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Sustainability Marketing

ISBN : 978-1-80071-245-4 , eISBN : 978-1-80071-244-7

Publication date: 5 May 2021

Sharma, R.R. , Kaur, T. and Syan, A.S. (2021), "Market Segmentation, Targeting and Positioning", Sustainability Marketing , Emerald Publishing Limited, Leeds, pp. 119-132. https://doi.org/10.1108/978-1-80071-244-720211009

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Segmentation research: What is it and how to make it actionable

Segmentation research helps companies find target segments that can propel their growth and profitability. This article establishes what segmentation research is and how companies effectively implement it.

Segmentation in marketing research

Editor’s note: Michaela Mora is the CEO of Relevant Insights. This is an edited version of an article that originally appeared under the title “ How to Make Segmentation Research Actionable. ”

Segmentation research helps companies identify groups of current and potential customers or users with the highest profitability potential. This, along with product positioning and target marketing, is one of the pillars of strategic marketing and product development.

This is broadly called “market segmentation”   in marketing and   market research because we effectively study current and potential buyers and users of products and services. Buyer and user behaviors may differ depending on the roles they adopt at different points in the user experience journey. For example, in video game purchases for minors, in specific segments of the gaming population, the parents are more likely to play the role of buyers as they are the authority figures. At the same time, their kids are most likely to be product users, but they also influence the purchase decision, often by nagging their parents to grant permission or give them money. Depending on the business goals, we may focus on segmenting customers on their buying or user behaviors or a combination of both.

The division (or merging) of roles between buyers, decision-makers, decision influencers and users can be found in many product categories. It is a mistake to assume that only users play a particular role. The roles change based on many factors including demographics, psychographics, market trends and purchase and usage scenarios.

Addressing the myriad roles a customer can take requires different marketing tactics and product positioning to reach the intended market. On the product development side, it may require the development of features to satisfy different needs.

The key concepts

Markets are not homogeneous. They are comprised of individual consumers/users with unique needs and desires. This is why   segmentation research is a powerful tool for creating better user experiences and leveraging competitive advantage.   This applies to both B2C and B2B markets.

A market segment is a portion of a market whose needs differ from the larger market and potentially from other segments.   When we do segmentation research, we need to consider the current and potential organizational capabilities. Capabilities include existing products and services, technologies, brand reputation, innovation pipeline, etc.

The first step is to identify   the need sets   the organization can meet. It is not pertinent to segment needs that lack the required skillset or resources.  

We talk about need sets because most products satisfy more than one need. Customer needs are not restricted to those satisfied by product features or user interactions. Their needs also include those that happen at different points in the journey of becoming a customer connected to:

• Types and sources of information about the product.
• Channels where the product is available.
• Product price.
• Services associated with the product.
• Perceptions and image of the product or brand.
• Where and how the product is produced or developed.
• User’s life stages and lifestyle.

Identifying relevant need sets that the organization’s current and potential products may satisfy requires qualitative and quantitative research.

Product features vs. benefits

Customers buy need satisfaction, not product features or attributes. Behind a preference for a feature or attribute there is a need searching for satisfaction and driving behavior. A consumer may buy cosmetics to satisfy the need to feel beautiful or transformed. Another will buy a drill for a DIY project, giving them a sense of accomplishment. A product manager may buy software to save time and better manage her job’s daily tasks. Segmentation studies based on product features tend to be less actionable than those based on needs.

The role demographic variables play

Users’ needs don’t exist in a vacuum. They are often associated with demographic variables such as gender, age, ethnicity, marital status, family composition, education, social class, occupation and geographic location. A segmentation solution may start by grouping users with similar product need sets despite different demographics. However, it is essential to understand these demographic differences to design effective marketing programs to reach them through different channels and messages.

Demographic information provides insights into the context in which products are purchased and used, how they think about the products beyond its features and the language they use to describe their user experience. For example, while singles, young families with children and middle-aged couples may want the same features in a mobile app, website or car display, they likely differ in how they perceive different aspects of product design, messages, channels we use to communicate with each group and the points of friction in product interactions.

Excluding demographic information leads to a lack of diversity, unintended discrimination and missed product development opportunities. For specific product categories, demographic variables can be used as segmentation criteria if they identify segments with distinctive needs and behaviors. In other categories, demographic information may not be as discriminating but still can be used to profile the segments and understand the context in which products are purchased and used.

Key demographics that can affect product use

Research has shown that age shapes the products we buy, how we use them, where we shop, how we use technology and media and how we think and feel about marketing activities.  

Many products are created with a gender in mind however sometimes gender-specific products can be based on obsolete ideas of what each gender may need or prefer. There are also product designs that intentionally or unintentionally ignore the needs of the other gender.

Race and ethnic origin are connected to ethnic subcultures in which members share unique behaviors based on a common racial, language or cultural background. It is important to remember that all subcultures are diverse and general descriptions don’t apply to every member. It is important to remain vigilant about unconscious biases that can lead to stereotypes.

Nonetheless, shared cultural traditions, values, language and behaviors within those subcultures are rooted in their histories that influence how some of their members see their needs represented in the products they buy and use. The cultures we identify with influence how we use language, how we interpret visual design elements and what mental models we have about how a product should work based on personal experiences connected to that culture.

Household cycle stages  

As social species, we usually grow up in families and go through different stages in life, each with specific needs. As we age, we may get married, have children, become empty nesters or be caregivers of older parents. Our family may shrink or expand over time depending on the paths we take and the relationships we develop. The needs for products and services in each stage will change and influence what we buy and how we use products.

Income and education  

Education often determines occupation and income and often influences our opportunities and purchasing abilities. Education also influences how we think, make decisions and relate to others.

Intersectionality

We don’t just identify with a gender, race or a particular age. We are all those things together all the time. This means research must consider the intersectionality of many of these variables at the segment level. The experiences of young Black American men in America are very different from that of young white American men. They will share preferences and use certain products in similar ways. Still, they could differ in perceived barriers to product use, depending on design elements and messaging about the product connected to their identity groups.

Firmographics

In B2B markets we use “firmographics” as equivalents to demographic information in B2C. Variables such as company size in terms of employees and revenue, industry, product category, structure, decision-making chain and processes and geographic location often correlate with the products and services they buy and how they are used internally.  

These are just some of the demographics or firmographic variables that may be relevant for your product category. Qualitative and secondary research from internal data systems or prior primary research can shed light on which variables may influence product use in your case.

Segment profiling

The selection of a segmentation solution is often based on a combination of technical know-how and judgment calls that consider the consistency and viability of the segments. In exploratory segmentation research, in which we don’t know what the segments are a priori, we use multivariate statistical techniques to identify segments with similar needs sets, behaviors, attitudes, perceptions, demographics and other relevant variables. We also need to describe the segments across all the measured variables and check if their profiles make sense.

The smaller the segments, the more likely the product will meet the segment’s needs. Smaller segments tend to have dominant and specific needs and behaviors that separate them from the rest but servicing a small segment can be very costly. A segment must be large enough to be worth investing in to be viable.  

Segment profiles, also called   personas, should highlight the most prominent common traits within the segments that act as differentiators against other segments. These could be behaviors, use occasions, buyer and user roles, attitudes, barriers and pains, motivators, demographics, etc.

Segments are probabilistic constructs, which means they summarize needs sets, behaviors, attitudes, etc., that are more likely to be shared by a group of people (or companies in B2B). This doesn’t mean that each individual classified in a segment will perfectly fit the segment. We are all individuals with unique needs, yet we share commonalities with the different groups we belong to. Segment profiles help understand a group’s core needs and distinctive user behaviors so the company can develop products that satisfy those needs.  

Making segmentation research actionable  

Despite significant investment in segmentation studies, these may have little impact on organizational decision making unless some conditions are in place for insight implementation.  

Define your desired business decisions and outcomes  

The key to an actionable segmentation study is a precise translation of desired business actions and outcomes into the information needs the research should meet to support those actions and outcomes. In survey-based segmentation studies, this must go further to operationalizing those information needs into good question design grounded in how the team plans to use the results.

It’s not uncommon to see clients bring very indefinite descriptions of how they plan to use the research results. As someone who does market segmentation studies for clients, I often help them define the jobs they want done to support particular function(s) (marketing, product development, operations, etc.) and how they support business outcomes.

Failure to define the specific actions the team plans to make (create content for different media, identify keywords for SEO, etc.) to achieve business outcomes, can take the segmentation study in a direction that is likely to provide less than valuable insights.

Assign a C-suite research champion  

Segmentation studies generate a lot of insights that are often difficult to socialize internally. The sheer amount of data can be overwhelming. Consequently, an action plan is needed to share the insights and help the organization to adopt them. Internal research teams are often responsible for this task but are rarely successful without a mandate from the top. Any strategic research effort needs a champion in the C-suite from its conception to its implementation.

With support from the executive team, researchers connected to marketing or product development need to educate internal stakeholders on the value of both the tactical and strategic implications of the segmentation research the company may have conducted. They need to understand the organization’s ability to adapt to the study’s findings and create an implementation plan to help manage internal clients’ expectations.

By connecting the tactical changes recommended by the findings to the overall strategic business goals, the research team can help internal teams, including the C-suite, to become educated on needed strategic changes.

Allow for a flexible organizational structure

Segmentation research provides insights with both tactical and strategic recommendations. Tactical recommendations may include changing a product configuration, adding new features, changing how is presented in advertising, etc. These changes can be implemented without significant organizational changes.

However, serving identified segments long-term may require a new structure to help manage them if the segmentation solution doesn’t align with the current organizational structure. In cases like these, the solution companies use is to create cross-functional teams, but depending on how rigid the structure is, these teams may get little accomplished.

To implement the strategic insights stemming from segmentation studies, the organization must be willing to change its structure to manage the market segments efficiently.

Create a balance between long- and short-term goals

In many organizations, there is often tension between marketing, sales and product development functions as they own channels and goals with different time horizons. A segmentation study may have recommendations that impact the design of channels these functions own. The marketing team may be receptive to changes the sales team resists because it may upset established client relationship patterns and short-term sales goals.

To balance short- and long-term goals, the management team must consider all research outcomes and decision possibilities of strategic value at the research design stage. If there is no commitment to implement strategic insights from the segmentation study, it is best to narrow its scope to find tactical solutions.

Prioritize certain market segments

A segmentation, by definition, implies discriminating among the segments in some respects. This means the marketing and product development will also discriminate certain segments if the segmentation solution is adopted. In practical terms, this will require prioritizing specific customer segments considering the risk of dedicating fewer resources to others.

If the company doesn’t want to take the risk of discriminating between segments and tries a middle-of-the-road strategy to reach all, it is likely to forfeit the competitive edge the segmentation insights may provide.

Have an experienced team

Understanding the value of the insights that can come from segmentation research and being willing to implement them requires prior experience with segmentation work. A marketing or product team not exposed to a well-designed segmentation study will have difficulty translating the insights into business implications.

If this is the case at your company, experienced internal researchers or external research suppliers should be called to help the teams think through the implications of decision-making based on different findings. Both internal researchers and external research suppliers should have experience in this methodology to help internal teams derive actionable insights.  

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Proceedings of Fourth International Conference on Soft Computing for Problem Solving pp 87–98 Cite as

Data Mining in Market Segmentation: A Literature Review and Suggestions

• Saibal Dutta 7 ,
• Sujoy Bhattacharya 7 &
• Kalyan Kumar Guin 7  
• Conference paper
• First Online: 25 December 2014

1725 Accesses

5 Citations

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 335))

The importance of data mining techniques for market segmentation is becoming indispensable in the field of marketing research. This is the first identified academic literature review of the available data mining techniques related to market segmentation. This research paper provides surveys of the available literature on data mining techniques in market segmentation. A categorization has been provided based on the available data mining techniques used in market segmentation. Eight online journal databases were used for searching, and finally, 103 articles were selected and categorized into 13 groups based on data mining techniques. The utility of data mining techniques and suggestions are also discussed. The findings of this study show that neural networks is the most used method, and kernel-based method is the most promising data mining techniques. Our research work provides a comprehensive understanding of past, present as well as future research trend on data mining techniques in market segmentation. We hope this paper provides reasonable insight and clear understating to both industry as well as academic researchers.

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Dutta, S., Bhattacharya, S., Guin, K.K. (2015). Data Mining in Market Segmentation: A Literature Review and Suggestions. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 335. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2217-0_8

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Monetary Policy, Segmentation, and the Term Structure

We develop a segmented markets model which rationalizes the effects of monetary policy on the term structure of interest rates. When arbitrageurs’ portfolio features positive duration, an unexpected rise in the short rate lowers their wealth and raises term premia. A calibration to the U.S. economy accounts for the transmission of monetary shocks to long rates. We discuss the additional implications of our framework for state-dependence in policy transmission, the volatility and slope of the yield curve, and trends in term premia accompanying trends in the natural rate.

An early version of this paper circulated under the title “Heterogeneity, Monetary Policy, and the Term Premium”. We thank Michael Bauer, Luigi Bocola, Anna Cieslak, James Costain, Vadim Elenev, Pierre-Olivier Gourinchas, Robin Greenwood, Sam Hanson, Ben Hebert, Christian Heyerdahl-Larsen, Sydney Ludvigson, Hanno Lustig, Anil Kashyap, Arvind Krishnamurthy, Matteo Maggiori, Stavros Panageas, Carolin Pflueger, Monika Piazzesi, Walker Ray, Alp Simsek, Eric Swanson, Dimitri Vayanos, Olivier Wang, Wei Xiong, and Motohiro Yogo for discussions. We thank Manav Chaudhary, Lipeng (Robin) Li, and Jihong Song for excellent research assistance. This research has been supported by the National Science Foundation grant SES-2117764. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Title: visual prompting for generalized few-shot segmentation: a multi-scale approach.

Abstract: The emergence of attention-based transformer models has led to their extensive use in various tasks, due to their superior generalization and transfer properties. Recent research has demonstrated that such models, when prompted appropriately, are excellent for few-shot inference. However, such techniques are under-explored for dense prediction tasks like semantic segmentation. In this work, we examine the effectiveness of prompting a transformer-decoder with learned visual prompts for the generalized few-shot segmentation (GFSS) task. Our goal is to achieve strong performance not only on novel categories with limited examples, but also to retain performance on base categories. We propose an approach to learn visual prompts with limited examples. These learned visual prompts are used to prompt a multiscale transformer decoder to facilitate accurate dense predictions. Additionally, we introduce a unidirectional causal attention mechanism between the novel prompts, learned with limited examples, and the base prompts, learned with abundant data. This mechanism enriches the novel prompts without deteriorating the base class performance. Overall, this form of prompting helps us achieve state-of-the-art performance for GFSS on two different benchmark datasets: COCO-$20^i$ and Pascal-$5^i$, without the need for test-time optimization (or transduction). Furthermore, test-time optimization leveraging unlabelled test data can be used to improve the prompts, which we refer to as transductive prompt tuning.

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