The Behavioral Economics of Consumer Brand Choice
The behavioral economics of consumer brand choice: patterns of reinforcement and utility minimization Gordon R. Focal a, , Jorge M. Oliver-Castro b , Teresa C. Schwarzenegger a b a Cardiff Business School, Cardiff University, Cardiff, UK Institutor De Psychological, Universities De Bras Lila, Bras Lila, UDF, Brazil I I Abstract Purchasers of fast-moving consumer goods generally exhibit multi-brand choice, selecting apparently randomly among a small subset or “repertoire” of tried and trusted brands.
Their behavior shows both matching and minimization, though t is not clear Just what the majority of buyers are maximizing. Each brand attracts, however, a small percentage of consumers who are 100%-loyal to it during the period of observation. Some of these are exclusively buyers of premium-priced brands who are presumably maximizing informational reinforcement because their demand for the brand is relatively price-insensitive or inelastic.
Others buy exclusively the cheapest brands available and can be assumed to maximize utilitarian reinforcement since their behavior is particularly price-sensitive or elastic. Between them are the charity of consumers whose multi-brand buying takes the form of selecting a mixture of economy- and premium-priced brands. Based on the analysis of buying patterns of 80 consumers for 9 product categories, the paper examines the continuum
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More specifically, such results indicate that, in steady-state markets: (a) only a small portion of consumers buy Just one brand on consecutive shopping occasions, that is, few consumers remain 100% loyal to one brand; (b) each brand attracts a small group of 100%-loyal consumers; (c) the majority of consumers buy several different brands, selected apparently randomly from a subset of existing brands; (d) existing Corresponding author. Tell. : +44-2920-874-275. E-mail address: [email protected] C. UK (G. R. Focal). Rand’s usually differ widely with respect to penetration level and not so much in The Behavioral Economics of Consumer Brand Choice: Patterns of Reinforcement and Utility Minimization By mimicry utility minimization Gordon R. Focal , Jorge M. Oliver-Castro b , Teresa C. Apparently randomly from a subset of existing brands; (d) existing * analysis period); and (e) brands with smaller penetration levels (or market shares) also tend to show smaller average buying frequency and smaller percentages of 100%-loyal consumers (I. E. , “double Jeopardy’).
These results have been replicated for mom 30 food and drink products (from cookies to beer), 20 cleaning and personal care products (from cosmetics to heavy cleaning liquids), gasoline, aviation fuel, automobiles, some medicines and pharmaceutical prescriptions, television channels and shows, shopping trips, store chains, individual stores, and attitudes toward brands (CB. Doll’s Riley et al. , 1997; Remembering, 1972; Remembering et al. , 1990; Remembering and Scrivener, 1999; Goatherd et al. , 1984; Uncles et al. , 1995). 0376-6357/$ – see front matter 2004 Elsevier B. V. All rights reserved. Ii:10. 1016/ j. Beeper. 2004. 03. 007 G. R. Focal et al. Behavioral processes 66 (2004) 235-260 So sure are the relationships involved that a mathematical model has also been developed to describe such regularities, the Directly Model (e. G. , Remembering et al. , 1990), which has been used to predict the market insertion of new products (Remembering, 1993), to analyze the effects of promotions (Remembering, 1986; Remembering et al. , 1994), and to evaluate patterns of store loyalty (Remembering and England, 1990; Keen and Remembering, 1984; Sharp and Sharp, 1997; Uncles and Remembering, 1990).
Nonetheless, despite the wide replication of such patterns, which have been raised y some authors to the status of “empirical generalizations” in marketing (e. G. , Uncles et al. , 1995), little is known about the variables and the underlying behavioral mechanisms that influence and explain consumers’ brand choices. The marketing literature is not forthcoming, for instance, about the factors responsible for shaping the subset of the brands that compose a product category among which consumers choose in practice (their “consideration sets”) and what Remembering calls the “repertoire” of such brands actually purchased (their “purchase sets”).
It is a basic axiom of modern marketing thought that sales are produced not simply by price acting alone, any more than by product attributes, or advertising and other promotional means, or distribution effectiveness acting singly, but by a combination of all four of these influences on demand that constitute the “marketing mix. As marketing science has developed as a separate discipline, it has De-emphasized the influence of price on demand (the principal focus of the economist’s purview) and stressed the non-price elements of the marketing mix, notably the promotional activity involved in brand differentiation (De Charleston and McDonald, 2003; Ajar ND William, 1998; Watkins, 1986).
Behavioral economics, partly because of the stress it has placed on the economics of animal responding in experimental situations, where the sole reliable analogue of the influences on consumer demand ruling in the market place relates to price, has necessarily followed the reasoning and methodology of the economist rather than the marketing scientist. The non-price economics. The assumption that consumers maximize utility in some way or other?a preoccupation of the economics approach?is, nevertheless, common in the marketing literature.
Criminality and Raja (1988), for example, state that “the consumer hoses that alternative which maximizes his (or her) utility,” although they recognize that this is a latent or unobservable utility which is assumed rather than tested (CB. Archaic, 1980). Based on this minimization assumption, one could expect consumers to choose the cheapest brands that offer the attributes and characteristics that they are looking for.
Although the price of different brands is certainly one variable that is expected to influence brand choice, as exemplified by the literature on the effects of promotions (e. G. , Remembering, 1986; Remembering et al. , 1994; Bell et al. , 1999), empirical evidence showing that consumers tend to maximize when choosing across brands was not available before recent research on the behavioral economics of brand choice (Focal and James, 2001, 2003; Focal and Schwarzenegger, 2003).
In this paper, we extend this research from the analysis of single cases to that of panel data for some 80 consumers purchasing 9 product categories, examining in detail the relationship between price and quantity demanded in relation to the functional and symbolic attributes of brands which influence the composition of consumers’ consideration and purchase sets. 1. 1 . Previous research Focal (AAA), Focal and James (2001 , 2003), and Focal and Schwarzenegger (2003) adopted techniques refined in choice experiments in behavioral economics and behavior analysis to investigate brand choice.
Three types of analysis were used: matching, relative demand, and minimization. 1. 1. 1 . Matching analysis The results of choice experiments with nonhuman animals in behavior analysis gave support for the development of the matching law, which in its simplest form asserts that organisms in choice situations match the relative distribution of responses to the relative distribution of the enforcers they obtain (Hermiston, 1961, 1970).
In its more general form, the generalized matching law (Beam, 1974, 1979) states that the ratio of responses between two alternatives is a power function of the ratio of reinforces, that is, Bal RI -b 82 RE s 237 where B represents responses, R represents reinforces, and the subscripts 1 and 2, choice alternatives. The parameter b, obtained from the intercept of the linear log- log formulation of the law, is a measure of biased responding between the alternatives, usually related to asymmetrical experimental factors such as differences n response cost between the alternatives.
The parameter s, the slope of the linear log-log formulation, is interpreted as a measure of sensitivity in response individual favors, more than predicted by precise matching, the richer (s > 1) or poorer (s < 1) schedule of reinforcement. In behavioral economics, the parameter s can also be used as an estimate of the level of substitutability of the reinforcers in the situation, in which case there is evidence suggesting that it should be equal or close to 1 for substitutable commodities, and negative for complementary commodities (cf.
Beam and Nevi, 1981; Focal, AAA; Gaggle et al. , 1995). Focal and James (2001 , 2003) applied this type of analysis to data obtained from consumers’ brand choice. Consumer choice was analyzed for brands that were substitutes, non- substitutes and independent, for 1-, 3-, and 5-week periods. Matching and minimization analyses were based on relative measures of price paid and amount bought, which considered the relation between the amount paid for (or amount bought of) the preferred brand and the amount paid for (or amount bought of) the other brands in the consumer repertoire.
As predicted, substitute brands showed itching whereas independent brands showed some evidence of anti-matching. Their results also showed some evidence that consumers tend to maximize the amount they pay in relation to the amount they buy within their brand repertoire by purchasing the cheapest brand (although they sometimes also bought some more expensive brand). Similar results have also been reported by more recent research (CB. Focal and James, 2003; Focal and Schwarzenegger, 2003). 1. 1. 2.
Relative demand analysis Whereas matching analysis relates the actual amount of a reinforced obtained to the actual amount of behavior expended in obtaining it, an understanding of consumer decision making in the face of competing sources or reinforcement offered at a variety of programmed behavioral costs or prices requires a different kind of analysis. Matching analysis plots the quantity obtained of a commodity as a positively accelerating function of the amount paid for it. By contrast, the sensitivity of the quantity demanded of a commodity to its ruling market price is expressed by economists in terms of the demand curve.
One of the assumptions underlying the demand curve is that as the unit price of a commodity increases, its consumption will decrease (Madden et al. 2000). This is demonstrated when demand curves plotted on logarithmic coordinates show consumption to be a positively decelerating function of unit price. The sensitivity of quantity demanded to price is expressed in economic terms as “price elasticity of demand” which at its simplest relates the percentage change in amount consumed to the percentage change in price (Houston and McFarland, 1980; see also Harsh, 1980; Harsh and Banana, 1987).
In an attempt to incorporate some of the features of naturalistic marketing settings involving consumer choices among competing brands whose relative prices might influence selection decisions, Focal and James (following Gaggle et al. , 1980) employed relative demand analysis which presents the relative amounts of brands A and B as a function of their relative prices. Their results, albeit for a restricted sample of individual consumers and covering a small number of product categories, found downward-sloping demand curves which indicated a degree of price sensitivity on the part of the buyers investigated (Focal and James, 2001, 2003). . 1. 3. Minimization analysis Analyses to reveal whether the observed consumer behavior was maximizing Hermiston and Loveland (1975), Hermiston and Vaughan (1980). On con ratio schedules,l there is a fixed probability of reinforcement for each response, 1 A ratio schedule is one in which a specified number of responses has to be performed before reinforcement becomes available. Fixed ratio schedules keep the number of required responses equal from reinforced to reinforced; variable ratio schedules allow the required number of responses to change from one reinforced to the next.
Concurrent variable ratio schedules, usually abbreviated to con IVR IVR, allow simultaneous choice to be investigated. It is this arrangement that most clearly assembles the purchases of brand within a product class. Which can be expressed as the reciprocal of the schedule parameter. Concurrent VRRP VRRP refers to response alternatives which have respective reinforcement probabilities of 1/30 and 1/60. On ratio schedules, the probability of reinforcement is independent of response rate (something not true of VI schedules where the probability of reinforcement is inversely proportional to rate of responding).
Although most research on matching and minimization has been undertaken in laboratory settings which incorporate VI schedules, IVR schedules are more probable in attraction settings (Hermiston, 1982; Hermiston and Loveland, 1975; Hermiston and Propel, 1991; Hermiston and Vaughan, 1980; Vaughan and Hermiston, 1987). Faced with con VRRP VRRP schedules, the individual’s maximal probability of reinforcement is obtained by responding exclusively on the VRRP schedule. Matching theory makes the same prediction for con IVR IVR schedules, claiming that minimization is under these circumstances a special case of matching (CB.
Archaic, 1980). Previous research, subject to the limitations of scope noted above, confirmed that consumers tend to examine by generally purchasing the least expensive brand available within their consideration set (Focal and James, 2001, 2003). 1. 2. Research issues Taken together, these results indicate that, within their repertoire of brands, consumers show price sensitivity, maximizing (most of the time), and matching (which refers to the relation between the amount they spend and the amount they buy).
Based on such findings, one can predict that consumers will buy, more often than not, the cheapest brand among those that they usually buy, although one still does not know why they usually buy a certain set of brands and not others. The fact that consumers tend to buy the cheapest brand within a restricted set of brands rather than the cheapest of all brands available in the product category indicates that not all brands are perfect substitutes for the others. Even though they may be functionally equivalent for the consumer, the brands are not entirely equivalent, that is, consumer preferences reflect more than functional utility.
This additional source of utility is usually rationalized in the marketing literature as stem- mining from rather nebulous “branding” considerations. Branding is not, however, a to clarify its basis as an extra-functional source of reinforcement. Although research to date is indicative that the principles and methods of behavioral economics can be usefully applied to consumer brand purchasing, there is clearly need for a more extensive investigation of a larger, systematically-selected sample of consumers purchasing a wider range of products in order to ascertain how far previously reported results are generalize.
It is necessary to take into greater consideration the differences between the typical consumption patterns of laboratory subjects which can be shown to be sensitive to price (or its analogue) and those of consumers n supermarkets who are subject to a much wider spectrum of choice under the influence of the entire array of marketing mix variables available to retailers.
For example, an expectation of demand analysis as it is employed in the behavioral economics literature is that when consumers choose between qualitatively identical reinforces which vary in terms of the unit prices that must be paid for them, the brand with the lower or lowest unit price will be exclusively chosen (Madden et al. , 2000). This is the prediction of both matching and minimization theories with regard to choice on con IVR IVR schedules.
However, research in these theoretical traditions typically takes place within laboratory settings that restrict choice to two alternatives, one or other of which must be selected at any choice point. Consumer brand choice is more complicated than this in that numerous choices are usually available to the consumer within a given product category, more than one of which may be selected on a single shopping occasion (Focal and Schwarzenegger, 2003).
A source of difference among brands, related to this and other aspects of consumer choice, stems from the distinction between utilitarian and informational benefits offered by efferent brands, as proposed by the Behavioral Perspective Model (Focal, 1990, 1994, 1996, 1997, 1998). According to this proposal, the behavior of the consumer can be explained by the events that occur before and after the consumer situation, which influence directly the shaping and maintenance of consumer behavior in specific environments.
The consumer situation, in turn, is defined 239 as the intersection between the consumer behavior setting and the consumer learning history. The consumer behavior setting?a supermarket, a bookstore, or a rock concert?includes the stimuli that form the social, physical and temporal nonuser environments. As purchase and consumption are followed by different consequences in different settings, the events in the setting become predictive of such consequences, building a learning history that relates elements of the setting to different consequences.
According to the proposal, antecedent events present in the consumer behavior setting signal the possibility of three types of consequences: utilitarian reinforcement, informational reinforcement, and aversive events. One major characteristic of economic behavior is that it involves both aversive and reinforcing consequences, for one has to give away money or rights (I. . , loss of Utilitarian reinforcement consists in the practical outcomes of purchase and consumption, that is, functional benefits derived directly (rather than mediated by other people) from possession and application of a product or service.
It is reinforcement mediated by the product or service and refers to consequences associated with increases in the utility (I. E. , use value) for the individual (“pleasant”) obtained from the product or service. The utilitarian, most obvious, consequence of owning a car, for example, is to be able to go from one place to the other, door to or, not depending on other people’s time schedules and avoiding being exposed to weather conditions, as usually happens when one uses public transportation.
Informational reinforcement, on the other hand, would be symbolic, usually but not exclusively mediated by the actions and reactions of other persons, and would be more closely related to the exchange value of a product or service. 2 It does not consist in inform Following Warden (1988), we use “informational reinforcement” to refer to performance feedback. The term “informational” carries excess baggage for many behavior analysts since it may appear to make cognitive inferences.
Given the examples we provide in the text, it may appear that “social” would be a more acceptable and accurate alternative. However, “social” does not entirely capture what we mean by “informational” which includes rewards for adhering to social mores, and physical sources of feedback such as lines on the road that convey an impression of speed, or the fullness of one’s shopping trolley. A nation per SE but in feedback about the individual’s performance, indicating the level of adequacy and accuracy of the consumer’s behavior.
Whereas utilitarian reinforcement is associated with the functional and economic consequences of arching and consuming goods or services, informational reinforcement is derived from the level of social status and prestige that a consumer obtains when purchasing or using certain goods. According to Focal, informational and utilitarian reinforcements would be orthogonal, and most products and services would involve, in different levels or proportions, both types of reinforcement.
Then, according to this analysis, the person who drives a Jaguars or Bentleys gets, in addition to door-to- door transportation (utilitarian), social status and approval from friends and acquaintances who see that car as a prestigious product, and from the general public that sees him or her driving around in a socially desirable car. The social status and prestige received are the informational, symbolic, consequences that the consumer obtains, which are usually related to branding or the level of brand differentiation of the product (CB. Focal, AAA).
The specific combination of utilitarian and informational reinforcement made available by purchase or consumption of a particular product is known as the “pattern of reinforcement” controlling these responses. Focal and James (2001 , 2003) argued that pattern of reinforcement influences consumers’ brand choices and that it is a key to understanding what consumers maximize. Different consumers might, for example, select brands belonging to different levels of informational reinforcement, some buying mostly highly differentiated whereas others buy relatively undifferentiated brands.
The differences in patterns of brand choice, including the set of brands that constitute responsiveness to different types of benefits. This idea gains even concomitant consideration arises in the functional definition of rules as “plays,” which involve the ideation of other people and which are therefore social, or as “tracks,” which depend on the rule-follower’s “reading” the physical environment, e. G. , in the process of following directions to get to a supermarket (Settle and Hayes, 1982).
Informational reinforcement thus remains our designation of choice for this phenomenon since it includes both personally-mediated and nonparallel-mediated performance feedback. 240 more force when we consider that branding is usually related to price, higher- differentiated brands being more expensive than less differentiated ones, and that consumers have different income levels. Then, individual buying patterns may be predominantly related, for example, to minimizing costs, maximizing utilitarian reinforcement, maximizing informational reinforcement, or to particular combinations of these.
If this is so, consumers may differ with respect to price responsiveness related to informational and utilitarian benefits. The research reported here tested predictions arising from these considerations using data from a consumer panel. Panel data are especially valuable for longitudinal studies because changes in purchasing behavior can be monitored very accurately by continuous agreements (Crouch and Housemen, 2003). Furthermore, diary panel data are considered to be very precise and less susceptible to errors than those obtained through consumers’ reporting their past behavior in surveys (Churchill, 1999).
Hence, they are particularly valuable when collecting multifarious information on variables such as price, shopping occasion, brand name, and so on. The special significance of this research technique for the present research lies in the fact that the data were obtained non-experimentally, by electronically tracking real consumers spending their real discretionary income. The two main purposes of the investigation were as follows.
First, in order to ascertain the generalizations of earlier research findings to consumer behavior in marketing-dominated contexts, three analyses were undertaken in order to determine whether the brands in question were in fact close substitutes (matching analysis), whether brand choice was sensitive to price differentials (relative demand analysis), and whether consumers could be said to maximize returns (minimization analysis). Second, in order to gauge consumers’ responsiveness to price and non-price marketing mix elements, the brands of 9 food reduce categories were ranked according to their informational and utilitarian levels.
The proportion of purchases made by each consumer at each brand level was computed, which served as basis for grouping consumers according to the level of brands they bought most. To test for differences in price responsiveness, price elasticity for consumer groups and individual consumers were compared. Softer, provided consumer panel data for 80 British consumers and their total weekly purchases in 9 fast-moving consumer goods categories over 16 weeks.
Taylor Nelson Softer is one of the largest and best-known companies in its field and clusters nonuser purchasing data on its so-called TENS Supernal on a range of consumer goods from 15,000 randomly selected British households. Data collection is personalized as follows: after each shopping trip, members of the panel scan their purchased items into a sophisticated handheld barded reader by passing the scanner across the Barbados, which nowadays are printed on all packaged supermarket products.
The data are then automatically sent to Taylor Nelson Softer for central processing without any further voluntary contribution from the panel participants. The retail outlets at which purchases were made was also identified for ACH shopping occasion, and included major I-J supermarkets such as Sad (a subsidiary of Wall-Mart), Tests, and Ginsburg. The 9 product categories that served as basis for this research were: baked beans, cookies, cereals, butter, cheese, fruit juice, instant coffee, margarine, and tea.
In more detail, the following information was recorded on each shopping occasion for each consumer: brand specification (I. E. , different versions of the same product category were classified as different brands, e. G. , Corn Flakes and Rice Crispiest by Kellogg), package size, name of the supermarket/shop, date, number of units, and total amount spent. As the analysis of brand choice requires information concerning actual purchase across several buying opportunities, data from consumers who bought, within each product category, fewer than four times during the 16-week period were disregarded. . 2. Measures and analyses 2. 2. 1 . Matching In consumer research, the matching law becomes the proposition that the ratio of amount of money spent for a brand to the amount spent on other brands 241 within the product category will match the ratio of reinforces earned (I. E. , purchases made as a result of that spending) of that brand to the amount bought of other Rand’s within the product category. The first of these, the amount paid ratio, was personalized as the ratio of money spent on “Brand A,” defined as the most frequently purchased brand, to money spent on “Brand B,” I. . , the amount spent on the remaining brands purchased within the requisite product category: Amount paid for Brand A/Amount paid for the remaining brands in the product category (B). The amount bought ratio was calculated, in terms of the physical quantity acquired, as: Amount bought of Brand A/Amount bought of Brand B (the remaining brands of the product category). Logarithmic transformations were used for the analyses. 2. 2. 2.
Relative demand In order to devise relative demand curves for the product categories, a demand analysis expressed the ratio of amount bought of the dominant brand (A) to the amount bought of the remaining brands in that category (B) as a function of the ratio of the relative average prices of the dominant brand to the relative price ratio). In operational terms, the relative price ratio = mean price of Brand A/Mean price of other brands in the repertoire (B). The amount bought ratio was calculated as in the case of the matching analysis.
Again, log transformations were used for the analyses. 2. 2. 3. Minimization To ascertain whether minimization is occurring, following Hermiston and Loveland (1975), Hermiston and Vaughan (1980), we plotted the amount bought ratio against probability of reinforcement. The latter is personalized as the reciprocal of the price of brand A over the reciprocal of the price of brand A plus the reciprocal of the mean of the prices of the other brands in the consumer’s consideration set (“Brand B”): I/PA PIP + I/BP ).
If the step function described by the data points falls to the right of the 0. 5 line on the abscissa then the researcher is maximizing by selecting the favorite brand (A) which is also the least expensive (Hermiston and Loveland, 1975). 2. 2. 4. Schedule analogies To ascertain how consumers make decisions, it is necessary to have some idea of how they integrate price data and brand choice responses over time, notably from shopping trip to shopping trip.
In the laboratory this can be achieved without undue difficulty by the imposition of a schedule of reinforcement which programs the relationships between dependent and independent variables. Researchers who are concerned with the behavioral analysis and explanation of non- experimental behavior face the difficulty of ascertaining with precision whether brand choice in naturalistic settings occurs, by analogy, on a series of fixed ratio schedules (represented by the prices of each brand obtaining on each purchase occasion) or, aggregated over several such occasions, on variable ratio schedules.
The question we are seeking to answer is whether consumers take into consideration only the prices of the brands in their consideration set that are in force on each discrete shopping trip, or whether their behavior (brand choice) reflects the price-quantity legislations for competing brands that are in force over the extended period represented by a series of shopping trips. This led us to undertake two analyses for each product category studied.
The first treated the schedules as a sequence of fixed ratio relationships by expressing measures of amount bought as a function of measures of prices for (a) weekly periods, representing (albeit by analogy rather than programming) the situation in which experimental subjects face a sequence of FRR schedules, and (b) periods of 3 weeks, for which the data were averaged, similarly representing an experimental situation governed by IVR schedules. . 3.
Utilitarian and informational reinforcement To investigate possible effects of informational and utilitarian reinforcement values on brand choice, an attempt was made to identify different levels or magnitudes of informational and utilitarian reinforcement offered by the brands available (I. E. , bought by consumers in the sample) in each product category. The set of alternative brands and product characteristics available in a supermarket within each product category can be interpreted as a set of programmed contingencies of reinforcement, which specify what responses (e. G. , how much one has to pay) are followed by what 242