Competitive analysis

Published: 2021-07-01 05:37:53
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Competitive analysis is considered essential for understanding the environment in which a firm operates. It has a significant effect on the strategies that the firm adopts in order to achieve and to sustain a competitive advantage and, consequently, to improve performance. However, there is surprisingly little empirical work on competitive decision making, especially in marketing. This paper describes the major perspectives that have been used to understand competitive analysis by showing how such analyses can be or are framed by decision makers.

Figure 1 provides an overview of how information processing and cultural biases influence the strategies and p e r f o r m a n c e of firms. As can be seen in Figure 1, we can conceive of competitive anlayses being framed within individual and organizational contexts. Each of these contexts produce certain biases that affect the nature of decisions made. Our paper proceeds by first describing the methods for understanding competitive structure - for understanding the actions taken and strategies developed by competitors - and concludes with a discussion of individual and organizational biases that affect competitive analyses.

1. Methods and information for competitive analysis in marketing Management gathers information about its competitive environment in order to develop a meaningful strategy. Information about competition is obtained from a We are grateful for the comments of George Day, Peter Dickson, Pete Fader, Don Lehmann, Rajan Varadarajan, and David Wheaton on an earlier version of this paper. The question addressed in this section concerns how managers can define their set of competitors (brands and firms). Management must know the brands and firms that are currently competing against them so that competitive advantages can be assessed.
This necessitates finding which brands are current substitutes and by what firm th ey are manufactured. It also necessitates that potential competitors for the future be anticipated. There is a vast literature in marketing and in economics on this topic. Three basic approaches have been proposed in that titerature: an approach based on the analysis of actual purchases made by consumers, an approach based on the analysis of consumer judgments, and an approach that does not use information provided by customers but is based on inferences made from the strategies exhibited by the competitors.
The analysis ofpurchases or of the usage behavior exhibited by consumers concerns the pattern of purchases made over time by individnals (at the individual consumer level) or exhibited in the market (at the aggregate level). The observation that consumers switch between brands over time indicates that these brands may be substitutes for the consumer. Therefore, these brands will be considered to be in competition, especially if brand switching is induced by changes in the marketing of these brands.
We review first the analysis performed at the aggregate level to estimate demand cross-elasticities and then turn to individual-levet analyses of consumer switching behavior. When products are substitutes, and therefore cornpete against each other, an increase in the sales of one of them causes a decrease in the sales of the other. Cross-elasticity of sales can, however, be misleading because this relationship could be due to an environmental factor that affects the sales of the two products in the opposite direction.
Sales of umbrellas and tanning totion might be negatively correlated; however, it is unlikely that these two products are in competition since they do not satisfy the same needs for consumers. Marketing mix crosselasticities indicate the effect of the marketing action of firm i on the sales of firm i. For example, Reibstein and Gatignon (1984) estimate the price cross-elasticities of eggs in a supermarket. The lack of cross-elasticity for a pair of products indicates that the two products are not in competition.
The cross-elasticity matrix can be used to derive the set of competing products. Market structure can, however, be complex. Reibstein and Gatignon (1984) show 274 ROHIT DESHPANDE AND HUBERT GATIGNON that the cross-elasticity matrix is not symmetric. For example, while the price of medium eggs influences the sales of large eggs, the reverse is not true. This indicates that competition is asymmetric since the manager Of one product (mediumsize eggs) can affect the sales of another product (large-size eggs) while the reverse is not true.
This asymmetry is, therefore, a source of competitive advantage since the medium egg product can h u r t a competitor without fear of retaliation. These asymmetric elasticities and cross-elasticities can also be estimated with market share models (Cooper and Nakanishi, 1988; Carpenter, Cooper, Hanssens, and Midgeley, 1988). While this method can help identify sources of competitive advantage due to asymmetric effects of competitive marketing mix variables, Day and Shocker (1976) have listed a number of drawbacks. First, changes in marketing mix variables by one firm often lead to a reaction from competitors.
The implication of this is that competitive reactions taust be modeled when estimating the response functions. Second, marketing mix cross-elasticities might not be static, as changes in the environment and in product composition occur over time. Third, the variability or the collinearity in the data make it difficult in some situations to estimate cross-elasticities. Finally, the estimation of the cross-elasticities is subject to bias due to variables missing from the model specification. This type of analysis remains, however, a basic source of information about the competitive structure of a market.
Brand switching has been typically measured by the conditional probability of purchasing brandj at purchase occasion t when brand i was bought at the previous purchase occasion (t - 1). For example, Carpenter and Lehmann (1985) group all individuals who have bought brand i at a given peried and model the probability of buying any of the competitive brands at the next purchase occasion using a logit model specification. This method suggests that changing the brand purchased in two consecutive periods indicates that the brands are close substitutes. Lattin and McAlister (1985) point out that
some of that "switching" might not reflect substitutability but complementarity due to variety seeking. They propose a model of variety-seeking behavior to reveal substitute and complementary brands. While it is not our purpose to review the broad marketing literature on brand-switching models, this type of analysis is particularly well adapted to the data from supermarket checkout scanners (for recent work using brand-switching data for inferring market structure, see Grover and Srinivasan, 1987; Kamakura and Russell, 1989; or Zenor and Srivastava, 1993).
Instead of inferring market structure from the observed purchase behavior of consumers, information about substitutability can be inferred from a n a l y z i n g c o n sumerjudgments - the distinctiveness of the brands in the consumers' perceptual space. Information can be obtained on similarities or dissimilarities between brands to derive the structure underlying these perceptual differences. Additional information on preferences is also useful to evaluate the strength of competition between brands.
Perceptual maps, developed especially from multidimensional scaling studies, provide a distance measure between brands in a parsimonious space. The dis- COMPETITIVE ANALYSIS 275 tances on each of the dimensions present a measure of substitutability of the brands. Brands that appear at different ends of the perceptual map are unlikely to compete, and the more closely brands are perceived, the greater their substitutability. In addition to the perceptions of brands in a perceptual map, information about consumer preferences (with ideal points) is useful to understand the degree of competition or substitutability between brands.
For example, the competition might be more intense in a market where the ideal point is equidistant from the two brands in the market, as compared to the case where the ideal point is closer to one of the two brands (assuming all other distances are constant). Therefore, the distance between brands and the ideal brand as well as the distribution of these preferences are useful pieces of information to infer competitive structures. Instead of using a customer-oriented approach to infer the competitive structure, it has been argued that competitors are firms that follow the same strategy.
It is critical to identify firms with similar strategies and to identify mobility bartiers that prevent firms in one group from switching to a strategy from a different group. All the firms that fall into the same group form a "strategic group," and the products within each group are considered substitutes (Thomas and Venkatraman, 1988). Porter (1980) illustrates the concept of strategic groups for the U. S. chain saw industry. Two dimensions contributing to mobility barriers are proposed in this example. The strategic group notion is very powerful and has received wide acceptance in the strategic academic and business communities.
More recently, some promising methodologies have been advanced to determine these strategic groups through the development of a competitive strategic map based on strategic variables (Day, DeSarbo, and Oliva, 1987; Oliva, Day, and DeSarbo, 1987). These types of analyses are clearly well established in the marketing academic literature. Nevertheless, some critical questions remain: Which of these methods are most @en used in practice? Do companies use a single approach or multiple comp/ementary approa«hes? Can the use of certain analyses be associated with better pelformance? 1. 2. Understanding competitive behavior
The section above has described briefly the information used to establish the competitive structure of a market - that is, to identify the boundaries of a market. While critical to understanding competition, the term competition itself often refers to the behavior of the firms in a market rather than to the structure of that market, specifically the interdependence of the actions taken by the managers of competing brands. Understanding this behavior is necessary in order to anatyze the extent and the type of rivalry (or lack of rivalry) that exists between the competitors and to measure the intensity of competition that results.
The major objective is for each competitor to be able to anticipate what the other comnetitor 276 ROHIT DESHPANDE AND HUBERT GATIGNON will do in terms of its strategic moves, especially in response to competitive actions. The understanding of competitive rivalry necessitates (1) information on industry structure and (2) information on the past behavior of the competitors that can be used to predict competitors' responses. These two types of information correspond to methodologies that can be used to assess and predict competitors' behavior and reactions.
Using the industry structure approach, Porter (1980) indicates that the attractiveness of any industry depends on five driving forces: (1) the extent of competition from traditional rivals, (2) the extent of the threat of new entrants into the market, (3) the extent of the threat of substitute products or services, (4) the bargaining power of suppliers, and (5) the bargaining power of buyers. This analysis has focused strategic analysis on the existence and ability to build barriers to entry. It is not clear, from a marketing point of view, that this emphasis is entirely justified.
Indeed, the key potential marketing barriers to entry are (1) product differentiation, (2) buyer switching costs, (3) marketing economies of scale, and (4) access to distribution channels. However, the extent to which each of these constitute strong barriers has not really been established. Innovations get imitated relatively easily, even in industries with strong legal protection, such as the pharmaceutical industry. The empirical evidence on economies of scale in marketing is also weak (Buzzell, Gale, and Sultan, 1975). It might be that only the access by competitors to effective distribution channels can be prevented.
In addition to this general concept of barrier to entry, which can create differences in levels of rivalry across industries, different competitive reactions can be explained by the nature of the industry itself. Because the strategy of the firm, and especially the allocation of resources to a given business or SBU, depends on the attractiveness of the market and on the relative position of businesses in the market, the reaction strategies of firms to each others' moves should be different depending on the characteristics of the market and on the characteristics of the competitors (Ramaswamy, Gatignon, and
Reibstein, 1994). The market growth rate, market concentration, and the degree of standardization of products are industry characteristics that affect the type (simple versus complex) of marketing behavior exhibited by the competitors. Also, the extent to which competitors differ on their costs and positioning explains whether firms are more likely to cooperate or to exhibit increased levels of rivalry. A special situation arises when a new entrant comes into the market. Incumbent firms' reactions can be explained by differences in the markets.
Robinson (1988) shows that the reactions to an entrant by incumbent firms vary as a function of the entrant's strategy as well as depending on the industry. Industry characteristics found to affect the reactions are (1) the industry concentration, (2) the degree of product standardization, and (3) the growth rate of the market. The greater the concentration in an industry, the more likely the entrant will be noticed by the incumbent firms and the greater the share likely to be taken away from these incumbents. Because of these reasons, the reactions are more intense in concentrated markets than in unconcentrated ones.
In markets where the products are COMPETITIVE ANALYSIS 277 specialized to order, as opposed to standardized, competition is based on multiple, complex bases that correspond to product differentiations satisfying different needs in the marketplace. Therefore, competition is less direct than in undifferentiated markets where price becomes a single dimension for competing. Consequently, reactions are found to be stronger in markets where the products are standardized. Market growth has also been shown to be associated with stronger reactions from incumbent firms.
Even though firms might be working at or near capacity, growing markets are attractive markets where the strategic orientations of the firms that have decided to establish themselves in that market are at stake. These firms view an entry as an attack to their strategic focus and, therefore, react violently to prevent the newcomer from establishing a position. In summary, a number of market or industry characteristics have been shown to affect how competitors behave toward each other. Thus, management should gather information of this nature in order to anticipate competitive actions and reactions.
Much of the behavior of firms resides in management's understanding of the past behavior of their competitors. Therefore, over time, managers develop an understanding of what affects market position and profitability and act accordingly. The way competitors react to each other follows the same organizational learning. Assuming no causal interpretation ambiguity, historical data on what actions or decisions were taken by each firm in the market enables management to analyze the covariation of these actions. This analysis is formally performed in the specification of response funcfions that can be economically estimated.
The object of such reaction functions is to summarize the reaction patterns among competitors from a matrix of reaction elasticities - that is, the percentage change in the marketing mix decision variable i of brand X due to a 1 percent change in the marketing mix variable j of brand K This analysis requires time series data across the competitors in the industry. Examples of such analyses include Lambin, Naert, and Bultez (1975), Lambin (1976), Metwally (1978), Hanssens (1980), Gatignon (1984) and Leeflang and Wittink (1992).
Gatignon, Anderson, and Helsen (1989) model more especially the reactions by incumbent firms to a new entrant. The approaches described above make individual firm inferences about the behavior of competitors. A different approach consists of classifying firms into categories of strategic interdependent firm behaviors. Miles and Snow (1978) have proposed classifying firms according to three typical sets of behaviors: the defenders, the analyzers, and the prospectors. Based on PIMS data analysis, Hambrick (1983) has shown that each of the three groups can be characterized by specific functional strategies.
For example, product R;D-to-sales and marketing expenses-to-sales ratios are greater for prospectors than for defenders. Therefore, the group to which a firm belongs can be inferred based on the functional attributes of the firms in an industry. Other implications of the typology can then be examined to understand the strategy of these competing firms. And, as McKee, Varadarajan, and Pride (1989) note, these strategies are contingent on the specific environmental context operating.

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