Do consumers know what they want?

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Most marketers assume that customers know what they want. Unfortunately, customers may not know as much as the customers think they do. Decision.
An executive summary for managers and executive readers can be found at the end of this article

Do consumers know what they want? Hernan Riquelme

Lecturer, Graduate School of Management, La Trobe University, Victoria, Australia Keywords Self-assessment, Predictive validity, Customers Abstract Predicting one's own decision-making policies is evidently a useful skill. How good are consumers at it? In spite of its importance, the question has not been much studied directly, although hints can be found in several parts of the literature. This article describes an experiment that identifies how much knowledge consumers have about an important purchase: a mobile phone. A total of 94 consumers provided ratings of the importance of six attributes and preference for several choices of mobile phone plans that were advertised in the market. Consumers' self-knowledge was assessed by three methods: comparing the product attribute importance derived from the subject's model with the attribute importance derived from a conjoint analysis model; comparison of predicted judgments generated by the subject's model with their holistic judgments; and comparison of the actual purchase of a mobile phone with the prediction generated by the subject's model. Results show that consumers have a relatively good predictive power of a product they have chosen. However, this knowledge is not perfect. The results have important implications for companies that collect information about preferences from consumers.

Rationally bounded

Most marketers assume that customers know what they want. Unfortunately, customers may not know as much as the customers think they do. Decision makers are not perfectly rational, but they are ``rationally bounded'' (Newell and Simon, 1972). That is, it is impossible for decision makers to evaluate all pieces of information available in the environment. Decision makers rely on heuristics to choose the most salient information (Evans, 1990) and therefore are subject to biases in different parts of the inferential process (Fiske and Taylor, 1991). For example, the availability bias (Tversky and Khaneman, 1974) ± the ease with which instances or associations are brought to mind ± encourages decision makers to recall salient information from memory. People's biases not only have been proved to inhibit decision making, but they are also likely to impede the decision makers' ability to accurately report on their decision processes (Nisbett and Wilson, 1977; Hogarth and Makridakis, 1981). The understanding of self-knowledge, sometimes referred to as metacognition, insight (Gregory, 1987), or simply as awareness of one's own mental processes, is important for practical reasons. Self-knowledge has implications for various social processes, for example self-regulation, self-conceptions, and the correction of biases. For example, a bias resulting from an explicitly held and consciously applied judgment strategy might be expected to be corrected by verbal instruction, whereas one resulting from a pre-conscious heuristic and schemata might require a different approach. Self-knowledge is similarly important in its relationship to self-regulation . Studies have demonstrated that lack of knowledge (awareness) of what a person is doing (mindless processing) leads to a failure to monitor task contingencies effectively (Fiske and Taylor, 1991). This mindless processing occurs when a person knows a task too well (i.e. a person has over learned it), the subject no longer pays attention to individual components of the task; as a consequence the person may, at least partially, lose the ability to perform it effectively. From a consumer behavior perspective, the study of consumers' selfknowledge has important managerial implications. First, a firm's sales and profits can be adversely influenced by consumers' misperception of which



attributes are really important. Second, companies that design and manufacture new products rely on customers' knowledge. If this knowledge is not perfect, marketers may be emphasizing the wrong attributes in the product/service. Third, given that many industries are highly competitive, marketers need to differentiate their products from others in the market. This task is very hard to accomplish if consumers' perception of a company's products is erroneous. Self-awareness

The issue of self-awareness of one's decision-making policies has also an important implication on the efficiency of choice (i.e. the selection of what is good for them). Consumers can achieve efficient choices only to the extent that their predicted utilities (i.e. the psychological worth a person associates with an object) are valid and their decision utilities are rational (Khaneman and Snell, 1992). Considerable research attention has been given to how consumers evaluate product attributes (Alba et al., 1991) and the overall conclusion is that consumers are not perfect in their evaluation. It has been found that consumers distort their evaluation of automobiles on the basis of beliefs about how certain automobile attributes should go together (Elliott and Roach, 1991). Similar results were found in an evaluation of airline carriers (Elliott and Roach, 1993). The results from these studies could be to some extent optimistic in the sense that, although decisions are not optimal, at least they are consistent with the consumers' personal beliefs. That is, consumers have preconceived ideas of relationships between attributes and they will normally focus on data that support their beliefs and ignore data that do not (Hogarth and Makridakis, 1981). On the contrary, there are some studies that have found that consumers do not possess well-formed beliefs about attribute importance either (Wind and Devita, 1976; Fischhoff et al., 1980). Consumers incorrectly report correlation between events or between an object's attributes when, in fact, there is no correlation at all or there is correlation with another unrelated event (Fischhoff, 1978). For example, a consumer may believe that car safety and price are inversely related, however, consumer reports show, through crash testing procedures, that price and safety are not necessarily inversely related (Elliott and Roach, 1991). As in other areas of human information processing, research into self-knowledge or accuracy of product attribute covariance has tended to emphasize the negative aspects. Examples of biases in human information processes in general are abundant (see, for example, Slovic, 1972; Slovic et al., 1977; Osber and Shrauger, 1986).

Influence of memory

Literature on consumer behavior also indicates that consumers have poor and biased product attribute recall due to the influence of memory (Alba et al., 1991). However, it is not clear if these findings regarding consumers' lack of accuracy about product attributes are true findings or the result of the researchers' bias in the selection of experimental context. Indeed, poor inference of the subjects in Elliott and Roach's (1991, 1993) studies is said to be due to the fact that the subjects in the experiments were not provided with any information about the products that they were asked to rate (Mason and Bequette, 1998). Nor did they take into consideration the experience of the individuals. In order to overcome these limitations of previous studies, Mason and Bequette designed a 262 factorial experiment to demonstrate the moderating effect of experience and relevant information in the accuracy of automobile attributes. The conclusion was that when subjects have greater interest and experience with the product category and they are provided with up-to-date product attribute information the chances are that consumers' evaluations will be more accurate. The authors emphasize that this greater interest, experience, and knowledge do not necessarily mean that the



consumer's knowledge is right, only that there are more chances of current information about the product being included in the consumer's memory. Another study that has also examined the effects of situational factors concluded that analytic processing varied significantly as a function of memory load, processing goals, type of information search, and the relative perceptual salience of product attributes (Hutchinson and Alba, 1991). In this experiment, multi-attribute information about stereo speakers was presented to subjects, although only one attribute was diagnostic. Little multi-attribute processing was observed among nonanalytic subjects. Most of these subjects relied on a small subset of attributes and placed emphasis only on a single non-diagnostic attribute. Other evidence has shown that people tend to be overconfident in predicting their own behavior (Vallone et al., 1990). Such confidence has consistently exceeded objective accuracy across a wide range of social and self-predictions (Pulford and Colman, 1996). Other sources that influence accuracy are the weighing of the cues or attributes used in the judgment of a product and the way these attributes are used by the person (Hammond and Summers, 1972). As it was found in the study by Hutchinson and Alba (1991), individuals normally overstate the number of attributes they use in choosing a product and misallocate the weights of these attributes (Slovic et al., 1977), which leads to inaccuracy in the predictions. Lack of accuracy

Despite the importance of the evidence in the literature about the lack of accuracy of human judgment and decision making in general, there is also evidence of good predictive ability (see e.g. Ashton, 1974; Cook and Stewart, 1975; Kraut and Lewis, 1982; Reilly and Doherty, 1989) These studies show that people can make good predictions of their decisions or identify their decision policies fairly well, especially regarding issues that are familiar, simple and directly experienced (Fischhoff et al., 1980). Experiments conducted to measure the accuracy of hedonic experiences such as taste prediction of music and yogurt seem to corroborate the previous conclusion. Studies on misprediction of tastes have found failures of prediction only when participants have been asked to indicate how much they will enjoy yogurt later in the week (Khaneman and Snell, 1992). However, when subjects in the experiment are asked to indicate how much they will enjoy a song that is about to be played, participants in such experiments appear to predict quite well (Ratner et al., 1999). There has been substantive research on various aspects of consumer learning and evaluation of products (for a good review see Alba et al., 1991), especially the effect of particular product attributes on buyers' product evaluations (see, for example, Dodds et al., 1991; Hutchinson and Alba, 1991; Lichtenstein et al., 1991; Costley and Brucks, 1992; Lynch and Chakravarti, 1991). However, the majority of the studies in this area were not directly concerned with measuring self-knowledge.

Measuring consumers' self knowledge

The validity of measuring consumers' self-knowledge in regard to product utilities and predictive accuracy are important because many companies quite often collect information from consumers about their perception of value, and the attributes that should be emphasized in a product to make it attractive to customers (Brayman, 1966; Gale, 1994). Various models are used to identify the importance of product attributes for consumers. They range from a simple one as suggested by Gale (1994) to more sophisticated ones such as multidimensional scaling techniques, e.g. MDPREF, conjoint analysis and value maps (Carrol, 1972; DeSarbo et al., 1982; Takane and Shibiyama, 1991). The key question that these models attempt to identify is: what are the



product attributes that consumers care about in a product? This study is concerned with the question: how accurate are consumers' predictions about these product attributes and their role in their final selection of the product? In other words, can consumers predict their product selection? Two phases

Methodology The research design comprised two phases. The first phase consisted of the selection of the product to be investigated and the attributes that were considered relevant by the consumers in the selection of such a product. The mobile phone product category was selected for the following reasons. First, consumers are highly familiar with the product since it is frequently used. Second, different mobile phone plans are advertised in the market, thus making several of the product attributes more salient for the consumers to recall. Third, since the mobile phone plans are already in the market there was no need to generate hypothetical product scenarios. This provides face validity to the study. Finally, given that the product is frequently used, subjects would have current knowledge about the characteristics of the mobile phone plan they had purchased and the relevance the different attributes played in the purchase. The mobile phone is a product which has as much information as is needed to make a decision. It is a product that consumers are familiar with, that is relevant for them, therefore it is expected that these aspects will decrease the role of memory and eliminate contextual potential biases in the experiment. The mobile phone plans are described in terms of the following six attributes: (1) telephone features; (2) connection fee; (3) access cost; (4) mobile to mobile phone rates; (5) call rates; and (6) free calls.

A range of choice

The attribute levels were chosen to represent what was offered in the different plans to represent the variation that typically occurs in the decisionmaking environment of a consumer, therefore maintaining believability and response validity. All the attributes offered a range of choices: .

telephone features consisted of three levels;


the connection fee had two levels;


the monthly access fee had six levels;


the mobile to mobile phone rates had five levels;


the call rates consisted of nine levels; and


free calls had six levels.

During the second phase, a conjoint study examined the set of profiles (mobile phone plans) that were evaluated by the consumers (see Appendix 1 for a sample of the stimulus evaluated by the interviewee). Research instrument The research instrument contained instructions, the experiment instrument (i.e. the mobile phone plan profiles), and a questionnaire that elicited selfexplicated attribute importance (or weights) and actual mobile phone 440


characteristics. Relevant definitions of terms were also available on a detachable sheet that could be referred to if needed. Six attributes

Subjects and procedure Subjects were asked to distribute 100 points across six attributes that had been chosen as relevant to the consumers: telephone features, connection fee, access fee, call rate, mobile to mobile rates, and free calls. Appendix 2 shows a sample of the interviewing procedure for self-explicated attribute importance and the levels used in each attribute. Respondents were also asked to indicate the most preferred and second most preferred levels, and so on, assuming the mobile phone plans were the same in all other respects. Subjects were presented with 22 actual mobile phone plans that were offered in the market. The presentation of the 22 cards was random across respondents to control for any order effects. The mobile phone plans represented actual offers in the market that were advertised by three major telecommunication companies. Subjects were asked to participate in the study as they were intercepted in a shopping center. Subjects were told that the study aimed to measure the degree of knowledge they had about their purchasing decision in regard to a mobile phone. Only subjects that indicated they possessed a mobile phone were interviewed (previous agreement of consent). Out of 317 that were intercepted during a pre-specified period for the study, 94 subjects agreed to participate. Subjects were asked to indicate ± on a nine-point scale ± if they would purchase the mobile phone plan described in the profile (the dependent variable). Anchors ranged from (1) ``I would definitely not buy it'' to (9) `` I would definitely buy it''. After the completion of the evaluation of the 22 cards (each representing a mobile phone plan), the subjects were asked to describe the mobile phone plan that they had purchased according to the attributes presented in the plans.

Three tests

Analysis Analysis at the overall level resulted in a single conjoint utility function for all 94 respondents. As the main goal of this research was to test if consumers have self-knowledge of their product evaluations, three tests were performed. (1) Comparison of self-explicated attribute importance (weights) with attribute importance derived from conjoint analysis. The mean correlation between self-explicated and conjoint was 0.88 (p < 0.0000). This value indicates a fairly good level of insight, especially considering the number of variables (six) that consumers had to use in assessing the mobile phone plans. Table I lists the relative importance of all six factors as calculated by conjoint together with the self-explicated weights (provided by the subjects). (2) Assessing insight via predictive judgments. Correlation between 30 judgments predicted by the subject's model was compared with the Attributes Telephone features Connection fee Access fee Call rates Mobile to mobile Free calls



16.17 6.96 29.09 20.05 13.05 14.89

11.09 7.15 33.03 18.91 19.38 11.01

Table I. Comparison of attributes' relative importance (average values) JOURNAL OF CONSUMER MARKETING, VOL. 18 NO. 5 2001


holistic judgments of the 22 mobile phone plans. The correlational study of the accuracy of individual self-prediction shows what consumers know about their actual purchases of a mobile phone, beyond what they could forecast about a random stranger. The logic behind this test is that if subjective weights yielded predictive judgments highly correlated with the actual judgments, this would imply that subjective weights (attribute importance) are a satisfactory description of the decision-making policy. The resulting average correlation was 0.78 (p < 0.0001). Under this criterion, consumers exhibit relatively good understanding of their own policies. (3) Comparison of actual choice with the judgment generated by the subject's model. In the final test, the characteristics of the actual mobile phone plan were replaced in the regression obtained from the subjective reports. The prediction was compared with an assumed maximum probability of purchase of the product, that is, nine. The average Pearson correlation between these two values was 0.48. The logic behind this comparison is that if subjects have a good level of insight, the regression model based on the subjective weights and preferences should be able to predict reasonably well the actual purchase. Given that the choice of the maximum level is very conservative and that a manager may consider that a product with a probability of purchase of eight and above is sufficient to introduce a product, another correlation was performed with this value. The correlation improved marginally to 0.51. Direct comparison

Discussion The resulting Pearson correlation coefficients of the direct comparison of weights and the prediction of holistic judgments based on the subjects' model (i.e. derived from self-explicated attribute importance) are both very good (0.88 and 0.78 respectively) considering the large number of variables the consumers had to keep in the mind when making a decision. Consumers' knowledge of their own decision policies is not perfect, however. From Table I it appears that consumers, when they evaluated mobile phone plans, overestimated the importance of telephone features, call rates and free calls and underestimated the importance of a monthly access fee, mobile to mobile phone calls and (only slightly) the connection fee when compared with the attribute importance derived from conjoint analysis at the aggregate level. The top values in terms of their importance in the daily lives of purchasers (sample) of mobile phones were: monthly fee (29.09), call rates (20.05), and telephone features (16.17). The conjoint-derived utilities (in other words the ``in-use'' attributes) were in order of importance as follows: access fee (33.04), mobile to mobile rates (19.38), call rates (18.91). One must bear in mind that the comparison of mathematically-derived utilities with self-reported utilities is not the most appropriate test of insight (or at least not the strictest one). There are at least two reasons for this: (1) self-explicated weights can be biased by beliefs (Elliott and Roach, 1991); and (2) they have also been reported to be biased by the social desirability response (Brookhouse et al., 1986). A more rigorous measure of insight and predictive accuracy is predicting consumers' own holistic judgments based on their own subjective model. The resulting correlation, although lower than the one found in the first test, is still considerable and significant (0.78, p < 001).



Predictability not very accurate

It is interesting to note that when the third method was applied, i.e. predicting the actual choice of mobile phone plan, the predictability was not very accurate. The reasons for this discrepancy could be ascribed to different factors. One is the fact that the predicted value is compared with a fixed probability of purchase, i.e. nine, whereas the prediction will fall within a range. Second, conjoint analysis shows that there were a number of reversals in the consumers' decisions. In other words, the monotonocity (e.g. more features in the phone is preferred to less features) indicated in the preferences by the consumers was not observed in many attributes by conjoint analysis. The inconsistencies are not eliminated in the subjective model. Third, in some cases, standard errors of some attributes were high, revealing that a linear additive model for those particular attributes could not be the most appropriate one. Despite this, the linear model test proved to be significant F = 35 > F0.95;6;87 = 2.15. Overall, the conjoint analysis performed much better in predicting the values of the actual purchase of a mobile phone than the subjective model, as it was expected. Validity and consistency checks for the conjoint analysis were performed in the following way. Internal validity to test the goodness of fit of the model was assessed by using Pearson's product moment correlation. The Pearson's R statistics ranged between 0.42 and 0.98, with an average of 0.63, suggesting that the model is not the optimum to fit the data. The actual purchases of mobile phone plans were used as holdout cards for the conjoint to estimate the probability of purchase, in other words the predictive capabilities of the conjoint model. Ratings for the holdout profiles were calculated based on the model's estimated partworths. The Pearson correlation between the estimated ratings and the respondent average rating was 0.78, reflecting a relatively good predictive power. The finding that conjoint analysis performs better in predicting choices is not surprising as there is substantial evidence in the literature on decision making that mathematical models outperform judgmental models (Slovic et al., 1977).

Degree of knowledge

Managerial implications The specific goal of this investigation was to measure the degree of knowledge consumers have about their purchases, in other words, their capacity to predict their choices. The results of the investigation suggest that consumers have a relatively good predictive power of mobile phone plans. However, this knowledge is not perfect. This result has important implications for companies that collect information about preferences from consumers. This study suggests that consumers, when asked about a product that they are familiar with, and have direct experience of, can predict their choices relatively well. Companies must be careful though in taking customers' suggestions and preferences at face value as their self-reports are not perfect, as indicated in the discrepancy of the subjective weightings with conjoint weightings. Presently, many companies are attempting to target their products towards those attributes consumers say they use when evaluating a product. The implications of this study are that companies are better off relying on mathematically-derived utilities than those reported by consumers as they are more accurate in predicting preference. By targeting the attributes consumers actually do use in their preferences (the ``in-use'' attributes), companies increase the probability of their products being accepted by the market. The implications of consumers' lack of insight about their own choices is also relevant in regard to their commitment to choose a product and the



management of dissonance in the post purchase. For example, given limited knowledge the consumer can be more susceptible to experience cognitive dissonance, that is, to feel that perhaps they have not made the appropriate decision. Having good knowledge of the decision policies also allows for self control, in that customers are fully aware of what is important for them. If biases are present these could be more likely to be corrected than in cases of lack of awareness of the decision-making policies. Areas of concern

Limitations The study is not without its limitations. The first area of concern is the selection of actual mobile phone plans. Although working with real product description increases the external validity of the experiment, this has posed a problem in terms of the correlation among variables. The lack of orthogonality in the set of profiles has direct implications for the values obtained from the regression models. A second matter of concern is the sample selection. Given that the subjects in the sample had already acquired a mobile phone, it is possible that this has influenced their level of knowledge about the product. After all, these people are using the phone and paying the bill every month, therefore the knowledge they have about the product attribute is more salient and available. This was purposefully done in order to eliminate the role of memory in the reporting of the actual purchase. The third concern is in regard to the individual analysis. The number of profiles used to estimate six parameters is not the optimum. As a rule of thumb there should be at least five data points for each parameter to be estimated in order to avoid spurious correlation (Green and Srinivasan, 1978). Our study contained only 22 data points (profiles). Finally, there was no control for promotional offers, for example, companies offered free connection, thus affecting the expectations of the consumers. In spite of the limitations, this study provides empirical evidence about the accuracy of consumers in predicting mobile phone plans. The design was developed with external validity in mind, thus employing actual mobile phone plans rather than hypothetical scenarios or products. Also interviewees were actual users of these products rather than college students, as is normally the case in most studies of this nature. Reference Alba, J.W., Hutchinson, J.W. and Lynch, J. Jr (1991), ``Memory and decision making'', in Robertson, T. and Kassarjian, H. (Eds), Handbook of Consumer Behavior, Prentice-Hall, Englewood Cliffs, NJ. Ashton, R. (1974), ``Cue utilization and expert judgments: a comparison of independent auditors with other judges'', Journal of Applied Psychology, Vol. 59 pp. 437-44. Brayman, J. (1966), ``Building a better business tool ± customer value analysis'', American Marketing Association Conference of Customer Satisfaction. Brookhouse, K., Guion, R. and Doherty, M. (1986), ``Social desirability response bias as one source of the discrepancy between subjective weights and regression weights'', Organizational Behavior and Human Decision Processes, Vol. 37, pp. 316-22. Carrol, D. (1972), ``Individual differences and multidimensional scaling'', in Shapard, R.N., Rommney, A.K. and Nerlove, S.B. (Eds), Theories and Applications in the Behavioral Sciences, Seminar Press, New York, NY. Cook, R. and Stewart, T. (1975), ``A comparison of seven methods for obtaining subjective descriptions of judgmental policy'', Organizational Behavior and Human Performance, Vol. 13, pp. 31-45. Costley, C. and Brucks, M. (1992), ``Selective recall and information use in consumer preferences'', Journal of Consumer Research, Vol. 18 No. 4, pp. 464-75. DeSarbo, W., Carrol, R., Lehmann, R. and O'Shaughnessy, J. (1982), ``Three-way multivariate conjoint analysis'', Marketing Science, Vol. 1, pp. 323-50.



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Vallone, R.P., Griffin, D.W. et al. (1990), ``Overconfident prediction of future actions and outcomes by self and others'', Journal of Personality and Social Psychology, Vol. 58, pp. 582-92. Wind, Y. and Devita, M. (1976), ``On the relationship between knowledge and preference'', in Bernhardt, K. (Ed.), Marketing: 1776-1976 and Beyond, American Marketing Association, Chicago, IL, pp. 153-7. Appendix 1. A sample stimulus description Connection fee: $65 Monthly access fee: $20 Call rates (peak): 40 cents per 30 seconds Free calls: 20 minutes Telephone features: basic facility Please circle your intention to buy this mobile phone plan: Definitely would buy

Definitely would not buy it 9









Appendix 2. Interviewing format for self-explicated level desirability and attribute importance data In this part of the interview we'd like to obtain your preferences for various features of mobile phone plans. Here are the attributes that we'll be considering: .

Connection fee


Call rates


Free calls


Monthly access fee


Mobile to mobile call rates


Telephone features

(Ask for the preferences of the different levels in each attribute) Now that you have seen all six set of attribute descriptions, one at a time, we'd like to know how important the attributes themselves are to you. Assume you have 100 points you can assign in any way you wish to the following attributes: .

Connection fee ______


Call rates ______


Free calls ______


Monthly access fee _______


Mobile to mobile call rates _______


Telephone features _______


Total: 100

You can assign zero points if you wish to one or more attributes. We only ask that you make sure that the total point count equals 100. Please, assign the 100 points so as to reflect the relative importance of each of the attributes to your purchase decision.




This summary has been provided to allow managers and executives a rapid appreciation of the content of this article. Those with a particular interest in the topic covered may then read the article in toto to take advantage of the more comprehensive description of the research undertaken and its results to get the full benefit of the material present

Executive summary and implications for managers and executives Do customers really know what they want? Most marketers assume that customers know what they want. But customers may not know as much as the marketers think they do. It is often impossible for customers to evaluate all the information available to them before they choose a product or service. They may recall only the most salient information which, in turn, can be affected by their personal biases. These biases may not only influence the decisions which customers make, but also impede their ability to report accurately on how they arrived at their decision. There is also evidence that consumers tend to overestimate the number of attributes they use in choosing a product, and misallocate the weights of these attributes. They are frequently overconfident about their ability to get the decision right. How customers' misperceptions can affect marketers Customers' lack of awareness of their own mental processes can have important implications for marketers. First, a firm's sales and profits can be affected by customers' misperceptions of what features of a product or service are really important. Second, companies that design and manufacture new products and services rely on customers' knowledge. If this knowledge is not perfect, marketers may emphasize the wrong features of the product or service. Third, marketers operating in highly competitive industries need to differentiate their products from those of their rivals. This is very hard to do if customers have a wrong impression of a company's products. Customers may, for example, believe that the more they pay for a car, the safer it will be. But crash tests carried out by consumer organizations show that this is not always the case. Obviously, customers who have greater interest in, and experience with, a category of product (personal computers, for example) and are provided with up-to-date product information, are likely to make more accurate evaluations of what is on offer. But greater interest, experience and knowledge do not necessarily mean that the customer's knowledge is right, but only that there are more chances of the consumer having current information about the product in his or her memory. Choosing the right mobile phone Riquelme conducted an experiment to identify how much self-knowledge customers really have when choosing between mobile phones. Ninety-four customers provided ratings of the importance of six attributes (telephone features, connection fee, access cost, mobile-to-mobile phone rates, call rates and free calls) and preference for several choices of mobile-phone plans that were advertised on the market. The research reveals that consumers, when asked about a product they are familiar with and have direct experience of, can predict their choices quite well. But firms must be careful in taking customers' suggestions and preferences at face value, as their self-reports are not perfect. When evaluating the mobile phone plans, they tended to overestimate the importance of telephone features, call rates and free calls and underestimate the importance of a monthly access fee, mobile-to-mobile phone rates and the connection fee.



Target the attributes customers actually use Many firms currently attempt to target their products on the attributes consumers say they use when evaluating a product. But companies would be better off relying on mathematically-derived utilities, rather than those reported by customers, as the former are more accurate in predicting preference. By targeting the attributes consumers actually do use, firms increase the probability of their products being accepted in the marketplace. (A preÂcis of the article ``Do consumers know what they want?''. Supplied by Marketing Consultants for MCB University Press.)



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