research paper on competitive market

Competitive pricing on online markets: a literature review

• Research Article
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• Published: 14 June 2022
• Volume 21 , pages 596–622, ( 2022 )

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• Torsten J. Gerpott 1 &
• Jan Berends 1  

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Past reviews of studies concerning competitive pricing strategies lack a unifying approach to interdisciplinarily structure research across economics, marketing management, and operations. This academic void is especially unfortunate for online markets as they show much higher competitive dynamics compared to their offline counterparts. We review 132 articles on competitive posted goods pricing on either e-tail markets or markets in general. Our main contributions are (1) to develop an interdisciplinary framework structuring scholarly work on competitive pricing models and (2) to analyze in how far research on offline markets applies to online retail markets.

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Introduction

Setting prices relative to competitors, i.e., competitive pricing, Footnote 1 is a classical marketing problem which has been studied extensively before the emergence of e-commerce (Talluri and van Ryzin 2004 ; Vives 2001 ). Although literature on online pricing has been reviewed in the past (Ratchford 2009 ), interrelations between pricing and competition were rarely considered systematically (Li et al. 2017 ). As less than 2% of high-impact journal articles address pricing issues (Toni et al. 2017 ), pricing strategies do not receive proper research attention according to their practical relevance. This research gap holds even more for competitive pricing. In the past, the monopolistic assumption that demand for homogeneous goods mostly depends on prices set by a single firm may have been a viable simplification since price comparisons were difficult. Today, consumer search costs Footnote 2 shrink as the prices of most goods can be compared on relatively transparent online markets. Therefore, demand is increasingly influenced by prices of competitors which therefore should not be ignored (Lin and Sibdari 2009 ).

In the early 1990s, few people anticipated that business-to-consumer (B2C) online goods retail markets Footnote 3 would develop from a dubious alternative to conventional “brick-and-mortar” retail stores to an omnipresent distribution channel for all kinds of products in less than two decades (Balasubramanian 1998 ; Boardman and McCormick 2018 ). In 2000, e-commerce accounted for a mere 1% of overall retail sales. In 2025, e-retail sales are projected to account for nearly 25% of global retail sales (Lebow 2019 ). Traditional offline channels are nowadays typically complemented by online technologies (Gao and Su 2018 ). With digitization of various societal sectors in general and the COVID-19 pandemic in particular, the shift toward online channels is unlikely to stop in the future. Besides direct online shops, two-thirds of e-commerce sales are sold through online marketplaces/platforms like Alibaba, Amazon or eBay (Young 2022 ). The marketplace operator acts as an intermediary (two-sided platform) who matches demand (online consumers) with supply (retailers). Whereas the retailer retains control over product assortment and prices, he has to pay a commission to the marketplace operator (Hagiu 2007 ). However, these intermediaries often act as sellers themselves, thereby posing direct competition to retailers who have to decide between direct or marketplace channels (Ryan et al. 2012 ).

Online consumer markets fundamentally differ from offline settings (Chintagunta et al. 2012 ; Lee and Tan 2003 ; Scarpi et al. 2014 ; Smith and Brynjolfsson 2001 ). Factors which make competition even more prevalent for online than for offline markets are summarized in Table 1 .

To date, a number of scholarly articles reviews various aspects of pricing under competition or online pricing (Boer 2015a ; Chen and Chen 2015 ; Cheng 2017 ; Kopalle et al. 2009 ; Ratchford 2009 ; Vives 2001 ). Vives ( 2001 ) provides an overview of the history of pricing theory and its evolution from the early work of Bertrand ( 1883 ) who studied a duopoly with unconstrained capacity and identical products to Dudey ( 1992 ) who set the foundation for today’s dynamic pricing Footnote 4 research with constrained capacities and a finite sales horizon. Ratchford ( 2009 ) reviews the influence of online markets on pricing strategies. Although he depicts factors shaping the competitive environment of online markets and compares online versus offline channels, he does not include competitive strategies specifically. This also holds for review papers on dynamic pricing which treat competition rather novercally (Boer 2015a ; Gönsch et al. 2009 ). With emphasis on mobility barriers, multimarket contact and mutual forbearance, Cheng ( 2017 ) studies competition mechanisms across strategic groups. Kopalle et al. ( 2009 ) discuss competitive effects in retail focusing on different aspects such as manufacturer interaction and cross-channel competition. To the best of our knowledge, Chen and Chen ( 2015 ) are the only scholars who review existing competitive pricing research by classifying model characteristics along product uniqueness (identical vs. differentiated), type of customer (myopic vs. strategic), pricing policy (contingent vs. preannounced) and number of competitors (duopoly vs. oligopoly). However, competition is only one of three pricing problems they analyze forcing them to reduce scope and depth and to exclude online peculiarities. In addition, significant competitive pricing contributions were published since 2015 (chapter 2.2). Overall, given the limitations of previous reviews of the pricing literature makes revisiting the current state of research a worthwhile undertaking.

Most often, competitive pricing literature uses simplifying assumptions limiting the applicability of presented models. The simplifications are required to circumvent challenges like the curse of dimensionality (Harsha et al. 2019 ; Kastius and Schlosser 2022 ; Li et al. 2017 ; Schlosser and Boissier 2018 ), endogeneity problems (Cebollada et al. 2019 ; Chu et al. 2008 ; Fisher et al. 2018 ; Villas-Boas and Winer 1999 ), uncertain information (Adida and Perakis 2010 ; e.g., Bertsimas and Perakis 2006 ; Chung et al. 2012 ; Ferreira et al. 2016 ; Keskin and Zeevi 2017 ; Shugan 2002 ) and simultaneity bias (Li et al. 2017 ). As a consequence, early work on pricing strategies with competition was restricted to theoretical discussions (Caplin and Nalebuff 1991 ; Mizuno 2003 ; Perloff and Salop 1985 ). This holds especially true in combination with other practical circumstances such as capacity constraints, time-varying demand or a finite selling horizon (Gallego and Hu 2014 ).

Armstrong and Green ( 2007 ) find empirical evidence that competitive pricing, especially for the sake of gaining market share, harms profitability. Similarly, some researchers cursorily ascribe competitor-based pricing as a sign of a poor management because it signals a lack of capabilities to set prices independently (Larson 2019 ). Revenue management researchers therefore often assume that monopoly pricing models implicitly capture the dynamic effects of competition. The so-called market response hypothesis is the key rationale to neglect the effects of competition altogether (Phillips 2021 ; Talluri and van Ryzin 2004 ). According to this reasoning, competition does not have to be considered as all relevant effects are already included in historical sales data. However, this intuitive argument can be easily rebutted as Simon ( 1979 ) already showed that price elasticities change over time. Furthermore, Cooper et al. ( 2015 ) study the validity of the market response hypothesis and conclude that this monopolistic view is rarely adequate. Monopolistic pricing models can only be applied to stable markets with little time-varying demand and little expected competitive reactions.

Detrimental outcomes of ignoring competition in pricing strategies are shown by Anufriev et al. ( 2013 ), Bischi et al. ( 2004 ), Isler and Imhof ( 2008 ), Schinkel et al. ( 2002 ), and Tuinstra (2004). The negative effects are even more harmful in fierce competitive settings such as situations with a high number of competitors or price sensitive customers (van de Geer et al. 2019 ). Empirical evidence on the influence of competition on pricing decisions is provided by Richards and Hamilton ( 2006 ) who find that retailers compete on price and variety for market share. Li et al. ( 2017 ) observe that competition-based variables explained 30.2% of hotel price variations in New York—compared to 22.3% attributed to demand-side variables. Similarly, Hinterhuber ( 2008 ) assesses competitor-based pricing as a dominant strategy from a practical perspective. Li et al. ( 2008 ) argue that because of its relevance, competition should be considered in operational revenue management and not be treated stepmotherly as an abstract strategic constraint.

Although striving to simplify pricing models is desirable, researchers should thus not simply ignore effects of competition on price setting in a non-monopolistic (online) world. Blindly pegging pricing strategies to competitors or undercutting competitors to gain market share may favor detrimental price wars and not profit-maximizing market structures. Nevertheless, no significant market player can operate isolated on online markets—decisions made always affect competing firms and consumer demand (Chiang et al. 2007 ). In such dynamic markets (chapter 3.3), competition must be considered with time-varying attributes (Schlosser et al. 2016 ).

Against this background, we suggest a conceptual framework to structure research covering competitive online pricing. It can serve scholars as a map to direct future research on the one hand and provide practicing managers with a guide to locate relevant pricing contributions on the other hand. Although the framework can be applied to a variety of markets with competitive dynamics, we concentrate our review on research covering B2C online goods retail markets. Thus, related research with a focus on auction pricing, multichannel peculiarities, behavioral pricing and multi-dimensional pricing approaches such as Everything-as-a-Service (XaaS) or bundle pricing is only assessed when findings are crucial to the competition-related discussion. In the remainder, we proceed as follows. The next chapter provides a descriptive overview of the competitive pricing literature for the subsequent discussion. Chapter 3 puts the identified literature into the perspective of online retail markets considering product and environmental characteristics. Section 4 concludes with practical implications and directions for future research.

Overview of competitive pricing research

Initially, properties of the reviewed literature are briefly summarized. Besides (a) the journal representation, (b) the historical development of online market considerations and (c) research domains, we classify research according to (d) the geographical and industry context as well as (e) research design and empirical foundation.

We identified relevant references through a semi-structured multi-pronged search strategy. Following Tranfield et al. ( 2003 ), we firstly screened the literature reviews mentioned in chapter 1 to obtain an overview of existing research streams. Second, we created a set of potentially relevant contributions by searching multiple keywords in the journal databases EBSCO, Scopus and Web of Science (c.f. Baloglu and Assante 1999 ). Footnote 5 Third, high-impact journals (see Appendix 1) in the academic fields economics, marketing management, and operations were screened. With focus on highly cited (> 10 citations in Scopus), recent (published later than 2000) research, we identified an initial sample of 996 unique papers. Fourth, we studied the abstract and skimmed the text of all papers for relevance to competitive online pricing, reducing our initial set to 174 papers. Fifth, we screened the references of the papers and identified literature cited which we not already included in our set. Sixth, especially for research areas with limited coverage in peer-reviewed journals, we uncovered gray literature through searches with Google Scholar. As a result, this study concentrates on papers published between 2000 and 2022 and only sparsely utilizes literature from the pre-internet era. The final sample of the papers with relevance to competitive B2C online pricing encompasses 132 entries. A complete list of the papers reviewed in great depth is provided in Appendix 2. 94% are peer-reviewed articles. Book chapters, conference papers and preprint/working papers each account for 2%.

Journal representation

Competitive pricing literature is widely dispersed over a broad range of journals as roughly half of the articles considered are from journals with less than three articles in our review. Notably, journals with a higher density of competitive pricing contributions are from the fields of operations, economics or are interdisciplinary. Table 2 reports the distribution of articles among the journals with the highest representation. In addition, it provides the considered articles subject to a content analysis in chapter 3.

Online pricing contributions over time

Between 1976 and the end of the second millennium, the number of papers on competitive pricing in an internet context is naturally limited (Fig.  1 ). Parallel to the dissemination of online use among residential households, interest of researchers in online pricing in a competitive environment started to take off. 71.8% of the papers published from 2015 to 2022 consider online settings specifically. The corresponding statistic from 2000 to 2005 amounts to 43.8%.

Competitive pricing literature and its consideration of online peculiarities accumulated by year

Development of research domains

Competitive pricing literature typically can be assigned to one of the following research domains:

The economics domain takes a market perspective across individual firms. It elaborates on the existence and uniqueness of competitive equilibria also including all subjects regarding econometrics.

The marketing management domain analyzes competitive pricing problems from the perspective of a single firm with a focus on customer reactions to pricing decisions. It includes all subjects linked to marketing, strategy, business, international, technology, innovation, and general management.

The operations domain considers quantitative pricing solutions for, among others, quantity planning, choice of distribution channels, and detection of algorithm driven price collusion. It includes all subjects regarding computer science, industrial and manufacturing engineering, and mathematics.

Separating the last 47 years of competitive pricing research into three intervals, all reviewed papers are assigned to their most affiliated research domain. Although the domains are similarly represented in our review (see Fig.  2 ), we see differences in their temporal change. Whereas rather theoretical economic subjects are covered relatively constant over time, more practice-oriented marketing management and operations subjects gained momentum since 2000. This suggests a shift from model conceptualization toward applicable research, frequently based on empirical data.

Distribution of competitive pricing literature over research domain and time interval

Geographical and industry context

As the origin of revenue management lies in transportation and hospitality optimization problems, one could expect that competitive pricing research also originates in these dynamic sectors. However, our analysis reveals a different picture: Almost half of the papers in our review do not concentrate on a specific industry. Besides, most industry-specific competitive pricing articles focus on retail, with 38% concentrating on the retail industry versus 8% and 4% on transportation and hospitality, respectively (see Fig.  3 ). This supports our proposition in chapter 1 that effects of competition on industry-specific pricing are particularly relevant for online markets.

Competitive pricing research by focal industry and location of lead authors’ institution

Competitive pricing literature is predominantly driven by researchers employed by U.S. institutions (60%). The remaining 40% consist of Europe (19%), Asia (17%) and Canada (4%).

Research approach

A lack of empirical testing is an issue that hampers competitive pricing research. Liozu ( 2015 ) reported that only 15% of general pricing literature include empirical data. For competitive pricing, the situation appears even more aggravated. In addition to parameters such as price elasticities and stock levels of the company under study, comprehensive, real-time information of other market participants is crucial to add practical value.

For instance, to solve a simple Bertrand equilibrium, Footnote 6 full information of all competitors is needed, which is rarely available in real-life settings. Therefore, many problems covered in the literature are of a theoretical nature. In accordance with Liozu ( 2015 ), we find that only 18% of reviewed articles use empirical evidence to validate hypotheses. An additional 23% strive to ameliorate this shortage through simulation data and numerical examples. The remaining 59% fail to bring any empirical evidence or numerical examples.

As can be taken from Fig.  4 , missing empirical support is particularly prevalent for equilibrium models which use empirical data in only 7% of all papers.

Competitive pricing research by research design and empirical validation

Competitive pricing on online markets

In this chapter, we assess the applicability of competitive pricing work to online markets. Typical characteristics of competitive B2C pricing models were derived from literature described in chapter 2. Competitive pricing literature can be classified along four characteristics depicted in Table 3 that form the market environment in which firms compete for consumer demand.

In the remainder of chapter 3, we discuss the four key questions in more depth and elaborate on their applicability to online retail markets.

Product similarity

In general, products in competitive pricing models are either identical (homogeneous) or differentiated by at least one quality parameter (heterogeneous). In case of homogeneous products, pricing is the only purchase decision variable—a perfectly competitive setting (Chen and Chen 2015 ). However, many firms strive to differentiate their products as this shifts the focus from the price as competitive lever to other product-related features (Afeche et al. 2011 ; Boyd and Bilegan 2003 ; Thomadsen 2007 ). According to Lancaster ( 1979 ), there are two types of product differentiation: vertical and horizontal differentiation. Vertical differentiation Footnote 7 encompasses all product distinctions which are objectively measurable and quantifiable regarding their quality level. Horizontal differentiation Footnote 8 can manifest in many variants and includes all product-related aspects which cannot be quantified according to their quality levels. Footnote 9 A key difference in the modeling of substitutable yet differentiated versus identical goods is that customers have heterogenous preferences among products. Footnote 10 A recent stream of literature approaches unknown differentiation criteria by assessing online consumer-generated content (DeSarbo and Grewal 2007 ; Lee and Bradlow 2011 ; Netzer et al. 2012 ; Ringel and Skiera 2016 ; Won et al. 2022 ).

Besides the chosen price level, Cachon and Harker ( 2002 ) argue that firms compete with the operational performance level offered and perceived, i.e., service level in online retail, to differentiate an otherwise homogenous offering. In situations, where resellers with comparable service and shipping policies offer similar products, price is a major decision variable for potential buyers (Yang et al. 2020 ). Often, e-tailers do not possess the right to exclusively distribute a certain product. For example, Samsung’s Galaxy S21 5G was offered by 69 resellers on the German price comparison website Idealo.de. Footnote 11 As some products in e-tail can be differentiated and others cannot, both identical and differentiated product research have their raison d’être for competitive online pricing.

Most competitive pricing models only address the effects of single-product settings. This simplification is reasonable if there is no interdependence between products of an e-tailer (Gönsch et al. 2009 ). Taking up on the smartphone example, the prices of close substitutes, such as Huawei’s P30 Pro, nonetheless have an impact on the demand of Samsung’s Galaxy S21 5G. To further extent product differentiation, price models have to incorporate multi-product pricing problems in non-cooperative settings (Chen and Chen 2015 ). Such models have to account not only for demand impact of directly competing products but also for synergies, cannibalization/substitution effects of (own) differentiated goods. Although there is a recent research stream regarding product assortment (Besbes and Sauré 2016 ; Federgruen and Hu 2015 ; Heese and Martínez-de-Albéniz 2018 ; Nip et al. 2020 ; Sun and Gilbert 2019 ), multi-product work is still underdeveloped. Thus, competitive multi-product pricing constitutes an area which should be addressed in future research.

Product durability

The durability of products is an important feature to differentiate between competitive pricing model types. Durable (non-perishable) products do not have an expiration date, for example consumer durables such as household appliances. Perishable products can only be sold for a limited time interval and have a finite sales horizon. After expiration date, unused capacity is lost or significantly devalued to a salvage value. Footnote 12 Combined with limited capacities, the firm objective is thus most often to maximize turnover under capacity constraints and finite sales horizon (Gallego and van Ryzin 1997 ; McGill and van Ryzin 1999 ; Weatherford and Bodily 1992 ).

Perishability can be of relevance for products with seasonality effects or short product life cycles (i.e., finite selling horizon) such as apparel, food groceries or winter sports equipment. This is especially relevant because online retailers of perishable products are severely restricted in their shipment, return handle policies and supply chain length (Cattani et al. 2007 ). Sellers cannot replenish their inventory after the planning phase and cannot retain goods for future sales periods (Perakis and Sood 2006 ). Some products like apparel—albeit reducing in value after a selling season—still have a certain salvage value and can be sold at reduced prices (Anand and Girotra 2007 ).

It depends on the type of product to decide whether perishability should be included in competitive pricing models. There is a fundamental distinction in the underlying optimization objective for models with or without perishability. Whereas models with perishable products tend to focus on revenue maximization over a definite short-term time horizon, models with durable products tend to focus on profit maximization over an indefinite or at least long-term time horizon by balancing current revenues of existing and future revenues of new customers. To account for this trade-off, models with durable products need to discount future cash flows incorporating time value of money, stock-keeping, opportunity and other costs related to prolonged sales (Farias et al. 2012 ). To conclude, perishability cannot be treated as an extension to durable models but rather as a separate class of pricing models. Depending on the product and/or setting in focus, both are relevant for online retailing. Further research could investigate the performance of models with and without consideration of perishability in various (online) settings to determine when it is appropriate to use which class of pricing models. Also, an interesting field of future studies arises around the question which instruments (e.g., service differentiations or price diffusion) are used by online retailers to differentiate otherwise homogeneous offerings.

Time dependence

A key differentiator of competitive pricing models is the consideration of either a static (time-independent) setup with definite equilibrium or a dynamic (time-dependent) constellation with changing environmental factors and equilibria. Albeit static pricing models have no time component, many consist of multiple stages to investigate the interplay of different factors. Footnote 13 In contrast, dynamic models allow for varying competitive (re-)actions over time. Footnote 14 Within the latter category, there are models with a finite (Afeche et al. 2011 ; Levin et al. 2008 ; Liu and Zhang 2013 ; Yang and Xia 2013 ) and an infinite (Anderson and Kumar 2007 ; Li et al. 2017 ; Schlosser and Richly 2019 ; Villas-Boas and Winer 1999 ; Weintraub et al. 2008 ) time horizon.

Historically, competitive pricing models assumed fixed prices over the considered time horizon. Limited computational power made it impossible to appropriately estimate models dynamically due to dimensionality issues (Schlosser and Boissier 2018 ). A lack of reliable demand information, high menu and investment costs to implement dynamic approaches were additional reasons why pricing models remained inherently static without incorporating changing competitive responses (Ferreira et al. 2016 ). The focus in retail has conventionally rather been on long-term profit optimization and to a lesser degree on dynamically changing price optimizations (Elmaghraby and Keskinocak 2003 ).

The literature disagrees on whether firms should opt for static or dynamic pricing strategies. A static environment allows to simplify and concentrate on a specific topic such as equilibrium discussions. For instance, Lal and Rao ( 1997 ) study success factors of everyday low pricing and derive conditions for a perfect Nash equilibrium between an everyday low price retailer and a retailer with promotional pricing. With Zara as an example for a company with a successful static pricing strategy, Liu and Zhang ( 2013 ) argue that with the presence of strategic customers who prolong sales in anticipation of price decreases, firms might even be better off to deploy static over dynamic price setting processes. Studying the time-variant pricing plans in electricity markets, Schlereth et al. ( 2018 ) suggest that consumers might prefer static over dynamic pricing because of factors like choice confusion, lack of trust in price fairness, perceived economical risk or perceived additional effort. Further support for a static pricing strategy is found in Cachon and Feldman ( 2010 ) and Hall et al. ( 2009 ).

Nevertheless, to generalize that static should strictly be preferred over dynamic pricing models could be short-sighted. Firms cannot generally infer future behavior of competitors from past observations to assess how competitive (re-) actions may influence the optimal pricing policy (Boer 2015b ). Corresponding to the surge of revenue management systems in the airline industry during the 70s and 80s, increased price and demand transparency, low menu costs and an abundance of decision support software created fierce competition among online retailers (Fisher et al. 2018 ). Taking up on the above mentioned example by Liu and Zhang ( 2013 ), Caro and Gallien ( 2012 ) show that even Zara does not solely rely on static pricing. They supported Zara’s pricing team in designing and implementing a dynamic clearance pricing optimization system—to generate a competitive advantage in addition to the fast-fashion retail model Zara mainly pursues (Caro 2012 ). Zhang et al. ( 2017 ) discuss various duopoly pricing models with static and dynamic pricing under advertising. They find that market surplus is highest when one firm prices dynamically, profiting from the static behavior of the other. Chung et al. ( 2012 ) provide numerical evidence that a dynamic pricing model with an appropriately specified demand estimation always outperforms static pricing strategies—also in settings with incomplete information. Xu and Hopp ( 2006 ) show that dynamic pricing outperforms preannounced pricing, especially with effective inventory management and elastic demand. Further support for advantages of dynamic pricing can be found by Popescu ( 2015 ), Wang and Sun ( 2019 ), and Zhang et al. ( 2018b ). Empirical evidence of the negative consequences of sticking to a static strategy in a changing environment is found in the cases of Nokia, Kodak, and Xerox.

While some scholars distinguish between discrete and continuous dynamic pricing systems (Vinod 2020 ), we suggest to classify dynamic pricing models according to their level of sophistication into two evolutionary stages: the (in e-commerce widely applied) manual rule-based pricing approach and the data-driven algorithmic optimization approach (Popescu 2015 ; Le Chen et al. 2016 ). Footnote 15 For the rule-based approach, “if-then-else rules” are defined and updated manually. Footnote 16 However, the mere number of stock-keeping units (SKUs) in today’s retailer offerings aggravate the initial setup and handling of rule-based pricing and make real-time adjustments unmanageable (Schlosser and Boissier 2018 ). In addition, rule-based approaches are rather subjective than sufficiently data-driven. Faced with a large range of SKUs, competitor responses and heterogeneous demand elasticities, canceling out the human decision-making process on an operational level is the next evolutionary step for competitive pricing systems (Calvano et al. 2020 ). Data-driven algorithmic pricing strategies use observable market Footnote 17 data to predict sales probabilities based on consumer demand and competitive responses (Schlosser and Richly 2019 ).

As online marketplaces benefit from an increased number of retailers on their platforms, they typically support sellers to establish automated dynamic pricing systems (Kachani et al. 2010 ). Footnote 18 However, Schlosser and Richly ( 2019 ) claim that current dynamic pricing systems are not able to deal with the complexity of competitor-based pricing and therefore most often ignore competition altogether or solely rely on manually adjusted rule-based mechanics. Challenges include the indefinite spectrum of changing competitor strategies, asymmetric access to competitor knowledge, a large solution space under limited information and the black-box character of dynamic systems, which exacerbates an intervention in case of a pricing system malfunction. Besides, researchers did not yet identify an algorithm which consistently outperforms other methodologies in competitive situations. Instead, it depends on the specific setting and other competitors’ pricing behavior to assess which pricing algorithm is optimal (van de Geer et al. 2019 ) exacerbating the application of such systems.

Reflecting the literature findings for both static and dynamic pricing strategies, we conclude that pricing managers should develop dynamic pricing models in most e-commerce situations. As long as demand and competitor price responses vary over time on online markets, dynamic models are naturally superior to time-independent approaches. Static models on the other hand are only appropriate in market constellations with little time-varying demand and competitor behavior. As static research can be expected to remain a vivid field of literature, further research with regard to the transferability of static models to dynamic settings is desirable. In addition, more research is needed that helps to better understand the implications of widely applied rule-based dynamic pricing methods and their transition toward algorithmic approaches (Boer 2015a ; van de Geer et al. 2019 ; Kastius and Schlosser 2022 ; Könönen 2006 ).

Market structure

The market structure describes the number of competing firms such as duopoly or oligopoly in a demand setting with an indefinite number of consumers. 60% of the reviewed papers studied duopolies, 49% oligopolies, 7% monopolistic competition, and 3% perfect competition. Footnote 19

Especially for research in the economics stream, many papers assume a perfectly competitive market. Pricing research with perfectly competitive markets (e.g., van Mieghem and Dada 1999 or Yang and Xia 2013 ) is likely to be of very limited value to online retailers. Building on the notion of Diamond ( 1971 ), Salop ( 1976 ) argues that if customers have positive information gathering costs, no perfect competition can occur as firms have room to slightly increase prices without losing demand. Christen ( 2005 ) found evidence that even with strong competition and low information costs, cost uncertainty could decrease the detrimental effect of competition for sellers and could increase prices above Bertrand levels. Similarly, Bryant ( 1980 ) showed that perfect competition is not possible in a market with uncertain demand, even if the number of firms is large and customers have no search costs. Rather, price dispersion reflects uncertain demand (Borenstein and Rose Nancy L. 1994 ; Cavallo 2018 ; Clemons et al. 2002 ; Obermeyer et al. 2013 ; Wang et al. 2021 ). Israeli et al. ( 2022 ) empirically show that the market power of individual firms does not only depend on the number and intensity of competitors but also on the firm’s ability to adjust prices in response to varying inventory levels of product substitutes, especially with low consumer search costs. This is of relevance for e-commerce as e-tailers could exploit this dependence by incorporating competitors’ stock levels into pricing decisions (Fisher et al. 2018 ).

Some papers discuss (quasi) monopolistic competition (e.g., Xu and Hopp 2006 ) in which small firms charge the (higher) monopoly price rather than the (lower) competitive price. From an empirical study in the U.S. airline industry, Chen (2018) concludes that, as firms can price discriminate late-arriving consumers, competition is softened, profits are increased, and the only single-price equilibrium could be at the monopoly price. This supports Lal and Sarvary ( 1999 ) who show that online retailers enjoy a certain amount of monopoly power in cases where buyers cannot switch suppliers for repeated purchases (e.g., technical incompatibility reasons). In such cases, switching costs could increase online prices (Chen and Riordan 2008 ). However, this contradicts Deck and Gu ( 2012 ) who empirically show that, although the distribution of buyer values of competing products might theoretically lead to higher prices through competition, intensity of competition rarely allows for an occurrence of this phenomenon in e-tail settings.

Although duopoly settings can serve to assess the relevant strength of pricing strategies, which is not directly possible for oligopoly markets due to the curse of dimensionality (Kastius and Schlosser 2022 ), they cannot be transferred to more competitive environments (van de Geer et al. 2019 ). In online retail, a duopoly market structure is a rare exemption. Like for perfectly competitive markets, findings of duopoly research must be carefully assessed in terms of their applicability to online retail oligopolies.

Bresnahan and Reiss ( 1991 ) found empirical evidence that markets with an increasing number of dealers have lower prices than in less competitive market structures such as monopolies or duopolies. Although applicable to many online retail markets, where retailers face dozens, if not hundreds of thousands of competitors (Schlosser and Boissier 2018 ), few research attention is currently given toward a structure with a large number of competitors in an imperfect market (cf. Li et al. 2017 ). A way to assess the current competitive structure of markets is the utilization of online consumer-generated content such as forum entries (Netzer et al. 2012 ; Won et al. 2022 ) or clickstream data (Ringel and Skiera 2016 ) and actual sales data (Kim et al. 2011 ).

In many countries with well-developed B2C online markets, one or few major retailers dominate on an oligopolistic market. For example, the top three online retailers in the United States accounted for over 50% of the revenue generated on the national e-commerce market in 2021. Footnote 20 Due to lower locational limitations in conjunction with substantial economies of scale and scope, online markets tend to become more concentrated than their offline counterparts (Borsenberger 2015 ). Although one could expect that increased market transparency leads to a higher intensity of competition (Cao and Gruca 2003 ), limiting the market power of established firms and leaving growth potential for smaller firms (Zhao et al. 2017 ), it appears reasonable to predict that most online markets will ultimately resemble an oligopoly setting with a with a relatively small number of players—enabling increased tacit pricing algorithm collusion in the future (Calvano et al. 2020 ). With few exceptions (e.g., Noel 2007 ), there is little research (Brown and Goolsbee 2002 ; Wang et al. 2021 ; Cavallo 2018 ) exploring what type of competitor-based pricing strategies are used and what competitive dynamics are found on e-tail markets. Thus, more research is needed to investigate the current state of market structure and intensity of competition in today’s e-commerce markets as drivers of the selection and the outcomes of pricing approaches.

Implications and directions

We contribute to the literature by providing an interdisciplinary review of competitive online retail research. Competitive pricing problems can most often be assigned to one of the academic fields of economics, marketing management or operations. In a first step, this review offered a descriptive portrayal of the relevant literature. Motivated by practical issues and common features in competitive pricing research, we then structured competitive pricing contributions along four properties of pricing models. First, do firms compete with identical or quality differentiated products? Second, are products to be considered as perishable or durable goods? Third, is the market setting to be regarded time-independent or not? Fourth, which market structure prevails on e-tail markets? The framework is derived from an analysis of pricing research not exclusively restricted to online retail settings. Therefore, it could be extended to other online or offline markets, with little loss of generalizability.

We focused on e-tail markets because the relevance of competition for pricing strategies is disproportionally higher in such environments. On e-tail online markets, products are rarely offered exclusively so that the likelihood of substitutive competition is high. Nevertheless, products can be differentiated through other factors than prices such as generous shipping, customer retention (e.g., loyalty reward programs) or return and issue handling policies. With a look on product similarities, accounting for product interdependencies and multi-product situations are important improvements of prevailing pricing models. Second, pricing models with both a focus on perishable and/or durable products are relevant on e-tail markets. However, further research is needed exploring which of the respective perishability considerations are appropriate for different settings. Third, we conclude that, albeit time-independent static models may occasionally serve to simplify pricing issues, dynamic models outperform their static counterparts in constantly changing market environments such as in e-commerce. Fourth, we show that in most practical settings, online markets resemble either an oligopolistic market structure or a structure with many firms under imperfect competition. Thus, future research should consider these two “real” competitive settings instead of further looking at simplifying market structures such as monopolistic or duopolistic competition. This should ease a transfer of theoretical insights into practical applications. To sum, firms should be able to improve their competitive position by developing a profit optimizing dynamic pricing strategy for identical products in an oligopolistic setting with a varying number and relevance of competitors.

Due to space limitations, we had to focus on competitive pricing model characteristics related to four overall product and market attributes. Thus, more work is needed on other characteristics of competitive pricing models, particularly firm- and consumer-related characteristics. Firm-related characteristics encompass various additional properties of interacting firms (e.g., similarity or capacity constraints). Similarly, consumer-related characteristics entail further properties of interacting buyers (e.g., certainty, discreteness, sophistication, and homogeneity of demand).

In the selection process of literature, this study only considered papers in peer-reviewed journals and conference proceedings in English. Subsequent research could complement our findings by including industry-funded, unpublished and non-peer-reviewed articles, also in other languages. In addition, we do not claim that our research captures all competitive pricing publications of the considered field. As our study spans almost 50 years of a frequently discussed topic in the domains of economics, marketing management, and operations, we had to constrain the scope to the most influential work. Although we mutually evaluated our selection decisions and consulted outside peers for validation and further input, we cannot eliminate the element of subjectivity. Consequently, other authors could have selected slightly different papers. However, this shortcoming is unlikely to significantly affect our results as our literature selection was derived from a broad array of competitive pricing research and would therefore be only marginally influenced by a few omitted articles.

Competitive pricing includes all activities and processes to price products with the consideration of competitors. This does not only include rigidly pegging prices to competitor prices but rather a comprehensive consideration of current and expected price (re-)actions of competing firms to sustainably ensure profit maximization. In this article, the terms competitive pricing, competitor-oriented pricing and competitor-based pricing are used synonymously.

Search costs are defined as the costs of time and resources to acquire information with respect to price, assortment, and quality characteristics of the goods provided by different sellers. The internet dramatically reduces search costs through price comparison websites such as Google Shopping, Shopzilla (USA) or Idealo (Germany).

Business-to-consumer (B2C) online retail sales encompass all forms of electronic commerce markets in which residential end customers can directly buy goods from a seller over the internet through a web browser or a mobile app. In this paper, the terms business-to-consumer (B2C) online goods retail, online retail, e-tail, e-retail and e-commerce markets are used synonymously.

In contrast to classical quantity-based revenue management, dynamic pricing, also known as surge pricing, is the practice of adjusting prices according to current market demand (Boer 2015a ). Revenue management, also known as yield management, is a type of price discrimination which originates from the airline and hospitality industries. Typically, revenue management models assume fixed capacities, low marginal cost, varying demand and highly perishable inventory (Talluri and van Ryzin 2004 ).

Keywords used for abstract, title and keyword screening were “competitive pricing”, “competitor-based pricing”, “competition” AND “pricing”. To find literature for online pricing in particular, the search was combined with the keywords “online”, “e-retail”, “ecommerce” and “e-commerce”. Whereas the combination was scanned in great depths, the three competitive keywords were screened for influential papers with implications for online markets.

Bertrand competition is a simplified model of competition to explain price competition among (at least) two firms for an identical product at equal unit cost of production. Prices are set simultaneously, and consumers buy without search costs from the firm with the lowest price. When all firms charge the same price, consumer demand is split evenly between firms. A firm is willing to supply unlimited amounts of quantities above the unit cost of production and is indifferent to supply at unit cost as it will earn zero profit. The only Bertrand equilibrium exists when prices are equal to unit cost (i.e., competitive price) as each firm otherwise would have an incentive to undercut all other competitors and thereby rake in the entire market demand. Therefore, there can be no equilibrium at prices above the competitive price and price dispersion cannot occur.

In vertical differentiation, consumer choice depends on specific quality levels of product attributes. At the same price, all consumers prefer one product over other products, for example because of superior design. In the simplest form, products differ in one attribute and customers are willing to pay marginal increments of this attribute.

In horizontal differentiation, consumer choice depends on preferences for products. At the same price, some customers would buy one product and others other products.

We consider product differentiation only to product-related differentiation attributes. However, in competitive pricing literature firm-related differences such as firm loyalty or distribution channels are occasionally attributed to differentiation. For example, Abhishek et al. ( 2016 ) differentiate online distribution channels of otherwise homogeneous products and firms.

Heterogeneous customer preferences are a key requirement for product differentiation, otherwise price constitutes the only driver of the buying decision (Li et al. 2017 ). Without heterogeneity in consumers’ marginal willingness to pay for different levels of product quality, there can be no product differentiation (Pigou 1920 ).

Accessed 14–03-2022.

Salvage value is defined as the residual cash-flow of a good after its expiration date.

For example, game settings on the foundation of Stackelberg games necessarily comprise ≥ 2 stages (Geng and Mallik 2007 ; Gupta et al.; Wang et al. 2020 ; Yao and Liu 2005 ). Another example would be Anand and Girotra ( 2007 ) who propose a 3-stage model in which they include the supply chain configuration and determination of production quantities in addition to the actual price setting.

As such, we classify n-stage models as dynamic models when not all individual stages serve a specific time-independent purpose.

For instance, the Brandenburg consumer advice center (Verbraucherzentrale Brandenburg) examined dynamic price differentiation in online retail and found that 15 of the 16 observed German online shops dynamically changed their prices in 2018 (Dautzenberg et al. 2018 ).

A typical rule would be to set prices always x% lower than competitor prices up to a certain profit threshold.

Observable market data include price and stock levels of competitors (Fisher et al. 2018 ) or clickstream and keyword data of customers (Li et al. 2017 ).

Examples for support programs by online marketplaces are Amazon’s Seller Central ( https://sellercentral.amazon.com/gp/help/external/G201994820?language=en_US&ref=efph_G201994820_cont_43381 ; Accessed 14–03-2022), eBay’s Seller Tools ( https://pages.ebay.com/sell/automation.html ; Accessed 14–03-2022) or Idealo’s Partner Program ( https://partner.idealo.com/de ; Accessed 14–03-2022).

Cumulatively, these values exceed 100% as some articles discussed more than one kind of market structure. Applied by economists to simplify real markets as the foundation of price theory, perfect competition relates to a market structure which is controlled entirely by market forces and not by individual firms. Instead, individual firms only act as price takers and cannot earn any economic profit. The conditions for a perfect competition, such as full information, homogeneous products, fully rational buyers, no scale, network or externality effects, no entry barriers, and no transaction costs, are rarely attainable in practical settings (Stigler 1957 ). If not all conditions for perfect competition are fulfilled, the market structure is imperfect which applies to most practical settings. Besides a monopoly with only one seller on the market, three market structures with competing firms exist: Monopolistic, duopolistic, and oligopolistic competition. An oligopoly is characterized by a small number of firms in which the behavior of one firm drives the actions of other firms. A duopoly is a particular case of an oligopoly in which two firms control the market. An extreme case of imperfect competition is (quasi) monopolistic competition in which products are differentiated and firms maintain a certain spare capacity giving them a certain degree of pricing power to maximize their (short-term) profits. In consequence, prices can be higher than corresponding the competitive (Bertrand) price (Vives 2001 ).

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Gerpott, T.J., Berends, J. Competitive pricing on online markets: a literature review. J Revenue Pricing Manag 21 , 596–622 (2022). https://doi.org/10.1057/s41272-022-00390-x

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A step-by-step guide to competitive market analysis

I remember Blockbuster going bankrupt in 2010. As a 12-year-old, this news meant the end of walking down aisles with my family, looking for the perfect weekend flick. But to Netflix, this meant their competitive market analysis had paid off.

As much as I miss my three-day VHS and DVD rentals, video entertainment has improved significantly, thanks to innovation and competitive pressure. Your industry isn't immune to change either, which is why it's so important to get ahead.

Table of contents:

What is a competitive analysis?

The importance of competitive analysis in marketing, how to conduct competitive market analysis in 6 steps, competitive analysis example, internal operations as a competitive advantage.

Competitive market analysis is the process of determining who your competitors are, researching their strategies, and unpacking what they do well (and not so well).

From this process, you can learn a lot about your company's own strengths and weaknesses, as well as how to remain a strong competitor in your industry.

A deep dive into your competitors' strategies will reveal their product offerings, mission statements , target markets, marketing strategies, and more. You may even identify new competitors you didn't know existed.

Identify and fill crucial gaps in your business  

The greats learn from the greats. Yes, you're trying to beat your competitors, but that doesn't mean you can't learn from them—especially if they've been around the block a few times. Seeing what works for your competitors will point toward gaps in your own strategies (as well as gaps in their strategies)—and ideally give you some inspiration on how to fill those gaps.

Spot trends and get ahead

Competitive market analysis isn't just about understanding specific competitors. It can also uncover the direction that an entire industry is moving. For example, Blockbuster didn't move with the industry, and we all know how that ended. Competitive analysis can help you avoid that same fate.

Recognize product value and pain point solutions

By analyzing competitors, you uncover customer pain points they might've missed, allowing you to refine your product's value and directly address those needs. It's not just about watching rivals, but seizing opportunities to stand out.

Set future objectives for growth

By examining market leaders, you define bold markers for your growth, and you can emulate their tactics. You can also employ AI-driven tools like Google Analytics' predictive metrics, HubSpot's forecasting tools, or IBM Watson's analytics. These tools can decipher market patterns, anticipate future prospects, and help you set precise KPIs. With AI's assistance, you can unearth data-driven insights and opportunities even top-tier competitors might miss.

Competitive market analysis isn't just a matter of making sure you have a unique selling point. Here's how to dig into the competition and figure out where you stand—and how to stand out.

1. List your competitors

Start by creating a comprehensive list of everyone operating in your industry. You can weed out the less relevant competitors later, but for now, jot down anyone that offers similar products or services.

If you're a small local business, your competitors are located nearby—you probably know a lot about them, but at the very least, you know they exist. But for something like an eCommerce store or online service, it's important to figure out who your competitors even are.

Start with basic keyword research. Google your product category a few different ways, maybe with words like "price," "cheap," or "sale" to reinforce the commercial nature of your query.

This will help you identify any competitors and can shed some light on how saturated your industry is.

Next, categorize the companies you identify into direct and indirect competitors.

Direct competitors provide similar products or services to a target market similar to yours. For example, Gucci and Prada both offer high-end clothing and accessories to fashion-conscious consumers with high disposable income.

Indirect competitors provide different products or services (though ones that land in the same category as yours) to a target market similar to yours. For example, gyms and companies producing at-home workout equipment can be considered indirect competitors.

As the name suggests, direct competitors tend to pose a more direct risk to your business. That said, indirect competitors can still attract customers and pull business away from you. You need to identify and analyze both to truly understand your competitive landscape.

2. Identify their target markets

Sometimes, a competitor's target market is clear—especially if it's a niche brand. Take Dick's Sporting Goods—it's pretty obvious that they're targeting people who are into sports (or who want to be).

For other companies, it isn't as obvious. To determine your competitors' target markets, try the following:

Study their mission statement: Sometimes, a company's mission statement will explicitly state who their products and/or services are designed for. And even when it isn't explicit, it will almost certainly provide a hint. Navigate to the "About" page on their website, and soak up whatever insight you can.

Analyze their voice and tone: Stoic or cheeky? Packed with jargon or written for the novice? Your competitors' tone and wording have a purpose—to get through to their target market. Read company blog posts, join their newsletters, and watch promotional video content, all the while paying close attention to who they seem to be speaking to.

Pay attention to their social media engagement: Observe who comments on their content and how they respond to those comments. The influencers they partner with can also shed light on the audience they want to reach.

3. Unpack their 4 P's

Competitive market analysis requires you to analyze your competitors' four P's of marketing : product, price, place, and promotion.

The best way to learn about your competitors' customer experiences is to become a customer yourself. You'll gain incredible insights into what's working for them—and more importantly, where there's room to offer something better.

Whenever possible, give your competitors' products a spin. Get a free trial or even buy or sign up for the real deal, if your budget allows. When using their products, try contacting their customer support for additional insight.

Pricing can be one of the most sensitive aspects of marketing. For this reason, you need to understand and track what your competition is doing with their pricing. Here are some questions to ask yourself about each competitor:

What's their pricing model? Subscription-style, one-time purchase, or a mix of both?

Do they seem to be using skim pricing or penetration pricing?

Does their pricing model seem to align with the rest of the industry?

There are many tools out there to help, as well as specialized tools for specific industries, like hotel room price monitoring.

Once you have a grip on your competitors' pricing strategies, here are your options:

Reduce your prices: If pricing is the only differentiator in your industry (meaning product, place, and promotion strategies are all pretty uniform), you may consider lowering your prices to snag a higher share of the market. But be wary about how this will impact customers' perceptions of your quality, as well as the risk of a price reduction competition, driving everyone's prices to unprofitable levels.

Raise your prices: If your business already leads the market by providing something unique or higher-quality products than the competition, you may consider raising your prices. Be mindful of the price elasticity of your product before pursuing this strategy, as a small price increase can lose customers in certain industries.

Match your competitors' pricing: It's much more challenging to sell products at the same price as competitors. This strategy works if your business has been in the market for a while and can make a unique or niche offer. In this case, the customer will decide to purchase based on personal preferences rather than the price.

If you feel like none of the above strategies will work for you, you might explore nontraditional pricing structures like bundling.

Where are your competitors selling? Is everything online, or do they have brick-and-mortar stores?

Every industry trends differently here—shoe brands, for example, tend to have some in-person outlets since most people want to try on a pair of shoes before buying them. Furniture stores, on the other hand, don't always rely on brick-and-mortar locations. They can provide pictures, videos, and product details (such as dimensions and materials) to provide customers with a "good enough" idea of what they're getting.

Promotion encompasses more than just advertising, but since advertising is so measurable and public, it's the easiest to spy on. To avoid wasting your advertising budget , analyze your competitors' strategies.

Here's what you're looking for:

Cost per click: If the CPC is too high, you may not want to use paid ads as a promotion channel at all. It's better to learn this from your competitors than from your own experience.

Keywords: Examine the competition level for each keyword in your competitors' ad campaigns. This will help you find the best-performing keywords with low competition (and that means lower cost per click). You'll also be able to see which keywords aren't doing well and avoid those.

Long-running ads: If your competitor keeps running the same campaign for a long time, it definitely works for them. You can try to create something similar to replicate their success.

If most competitors rely on social media advertisements as opposed to TV or print ads, you should probably follow suit. Likewise, if they're putting their entire budget into advertising how crispy their chicken sandwich is, it's probably because chicken sandwich consumers really like their chicken crispy. Watch how your competitors allocate their advertising budget and use that information to inform your own strategy.

4. Get to know their SEO strategy

SEO is a beast, and there's no surefire way to fully dissect your competitors' strategy. But there are a few ways to at least figure out what's working for them.

Use a keyword research tool to see what topics your competitors are ranking for. Do they rely entirely on industry-specific queries, or do they branch into other tangential topics? What pages are bringing in the most traffic?

By discovering which keywords your competitors target (and rank for), you get insight into what's working for them and uncover new keywords you might have neglected. Even better, you might also find keywords they aren't targeting and identify the underserved customers. You can also use it as a chance to differentiate your message so that you're ranking for more targeted keywords .

The Ahrefs screenshot below shows the highest-traffic-driving pages from Slack's blog. A competing communication platform could use this data to learn what's working for Slack and replicate their success. To really be successful, however, you need to one-up the competition, bringing something new to the table.

Ahrefs also lets you see your competitors' backlinks. Figure out which websites are already linking to your competitors, and then you can try to get yourself a backlink from those sites as well.

Pro tip: As you do this research, actually click into some of your competitors' high-ranking pages to see what exactly is attracting users (and Google). Data is crucial, but nothing can replace the human eye.

5. Make note of their most popular content

If you can figure out which types of content—blog posts, videos, podcasts, guides, etc.—are most popular with your competitors' audiences, that can help inform your own content strategy . And here's a tech twist: you can employ ChatGPT to run a content analysis. Simply plug in a piece of content, ask it to run the analysis, and it will quickly dissect the post, highlighting themes, patterns, and potential gaps your brand can capitalize on.

The goal is to identify which content types get the most traffic or engagement. You can use a marketing analytics tool to analyze both of those things:

Do the highest-performing pieces of content tend to all be one specific medium (video, infographic, written content, or something else)?

Do the titles of the most-shared articles all have numbers or other specific characters (like brackets or hyphens) in them?

Is the most-shared content all about a similar topic?

Let's say you run a travel blog and discover that your competitor's article called "How to save money for a trip" is super popular among their audience. You now have an opportunity to create content on a similar topic but make it even better. Maybe "The ultimate guide to traveling on a budget" or even a real story about "How 21-year-old Mike saved $10K for his epic adventure." 

You should also be following all your competitors to learn more about their social media strategy , including which platforms work best for them.

Social media presents an opportunity to let your brand's voice and personality shine. Take Wendy's, for example. The brand publicly roasts its competitors on X (Twitter), and the internet loves it.

Not every brand can (or should) be quite as blunt and sassy as Wendy's, but showing some personality can pay off.

6. Conduct a final SWOT analysis

A SWOT analysis is a great way to spot opportunities to capitalize on and threats to prepare for before they strike. In short:

Strengths (S) and weaknesses (W) look internally at your company.

Opportunities (O) and threats (T) look externally to the industry and market.

While diving into your competitive market analysis, consider these factors:

Market share percentage: This metric reflects the proportion of sales a company has in the industry compared to its competitors. Tools like Statista or market research reports can offer insights into a brand's dominance or underperformance.

Competitive features: Understand the unique offerings that set a product or service apart from its rivals. Customer surveys and product comparison charts are useful methods to determine standout features.

Company culture: A company's values, work environment, and ethics can be a deciding factor for customers. Monitoring employer review sites like Glassdoor or observing a company's interactions on social media can provide insights.

Customer reviews: Customer reviews provide direct feedback on a product or service's quality and the company's reputation. Platforms like Yelp, Trustpilot, or Google Reviews offer a treasure trove of customer sentiments and concerns.

Geography (if applicable): Location can be pivotal in a company's success, especially for local businesses. Using tools like Google Trends can help identify where a product or brand is most popular geographically.

Let's be clear—this isn't going to shine a spotlight on the exact strategy you should pursue to beat your competitors. What it can do is provide a high-level look at your internal and external environment, making it easier to piece together your competitive strategy.

When promoting your brand, you're thinking about how to communicate your strengths. Conducting a SWOT analysis forces you to also view your brand with scrutiny. You may be working toward being "the best" at everything, but there will inevitably be things that other brands do better.

Download our SWOT analysis template and follow along with the example below to learn as much as possible about your brand and your competitive environment. You can also kick-start the process using ChatGPT.

Let's say you're the CEO of an automotive company conducting a competitive analysis. After doing a deep dive into your competition and your industry as a whole, organizing your notes and SWOT analyses in folders labeled by competitor, you notice a huge push toward electric vehicles.

In fact, you estimate that your closest competitor (who targets the same market as you) now allocates over half of its advertising budget to its newest electric vehicle release. But you only have one electric vehicle option, and you only allocate 15% of your advertising budget toward it. You reasonably guess that you're in trouble.

Additional research confirms your market is very interested in making the switch to electric. You decide to reallocate your budget toward researching, producing, and promoting electric vehicles. Five years pass, and you realize you would have been left in the dust had you failed to analyze the competition or ignored the warning signs.

Competitive market analysis can prevent wasted resources, inspire new opportunities, and help you stay one step ahead of the competition. Take these six steps to better understand your company's strengths and weaknesses—then identify where you need to improve and how to invest your resources to remain a strong competitor in your industry. 

Sometimes, a company's competitive advantage comes from its seamless internal operations. It can be hard to find out what's going on internally at another company, but you can be sure you streamline your own processes. Here are some tips to get started:

5 things you should automate today

9 ways to leverage automation in the workplace

How to write internal documentation that works

Streamline work across departments with automation

How to build an analysis assistant with ChatGPT

This article was originally published by Diana Ford in May 2021. The most recent update was in September 2023.

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Luke Strauss

Based in sunny San Diego, Luke is a digital marketer with 3+ years of experience developing and executing content strategy for eCommerce startups and SaaS enterprises alike—Airtable, Zoom, and yes, Zapier—to name a few. When he isn’t diving into a keyword research rabbit hole, you can find him at a music festival, thrifting, or spending time with his friends and family.

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How to create a competitive analysis (with examples)

Competitive analysis involves identifying your direct and indirect competitors using research to reveal their strengths and weaknesses in relation to your own. In this guide, we’ll outline how to do a competitive analysis and explain how you can use this marketing strategy to improve your business.

Whether you’re running a business or playing in a football game, understanding your competition is crucial for success. While you may not be scoring touchdowns in the office, your goal is to score business deals with clients or win customers with your products. The method of preparation for athletes and business owners is similar—once you understand your strengths and weaknesses versus your competitors’, you can level up. 

What is a competitive analysis?

Competitive analysis involves identifying your direct and indirect competitors using research to reveal their strengths and weaknesses in relation to your own. 

Direct competitors market the same product to the same audience as you, while indirect competitors market the same product to a different audience. After identifying your competitors, you can use the information you gather to see where you stand in the market landscape. 

What to include in a competitive analysis

The purpose of this type of analysis is to get a competitive advantage in the market and improve your business strategy. Without a competitive analysis, it’s difficult to know what others are doing to win clients or customers in your target market. A competitive analysis report may include:

A description of your company’s target market

Details about your product or service versus the competitors’

Current and projected market share, sales, and revenues

Pricing comparison

Marketing and social media strategy analysis

Differences in customer ratings

You’ll compare each detail of your product or service versus the competition to assess strategy efficacy. By comparing success metrics across companies, you can make data-driven decisions.

How to do a competitive analysis

Follow these five steps to create your competitive analysis report and get a broad view of where you fit in the market. This process can help you analyze a handful of competitors at one time and better approach your target customers.

1. Create a competitor overview

In step one, select between five and 10 competitors to compare against your company. The competitors you choose should have similar product or service offerings and a similar business model to you. You should also choose a mix of both direct and indirect competitors so you can see how new markets might affect your company. Choosing both startup and seasoned competitors will further diversify your analysis.

Tip: To find competitors in your industry, use Google or Amazon to search for your product or service. The top results that emerge are likely your competitors. If you’re a startup or you serve a niche market, you may need to dive deeper into the rankings to find your direct competitors.

2. Conduct market research

Once you know the competitors you want to analyze, you’ll begin in-depth market research. This will be a mixture of primary and secondary research. Primary research comes directly from customers or the product itself, while secondary research is information that’s already compiled. Then, keep track of the data you collect in a user research template .

Primary market research may include: 

Purchasing competitors’ products or services

Interviewing customers

Conducting online surveys of customers 

Holding in-person focus groups

Secondary market research may include:

Examining competitors’ websites

Assessing the current economic situation

Identifying technological developments 

Reading company records

Tip: Search engine analysis tools like Ahrefs and SEMrush can help you examine competitors’ websites and obtain crucial SEO information such as the keywords they’re targeting, the number of backlinks they have, and the overall health of their website. 

3. Compare product features

The next step in your analysis involves a comparison of your product to your competitors’ products. This comparison should break down the products feature by feature. While every product has its own unique features, most products will likely include:

Service offered

Age of audience served

Number of features

Style and design

Ease of use

Type and number of warranties

Customer support offered

Product quality

Tip: If your features table gets too long, abbreviate this step by listing the features you believe are of most importance to your analysis. Important features may include cost, product benefits, and ease of use.

4. Compare product marketing

The next step in your analysis will look similar to the one before, except you’ll compare the marketing efforts of your competitors instead of the product features. Unlike the product features matrix you created, you’ll need to go deeper to unveil each company’s marketing plan . 

Areas you’ll want to analyze include:

Social media

Website copy

Press releases

Product copy

As you analyze the above, ask questions to dig deeper into each company’s marketing strategies. The questions you should ask will vary by industry, but may include:

What story are they trying to tell?

What value do they bring to their customers?

What’s their company mission?

What’s their brand voice?

Tip: You can identify your competitors’ target demographic in this step by referencing their customer base, either from their website or from testimonials. This information can help you build customer personas. When you can picture who your competitor actively targets, you can better understand their marketing tactics. 

5. Use a SWOT analysis

Competitive intelligence will make up a significant part of your competitor analysis framework, but once you’ve gathered your information, you can turn the focus back to your company. A SWOT analysis helps you identify your company’s strengths and weaknesses. It also helps turn weaknesses into opportunities and assess threats you face based on your competition.

During a SWOT analysis, ask yourself:

What do we do well?

What could we improve?

Are there market gaps in our services?

What new market trends are on the horizon?

Tip: Your research from the previous steps in the competitive analysis will help you answer these questions and fill in your SWOT analysis. You can visually present your findings in a SWOT matrix, which is a four-box chart divided by category.

6. Identify your place in the market landscape

The last step in your competitive analysis is to understand where you stand in the market landscape. To do this, you’ll create a graph with an X and Y axis. The two axes should represent the most important factors for being competitive in your market. 

For example, the X-axis may represent customer satisfaction, while the Y-axis may represent presence in the market. You’ll then plot each competitor on the graph according to their (x,y) coordinates. You’ll also plot your company on this chart, which will give you an idea of where you stand in relation to your competitors. 

This graph is included for informational purposes and does not represent Asana’s market landscape or any specific industry’s market landscape. 

Tip: In this example, you’ll see three companies that have a greater market presence and greater customer satisfaction than yours, while two companies have a similar market presence but higher customer satisfaction. This data should jumpstart the problem-solving process because you now know which competitors are the biggest threats and you can see where you fall short. 

Competitive analysis example

Imagine you work at a marketing startup that provides SEO for dentists, which is a niche industry and only has a few competitors. You decide to conduct a market analysis for your business. To do so, you would:

Step 1: Use Google to compile a list of your competitors. 

Steps 2, 3, and 4: Use your competitors’ websites, as well as SEO analysis tools like Ahrefs, to deep-dive into the service offerings and marketing strategies of each company. 

Step 5: Focusing back on your own company, you conduct a SWOT analysis to assess your own strategic goals and get a visual of your strengths and weaknesses. 

Step 6: Finally, you create a graph of the market landscape and conclude that there are two companies beating your company in customer satisfaction and market presence. 

After compiling this information into a table like the one below, you consider a unique strategy. To beat out your competitors, you can use localization. Instead of marketing to dentists nationwide like your competitors are doing, you decide to focus your marketing strategy on one region, state, or city. Once you’ve become the known SEO company for dentists in that city, you’ll branch out. 

You won’t know what conclusions you can draw from your competitive analysis until you do the work and see the results. Whether you decide on a new pricing strategy, a way to level up your marketing, or a revamp of your product, understanding your competition can provide significant insight.

Drawbacks of competitive analysis

There are some drawbacks to competitive analysis you should consider before moving forward with your report. While these drawbacks are minor, understanding them can make you an even better manager or business owner. 

Don’t forget to take action

You don’t just want to gather the information from your competitive analysis—you also want to take action on that information. The data itself will only show you where you fit into the market landscape. The key to competitive analysis is using it to problem solve and improve your company’s strategic plan .

Be wary of confirmation bias

Confirmation bias means interpreting information based on the beliefs you already hold. This is bad because it can cause you to hold on to false beliefs. To avoid bias, you should rely on all the data available to back up your decisions. In the example above, the business owner may believe they’re the best in the SEO dental market at social media. Because of this belief, when they do market research for social media, they may only collect enough information to confirm their own bias—even if their competitors are statistically better at social media. However, if they were to rely on all the data available, they could eliminate this bias.

Update your analysis regularly

A competitive analysis report represents a snapshot of the market landscape as it currently stands. This report can help you gain enough information to make changes to your company, but you shouldn’t refer to the document again unless you update the information regularly. Market trends are always changing, and although it’s tedious to update your report, doing so will ensure you get accurate insight into your competitors at all times. 

Boost your marketing strategy with competitive analysis

Learning your competitors’ strengths and weaknesses will make you a better marketer. If you don’t know the competition you’re up against, you can’t beat them. Using competitive analysis can boost your marketing strategy and allow you to capture your target audience faster.

Competitive analysis must lead to action, which means following up on your findings with clear business goals and a strong business plan. Once you do your competitive analysis, you can use the templates below to put your plan into action.

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After 40 Years, How Representative Are Labor Market Outcomes in the NLSY79?

In 1979, the National Longitudinal Study of Youth 1979 (NLSY79) began following a group of US residents born between 1957 and 1964. It has continued to re-interview these same individuals for more than four decades. Despite this long sampling period, attrition remains modest. This paper shows that after 40 years of data collection, the remaining NLYS79 sample continues to be broadly representative of their national cohorts with regard to key labor market outcomes. For NLSY79 age cohorts, life-cycle profiles of employment, hours worked, and earnings are comparable to those in the Current Population Survey. Moreover, average lifetime earnings over the age range 25 to 55 closely align with the same measure in Social Security Administration data. Our results suggest that the NLSY79 can continue to provide useful data for economists and other social scientists studying life-cycle and lifetime labor market outcomes, including earnings inequality.

We thank Kevin Bloodworth II, Elizabeth Harding, and Siyu Shi for research assistance. The views in this paper are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or of the National Bureau of Economic Research.

Richard Rogerson acknowledges financial support in excess of $10,000 over the last three years from the Federal Reserve Bank of Atlanta, the Federal Reserve Bank of Minneapolis and the World Bank.

MARC RIS BibTeΧ

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Maintaining Hold: Balancing Roku’s Mixed Results and Competitive Risks in Streaming Market

In a report released today, Jeffrey Wlodarczak from Pivotal Research reiterated a Hold rating on Roku ( ROKU – Research Report ), with a price target of $75.00 .

Jeffrey Wlodarczak has given his Hold rating due to a combination of factors impacting Roku’s business. His analysis acknowledges the mixed results in the company’s first-quarter earnings and second-quarter guidance, where Roku experienced worse-than-expected net new streaming household additions but performed better in gross profit and free cash flow. Despite tough comparables due to previous accounting adjustments and an uneven advertising recovery, Roku’s impressive cost control management is a highlight that has positively influenced Wlodarczak’s EBITDA and free cash flow estimates for 2024. However, he has slightly lowered his expectations for net new streaming households and platform revenue. Furthermore, Wlodarczak notes Roku’s strong standing in the streaming market, given its substantial U.S. household penetration and high-quality streaming aggregation product. Yet the company faces significant competitive risks from large players with deep pockets who are recognizing the value in streaming aggregation and could subsidize their streaming ventures. The proliferation of advertising inventory and potential market consolidation also pose threats to Roku’s advertising-based revenue model. Despite these challenges, Roku’s market position and product strength are considered formidable, leading Wlodarczak to maintain his year-end target price and reiterate the Hold rating.

Based on the recent corporate insider activity of 80 insiders, corporate insider sentiment is positive on the stock. This means that over the past quarter there has been an increase of insiders buying their shares of ROKU in relation to earlier this year.

TipRanks tracks over 100,000 company insiders, identifying the select few who excel in timing their transactions. By upgrading to TipRanks Premium, you will gain access to this exclusive data and discover crucial insights to guide your investment decisions. Begin your TipRanks Premium journey today.

Roku (ROKU) Company Description:

Founded in 2002 and based in California, Roku, Inc. is a television streaming platform. It operates through two business segments: Player and Platform. The Player segment consists of net sales of streaming media players and accessories through retailers and distributors, as well as directly to customers through the company’s website. Its Roku platform allows users to personalize their content selection with cable television replacement offerings and other streaming services that suit their budget and needs.

Read More on ROKU:

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The U.S. labor market can affect ‘people who are not even here,’ research finds

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A recently published paper co-authored by Brian Cadena finds deep connections between the U.S. and Mexican economies

That the job market in Phoenix can affect a child’s education in Mexico may strain credulity, but it’s nevertheless true, according to a recent  paper co-authored by Brian Cadena , a University of Colorado Boulder associate professor of economics.  

People from specific regions in Mexico tend to migrate to specific regions in the United States, and when U.S. work dries up in some areas, those migrants tend to return to Mexico, Cadena and his co-authors, María Esther Caballero of American University and Brian K. Kovak of Carnegie Mellon, found.

Their paper, published in the Journal of International Economics in November, explores the U.S. labor market’s influence on the lives of people in Mexico by comparing how neighboring Mexican counties, or “municipios,” fared during the Great Recession.

Brian Cadena, a CU Boulder associate professor of economics, and his research colleagues explore the U.S. labor market’s influence on the lives of people in Mexico by comparing how neighboring Mexican counties fared during the Great Recession.

To perform their analysis, Cadena, Caballero and Kovak drew upon data from the Matrícula Consular de Alta Seguridad (MCAS), a governmental organization that issues identity cards to Mexican migrants.

Unlike either the U.S. or Mexican census, MCAS provides in-depth, granular information on migrant workers, specifying the municipios they leave and where in the United States they settle.

MCAS is a treasure trove, says Cadena. But it wasn’t long ago that researchers didn’t know how to use it. Cadena, Caballero and Kovak changed that with another paper they published in 2018, which validated the MCAS data and thereby opened up a whole range of potential research.

“This identity-card data really allowed us to drill down and make tight comparisons between municipios,” says Cadena.  

The strength of networks

A key finding that emerged from the MCAS data is that people from the same municipio often move to the same cities and states in the United States. “People follow their networks,” says Cadena. And these networks are so strong that migrants from nearby municipios often end up hundreds of miles apart in the States.

Migrants from the municipio of Dolores Hidalgo, for example, tend to move to Texas, while those from nearby Jaral del Progreso generally relocate to Chicago, California and the Southwest. Same region in Mexico, different time zones in the United States.

The close proximity of the municipios is important for the kind of research Cadena, Caballero and Kovak are doing, Cadena explains, because it cuts down on confounding variables. Neighboring municipios experience the same weather, suffer the same droughts, follow the same or similar laws, etc., which means differences in their economic outcomes are likely due to something they don’t share—the job market in the cities and states where their migrants moved.

To unearth these differences, Cadena, Caballero and Kovak measured the job-market losses in the U.S. regions linked to each municipio and then compared the economic outcomes in the municipios connected to harder-hit regions to those connected to softer-hit regions.

As it happens, labor demand in Texas survived the Great Recession relatively unscathed, so the municipios of the migrants who ventured there remained stable. The American Southwest, however, suffered some major blows, and so the municipios connected to that region exhibited several changes.

(Un)expected observations

Some of those changes were unsurprising, says Cadena.

“One of the things we’re finding is how connected these two economies are," says CU Boulder researcher Brian Cadena of the United States and Mexico. On the one hand, the stark differences in what someone can earn and what the labor market looks like in one country as opposed to the other suggests that we have made the separation between those countries real and meaningful. On the other hand, we are certainly not islands.”

“When work dried up, more immigrants returned to Mexico, and fewer new immigrants came from that source community.” This then led to a fall in remittances, or money transfers from migrant workers to their families back in Mexico.  

Yet Cadena, Caballero and Kovak also observed some changes they didn’t expect. One was that more women joined the Mexican workforce.

“This is called the added worker effect,” says Cadena. “When the primary earner of a household”—in this case, the migrant laborer—“loses their job, it’s a common reaction by the household to say, ‘Let’s send someone else to work.’”

Another unexpected change was a drop in school retention. “We found some suggestive evidence that a loss of jobs in the United States reduced investment in schooling in Mexico. We saw more schooling dropout, especially at transition ages, when kids move from one level of schooling to the next,” says Cadena.

Blurred lines and better choices

What do these findings suggest about the perceived separation between these two countries and their economies?

It makes that separation “a little fuzzier,” says Cadena.

“One of the things we’re finding is how connected these two economies are. On the one hand, the stark differences in what someone can earn and what the labor market looks like in one country as opposed to the other suggests that we have made the separation between those countries real and meaningful. On the other hand, we are certainly not islands.”

Realizing this, Cadena believes, could inform policymaking, specifically regarding immigration.

“When we’re thinking about immigration policy—when we’re thinking about all these things that affect the low-wage labor market—we are making policy that has a real and noticeable effect on the lives of people who are not even here,” he says.

“I’m not a politician, but I think that a more holistic sense of all the impacts of the choices we make as a country could help us make better choices.”

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Reporting by Mrinmay Dey in Bengaluru; Editing by Varun H K

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