algorithm performs a maximum likelihood estimation on given data points which consists of latent variables. It is an iterative approach that optimizes the mean and variance of the cluster distribution until it converges (Dempster et al. 1977 ). Spectral Clustering is a graph-based clustering approach in which distances between data points are represented by the edges. With the resulting graph’s Laplacian-matrix segments can be computed (Fiedler 1973 ; Donath and Hoffman 1973 ).

In the first decade (2000–2010) rule-based, Evolutionary Algorithms (EA), latent class, hybrid, and “other” clustering approaches were used twice. Both hybrid approaches were published in 2004. One hybrid approach combines k-means with a EA and the other combines a hierarchical approach with k-medoids. Hierarchical, fuzzy C-means, and rough set theory segmentation approaches are used once in the years between 2000 and 2010. Self-organizing map (SOM)-based segmentation was used three times which makes it the most applied method in this decade in our survey.

In the second decade (2011–2022), 89 of 105 (84.76%) relevant papers were published. K-means is used for the first time in 2011 (disregarding hybrid approaches). Since then, k-means has been used at least once a year. In 2014 k-means is used in two, 2015 in four, 2018 in five, 2020 in eight, and 2022 in eleven publications. Statistically, this indicates an upward trend. Also rule-based approaches are used repeatedly in the last years.

3.3 Overview customer targeting use cases

The underlying customer targeting process applies to a large amount of business and e-business use cases. In this section, we present an overview of which segmentation methods are used on which use case. Therefore, we briefly introduce the found e-commerce use cases.

The first category of use cases we want to introduce is Retailing . It is the sale of different goods that are not further specified and don’t belong to any other use case category. We also assign use cases to this category if it is not further specified. This means that a pure sports retailer is classified under the Sports use case, or a retailer that sells only clothing is classified under Fashion . Different to retailing, fashion is a dynamic industry (Brito et al. 2015 ). Like the fashion branch, Electronic is considered as a branch of e-commerce retailing. In the literature, some customer behavior segmentation use cases are related to Banking . Use cases in this category naturally have more information about the customer. In addition, the products and services don’t change as quickly as in retailing. In Mobile operators’ use cases the authors deal with data from mobile network providers. With Youtube, Netflix, and other companies, Video & music streaming platforms and services become very popular, and forecasts show that sales will also grow strongly in the coming years (statista.com 2022 ). In our literatur search we found some Book use cases that deal with book retailing or renting services. Nowadays, there a plenty of online services to plan a trip. In travel use cases we consider case studies that deal with trip-related action like hotel booking, reviewing, or trip and location recommendation. In our survey food use cases get their own category because in some cases it is difficult to distinguish between food retailing, restaurant reviews, or food production or manufacturing. Manufacturing in e-commerce comes with some benefits and new opportunities. One is product customization (Fan and Huang 2007 ). Another one is manufacturing-related services. In others use cases, we classify use cases that we could not determine explicitly or don’t fit in one of the other groups, e.g. online news or email campaigns of charitable organizations.

Table 1 shows in the rows all clustering method used in the literature. Each column represents one use case. A check mark indicates that we were able to identify an example for at least one use case. The number of checkmarks indicates the number of use cases we identify for the segmentation methods. Note, that in some publications, the utilized method is showcased on multiple use cases which leads to a mismatch between the number of publications and the number of use cases.

Retail is the most occurring use case in the surveyed litterateur with 43 case studies. The authors show with their publications that every segmentation method is usable to approach retail use cases. The retail use case is the only one that have examples for each segmentation method. Besides retail use cases, only travel and “other” use cases are approach by most of the segmentation methods for the customer analysis. The remaining use case categories have at least five different segmentation methods as an application example.

Regarding use case coverage, we found that k-means clustering are used to approach all use cases expect manufacturing. Thereby, k-means is utilized 16 times to approach retail and three times in bank, video and music, and “other” use cases each. Our literature review show that FCM is applied to seven different use cases. Rule-based, hierarchical, hybrid, and “other” segmentation approaches are applied on five different use cases.

3.4 Overview and examples of the interplay between customer representation and analysis for customer targeting use cases

The authors of the identified publications utilize different customer segmentation methods with different feature selection methods for the customer targeting process. In this section, we further investigate and describe these approaches to give a better insight into the interaction of the feature selection and segmentation methods. Table 2 provides an overview of the different segmentation methods with the corresponding feature selection approaches used. It also lists the number of times such a pair of segmentation method and feature selection was used in the paper corpus. The last column of the table shows the publication’s reference. In the following, we present some examples on how the different segmentation and feature selection methods are used in the found literature to approach customer targeting in e-commerce.

In nine publications rule-based clustering is used to segment the customers into different behavioral groups. Therefrom, seven use the RFM-analysis to represent their customers. An example retail use case that combines RFM-analysis and k-means is provided by Hsu and Huang ( 2020 ). In their research they want to identify VIP customers. VIP customers are buyers of critical products which are not purchased by the average customer. In their approach, they apply the RFM-analysis on over 600,000 transactions from around 3800 customers. The segmentation is based on the 20%-quantile of th RFM-values. Another example which utilzes rule-based segmentation with RFM-analysis is from Jonker et al. ( 2004 ). In their publication, the authors want to find the best marketing policy out of a set of policies for a customer. The data are from a mailing scenario of a charitable organization. They first utilise an on the email data adapted RFM-analysis and segment the customers based on defined thresholds. To identify the the best policy for a segment the authors used a markov decision process.

Two authors applied rule-based segmentation without applying a feature selection method. Hjort et al. ( 2013 ) want to investigate the impact of product returns in a fashion use case provided by Nelly.com which is a Scandinavian online fashion retailer. For the research, the scientists selected six features for each customer which are total sales, average sales per order, total contribution margin, average contribution margin, the total number of orders, and the total number of returns. Based on the feature information, they assign each customer to one of four groups. The groups are based on the buying and returning habits of the customers. The authors conclude from the customer analysis, that customers who tend to return goods are also the more valuable for the company.

In 16 publications the authors decide to not use a feature selection method but select features by hand before applying k-means clustering to the customer data. Authors of 21 publications use the value of RFM-analysis for the segmentation with k-means. Three research groups use a principal component analysis (PCA) for feature selection before clustering with k-means. Only Ding et al. ( 2019 ) use a graph representation before segmentation. The graph is built based on user-item interactions.

Griva ( 2022 ) analysis the customer of 140 e-commerce stores in European countries with k-means and hand crafted features. The features are extracted from 270,000 responses from a customer satisfaction survey and 1 million orders from 800,000 customers. They propose a framework which is capable to build automated marketing actions based on the created customer satisfaction segments. Example for such marketing actions are social media sharing strategies for the satisfied segments or discounts for the less satisfied customer segments.

Guney et al. ( 2020 ) are looking for the best campaign in movie rental use case (video on demand). In a first step they apply an modified RFM-approach which extract two additional features from the data. The two features are the number of days between the first and last rental and the standard deviation of the days between two rented movies. These five features are clustered via a k-means algorithm. The clustering results in four customer groups. An apriori algorithm namely an association rule mining approach is than used to assign the best marketing campaign to the customer segment.

In our selected literature six publications utilize an FCM approach. Ozer ( 2001 ) collects the data from customers of an online music service via a customer survey and doesn’t use a feature selection method before applying FCM on the features.

Nemati et al. ( 2018 ) search for the most appropriated marketing strategy for the customers of a telecommunication industry use case. First, they compute the customer lifetime value (CLV) for each customer and group them with FCM. To assign the right marketing strategy to the right segment they utilize a fuzzy TOPSIS technique.

For hotel businesses, customers’ satisfaction is crucial. Alghamdi ( 2022a ) investigate customers’ satisfaction of hotel visitors in Mecca and Medina (Saudi Arabia). Therefore, they apply PCA on data collected from TripAdvisor and segment the resulting features via FCM.

Hierarchical clustering is used in five publications. Three authors handcraft their features. Aghabozorgi et al. ( 2012 ) calculate the necessary features by applying a discrete wavelet transformation (DWT) on customer data of a bank use case. In their research, DWT is an appropriate approach because they consider customer activities as a time series which is not the norm. After using DWT on the data, the data is initially segmented with a hierarchical clustering method. The cluster is updated incrementally in a given period with new data. Zhou et al. ( 2021 ) combines hierarchical clustering with an extended the RFM-analysis for a retail use case. The RFM-analysis is extended by the interpurchase time which results in four different features. The interpurchase time is defined as the time gap between two consecutive purchases in the same location (same website). Afterwards, the customers are clustered by the calculated features.

In our research, we have one publication from Dhandayudam and Krishnamurthi ( 2014 ) that combines RFM-analysis for feature selection with rough sets for clustering. In addition, they add another feature to the RFM-values that describes the average time between purchase and payment. They categorize all four features in their 20%-quantiles and then utilize a slightly modified rough set theory approach for the clustering. Song and Shepperd ( 2006 ), Wu ( 2011 ) don’t use feature selection methods before segmenting the customers with a rough set approach.

Clustering based on latent class models is used six times in the surveyed literature. Four of them manually select the features and therefore, don’t use feature selection methods. Nakano and Kondo ( 2018 ) use psychographic, demographic, online store, social media, and device touchpoint data. The information is clustered with a latent class analysis approach which results in seven segments. Goto et al. ( 2015 ) propose a method based on latent class analysis that clusters items and customers. They assume valuable users purchase more often only browsing and valuable products are bought more often. They use the latent class model to cluster the customers into “good users” and “other users”. To analyse the resulting segments they use the Classification and Regression Tree (CART) Algorithmus.

Wu and Chou ( 2011 ), Apichottanakul et al. ( 2021 ) use RFM-analysis for the feature selection and apply a latent class approach for the clustering. Apichottanakul et al. ( 2021 ) use the proposed GRFM approach from Chang and Tsai ( 2011 ) to analyse the customers of a pork processing use case. First, the RFM scores are calculated for nine product categories and each feature is categorized in one of five categories based on the 20%-quantile. The features are clustered with a probabilistic latent class model. Apriori the optimal number of k is unknown therefore, a suitable number of clusters is determined with the Akaike Information Criterion (Akaike 1974 ). In the last step, the clusters are analyzed with the help of the RFM-values.

The only publication that uses the EM algorithm for clustering is from Rezaeinia and Rahmani ( 2016 ). The goal of their work is to recommend products in a retail use case. Therefore, they first compute the features via RFM-analysis and cluster them with an EM approach for customer targeting.

Spectral clustering is used by Chen et al. ( 2019 ) to segment customers buying behavior. Therefore, they use a Purchase Tree representation for customers transactions which was proposed earlier by Chen et al. ( 2018 ). For the customer segmentation, they propose a two-level subspace weighting spectral clustering algorithm. Spectral clustering approaches are used only once in our literature.

Our survey contains five publications that utilize EAs for customer clustering of which two use RFM-analysis and three don’t use a feature selection method on the available data. Both publications using RFM-analysis are published by or with Chu Chai Henry Chan. In his publication from 2007, the task is to determine an appropriated strategy for each customer of an Nissan automobile retailer. Therefore, Chan ( 2008 ) computes the features from the RFM-analysis and categorizes the values in one of five 20%-quantiles. Then the features are binary encoded with four bits. Based on the binary features a GA is used with the customer lifetime value (CLTV) as the fitness function. In 2016, Chan et al. ( 2016 ) apply the same feature preprocessing and PSO with CLTV as fitness function on a similar use case but with more data.

SOMs are used in five publications in total. Verdu et al. ( 2006 ); Nilashi et al. ( 2021 ) utilize handcrafted features to represent the customers. In the remaining three publications customers are represented by their RFM-values. For example, Hsieh ( 2004 ); Liu et al. ( 2009 ) combine an RFM-analysis feature extraction with a SOM clustering to segment the customers in their case study. A recent example of an SOM approach is proposed by Liao et al. ( 2022 ). They develop different marketing strategies for each segment for a retail use case. Therefore, they use an extended RFM-analysis approach to represent the customers. The extension is not only using RFM-analysis on customer purchase information but also on other behavioral information like clicks, add-to-cart, or add-to-favorite. For this, they utilize 2 million customer interaction records. The SOM approach is than applied on the different RFM-values of the customers to segment them in similar behavioral groups.

Nguyen ( 2021 ) presents a deep learning-based clustering approach named Deep Embedding Clustering that combines a deep neural network and a self-supervised probabilistic clustering technique. They state that their approach produces explainable customer segments. In the first step, they determine the optimal number of clusters with a spectral clustering approach and the elbow method. Then they encode their manually selected variables and apply the deep embedding clustering which is a deep autoencoder that is trained with the mean squared error (MSE) loss.

In our literature review, we found twelve research papers that use hybrid clustering methods. In ten publications no feature selecteion method is used. For example, Kang et al. ( 2012 ) don’t utilize a feature selection. They split the dataset into two sets of answering customers and not answering customers. The data points are clustered with a k-means and CSI Algorithm with different criteria. Kim and Ahn ( 2004 ) use (CHAID) as a feature preprocessing. The clustering is performed by a GA based on k-means clustering. Jadwal et al. ( 2022 ) use MCA as feature preprocessing and segment the customers of a bank use case with an segmentation approach based on k-means and hierarchical clustering.

In our survey, we classified ten publications as “other clustering”. Six authors have manually selected features. In three publications the RFM-analysis is used as feature selection method. For example, Abbasimehr and Shabani ( 2021 ) propose a time series clustering approach to get knowledge from customer behavior. First, they split the dataset into predefined time intervals. As a second step, they apply RFM-analysis on each interval and use the monetary value of the customer for the time series. On the resulting time series, a time series clustering approach is applied. Also, Hu and Yeh ( 2014 ) utilize RFM-analysis based features for the clustering. Therefore, they propose an RFM-pattern-tree to represent customers which also is used to approximate customers with less information. They can use this to detect similar customers with similar behavior. Simoes and Nogueira ( 2021 ) uses RFM-features and segment the customers with an ABC curve segmentation. Chen et al. ( 2018 ). represent the data as a Purchase Tree and propose for the segmentation an algorithm which they call PurTreeClust and is based on a partitional clustering algorithm.

3.5 An overview of the data dimensionality in the publications’ experiments

An essential component for the behavior analysis and customer targeting process is the information that is collected by the companies. In this section, we describe which methods are used for which data in respect to the order of magnitude. We distinguish between two different types of data amount. The first is the number of data points e.g. transactions and it describes the amount of data an algorithm can handle at least. The second type is the number of customers in a dataset.

The number of customers may indicate how much data an algorithm can process because customers and not data points are segmented. Therefore, it is important to consider the number of customers when analyzing the data dimensionality. Depending on the number of customers the number of data points can be reduced after a feature selection method. For example, in RFM-analysis the information of a user is aggregated for one period which leads to fewer data points the clustering algorithm needs to process. Other feature selection approaches like PCA doesn’t affect the number of data points or user but the number of features.

Table 3 shows which feature selection methods and clustering algorithms are used with which data dimensionality regarding the number of data points and the number of customers in the use case. The number of data points is described by six columns of which each has a different order of magnitude. We choose a similar representation for the number of customers in a dataset but only have five columns. We annotate the methods that deal with this amount of data with checkmarks. Note, that not all publications describe the data in a way it is possible to extract the information of the data dimensionality. In some cases, only the number of data points are given, in others, we only know about the number of customers, and sometimes we don’t have information at all. How often a method is used, is indicated by the number of checkmarks. In some publications, different datasets with different sizes are utilized. If two datasets have different orders of magnitude, we indicated it by using checkmarks in the appropriated cells. However, if the datasets in the same publication have the same order of magnitude, we indicated it only once per publication.

In terms of feature selection, we see that RFM-analysis is applied up to \(10^8\) data points, but above this number of data points it is not used anymore. For example, Akhondzadeh-Noughabi and Albadvi ( 2015 ) apply RFM-analysis on 35,537,276 customer activities from 14,772 customers.

Based on the survey literature, PCA and DWT can be applied to data with up to 1 million data points. The graph approach utilized by Ding et al. ( 2019 ) is used on around 50,000 user activities. Chen et al. ( 2018 , 2019 ) propose a purchase Tree approach which is tested on several datasets with different sizes in a range of a few thousand and 350 million transactions with customer numbers between 800 and 300,000.

The clustering method rows only refer to the clustering algorithms where no feature selection methods are applied. Regarding the number of users in datasets, we see that usually, their number doesn’t exceed 10,000. An expectation is provided by Kang et al. ( 2012 ). They test their hybrid approach on two datasets in which one dataset contains information about 101,532 customers. Another one comes from Goto et al. ( 2015 ) where they apply a latent class model on 37,278,907 browsing actions from 99,924 users. Abdolvand et al. ( 2015 ) apply k-means on 25,000 bank customers. Investigating which clustering methods are used for data with at least one million entries, we identify that it is k-means clustering, latent class models, and hybrid clustering approaches. K-means is used twice on over a million data points by Liu et al. ( 2015 ); Zhang et al. ( 2014 ). Liu et al. ( 2015 ) have access to 3 million transaction data from taoboa.com. Zhang et al. ( 2014 ) use the MovieLens datasets in which one has 100,000 movie ratings of 1682 different movies rated by 943 different users and the other has 1 million ratings for 3952 movies made by 6040 users.

3.6 Evaluation metrics

Usually, a clustering model learns in an unsupervised manner and the ground truth is unknown. Therefore different criteria need to be used to evaluate their performance. In the following the frequently used evaluation measures are described and briefly analyzed.

Clustering evaluation or cluster validation is an essential step in verifying the discovered groups in a data set. The fundamental challenge of evaluation lies in the missing ground truth, which can be a reason that we have not found a consensus between the evaluation methods in our literature research. Figure  6 presents the distribution of used evaluation methods for segmentation methods in the literature that also shows the missing consensus. We classified the evaluation criteria into seven different groups which are indicated by a color. In the following, we briefly introduce the evaluation criteria and give some examples. It should be noted, that in some publications the authors don’t apply evaluation metrics. Instead, they analyze the segments based on their plausibility. Chan et al. ( 2011 ) for example, measures the performance of the proposed method by comparing the company’s sails before and after the using the approach. Guney et al. ( 2020 ), Nie et al. ( 2021 ), Wu et al. ( 2020 ) evaluate the segments with the help of the RFM-values.

figure 6

Distribution of the segmentation methods used evaluation methods

Statistical significance test The underlying concept of a Statistical significance test is to determine whether the data points are randomly distributed or not. Krishna and Ravi ( 2021 ) have used a statistical t-test to evaluate their genetic algorithm approach on five different datasets. Another approach is the Kendall coefficient (Kendall 1938 ) that is used by An et al. ( 2018 ).

Analysis of variance The basic idea behind the analysis of variance (ANOVA) is to analyze whether the expected values of variables differ in distinct groups. By testing, if the variance of a variable is larger or smaller between the groups than within the groups, a statement about the meaningfulness of the group can be determined. ANOVA tests are used by Li et al. ( 2009 ), Hong and Kim ( 2012 ), Hjort et al. ( 2013 ), Hiziroglu et al. ( 2018 ).

Silhouette analysis The silhouette analysis is a (visual) validation method that is independent of the number of clusters and determines the consistency within a cluster. In addition to validation, this method can also be used to find the optimal number of clusters (Rousseeuw 1987 ). For example, the silhouette analysis is used by Akhondzadeh-Noughabi and Albadvi ( 2015 ), Peker et al. ( 2017 ), Christy et al. ( 2018 ).

Indices As shown in Fig.  6 many different index metrics were used to validate the clustering performance. The most used indices in our literature review are Davies-Bouldin (DB) index, Calinski-Harabasz (CH) index, and Xie-Beni (XB) index. The DB index describes the average similarity of each cluster with its most similar cluster. The DB index is to be interpreted in such a way that the lower the value is, the better the clustering (Davies and Bouldin 1979 ). The CH index, is the ratio of intra-cluster dispersion and inter-cluster dispersion (Caliński and Harabasz 1974 ). The XB index is used for fuzzy segmentation approaches and describes the separation and compactness of the clusters. The optimal number of clusters has the lowest XB value (Xie and Beni 1991 ). Chan et al. ( 2016 ) evaluate their proposed EA clustering with the DB index. Munusamy and Murugesan ( 2020 ) evaluate their fuzzy c-means clustering approach with XB index but also with the Kwon index, and the Tang index. They also use error measures for the cluster evaluation.

Information criteria These measures are used to select the models that fit the given data best but also take the number of parameters into account to prevent overfitting. One popular information criterion is the Akaike information criterion (AIC) which describes the model’s information based on the number of parameters and the model’s log-likelihood (Akaike 1974 ). Apichottanakul et al. ( 2021 ) utilize the AIC for evaluation to determine the optimal number of clusters in their latent class model.

Error measures Another evaluation method that is used in the surveyed literature is based on error measures like the mean absolute error (MAE), sum of squared error (SSE), root mean squared error (RMSE), or symmetric mean absolute percentage error (SMAPE). Abbasimehr and Shabani ( 2021 ) measure the cluster performance with SMAPE. Aghabozorgi et al. ( 2012 ) evaluate their proposed hierarchical clustering with SSE. Also, Lam et al. ( 2021 ) evaluate their clustering approach with SSE.

Others Some authors combine several evaluation metrics to express the usefulness and quality of their clustering models or use methods which donot fit in the six categories above. The mostly used “other” metric is cluster distance. We classify all inter and intra-cluster distance metrics as cluster distance if they are not further explained by the authors. For example, Wan et al. ( 2010 ) utilize an inter and intra-cluster distance to show that their CAS clustering approach has better distances and is more stable than k-means. Sivaguru and Punniyamoorthy ( 2021 ) apply a within/total clustering error index (which we consider as a cluster distance metric) to evaluate their k-means approach. In addition, they utilize DB index and t-test too. Umuhoza et al. ( 2020 ) utilize the elbow method, silhouette score, and CH index to determine the optimal number of segments. Another metric is the concordance (C) statistic (C-index) also known as receiver operating characteristic (ROC) and associated area under curve (AUC) score is for example used by Hsu et al. ( 2012 ) (also use SVM, isolation, and AVG index) or Barman and Chowdhury ( 2019 ). Dhandayudam and Krishnamurthi ( 2014 ) uses cohesion and coupling to evaluate the cluster quality for their rough set theory approach. Griva et al. ( 2021 ) use cohesion, inter and intracluster distance, similarity, and separation for cluster validation and gap statistic plus silhouette analysis to determine the optimal number for their latent class model clustering. Ramadas and Abraham ( 2018 ) validate the hybrid clustering which combines GA and fuzzy c-means with a partition coefficient (degree of intersection of clusters), classification entropy (the fuzziness of clusters), XB index, separation index, and partition index. Abdolvand et al. ( 2015 ) utilize the DB index to determine the optimal number of segments for their k-means approach and data envelopment analysis (DEA) for the evaluation.

4 Analysis and discussion

As previously shown in Fig.  3 , the reviewed publications were not equally distributed over the years. An upward trend in the number of publications can be recognized which indicates the importance of customer behavior analysis and therefore, their segmentation even after twenty decades of research. Especially, in the years 2020, 2021, and 2022, we have found more publications than the years before. There may be several reasons for this. The first reason that comes to mind is the current covid pandemic. This has increased the growth in e-commerce services. This could have prompted less digitalized companies to digitalize more and offer their services online. In many publications the company remains unknown. However, in some other publications the companies are named. Two examples are taobao.com or nelly.com that are established online companies which is an indication against our statement. From the literature conducted experiments did not show the state of digitization of the companies. Therefore, whether this connection exists remains open, and is not further investigated by us. Another reason, and in our opinion a more decisive one, is the increasing availability of the internet regardless of location. This means that a user can access the available online services at any time and from any place. For example, watching a series during a train ride or buying a new product at the online retailer of choice. With new requirements and necessities, the topic is also becoming more relevant in science and thus more is being published.

4.1 Analysis of feature selection methods

Based on our research, feature selection to represent customers is a fundamental step in the customer targeting process. For feature selection, customer information is indispensable. It is a challenge to get customers’ demographic information, physiographic information, or information about their preferences. As already stated, there are two possible ways to collect such data. Explicit information collection is done by questionnaires or user surveys that require customers’ accommodation to participate. Another, more implicit way is to collect demographic information via registration. Information can be collected by setting them as mandatory. Nevertheless, collecting data via registration is often limited to the usual information like age, gender, or address. In some use cases, like fashion, additional information about height and weight can be collected. It needs to be considered, that some users don’t want to provide any information and wish to remain anonymous. They either give false information or leave the website (service). In both cases, it is not possible to gather useful information and in the worst case, the former leads to false conclusions regarding the customers. Furthermore, user groups that don’t participate in a survey or are signed up are not represented in the data which makes the acquisition of unknown and new customers harder.

It is possible to gather customers’ preferences with the aforementioned method. Nevertheless, this comes with a huge disadvantage. The information is outdated soon and needs to be constantly updated which increases the maintaining effort. Constantly asking the customer for an information update can also cause him to quit as a consequence. Therefore, customer preference should be estimated based on their recent behavior. Customer behavior information can be recorded implicitly. Usually, purchase information with product information, timestamp, etc., is stored for a company’s financial overview. In addition, online touchpoints with the customer can be logged by the system. These logs can include various touchpoints like product views, click events, reviews, (dis)like, and many more. The advantages are that the customers do not disclose any personal information. Also, they are likely not interrupted on their shopping journey by unwanted questions. Nevertheless, disadvantages exist too. Predicting customer information from their behavior is not always correct that is for example caused by customers’ heterogeneity. Additionally, a large amount of data is required to make such predictions. Another challenge of implicit data collection is that the information needs to be linked to the customer. However, there are plenty of tracking-techniques to link the data with customers by using cookies or the browser identifier to name two examples.

As shown in Fig.  4 , for the customer process as a whole, it makes no difference whether a feature selection method is used or the features are selected or handcrafted by an expert. However, manual feature selection and feature selection methods have their pros and cons.

One advantage of manual feature selection is that no additional computation is required. However, it requires expertise and domain knowledge to select customer information that is meaningful and representative. Feature selection methods are designed to automate the selection of features. One advantage is that domain knowledge is no longer required. However, this doesn’t mean that domain knowledge should generally be dispensed with. Another argument in favor of feature selection methods is that information redundancy can be removed. Redundancies come in hand with the amount of data collected. Removing unnecessary and redundant information can speed up the customer analysis algorithms. This information is hard to determine and select manually even with domain knowledge. Regarding Table 3 , we notice that feature selection methods have processed larger amounts of data in our literature. Considering our second question from the introduction, we can state that feature selection methods allow larger amounts of data for customer behavior analysis. Particularly, the RFM-analysis and Purchase Tree have no limitation concerning the data dimensionality based on our research.

Our literature research shows that the RFM-analysis is by far the most popular feature selection method. Therefore, we analyze the RFM-analysis method in more detail hereafter and discuss the advantages and disadvantages. During the literature research, several points caught our attention. The RFM-analysis could be applied to almost any type of purchase or activity data since only three features need to be calculated. Furthermore, the calculation is very simple and requires only the basic arithmetic operations. So there is valuable customer representation in only three values. These values can be represented either numerically or categorically. For the categorical representation, the values were typically divided into five categories, each with 20%-quantiles. Thus, the obtained features are used for any clustering method. In addition, we notice that the RFM-analysis is often extended with additional features. The feature extension is usually use case-specific. Besides adding new features, the RFM-features are extended on different activity levels. For example, the RFM-values are calculated for all product categories or different customer activities. This provides additional information about the customer’s product preference at the category or activity level. Another advantage of RFM-analysis is that it can handle all sizes of data sets without having a scalability problem. This has been sufficiently demonstrated in the publications and is illustrated by Table 3 . We also like to note that in some publications, the RFM-analysis is used to explain the resulting clusters and helps with the customer behavior analysis which shows that decision makers can easily understand and interpret the RFM-values. Based on our findings to feature selection methods, we can answer the third question as follows. For feature selection methods no time-depended methodological trend could be determined. However, the most popular feature selection method is the RFM-analysis.

These versatile properties of the RFM-analysis are the reason for its popularity which is also stated by Chan et al. ( 2011 ), Alberto Carrasco et al. ( 2019 ). Despite it being the most used feature selection method, we also identified weaknesses in the RFM-analysis that all found customer representation has in common. The RFM-analysis, other feature selection methods like PCA, or manual feature selection don’t consider the whole information content of the accessible data. However, to represent more information, more features and therefore, more memory is required, which also increases the computation time for the segmentation methods. Another issue is that there is information in the data that cannot be extracted using feature selection methods or expertise. Recently, embeddings become a popular approach for representations. Embeddings are capable to represent words as shown by Mikolov et al. ( 2013 ), time series (Nalmpantis and Vrakas 2019 ), or products (Vasile et al. 2016 ) but are not limited to them. With embeddings, it could be possible to encode additional behavioral information that could improve the customer targeting process. This was already demonstrated for product recommendation (Vasile et al. 2016 ; Tercan et al. 2021 ; Alves Gomes et al. 2021 ; Srilakshmi et al. 2022 ) or customers’ purchase behavior prediction (Alves Gomes et al. 2022 ). Despite the popularity in several e-commerce tasks, no author used an customer embedding representation in the reviewed literature. From our perspective, the reason is that embeddings are less interpretable, and therefore, non-automated customer targeting is more difficult.

4.2 Analysis of segmentation methods

We found 13 different types of segmentation methods. K-means is by far the most used approach. Especially, in the last years from 2020 to 2022 k-means is used 24 times. In regard to the third guiding question, we can conclude that besides a k-means upwards trend no other trend can be spotted. The question that now arises is “why is k-means becoming so popular recently”? One answer is that k-means is simple to implement and an established approach. In contrast, other approaches like EAs, hierarchical clustering, or SOMs are more complex according to how the run time or space requirements grow as the input size grows (Bachmann-Landau notation) and it needs more effort to implement them (Firdaus and Uddin 2015 ). The ever-increasing amounts of data in e-commerce amplifies this trend because simple methods can be used more quickly, and thus, results can be obtained faster. However, if this is the reason, then the question that follows is why are rule-based approaches not popular as well? As shown by Fig.  5 the density of rule-base approaches increased in the years between 2018 and 2021 but some other influencing factors play a major role on the methods popularity. While we can only make assumptions at this point, rule-based segmentation approaches have significant drawbacks. For example, they require domain knowledge to set appropriate thresholds for separating customer segments. The increasing and heterogeneous amount of data complicates this setting of appropriate thresholds or requires a higher dynamic, which in turn results in more rules and complex relationships. Our assumption is supported by the aggregated information in Table 3 that shows that k-means is applicable on 100 million data points.

Considering the data dimensionality which is used in the publications we see that k-means approaches can handle a larger amount of data and is in pair with latent class approaches. As we mentioned, the hybrid approach that uses the largest amount of data is a combination of the latent class model. However, concerning the number of customers in the data which are the objective of the clustering, the numbers rarely exceed the 10,000. This indicates that clustering approaches need an appropriate feature selection method to deal with a larger amount of data. All this doesn’t mean that the methods cannot be applied to larger data sets. Our argumentation is based solely on the paper corpus we saw. Based on the findings concerning the data dimensionality, we can state for guiding question number three, that k-means and latent class models can process the largest amount of data among all segmentation methods. However, as already stated this applies only in case of manual feature selection. We recommend using a feature selection methods namely the RFM-analysis that allows to process any kind of data dimensionality. Note that we don’t address the time or memory complexity of the segmentation methods, which is also a performance indicator, but evaluate them based solely on the amount of data used in the literature.

In terms of use cases, we can state that each clustering method is usable in retailing use cases. We cannot make such a generalized statement for other domains. However, it is not unlikely that all segmentation methods can be used independently of the domain. Especially with k-means, we can see that it has the largest variant of different use cases. Nonetheless, the reason for being used in different domains can be because k-means is applied in most publications.

Apart from a quantitative analysis of the segmentation method, we would like to make a qualitative analysis. Unfortunately, there is no way to determine which segmentation method performs best. The major issue in our opinion is that there is no ground truth for the customer segments to determine a score. Therefore, there is no unified method for qualitative evaluation which is necessary to state which segmentation method is superior to the other. We noticed that there are a vast amount of different evaluation methods as presented in Sect.  3.6 . Different evaluation approaches are required for different clustering approaches, i.e. fuzzy (soft) clustering has different properties than hard clustering. It would simplify qualitative segmentation analysis if the scientific community agree on a small set of evaluation methods. The urge is there which we can see in the number of different evaluation metrics and the considered publication where the authors try to show that their approach is superior to others. If everyone would use the same metrics, the authors’ efforts would have more significance and the performance of the method could be compared over different publications which are usually done in other scientific disciplines. Nevertheless, due to the absence of ground truth, correctness can never be shown, and therefore, the purpose of unified evaluation methods may be questioned. Another aspect we want to consider is evaluation metrics with semantic interpretability. Such metrics would have the advantage to show which segmentation algorithm partitions the customers in a desirable way. Furthermore, it would create comparability between multiple segmentation methods for identical use cases. However, the challenge is to define evaluation metrics that have the capacity to be semantic interpretable and, at the same time, can be applied to different segmentation methods and use cases. In numerous publications, evaluation methods are used to find the optimal number of segments. Therefore, even if there is no defined uniform way to compare clustering approaches, they still have their reason of existence and are necessary methods for determining an optimal number of segments.

Before the study, we would not have expected such a distribution, as we thought that a relatively old method like k-means (first proposed around 1960 and published in 1982) is not so often used especially not so often in the last years of the considered literature. In addition, we assumed that there would be newer and more innovative approaches like deep learning-based approaches. The reason for our assumption is that deep learning techniques archived great results in a broad range of applications such as computer vision and natural language processing and we expected to see these methods transferred to customer segmentation and analysis. However, deep learning-based segmentation only appeared once in the literature. Regarding our initial assumption, an open question still remains. Will deep learning methods be used for customer segmentation in the future? As with embeddings used as feature representations, one advantage might be that the feature representation phase can be omitted, and thus less information is lost. However, a disadvantage and probably the reason why we did not find more than one deep learning-based segmentation method is that the customer segmentation needs to be formalized as a learning problem. Furthermore, segmentation is by design an unsupervised process and no ground truth exists. Another point that speaks against deep learning segmentation is that deep learning models are black boxes and therefore, interpretation, explainability, and reasoning for decision making are no longer achievable.

Based on our findings and analysis, we recommend using k-means or rule-based segmentation approaches which are easy to use and implement, to partition different customers for e-commerce use cases. In addition, if massive transaction data is available, we recommend RFM-analysis for the customer representation that can be extended with additional features.

5 Conclusion and future research

In this survey, we provided an extensive literature review on customer targeting process for e-commerce use cases whose main focus lies in the segmentation methods for customer behavior analysis. Our goal was to provide an overview of segmentation methods used in the literature and to determine best-practice approaches and their limitations. We introduced the steps of the research and key criteria for the paper selection and analyzed as well as discussed our findings afterward. In our work, we considered 105 publications with different case studies that focused on customer analysis with segmentation methods.

Summarizing the approaches examined, the identified four-step process emerges as the current gold standard for personalized customer targeting in e-commerce. For the customer representation, either hand-crafted features or an RFM analysis adapted to the use case are generally used. Subsequently, for customer analysis, the generated customer representation is segmented using a k-means approach.

Based on our research and literature analysis we made several findings regarding our investigated topic.

We identified a common process for personalized customer targeting which includes feature selection methods, customer segmentation, and customer targeting. This process is illustrated by Fig.  2 and can be utilized to plan customer targeting campaigns. Each of the four steps has its own requirements and its a discipline of its own worth to be investigated. We focused on the customer analysis and customer representation part.

Over the years, the number of publication that deals with customer targeting in e-commerce are continuously increasing. This supports the preceding assumption that it is a time-relevant subject.

Feature selection methods enable the usage of larger datasets and among the utilized methods the RFM-analysis is by far the most popular one. There are many reasons for this: first, the method is easy to use, and second, it is based on features that can be extracted and understood. Another advantage of RFM analysis is the possibility of its easy adaptation to specific use cases by adding further or changing existing features.

In approximately half of the publications (47.6%), manual feature selection was used.

Among all the used clustering methods, k-means has emerged as the most popular approach (39% in total). Since 2011, it was repeatably used. Besides that, no other over-time trend was identified. The popularity of k-means can be explained by its simplicity and applicability to large scale datasets.

We were not able to define the best clustering approach based on its performance because many different evaluation methods exist and were used to evaluate the cluster quality.

Some evaluation methods can be used to determine the optimal number of segments which is unknown from the beginning and is often a tunable hyperparameter.

The literature review doesn’t show that a segmentation method exists that is applicable to every e-commerce use case that involves customer analysis. This could only be suggested, if at all, for the retail use case. In terms of method, k-means has been used in every use case identified, with the exception of the manufacturing use case.

New insights always come with new challenges and opportunities. Based on our research and findings we propose future research ideas which should be investigated. Especially with regard to recent developments in the field of Deep Learning, there are many approaches that can be adapted and, according to the our assessment, display a lot of potential.

Deep learning introduced innovations in many domains such as natural language processing and computer vision. Nevertheless, we only found one DL-based segmentation approach in our research. Therefore, we see potential and a research gap in DL techniques for segmentation.

The process steps in the identified four-phase process for customer targeting are essentially based on a high level of understanding of the customers, i.e. their needs and behavior. This is necessary for marketing and domain expert to tailor personalized marketing strategies for the customers. However, with the advent of deep learning-based approaches personalized customer targeting can be done fully automated e.g. end-to-end model and therefore, the customer analysis step which includes customer segmentation can be omitted. This development can be seen for example in deep learning-based recommendation systems which make personalized recommendation without the need of the customer analysis. This leads to the question; How customizable are the individual phases of this process and can individual steps be omitted to increase efficiency or are all steps so fundamental that a deviation from these procedures would have a negative impact on the goal, customer targeting?

Manual feature selection is still frequently used. The feature quality is thereby highly depended on the underlying expertise to select or define important features for clustering. Progressive digitization is leading to growing challenges, especially in dealing with data volumes and data diversity. To meet these challenges, manual feature selection is reaching its limits as it is not able to tap the insight potential within this data. Hence, the question arises if approaches exist that can help experts to create meaningful and representative features for customer representation?

In this regard a look outside the box to other e-commerce research, e.g. click-through rates prediction can yield new approaches. There researchers and professionals have started using feature embeddings on manual selected features with the underlying assumption that the learning models will learn meaningful representations from the data. This would simplify the manual feature selection process. However, these learning models are usually based on deep neural networks which are unfortunately black boxes and not interpretable. The question rises, if segmentation methods can be used as a post-processing to provide interpretability for the embedded features and therefore, an insight over the customers? (Which got lost by not using the customer analysis step).

In our research, we identified many different evaluation metrics to evaluate the performance of segmentation methods. Nevertheless, we could not find a consensus on evaluation metrics as in other domains. The reason is the missing ground-truth. This circumstance makes it difficult to determine the effectiveness and transferability of a segmentation approach from one use case to another. The open question that remains is, is it necessary, to develop evaluation metrics with semantic meaning and is it possible to transfer such metrics to different experiments to enable comparision of the segmentation methods?

In our literature review, we covered the usage of feature selection and segmentation method for personalized customer targeting. E-commerce is a dynamic environment with ever new challenges and therefore, new research opportunities.

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Alves Gomes, M., Meisen, T. A review on customer segmentation methods for personalized customer targeting in e-commerce use cases. Inf Syst E-Bus Manage 21 , 527–570 (2023). https://doi.org/10.1007/s10257-023-00640-4

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Market Segmentation -A framework for determining the right target customers BA-thesis May 2010

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The purpose with the thesis is to provide a framework for exemplifying how market segmentation can determine the right target customers. This will be done by using the landline telephone and the mobile telephone as examples. First by explaining the market segmentation process and secondly followed by an analysis according to a questionnaire conducted and using respectively the Minerva model and the Mosaic model. During the first part of the thesis, theories by Kotler et al. and Gunter el al. will be predominant, whereas in the last part internet articles from AC Nielsen -AIM and dobney.com, amongst others will be the predominant sources.

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This dissertation explores options for improving the success of market segmentation research by testing different market segmentation methods and effects of information and communication technologies (ICTs) in tourism research. The purpose of this study is to increase the success of market segmentation research in the field of travel and tourism. The context of this study is rural tourism in Finland, which is regarded as an important source of revenue for many rural areas and a field where information on data-driven market segmentation is practically non-existent. This dissertation consists of four papers, all discussing the topic of market segmentation in tourism. The theoretical basis of this study lies within the discipline of marketing and relies on the assumption that markets are heterogeneous, and that through market research it is possible for businesses to diversify their offerings to suit the needs and wants of specific segments in a way that creates value both for the customer as well as the company. Market segmentation is one of the cornerstones of marketing the management paradigm and its usefulness has been demonstrated repeatedly both in the academic literature and by practitioners. This study adapts a postpositivistic research paradigm to study the possibilities for improving market segmentation theory and methodology. By means of a literature review and two surveys of Finnish rural tourism websites data is collected on the impact of ICTs on market segmentation in tourism as well as the needs and wants of Finnish rural tourists. This study provides evidence that the academic market segmentation literature does indeed identify segments that also exist in practice, thus bridging the gap between academic and practice, and contributes to the way market segmentation is conducted in travel and tourism.

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Abstract The purpose of this study is to investigate the factors affecting the decision of buying mobile phone devices in southwestern Nigeria. In order to accomplish the objectives of the study, a sample of 246 consumers were taken by using simple random sampling technique. Both primary and secondary data were explored. Moreover, six important factors i.e. price, social group, product features, brand name, durability and after sales services were selected and analyzed through the use of correlation and multiple regressions analysis. From the analysis, it was clear that consumer’s value price followed by mobile phone features as the most important variable amongst all and it also acted as a motivational force that influences them to go for a mobile phone purchase decision. The study suggested that the mobile phone sellers should consider the above mentioned factors to equate the opportunity.


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Essays on Market Segmentation and Retailers' Competing Strategies

This dissertation focuses on exploring U.S. food retailers’ strategic interactions and the impacts on consumers. Specifically, I examine food retailers’ strategies on segmenting consumers, conducting price discrimination, and designing their product portfolio in the context of the U.S. yogurt market. The first essay examines the segmentation strategies employed by food retailers, with a focus on the use of advanced machine learning techniques (i.e., K-means clustering) to group consumers based on various characteristics, including demographics and purchase history. The second essay applies the data-driven market segmentation obtained in the first essay to a second-degree price discrimination model. The third essay relaxes the implicit assumption made in the first two essays that consumers’ choice set is fixed, and studies a non-price strategy, namely, adjusting assortment, that is adopted by food retailers in response to regulations. By analyzing the retailers’ strategies on market segmentation and responses to regulations, this dissertation aims to shed light on the strategic interactions of food retailers and consumers, and the competitive landscape of food market in general.

Understanding the strategies employed by food retailers is of utmost importance in agricultural and food economics as it directly influences consumers and their purchasing decisions. The food retail industry in the U.S. is highly competitive, with retailers continuously devising tactics to attract and retain customers. Dimensions of competition such as pricing strategies, product assortment, promotional activities, and customer service can significantly impact consumers’ choices and behaviors. Investigating the strategies employed by food retailers not only provides insights into their business operations but also sheds light on how these strategies affect consumers.

The first essay explores the application of machine learning methods in consumer segmentation under different information environments. Machine learning methods become popular in economic and marketing research, partly because of their flexibility in application. Although recent studies apply these advanced methods to various topics including water, housing, health, and food markets, much is less known about using machine learning methods to facilitate firms’ market segmentation decisions. Using Nielsen Consumer Panel data, I show that K-means clustering, one of the unsupervised learning methods, can be applied to conduct market segmentation. From the retailers’ perspective, incorporating more consumer information (i.e., purchase history) leads to the change in segments consumers belong to.

The second essay assesses the effectiveness of data-driven market segmentation in enhancing price discrimination models. Price discrimination models are commonly adopted by firms to optimize revenue and profitability by customizing prices to different customer segments. Existing studies often rely on exogenous assumptions for consumer segmentation, which may or may not be applicable in practice. This study advances the existing literature by replacing the consumer segment assumption with data-driven market segmentation obtained through K-means clustering. The results are then applied to the second-degree price discrimination model to analyze how sensitive the firms optimal profits are under different consumer information environments. The findings reveal that adding consumer information to consumer segment leads to a more inelastic demand for the consumer segments and an increase in firm’s profits.

The third essay focuses on the non-price strategies retailers adopt to respond to the Unit Pricing Regulation (UPR). UPR requires retailers to display unit prices in addition to product prices and helps consumers make more informed decisions. Despite extensive research on consumers perceptions of unit prices, little is known about retailers price and non-price responses under intensified price competition brought by UPR. Relying on the geographic variation in UPR implementation across U.S. states, we use product-store-level scanner data on the U.S. yogurt market and identify UPR effects on store product offerings and pricing. We find that mass merchandisers reduce product offerings under UPR. Grocery stores that belong to a retail chain entirely under UPR add brands, while other grocery stores make no significant assortment responses. UPR price effects are limited for mass merchandisers as well as grocery stores. Using a structural demand model, we find that the average consumer surplus falls under UPR, highlighting an unintended policy effect.

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Dissertations / Theses on the topic 'Market segmentation – United States'

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Bellemare, Guy. "Capital market segmentation, the case of Canada and the United States." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0001/NQ27876.pdf.

Altawail, Ghassan Mohammed. "Gender segmentation and its implementation in Saudi Arabia." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2281.

Mahmoudi, Dillon. "Making Software, Making Regions: Labor Market Dualization, Segmentation, and Feminization in Austin, Portland and Seattle." PDXScholar, 2017. https://pdxscholar.library.pdx.edu/open_access_etds/3768.

Senger, Saesha. "Gender, Politics, Market Segmentation, and Taste: Adult Contemporary Radio at the End of the Twentieth Century." UKnowledge, 2019. https://uknowledge.uky.edu/music_etds/150.

Dasso, Michael W. "Analysis of the United States Hop Market." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1419.

Qureshi, Zaina Parvez. "Market Discontinuation of Pharmaceuticals in the United States." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1250572741.

Clark, John H. Tucker Joshua L. "A strategic market analysis of the Open Market Corridor /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FClark.pdf.

Zhu, Liye. "Three essays on the United States health insurance market." Ann Arbor, Mich. : ProQuest, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3220413.

Conibear, Anthony. "Labour market segmentation and regulation theory : an application to the United Kingdom." Thesis, Open University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340714.

Lyon, Mark Evan. "Improved market research in United States Marine Corps field contracting." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA293671.

Choi, Seok Joon. "Three essays on agent behavior in United States housing market." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2005. http://wwwlib.umi.com/cr/syr/main.

Kulkarni, Veena S. "Asians in the United States labor market 'winners' or 'losers' ? /." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8581.

Senteri, Zulkifli Bin. "An econometric analysis of the United States palm oil market." The Ohio State University, 1985. http://rave.ohiolink.edu/etdc/view?acc_num=osu1392718329.

Senteri, Zulkifli Bin. "An econometric analysis of the United States palm oil market /." The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487262513408123.

Weber, Matthew August. "Riparian Valuation in the Southwestern United States." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/195121.

Economopoulos, Andrew James. "Impact of free banking on the free banking market." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/54288.

Cann, Joseph Patrick. "Structural Change of the Western United States Alfalfa Hay Market and its Effects of the Western United States Dairy Industry." DigitalCommons@USU, 2014. https://digitalcommons.usu.edu/etd/2118.

Cann, Joseph. "Structural change of the Western United States alfalfa hay market and its effect on the Western United States dairy industry." Thesis, Utah State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1584291.

Ide, Yoshinori. "Liberalization of international air transport in the Japan-United States market." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0001/MQ44062.pdf.

Brett, Bridget C. "Potential market for LNG-fueled marine vessels in the United States." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44920.

Gamber, Joanne. "The Level of Market Alignment Between the United States and France." Miami University Honors Theses / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=muhonors1111685544.

Cacho, Joyce Agnes Sabina. "United States competitiveness in soybean trade : loss market share in the Japanese soybean import market /." Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-08222009-040252/.

Bizzotto, Magalhaes Garcia Rafael. "International Market Assessment and Entry – United States’ Fast Casual Firm Entering the Brazilian Food Market." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1560964690816666.

Keliher, Macabe. "Americans in eastern Asia, revisited Anglo-American rivalry and the China market /." online access from Digital Dissertation Consortium, 2007. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?1442231.

Vassiliou, Constantinos. "U.S. terrorism insurance market the case of government intervention /." Diss., Connect to the thesis, 2006. http://hdl.handle.net/10066/595.

Ramaswami, Narayanaswamy Accounting Australian School of Business UNSW. "Voluntary Disclosure and the Role of Product Market Competition: A Study of Disclosures in Press Releases by U.S. Companies." Awarded by:University of New South Wales. School of Accounting, 2001. http://handle.unsw.edu.au/1959.4/18643.

Unti, Marco <1981&gt. "The secondary market for life insurance policies in the United States market evolution and product valuation." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2010. http://amsdottorato.unibo.it/2432/.

Kuo, Yu-Chen. "Marriage, fertility, and labor market prospects in the United States, 1960-2000." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2561.

Youn, Hwa-young. "Co-branding strategy for imported children's programming in the United States market /." Available to subscribers only, 2005. http://proquest.umi.com/pqdweb?did=1079668391&sid=26&Fmt=2&clientId=1509&RQT=309&VName=PQD.

Mah, Hared. "Labor Market Experiences of Hispanic and Black Immigrants in the United States." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/dissertations/1700.

Sethi, Rosh Kumar Viasha. "Technology Adoption in the United States: The Impact of Hospital Market Competition." Thesis, Harvard University, 2014. http://etds.lib.harvard.edu/hms/admin/view/57.

Johnson, Jodien M. Mencken Frederick Carson. "Federal employment concentration and regional process in nonmetropolitan America." Waco, Tex. : Baylor University, 2008. http://hdl.handle.net/2104/5238.

Gross, Michael. "Labour market segmentation : the role of product market and industry structure in determining labour market outcomes; a test for the United Kingdom." Thesis, University of Cambridge, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292028.

Yang, Hengsheng. "China market between myth and reality : U.S.-China economic entanglements during China's age of reform /." access full-text online access from Digital dissertation consortium, 1997. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?9808859.

McLean, Caitlin Camille. "Market-based childcare & maternal employment : a comparison of systems in the United States & United Kingdom." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/25694.

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Malevich, Steven Brewster, and Steven Brewster Malevich. "Cool-Season Moisture Delivery and Multi-Basin Streamflow Anomalies in the Western United States." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/624160.

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Iniguez, Christian R. "Demand shifts in outlet selection in the United States market for fresh flowers." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011838.

Bruin, Thomas M. "Real estate investment trusts and market sentiment in the United States & Europe." View electronic thesis (PDF), 2009. http://dl.uncw.edu/etd/2009-2/rp/bruint/thomasbruin.pdf.

Kess, Lauren. "Creating a Risk Pool of Defunctness in the United States’ Higher Education Market." Otterbein University Distinction Theses / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=otbndist1620463295558415.

Laffman, John D. "A Random Coefficient Analysis of the United States Gasoline Market From 1960-1995." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/34489.

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Marketing Dissertation Topics

Published by Jamie Walker at January 11th, 2023 , Revised On August 18, 2023

Marketing is a business-focused subject, so you’ll be exposed to much more than just creativity. You’ll learn how to set budgets, find new customers, enter international markets, and decide on prices or profits.

As a marketing student, you will be required to complete a marketing dissertation to complete your degree programme. Your dissertation topic can relate to branding, relationship marketing, online/digital marketing, marketing ethics, and any other field of marketing.

To help you get started with brainstorming for marketing topic ideas, we have developed a list of the latest topics that can be used for writing your marketing dissertation.

These topics have been developed by PhD-qualified writers of our team , so you can trust to use these topics for drafting your dissertation.

Review the step-by-step guide on how to write your dissertation here.

You may also want to start your dissertation by requesting  a brief research proposal  from our writers on any of these topics, which includes an  introduction  to the topic,  research question , aim and objectives,  literature review , and the proposed research methodology conducted.  Let us know  if you need any help in getting started.

Check our  dissertation example to get an idea of  how to structure your dissertation .

You can review step by step guide on how to write your dissertation here .

Review Our Best Dissertation Topics 2021 complete list.

2022 Marketing Dissertation Topics

Topic 1: assessing the role of communication strategies in fashion marketing- a case study of uk.

Research Aim: The purpose of this study is to investigate the role of communication strategies in the world of   UK fashion marketing. This will also give us an understanding of how new fashion remanufacturing should be communicated to the consumers. Focusing on how information and messages about the brands or products should be labelled to attract the audience.

Topic 2: How Influential are Retail Stores and Fast Fashion on each other? A case study of Zara

Research Aim: Fast fashion is an idea in which retailers target their business strategies, reducing the time it takes to get products into the store, using an in-season purchasing strategy to keep the products in the market updated during the season. This study aims to find the impact or influence of fast fashion and retail stores on each other, focusing on Zara as it is considered as the famous brand among consumers. It will look at what happens to fast fashion when it is delivered to retail outlets, as well as the adaption of fast fashion in the retail sector and how it is communicated to customers.

Topic 3: Assessing the Key Ethical Issues in the marketing of Pharmaceutical Products in the UK.

Research Aim: Pharmacists may occasionally be led to unethical behaviours during contact; that’s why it is important to understand these behaviours. Pharmaceutical products are sensitive to advertising, and minor misconduct can lead to loss of public trust in the industry. The current study will examine the challenges faced and the key ethical issues that occur during the marketing of pharmaceuticals products focusing on the UK pharmaceutical industry; through quantitative research analysis.

Topic 4: Brand Marketing to a Global World and its impact on local cultural beliefs and attitudes- A literature review.

Research Aim: Different cultures in different countries act as challenges to global marketing. The aim of this study is to develop an understanding of how brand marketing in a global world has a huge impact on the cultural beliefs and attitudes of people. It will provide an experience of international and local consumer cultures and their mutual influence on many consumer behaviours and their effect on their decision-making process.

Topic 5: E-sports marketing- Investigating purpose and scope in current times.

Research Aim: This study aims to find the emerging trend of esports marketing and its scope in current times. It will also explore the collaborative efforts of gaming companies, players and different online communities and how they play an important role in maintaining and enriching the value of Esports consumption.   of   It will provide a societal impact of esports and by applying different strategies

Marketing Dissertation Topics for 2021

Topic 1: impact of product packaging on organisational sales: a case study of the uk retail sector.

Research Aim: Due to intense competition in the UK retail sector, product packaging has gained significant importance concerning consumer purchase decisions. This research will focus on how product packaging (colour, shape, and other attributes) influences consumer purchase behaviour which in return increases or decreases the sales of the organisation.

Topic 2: Impact of E-marketing on consumer purchase decisions: Case of the UK luxury industry

Research Aim: The main purpose of the research is to analyze the impact of electronic marketing on consumer purchase decisions. Different modes of e-marketing will be assessed, and based on the results of each e-marketing channel, the dissertation will be concluded. The focus of this research will be the UK luxury industry.

Topic 3: Analysing the customer-centric marketing strategies in attaining competitive advantage for the firm and sustaining business success

Research Aim: In today’s competitive corporate world, organizations are formulating and implementing customer-centric marketing strategies. These strategies are devised, keeping in mind customer behaviour, customer pattern, customer preferences, customer trends, etc. Considering all these and many other customer-related aspects, companies assess what is successful for their business. This research will discuss the different characteristics of customers that should be studied and how formulating related strategies will help the company gain a competitive advantage and generate profits.

Topic 4: The role of information technology in revolutionizing marketers' approach towards manipulative advertisement

Research Aim: The digital media or the digital world has provided a very effective and large platform for marketers to market and advertise their products. However, this platform can also be used to manipulate customers through deceptive marketing techniques. The main purpose of the research is to analyse the role of information technology in revolutionizing marketers’ approach towards manipulative advertisements. In addition to this, the research will also talk about how marketers use digital media channels to deceive customers who also harm the company’s reputation.

Topic 5: Assessing the impact of integrated marketing communication on consumer impulsive buying behaviour

Research Aim: Consumer impulsive buying behaviour has become an important phenomenon in today’s global world. Companies have been able to acquire a high market share through the impulsive buying behaviour of the consumer. Therefore, this research focuses on analyzing the impact of integrated marketing communication on consumer impulsive buying behaviour.

Topic 6: The Impact of digital marketing on businesses throughout the world

Research Aim: Digital Marketing has changed the face of marketing in today’s world. More and more companies are now adopting this new technique to gain a competitive edge over traditional marketing methods. This research will address the impact of different digital marketing channels on businesses and how each channel can help companies earn more.

Topic 7: Traditional vs Digital Marketing: A comparative study of the last ten years

Research Aim: With the emergence of digital marketing in the global world today, more and more companies are abandoning traditional marketing techniques. This research will compare traditional and digital marketing methods and present data over the past ten years. Through this data, a conclusive analysis will be conducted to determine which marketing is more successful in today’s times.

Topic 8: Studying customers’ responses to automated interactions in the services industry: How does it impact the business?

Research Aim: Many companies have now completely automated their business operations. They have streamlined standard responses given to customers. This research will mainly assess the impact of automated responses on customers, whether or not they impact them in terms of generating and converting leads, and ultimately how it impacts the business overall.

Topic 9: Capturing and analyzing the Voice of Customer (VOC) through Artificial Intelligence: How effective is the technology?

Research Aim: Voice of Customer (VOC) is not a new concept. Companies have been working and collecting data on it for the past several years. It is a method to gather customers’ feedback about their expectations and experiences with respect to your product or service. This research will study how companies gather, assess, and analyse this data through artificial intelligence and how effective it is for businesses. The research will utilise quantitative analysis to conclude whether or not this new technology and strategy is successful.

Topic 10: Online search queries – Can businesses benefit from them and better market their products and services?

Research Aim: Keyword targeting, search engine optimization (SEO), click trend, search trend, etc., are all ways to find how consumers search for a particular product, brand or website online. With more business being done online and with companies focusing more on online marketing, understanding online search queries have become crucial for the business’s success. This research will focus on the different ways through which companies can assess online search queries and whether or not they can benefit from them. Data from past years will be fetched and included to conduct authentic research and conclude accurately.

Also read: Management Dissertation Topics

“Our expert dissertation writers can help you with all stages of the dissertation writing process including topic research and selection, dissertation plan, dissertation proposal, methodology, statistical analysis, primary and secondary research, findings and analysis and complete dissertation writing”. Learn more here .

Relationship Marketing Dissertation Topics

Relationship marketing is a form of marketing that focuses on long-term goals such as building customer loyalty and increasing customer retention. In relationship marketing, products are provided based on relationships and not traditional marketing. This type of marketing helps firms acquire more customers and build loyalty. The more loyal and satisfied a customer is, the more likely they are to make a purchase.

Under relationship marketing, the purchasing pattern, the contact details, and the entire profile of customers are maintained. Normally, firms assign executives to one or more major customers to maintain relationships and satisfy their needs. It is a very useful marketing tool and also an excellent topic to research on. You can choose a topic for your relationship marketing dissertation topic from any of the topics listed below:

Topic 11:Customer loyalty – Behaviour or an attitude? A mixed-method analysis

Research Aim: This research will analyse how customer loyalty is determined, whether it is an attitude or behaviour. A comparative analysis, comparing different elements of attitudes and behaviours, will be conducted.

Topic 12: The usefulness of relationship marketing in the UK fashion industry: To what extent have organisations incorporated club-style membership schemes for their customer and their impact on businesses?

Research Aim: This research will focus on one important aspect of relationship marketing – memberships. The research will revolve around the UK fashion industry, and the impact memberships have on customers and business organisations.

Topic 13: The impact of relationship marketing on customer loyalty: An analysis of Honda Motors

Research Aim: This dissertation will assess how customer loyalty is impacted by relationship marketing. The main focus of this study will be Honda Motors, how the company maintains customer relationships.

Topic 14: Loyalty schemes and customer satisfaction: Do they really have an impact?

Research Aim: This research will analyse whether or not loyalty schemes impact customer satisfaction. If yes, then the various means will be explored.

Topic 15: The interrelationship between switching costs and consumers' resistance to switching brands' loyalty

Research Aim: The relationship between costs and brand loyalty will be assessed in this research. This research will discuss circumstances under which customers decide to switch brand loyalty.

Topic 16:Loyalty schemes and their relationship with sales: An exploratory analysis of the UK retail industry.

Research Aim: This dissertation will conduct an exploratory analysis to conclude whether or not there is a relationship between loyalty schemes and sales of companies.

Topic 17:Maintaining customer relations through relationship marketing. A case of ASDA

Research Aim: The main focus of this research will be to study how ASDA maintains customer relations and whether they prove to be successful for the business or not.

Topic 18:Exploring the effectiveness of online marketing – Does digital marketing help companies build customer loyalty?

Research Aim: This dissertation will analyse how effective online marketing is for companies to help build and maintain customer loyalty. And whether online marketing can be used to build customer loyalty.

Topic 19: Assessing customer satisfaction in the UK tourism and hospitality industry

Research Aim: The UK tourism and hospitality industry will be assessed in this study for customer satisfaction. The dissertation will conclude to answer how the UK tourism and hospitality industry has maintained customer satisfaction.

Topic 20:Technology driven customer engagement – Does it lead to better customer satisfaction as compared to traditional engagement methods?

Research Aim: This dissertation will discuss how technology has impacted customer engagement. Furthermore, it will analyze how effective technology has been in driving customer engagement compared to traditional methods.

Branding Dissertation Topics

Branding involves creating a unique image and name for a product in the minds of the customers. This is done through creative advertising using a brand theme used consistently in all the advertisements. Branding also entails creating a unique logo and name for a distinguished product.

Some consumers compare prices before purchasing a product, but mostly a purchase is made by focusing on the quality of goods and brand loyalty. There is a misconception that branding is the same as marketing, but it can be distinguished based on the former being one of marketing strategy fundamentals.

For successful branding, there should be truthfulness and clarity in every phase through interaction with customers, which will help improve the value and brand perception of a company.

Branding provides companies with a competitive edge over other organizations and has become a very popular topic for research among undergraduate and postgraduate students. When looking to work on a branding related dissertation, you can choose from the dissertation topics below:

Topic 21:Maintaining brand equity through innovation: A case study of Apple Inc.

Research Aim: Innovation has a huge impact on brand equity. The same will be discussed in this research, with Apple Inc. as the main focus.

Topic 22:Building brand equity through celebrity endorsement: Analysis of the fashion industry

Research Aim: Celebrity endorsement is an excellent way to build brand equity. In this dissertation, the same will be discussed concerning the UK fashion industry or another country of your choice.

Topic 23:Brand attitudes and advertisements: Evidence from the past five years

Research Aim: This study will talk about how advertisements shape brand attitudes. Evidence from the past five years will be presented to conclude whether advertisements impact the brand attitude or not.

Topic 24: Packaging as a brand marketing strategy: Assessing its effectiveness in the retail sector

Research Aim: The success of a brand marketing strategy depends on several factors. This dissertation will assess how important packaging is in a brand marketing strategy.

Topic 25:Effect of branding on consumers of Coca Cola and Pepsi: A comparative analysis

Research Aim: Branding has a huge impact on consumers. Competitors utilise this strategy to build customer loyalty. This research will compare two big rivals – Coca-Cola and Pepsi concerning branding.

Topic 26:Branding strategies: Impact and application

Research Aim:  The different types of branding strategies and their implementation process will be discussed in this study.

Topic 27:Analysis of the consumer: Comparative analysis between good quality products and brand loyalty.

Research Aim: This study will discuss how good quality products impact consumers and how it helps companies build brand loyalty.

Topic 28:Building, retaining and maintaining the brand image in the market – Studying MNCs in the UK industry

Research Aim: Brand image and reputation are something that companies should pay close attention to. This research will talk about leading MNCs and how they should build and retain the brand image.

Topic 29:Importance of brand and reliability in the automotive industry – Case of Toyota Motors

Research Aim: Reliability is a huge factor in building a brand. With a specific focus on Toyota, this study will discuss how reliability impacts the brand.

Topic 30:Building brand awareness and equity through online marketing – Assessing its effectiveness

Research Aim: This research will assess the effectiveness of online marketing in building brand awareness and equity.

Topic 31:International brand building in the digital age: The role of digital marketing

Research Aim: Building a brand with the help of digital marketing will be discussed in this research.

Topic 32:Corporate social responsibility and brand management: A case of Nestle

Research Aim: Giving back to the community creates a positive image of the company. This research will discuss how fulfilling corporate social responsibility helps the company maintain its brand.

Also Read:   Chanel’s Brand Identity and Personality

Direct Marketing Dissertation Topics

Direct marketing is a marketing phenomenon that involves direct selling to customers. This includes telephone selling, email selling, direct mail selling, etc. No retailer is involved in the process. The product/service flow includes only two parties, the company and the consumer.

Direct marketing allows businesses and non-profit organizations to communicate with customers directly. It relies on advertisements on the internet, television, or radio.

There are different types and forms of direct marketing, with internet marketing being the most popular. Online marketing helps companies to interact directly with their customers without any middleman. In this manner, companies can gain insight into customers, expectations, and feedback on the product/service.

Below is a list of topics that you can base your dissertation on under the direct marketing theme.

Topic 33:Loyalty schemes and direct selling – Does it help businesses to market directly to customers?

Research Aim: Loyalty schemes are an old but extremely effective marketing tool. This research will discuss and analyze whether direct marketing can be done through these schemes or not.

Topic 34:How customers can protect themselves from deceitful direct marketing techniques?

Research Aim: This study will highlight the unlawful and unethical ways companies adapt through digital marketing and how customers can protect themselves.

Topic 35:Direct Marketing: Effects and implications

Research Aim: The main concept, theory, and framework of direct marketing will be discussed and analysed in this research. The effects and implications of direct marketing will be the main focus of this study.

Topic 36:Do customers respond differently to direct and digital marketing?

Research Aim: Direct and digital marketing will be compared and analysed in this research. Their responses will then be evaluated as to which one is the most effective.

Topic 37:The relationship between the duration of a voice message and the success of Tele-marketing? A case of mobile Industry.

Research Aim: Telemarketing is a successful marketing tool. This research will study the relationship between the duration of a voice message and its success for companies operating in the mobile industry.

Topic 38:Developing a marketing information system for direct marketing: Analysing its effectiveness

Research Aim: A marketing information system is extremely essential for companies today. This research will discuss how a marketing information system can be developed and how effective it is for direct marketing.

Topic 39:The role of business and artificial intelligence in direct marketing – How can companies gain advantage?

Research Aim: Artificial Intelligence is the big thing in the marketing industry these days. Incorporating it into your business for marketing will help you achieve a competitive advantage. The same will be studied and evaluated in this research.

Topic 40:Internet marketing as a direct marketing technique – Assessing its effectiveness and profitability

Research Aim: Internet marketing can be used a direct marketing technique. This research will assess how effective and profitable this technique can be for businesses.

Topic 41:Protecting consumer data and privacy in direct marketing techniques – Evaluating its importance.

Research Aim: Companies do not pay much attention to customer privacy. This research will discuss how direct marketing can help companies protect customer data and privacy.

Marketing Across Cultures Dissertation Topics

Every culture is different. Thus, what is acceptable in one, may not be acceptable in the other. This is why firms must adopt different techniques while operating in different cultures. Before introducing any product, companies need to analyse the cultural aspect of the market.

This has become a very important and deciding factor for the successful operation of a business. Cultures have a deep impact on consumer behaviour, and it plays a key role in shaping the buying behaviour and the attitude of the customer.

There is no doubt that this marketing aspect is worth some research. Some intriguing and current dissertation topics in the field of cultures and marketing are given below:

Topic 42:Impact of culture on the trading Market: A case of general motors through an analysis of the imports and exports.

Research Aim: Marketing is based on different cultures. This research will discuss the impact of culture on the trading market, focusing on general motors’ imports and exports.

Topic 43: Does language impact the identity of a brand? A case of Coca-Cola

Research Aim: Language is an important element of a culture. This study will research and analyze whether or not the language impacts a brand and will utilize Coca-Cola as its main focus.

Topic 44: Amalgamating and assessing the regional differences within the national culture of marketing

Research Aim: This research will study the various regional differences that exist in the marketing culture and how they impact businesses.

Topic 45: The impact of collectivism and individualism on purchasing smartphones

Research Aim: Different cultures have a different impact on society. This research will conduct a cross-cultural analysis to understand how culture impacts marketing strategies

Topic 46:Cross-cultural marketing and how it impacts a business – A specific focus on the FMCG sector

Research Aim: Cross-cultural marketing is utilised by companies operating in different cultures. This research will talk about how cross-cultural marketing is formulated, devised, and implemented in the FMCG Sector and whether it is successful for the company or not.

Topic 47:Belief, religion and values: Do they have an impact on businesses across the world

Research Aim: There can be many cultures in one market. Thus, it becomes challenging for companies to market their product according to cultures. This research will assess this issue by focusing on two different cultures.

Topic 48: Application of marketing mix in a culturally diverse society: Assessing two different cultures

Topic 49: same marketing tact in different markets: how it leads to business failures.

Research Aim: Companies cannot utilise similar marketing techniques for different cultures. This research will assess how this act can lead to the failure of businesses.

Topic 50:Cultural differences and the subsequent effect on supermarkets across the United Kingdom

Research Aim: This research will talk about how various cultural differences impact supermarkets operating in the United Kingdom.

Also Read :   Fashion and Culture Dissertation Topics

Online Marketing Dissertation Topics

When marketing evolved from traditional to online or digital marketing, it was observed that all the rules that traditional marketing followed were no longer useful. The needs and demands of the market had changed, thus online marketing emerged. Digital marketing has been a game-changer in the field of marketing.

New tools, new rules, and new methods have set the marketing game field. Every player entering the business world needs to be well versed with all these new aspects or else it can go out of business quickly.

Online marketing helps business organisations to understand and evaluate customers’ responses to a particular marketing strategy very efficiently. So businesses are now aware of their customer behaviour, trends, what they look for in a product, what are they interested in, etc.

However, while collecting and storing all this customer information, organisations need to ensure the privacy of their consumers to avoid losing their trust. Companies are now in an era where they can efficiently interact and engage their consumers.

Based on their responses, they can devise subsequent marketing strategies. Online marketing is now a powerful marketing tool as it allows organizations to develop specific strategies to suit the needs of their consumers.

The field of digital marketing is worth the research. You can spend hours learning about this facet of marketing, and still will be left with the urge to learn more. Some interesting topic suggestions are given below if you want to base your dissertation on online marketing.

Topic 51:How online marketing impacts the retail sector during the Covid-19 pandemic? - Entering the field of internet marketing

Research aim: This research will aim to discover the impact of the covid-19 pandemic on the performance of the retail sector in any country of your choice. How did the retail stores move to online marketing to overcome the losses?

Topic 52: Organisations and their use of personalised products: How do companies decide who to market?

Research Aim: There are times when companies launch personalised services or products for a specific group of customers. To identify this need, traditional research is not useful. Customers do not want to give out this type of information. With online marketing and its various tools, companies can now gather this data. This research will delve deep into how that happens.

Visit our topics database to view 100s of dissertation topics in your research area.

Topic 53:The role of online marketing in driving sales: Studying the retail sector

Research Aim: The main focus of this research will be to understand how the retail sector is impacted through online marketing and its role.

Topic 54:Implications and application of online marketing

Research Aim: Online marketing tools are extremely powerful. Various tools will be discussed and analysed in this research to conclude how well they perform.

Topic 55:How can companies overcome the hate speech of unhappy customers?

Research Aim: With the ease of gathering data, online marketing and tools can also lead to hate speech from customers. This research will evaluate different ways through which companies can overcome this issue.

Topic 56:What attributes are preferred by customers for online search, purchasing and comparison?

Research Aim: The different types of research, search, purchasing, and comparing attributes undertaken by customers will be assessed in this study. Moreover, their impact on business will be studied.

Topic 57:The power of online marketing tools – Assessing their effectiveness with respect to sales

Research Aim: Different marketing tools will be analysed and studied in this research. They will be assessed based on their effectiveness concerning sales.

Topic 58:Analysis of change in behaviour of customers in offline and online marketing

Research Aim: Online and offline marketing are extremely different. This research will analyse how customers behave differently in an online marketing setting as compared to an offline marketing setting.

Topic 59:Impact of appearance and visual effects for the effectiveness of online marketing

Research Aim: Online marketing utilises different appearance and visual effects to attract customers. This research will analyze how effective these techniques are for the company.

Topic 60:Websites and E-commerce – Do they influence customer behaviour? A case study of the UK fashion industry

Research Aim: With online marketing, it is essential that you have a website and an online store if you’re selling products. The impact of both website and e-commerce on marketing in the UK fashion industry will be assessed.

The 4Ps (Price, Product, Promotion, Place) of Marketing Mix Dissertation Topics

Price, product, promotion, and place are also known as the four pillars of marketing. Referred to as the marketing mix, these four components help companies decide on a product and/or marketing strategy.

These four factors (4Ps) are the key ingredients of a successful marketing strategy since they allow for an in-depth analysis of the market and marketing strategies concerning any particular product. The companies analyse the culture, the product itself, and the pricing of other similar products to gain a competitive edge for their business and production processes.

To understand more about these components and how they impact businesses, you can research this area. Some relevant topics in this area of marketing are listed below for you to base your dissertation on:

Topic 61:Understanding the importance of location for customers Starbucks USA vs Starbucks UAE

Research Aim: Location has a great impact on the company’s sales and marketing efforts. This research will assess how impactful location is for customers by comparing Starbucks located in the US and the UAE.

Topic 62: Pricing war between competitors: Analysing the case of Coca-Cola and Pepsi

Research Aim: Companies usually price their product to competitors to stay relevant and to help their products succeed. This research will analyze how competitors price their products by assessing the pricing strategies of Coca-Cola and Pepsi.

Topic 63:Impact of point-of-purchase promotion on sales: A case study of ZARA

Research Aim: Promotions are an effective way of selling products. This research will study the point of purchase promotion and its impact by focusing on ZARA.

Topic 64: Product packaging and its impact on buying decision – An exploratory analysis

Research Aim: The packaging of a product has a huge impact on the buying and purchasing decisions of customers. This research will conduct an exploratory analysis to understand this impact.

Topic 65:International pricing strategies and their Impact on the brand image: A case study of iTunes

Research Aim: Pricing strategies may or may not differ in different locations. This research will analyze whether iTunes has gained or not by its pricing strategies in different locations.

Topic 66: Impact of price adjustment strategies in online and offline setting

Research Aim: Prices vary in different settings. This research will study the price strategy adjustment in online and offline marketing.

Topic 67:Should Online Reviews and Word of Mouth be a New Component in the Marketing Mix?

Research Aim: Word of mouth and online reviews have proved to be extremely effective marketing tools in recent times. These components concerning the marketing mix will be studied in this research.

Topic 68:Difference between Online and Offline Promotions – How do They Impact Brand Image

Research Aim: A variety of marketing promotion techniques exist. This research will talk about the different online and offline promotional tools and how they impact brand image.

Topic 69:Impact of Traditional Promotions vs Social Media Promotions – Analyzing Burberry’s Promotional Campaigns

Research Aim: Social media promotional campaigns gain a lot of traction. With a specific focus on Burberry’s promotional campaigns, this research will analyse traditional and social media campaigns.

Topic 70:Effect of Premium Pricing Strategies on Consumers. A case of Apple Products

Research Aim: Of different pricing strategies, premium pricing strategies are adopted for luxury products. The effect of this type of pricing strategy on luxury products (Apple products) will be analyzed in this study.

Topic 71:Impact of Cultural Values in Promotional Activities

Research Aim: Culture has a huge impact on the marketing efforts of a company. This research will talk about the various cultural values and how they impact the promotional activities of businesses.

Topic 72:Placing Products in a Central Location and Ease of Access: Assessing its Impact on Customers

Research Aim: Location affects the sales of products and services. This research will assess the impact of customers when products are placed in a central location and when they are offered ease of access.

Topic 73:Influence of celebrity endorsement on sale: A comparative analysis of Nike and Rebook

Research Aim: Celebrity endorsement is a highly effective way to increase sales. A comparative analysis between celebrity endorsement done by Nike and Reebok will be evaluated in this research.

Topic 74:Impact of promotions upon customer’s perception

Research Aim: Customers may or may not change their perception after marketing promotion efforts. This research will discuss whether promotions can change perceptions or not.

Topic 75: Analysing the impact of cartoon characters on children

Research Aim: Products marketed towards children are tricky to market. This research will study whether including a cartoon character to attract children helps businesses or not.

Marketing and Consumer Psychology Dissertation Topics

Marketing is fundamentally based on consumer behaviour. Studying consumer behaviour helps businesses understand the customer in a better manner. Not only this, but it also helps them improve their marketing strategies by understanding the problems of a consumer with a specific focus on their perception of products. It is very important to understand the psychology of consumers and the various influences that the environment may have on their psychology. Studying these behaviours and patterns helps companies know how they should target their customers and what aspects they should focus on.

Consumer psychology comes in very handy for online marketing. When marketing digitally, companies have little or no information regarding their consumers. Thus, understanding their way of thinking, behaviour, buying patterns, trends, etc., helps businesses understand what the customer expects.

The study of consumer behaviour is very interesting and therefore provides an ideal topic for dissertations.

Topic 76:An investigation of consumer psychology and perceptions and their impact on marketing fashion products

Research Aim: Consumer psychology and their perceptions will be evaluated in this research. These two factors concerning the marketing of fashion products will be assessed.

Topic 77:How does consumer knowledge affect the purchase of products and their buying decision

Research Aim: Consumer knowledge influences their buying or purchasing decision. This research will talk about how this knowledge and its impacts the marketing decisions of a company.

Topic 78:The impact of negative publicity on consumer behavior

Research Aim: Consumers cannot be tricked. They are aware of when companies utilize techniques or tools to create a negative image of other companies. This research will talk about such techniques and their impact on consumers.

Topic 79:Consumer attitude towards in-store shopping and online shopping in Wall-Mart

Research Aim: This research will investigate the attitude of customers shopping in-store (physical stores) versus customers shopping online (digital stores). Walmart’s customers will be the focus.

Topic 80:Understanding consumer psychology to devise effective marketing strategies

Research Aim: Customer psychology will first be discussed in this research. Then, the research will talk about how effective marketing strategies will be devised.

Topic 81:Assessing the consumer behaviour and perceptions in relation to luxury

Research Aim: This research will discuss consumer behaviour when customers opt for luxury products, i.e. what drives them to purchase high-priced products.

Topic 82:Measuring consumer response to new products launched by Nestle

Research Aim: Companies should always measure consumer response to assess their marketing activities. This research will discuss different ways through which customer response to new products launched by nestle is assessed.

Topic 83:Consumer perceptions related to discounts and promotions when purchasing products

Research Aim: Every customer likes to purchase products at discounted prices. This research will discuss consumer perceptions concerning discounts, sales, and promotions when purchasing products.

Topic 84:Creating profitable relationships with consumers

Research Aim: This research will analyse the various ways through which companies can create profitable relationships with customers.

Topic 85:Switching costs – Do consumers think about it when abandoning a brand?

Research Aim: There are different reasons for switching a brand or abandoning it completely. These reasons will be the main focus of this research, and customer perceptions will also be studied.

Marketing and Social Networks Dissertation Topics

Social networks (Facebook, Linked In, and Twitter) have played a decisive role in using the internet and purchasing online. Companies need to understand these social networks and tools from a marketing perspective in today’s business world. Businesses that do not make use of the different social media platforms are entirely out of the race.

This is the power of social networks in today’s corporate world. Not only is it competitive, but these networks also help companies interact with their customers and gain feedback in real-time.

This means that they can launch a product, post and market it on social networks, and assess customer reaction. Companies have done well by utilizing these platforms, and all businesses must have a social media presence and interact with customers.

However, it should be noted that organisations face various challenges using social media as a tool to market their products and services. Social media can make or break things for businesses.

If done right and if the accounts are handled appropriately, nothing can stop the business from achieving success. However, one small mistake can cause a lot of trouble for the company. The backlash on social media is extreme, and the company will have to spend months to bring back its reputation.

Thus, considering the challenging nature of these platforms, it is interesting to conduct researches and studies around various related topics. The following is a list of topics that can be undertaken as a part of social networks and marketing dissertation:

Topic 86:The role of Facebook as a marketing tool

Research Aim: The research will explore the various events in Indian film history that have allowed it to become a global sensation. The paper will analyse its market-driven triumph against Hollywood imports starting from the 1930s. The paper will also examine the nationalist social views of films produced in Bollywood during the 1950s.

Topic 87:Social media marketing vs. traditional marketing evaluating the success rate

Research Aim: Social media marketing is the new trend. But does it really reap results? This will be the main focus of this research, and the results of online marketing and traditional marketing methods will be compared.

Topic 88:Building relationships with customers through social media.

Research Aim: Social media not only helps in networking and connecting people but also enables companies to get in touch with their customers. This research will talk about companies use it as a medium to build relationships with their customers.

Topic 89:How social media influences consumers’ buying preferences

Research Aim: Social media trends are followed by everyone. This research will discuss how these trends are shaped and how it influences the buying and purchasing decision of customers.

Topic 90:How businesses gather Information from social media: A deep insight into customer privacy concerns

Research Aim: A lot is argued about the loss of privacy and data for online customers. This research will investigate the various ways data is collected online and whether or not there are data security breaches.

Topic 91:Consumer perception of social media marketing and its impact on brand image

Research Aim: Consumer perception regarding social media marketing will be assessed in this research. Moreover, the impact of this perception on the brand image will be evaluated.

Topic 92:Is Banner advertisement a good idea in social media marketing? A global comparative analysis

Research Aim: Online Banner advertisements are utilised by almost all companies in the market. This research will discuss various banner advertisement campaigns and their effectiveness.

Topic 93:The role of online stores in the traditional marketing mix

Research Aim: The traditional marketing mix does not take into consideration online marketing. This research will talk about the importance of online and social media marketing in the corporate world today and the role of online stores in the marketing mix.

Topic 94:Why is there more focus on the use of Facebook for marketing rather than other platforms for social media marketing?

Research Aim: Facebook advertising is considered the most powerful amongst all other social media marketing tools. There are various reasons due to which Facebook is considered a powerful tool. All these will be discussed, analyzed, and evaluated in this research.

Also Read:   How to Use Social Networks for your Dissertation

Marketing Ethics Dissertation Topics

Marketing Ethics Dissertation Topics Marketing ethics is a thought-provoking issue in the field of marketing. Where marketers are making efforts to run effective and profitable marketing campaigns for their companies, they should also consider marketing ethics.

The continuous evolution of customers’ attitudes customers over media has a significant impact on businesses worldwide. People nowadays are more concerned about the company’s ethical behaviour and the use of ethics employed by their marketing experts.

They are more concerned about their corporate social responsibility programs and the values of society. Companies must run various social corporate responsibility campaigns, through which they not only create a good reputation but also give back to the community.

These campaigns indeed help businesses to build a reputation and become a preferred brand for consumers. Acts such as animal cruelty and the use of prohibited products hit hard, and a company can lose its long-built strong reputation in a matter of minutes.

There are various ethical concerns that organisations must abide by to have a successful operating and marketing campaign. A dissertation on marketing ethics can be based on any of the following topics:

Topic 95:Ethics and consumer perception: What do consumers really expect from companies?

Research Aim: Corporate and marketing ethics are extremely important for companies. This research will talk about what customers expect from the company regarding ethics and how it shapes their perceptions.

Topic 96:Impact of unethical behaviour of an organisation on sales: Studying unsuccessful marketing campaigns

Research Aim: Unethical organizational behaviour leads to unsuccessful marketing campaigns. The main focus of this research will be the unethical behaviours undertaken by companies and how it adversely affects their sales.

Topic 97:How firms mislead people to enhance product sales and the effect this has on their business

Research Aim: A number of companies mislead their consumers only to enhance their sales. This research will discuss the different ways through which companies mislead people and the impact it has on their business.

Topic 98:How country laws shape business and marketing

Research Aim: When operating in a country, companies have to abide by the laws, rules, and regulations set out by the government. This research will talk about how these laws and regulations shape the business environment.

Topic 99:Ethical considerations and brand loyalty

Research Aim: This research will discuss whether or not ethical business operations have an impact on brand loyalty or do consumers continue to buy from companies who operate unethically.

Topic 100:Spam laws and online marketing – A critical analysis

Research Aim: Online marketing has its own rules. Companies have to abide by spam laws, or else they will be blacklisted. These rules and how companies should abide by them will be analyzed in this study.

Topic 101:Exploring the relationship between marketing ethics and corporate social responsibility

Research Aim: Companies have a responsibility to fulfill. They have to give back to the community, thus operate with corporate social responsibility. This research will discuss whether or not marketing ethics are directly related to corporate social responsibility.

Topic 102: Building company reputation and brand equity through various corporate social responsibility initiatives

Research Aim: The main focus of this research will be to explore whether or not corporate social responsibility initiatives build company reputation or brand equity.

Topic 103: Do cause-related marketing campaigns impact consumer purchase decisions?

Research Aim: This research will explore whether cause-related marketing has an impact on consumer purchase decisions or not.

Topic 104: Public relations and consumer boycotts: Learning lessons from Shell and Nestle

Research Aim: Consumers can boycott a company based on a variety of reasons. This research will discuss the different reasons why consumers boycott and how it impacts public relations, with a special focus on Shell and Nestle.

Important Notes:

As a student of marketing looking to get good grades, it is essential to develop new ideas and experiment on existing marketing theories – i.e., to add value and interest in the topic of your research.

The field of marketing is vast and interrelated to so many other academic disciplines like civil engineering ,  construction ,  law , engineering management , healthcare , mental health , artificial intelligence , tourism , physiotherapy , sociology , management , and nursing . That is why it is imperative to create a project management dissertation topic that is articular, sound, and actually solves a practical problem that may be rampant in the field.

We can’t stress how important it is to develop a logical research topic; it is the basis of your entire research. There are several significant downfalls to getting your topic wrong; your supervisor may not be interested in working on it, the topic has no academic creditability, the research may not make logical sense, there is a possibility that the study is not viable.

This impacts your time and efforts in  writing your dissertation  as you may end up in the cycle of rejection at the very initial stage of the dissertation. That is why we recommend reviewing existing research to develop a topic, taking advice from your supervisor, and even asking for help in this particular stage of your dissertation.

While developing a research topic, keeping our advice in mind will allow you to pick one of the best marketing dissertation topics that fulfill your requirement of writing a research paper and add to the body of knowledge.

Therefore, it is recommended that when finalizing your dissertation topic, you read recently published literature to identify gaps in the research that you may help fill.

Remember- dissertation topics need to be unique, solve an identified problem, be logical, and can also be practically implemented. Take a look at some of our sample marketing dissertation topics to get an idea for your own dissertation.

How to Structure your Marketing Dissertation

A well-structured   dissertation can help students   to achieve a high overall academic grade.

  • A Title Page
  • Acknowledgments
  • Declaration
  • Abstract: A summary of the research completed
  • Table of Contents
  • Introduction : This chapter includes the project rationale, research background, key research aims and objectives, and the research problems to be addressed. An outline of the structure of a dissertation  can also be added to this chapter.
  • Literature Review :  This chapter presents relevant theories and frameworks by analysing published and unpublished literature available on the chosen research topic, in light of  research questions  to be addressed. The purpose is to highlight and discuss the relative weaknesses and strengths of the selected research area whilst identifying any research gaps. Break down of the topic, and key terms can have a positive impact on your dissertation and your tutor.
  • Methodology:  The  data collection  and  analysis  methods and techniques employed by the researcher are presented in the Methodology chapter which usually includes  research design, research philosophy, research limitations, code of conduct, ethical consideration, data collection methods, and  data analysis strategy .
  • Findings and Analysis:  Findings of the research are analysed in detail under the Findings and Analysis chapter. All key findings/results are outlined in this chapter without interpreting the data or drawing any conclusions. It can be useful to include  graphs ,  charts, and  tables in this chapter to identify meaningful trends and relationships.
  • Discussion  and  Conclusion: The researcher presents his interpretation of results in this chapter, and states whether the research hypothesis has been verified or not. An essential aspect of this section of the paper is to draw a linkage between the results and evidence from the literature. Recommendations with regards to implications of the findings and directions for the future may also be provided. Finally, a summary of the overall research, along with final judgments, opinions, and comments, must be included in the form of suggestions for improvement.
  • References:  This should be completed in accordance with your University’s requirements
  • Bibliography
  • Appendices:  Any additional information, diagrams, graphs that were used to  complete the  dissertation  but not part of the dissertation should be included in the Appendices chapter. Essentially, the purpose is to expand the information/data.

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For further assistance with your dissertation, take a look at our full dissertation writing service .

Our team of writers is highly qualified. Our writers are experts in their respective fields. They have been working in the industry for a long time. Thus, they are aware of the issues and the trends of the industry they are working in.

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Identifying and assessing risks in various life situations is the focus of risk management dissertation topics. The majority of them are natural, but there are also artificial ones. In addition to mitigating the effects of various types of risks



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What Is Market Segmentation?

  • How It Works
  • Determining Your Market Segment
  • Limitations
  • Market Segmentation FAQs

The Bottom Line

  • Marketing Essentials

Market Segmentation: Definition, Example, Types, Benefits

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dissertation segmentation marketing

Market segmentation is a way of aggregating prospective buyers into groups or segments, based on demographics, geography, behavior, or psychographic factors in order to better understand and market to them.

Key Takeaways

  • Market segmentation seeks to identify targeted groups of consumers to tailor products and branding in a way that is attractive to the group.
  • Markets can be segmented in several ways such as geographically, demographically, or behaviorally.
  • Market segmentation helps companies minimize risk by figuring out which products are the most likely to earn a share of a target market and the best ways to market and deliver those products to the market.
  • With risk minimized and clarity about the marketing and delivery of a product heightened, a company can then focus its resources on efforts likely to be the most profitable.
  • Market segmentation can also increase a company's demographic reach and may help the company discover products or services they hadn't previously considered.

Investopedia / Matthew Collins

Understanding Market Segmentation

Companies can generally use three criteria to identify different market segments:

  • Homogeneity , or common needs within a segment
  • Distinction , or being unique from other groups
  • Reaction , or a similar response to the market

For example, an athletic footwear company might have market segments for basketball players and long-distance runners. As distinct groups, basketball players and long-distance runners respond to very different advertisements. Understanding these different market segments enables the athletic footwear company to market its branding appropriately.

Market segmentation is an extension of market research that seeks to identify targeted groups of consumers to tailor products and branding in a way that is attractive to the group. The objective of market segmentation is to minimize risk by determining which products have the best chances of gaining a share of a target market  and determining the best way to deliver the products to the market. This allows the company to increase its overall efficiency by focusing limited resources on efforts that produce the best return on investment (ROI).

Market segmentation allows a company to increase its overall efficiency by focusing limited resources on efforts that produce the best return on investment (ROI).

Types of Market Segmentation

There are four primary types of market segmentation. However, one type can usually be split into an individual segment and an organization segment. Therefore, below are five common types of market segmentation.

Demographic Segmentation

Demographic segmentation is one of the simple, common methods of market segmentation. It involves breaking the market into customer demographics as age, income, gender, race, education, or occupation. This market segmentation strategy assumes that individuals with similar demographics will have similar needs.

Example: The market segmentation strategy for a new video game console may reveal that most users are young males with disposable income.

Firmographic Segmentation

Firmographic segmentation is the same concept as demographic segmentation. However, instead of analyzing individuals, this strategy looks at organizations and looks at a company's number of employees, number of customers, number of offices, or annual revenue .

Example: A corporate software provider may approach a multinational firm with a more diverse, customizable suite while approaching smaller companies with a fixed fee, more simple product.

Geographic Segmentation

Geographic segmentation is technically a subset of demographic segmentation. This approach groups customers by physical location, assuming that people within a given geographical area may have similar needs. This strategy is more useful for larger companies seeking to expand into different branches, offices, or locations.

Example: A clothing retailer may display more raingear in their Pacific Northwest locations compared to their Southwest locations.

Behavioral Segmentation

Behavioral segmentation relies heavily on market data, consumer actions, and decision-making patterns of customers. This approach groups consumers based on how they have previously interacted with markets and products. This approach assumes that consumers prior spending habits are an indicator of what they may buy in the future, though spending habits may change over time or in response to global events.

Example: Millennial consumers traditionally buy more craft beer, while older generations are traditionally more likely to buy national brands.

Psychographic Segmentation

Often the most difficult market segmentation approach, psychographic segmentation strives to classify consumers based on their lifestyle, personality, opinions, and interests. This may be more difficult to achieve, as these traits (1) may change easily and (2) may not have readily available objective data. However, this approach may yield strongest market segment results as it groups individuals based on intrinsic motivators as opposed to external data points.

Example: A fitness apparel company may target individuals based on their interest in playing or watching a variety of sports.

Other less notable examples of types of segmentation include volume (i.e. how much a consumer spends), use-related (i.e. how loyal a customer is), or other customer traits (i.e. how innovative or risk-favorable a customer is).

How to Determine Your Market Segment

There's no single universally accepted way to perform market segmentation. To determine your market segments, it's common for companies to ask themselves the following questions along their market segmentation journey.

Phase I: Setting Expectations/Objectives

  • What is the purpose or goal of performing market segmentation?
  • What does the company hope to find out by performing marketing segmentation?
  • Does the company have any expectations on what market segments may exist?

Phase 2: Identify Customer Segments

  • What segments are the company's competitors selling to?
  • What publicly available information (i.e. U.S. Census Bureau data) is relevant and available to our market?
  • What data do we want to collect, and how can we collect it?
  • Which of the five types of market segments do we want to segment by?

Phase 3: Evaluate Potential Segments

  • What risks are there that our data is not representative of the true market segments?
  • Why should we choose to cater to one type of customer over another?
  • What is the long-term repercussion of choosing one market segment over another?
  • What is the company's ideal customer profile, and which segments best overlap with this "perfect customer"?

Phase 4: Develop Segment Strategy

  • How can the company test its assumptions on a sample test market?
  • What defines a successful marketing segment strategy?
  • How can the company measure whether the strategy is working?

Phase 5: Launch and Monitor

  • Who are key stakeholders that can provide feedback after the market segmentation strategy has been unveiled?
  • What barriers to execution exist, and how can they can be overcome?
  • How should the launch of the marketing campaign be communicated internally?

Benefits of Market Segmentation

Marketing segmentation takes effort and resources to implement. However, successful marketing segmentation campaigns can increase the long-term profitability and health of a company. Several benefits of market segmentation include;

  • Increased resource efficiency. Marketing segmentation allows management to focus on certain demographics or customers. Instead of trying to promote products to the entire market, marketing segmentation allows a focused, precise approach that often costs less compared to a broad reach approach.
  • Stronger brand image. Marketing segment forces management to consider how it wants to be perceived by a specific group of people. Once the market segment is identified, management must then consider what message to craft. Because this message is directed at a target audience, a company's branding and messaging is more likely to be very intentional. This may also have an indirect effect of causing better customer experiences with the company.
  • Greater potential for brand loyalty. Marketing segmentation increases the opportunity for consumers to build long-term relationships with a company. More direct, personal marketing approaches may resonate with customers and foster a sense of inclusion, community, and a sense of belonging. In addition, market segmentation increases the probability that you land the right client that fits your product line and demographic.
  • Stronger market differentiation. Market segmentation gives a company the opportunity to pinpoint the exact message they way to convey to the market and to competitors. This can also help create product differentiation by communicating specifically how a company is different from its competitors. Instead of a broad approach to marketing, management crafts a specific image that is more likely to be memorable and specific.
  • Better targeted digital advertising. Marketing segmentation enables a company to perform better targeted advertising strategies. This includes marketing plans that direct effort towards specific ages, locations, or habits via social media.

Market segmentation exists outside of business. There has been extensive research using market segmentation strategies to promote overcoming COVID-19 vaccination hesitancy and other health initiatives.

Limitations of Market Segmentation

The benefits above can't be achieved with some potential downsides. Here are some disadvantages to consider when considering implementing market segmentation strategies.

  • Higher upfront marketing expenses. Marketing segmentation has the long-term goal of being efficient. However, to capture this efficiency, companies must often spend resources upfront to gain the insight, data, and research into their customer base and the broad markets.
  • Increased product line complexity. Marketing segmentation takes a large market and attempts to break it into more specific, manageable pieces. This has the downside risk of creating an overly complex, fractionalized product line that focuses too deeply on catering to specific market segments. Instead of a company having a cohesive product line, a company's marketing mix may become too confusing and inconsistently communicate its overall brand.
  • Greater risk of misassumptions. Market segmentation is rooted in the assumption that similar demographics will share common needs. This may not always be the case. By grouping a population together with the belief that they share common traits, a company may risk misidentifying the needs, values, or motivations within individuals of a given population.
  • Higher reliance on reliable data. Market segmentation is only as strong as the underlying data that support the claims that are made. This means being mindful of what sources are used to pull in data. This also means being conscious of changing trends and when market segments may have shifted from prior studies.

Examples of Market Segmentation

Market segmentation is evident in the products, marketing, and advertising that people use every day. Auto manufacturers thrive on their ability to identify market segments correctly and create products and advertising campaigns that appeal to those segments.

Cereal producers market actively to three or four market segments at a time, pushing traditional brands that appeal to older consumers and healthy brands to health-conscious consumers, while building brand loyalty among the youngest consumers by tying their products to, say, popular children's movie themes.

A sports-shoe manufacturer might define several market segments that include elite athletes, frequent gym-goers, fashion-conscious women, and middle-aged men who want quality and comfort in their shoes. In all cases, the manufacturer's marketing intelligence about each segment enables it to develop and advertise products with a high appeal more efficiently than trying to appeal to the broader masses.

Market segmentation is a marketing strategy in which select groups of consumers are identified so that certain products or product lines can be presented to them in a way that appeals to their interests.

Why Is Market Segmentation Important?

Market segmentation realizes that not all customers have the same interests, purchasing power, or consumer needs. Instead of catering to all prospective clients broadly, market segmentation is important because it strives to make a company's marketing endeavors more strategic and refined. By developing specific plans for specific products with target audiences in mind, a company can increase its chances of generating sales and being more efficient with resources.

What Are the Types of Market Segmentation?

Types of segmentation include homogeneity, which looks at a segment's common needs, distinction, which looks at how the particular group stands apart from others, and reaction, or how certain groups respond to the market.

What Are Some Market Segmentation Strategies?

Strategies include targeting a group by location, by demographics—such as age or gender—by social class or lifestyle, or behaviorally—such as by use or response.

What Is an Example of Market Segmentation?

Upon analysis of its target audience and desired brand image, Crypto.com entered into an agreement with Matt Damon to promote their platform and cryptocurrency investing. With backdrops of space exploration and historical feats of innovation, Crypto.com's market segmentation targeted younger, bolder, more risk-accepting individuals.

Market segmentation is a process companies use to break their potential customers into different sections. This allows the company to allocate the appropriate resource to each individual segment which allows for more accurate targeting across a variety of marketing campaigns.

PubsOnline. " Millennials and the Takeoff of Craft Brands ."

Crypto.com. " Fortune Favors the Bold ."

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10,000 citations strong: How a dissertation topic defied skepticism and redefined marketing narratives

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BGSU Distinguished Research and Teaching Professor Dr. Dwayne Gremler trailblazed the digital age in a quest to decode online word-of-mouth communication.

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While considering his dissertation topic some 30 years ago, a certain Ph.D. candidate, now professor at Bowling Green State University, was interested in researching word-of-mouth communication, but he ran into a stone wall.

A senior professor offered this terse rebuke: “Word-of-mouth is dead. Move on to a different topic.”

After quietly and respectfully disagreeing, the allure of examining the power and potential in this area of the exchange of information remained a topic of interest, if the right project presented itself.

“I thought the topic of word-of-mouth communication was intriguing,” said Dr. Dwayne Gremler, a professor of marketing who has been named as both BGSU Distinguished Research Professor and BGSU Distinguished Teaching Professor.

He found another route to complete his Ph.D. studies, but his interest in researching word-of-mouth persisted, even as the primary focus early in his career centered on other issues.

The growth of the internet changed word-of-mouth communication, which is “the informal spread of information, opinions and recommendations about a product, service or brand from one person to another.” This communication morphed from a casual conversation in the breakroom at work or a brief exchange over the hedgerow with a neighbor into something that could involve thousands or millions of other individuals.


After his own research on the topic, Gremler was invited about 25 years ago to be part of an extensive study led by Professor Thorsten Hennig-Thurau in Germany. Their research was published in 2004 in the Journal of Interactive Marketing, and it has proven to be groundbreaking, at a minimum. 

The research has been cited more than 10,000 times and is regarded as a foundational element in the study of the digital communication and marketing landscape. It is the most cited work among all research about word of mouth and consumer communication.

Their collaborative effort looked at what eventually would be called electronic word-of-mouth communication - or EWOM. Gianfranco Walsh, another German professor, along with Kevin Gwinner, a marketing professor from Kansas State, were also part of the team. 

The four scholars came together to examine what prompted people to share their opinions online. They collected data from an online sample of more than 2,000 German opinion-platform users who had previously written comments about products they had experienced.

As the research ideas, drafts of papers and exchange of information zipped back and forth across the Atlantic via the internet, a scholarly document titled “Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet?” eventually was generated by the group.

“The results revealed that the motivation for providing such comments was social (being an active part of a community and concern for those consumers who would read the reviews), intrinsic (self-expression) and extrinsic (rewards by the platform) incentives,” Gremler said.

Gremler said the two American professors were called on to provide additional insight into the research, plus they had experience with the publication process needed to get the study published in a U.S. journal. 

The BGSU professor said he and his co-authors were fortunate to address the topic at the same time the use, reach and capabilities of the internet were experiencing explosive growth.

“I think in part it was a timing issue, since had the research been four or five years later, someone else might have beaten us to the punch,” he said. 

The paper was not an immediate hit, but researchers around the globe eventually found that the work offered an extensive examination of a topic few others had visited.

“Once the momentum started, since we came along early on and our research had started the conversation, others have used our work and have built on that,” he said. “It certainly has surprised us that interest in our research has grown exponentially, especially in recent years. It is surprising to me it is still being heavily cited.”

Gremler, who joined the BGSU faculty in 2000, said the German-American research team was able to document the early stages of a paradigm shift that continues to grow as it cascades across the globe. Word-of-mouth in the comment and review sections of company websites, as well as crowd-sourced entities such as Yelp, have proven to be a game-changer.

“Historically, word-of-mouth was one-on-one, or one-on-three, and consumers had very little power,” he said. “What has changed with the internet is the power has shifted to the consumer. In a matter of seconds, someone sitting on a flight with their phone can capture a guy being hauled off an airplane, and within very little time a video can be sent out and shared with hundreds, thousands or millions of people.”


His interest in word-of-mouth communication began in the late 1980s before his time in academia, while Gremler was working for a large corporation in Phoenix. The company had an in-house communication network called “Forum” that was a forerunner to social media. If an employee was looking for a house painter, a babysitter or a Volkswagen repair shop, they could post that need on the internal site and review the feedback they would receive from the 5,000-person workforce.

“At that point, I realized how powerful this was, and I could envision that this online thing might make it fairly easy to see responses from hundreds or even thousands of other people. I might have a limited circle of friends, but this kind of connection and capability was both interesting and fascinating.”

He said word-of-mouth communication is often considered highly influential and trustworthy because it comes from personal connections rather than direct advertising and is therefore thought to be unbiased. What resulted from the growth of the internet was a mechanism for a single consumer to reach many others via word of mouth.

Gremler said he jumped at the opportunity to be part of the research team, although the trans-Atlantic operation was a bit cumbersome more than two decades ago.

“We didn’t have all of the cool tools we have now, so no Zoom meetings or screen sharing. We would each work on our aspect of the project and send drafts of papers back and forth,” he said. “It took a lot of planning and coordination since there were different timetables and different cultures involved.”

While the German professors excelled at the quantitative aspect of the work, Gremler focused on making their study more readable and digestible, and finding the pathway to getting the paper into a journal publication. He is still sharing that skill set today.

Gremler has developed a seminar on scholarly research and how to get such works published, and he has presented that at 20 universities in 10 countries with more than 450 participants, primarily Ph.D. candidates seeking advice on how to get their research published in academic journals.

“Most universities don’t teach their Ph.D. candidates how to do that,” he said.

The distinction that has accompanied the frequently cited work has taken place inside the academic ranks, but Gremler said he remains proud of the longevity of the research and its sustained place in the field of communication.

“It is surprising to me that it is still being cited so often, and the exponential growth it has experienced, especially in recent years,” he said. “We found and examined a research question that has generated a lot of interest.”


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Media Contact | Michael Bratton | [email protected] | 419-372-6349

Updated: 02/21/2024 12:10PM


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