- Email: [email protected]
The intersection of modelling potential and practice
Intern. J. of Research in Marketing 17 Ž2000. 127–134 www.elsevier.comrlocaterijresmar
The intersection of modelling potential and practice John H. Roberts ) Australian Graduate School of Management, UniÕersity of New South Wales, Sydney, NSW 2052, Australia
Abstract This paper sets up a framework of the environment in which marketing modelling applications take place. It examines the grist to the mill of marketing modelling Žmarketplace consumer behaviour. and the avowed users of its output Žpractising managers.. From this, it establishes the desirable features of effective marketing models, evaluates our performance as modellers, and suggests ways in which we need to go in the future, both to improve our success in the current environment and to ensure success in future ones. In doing so, the paper critiques and builds on the work of Leeflang and Wittink wInternat. J. Res. Marketing 17 Ž2000. 105x. q 2000 Elsevier Science B.V. All rights reserved. Keywords: Marketing models; Management applications
1. The role of marketing modelling Marketing modellers use their tools to help marketing managers focus their actions more finely towards meeting the needs of their target consumers. To the extent that they are able to achieve this consumers are likely to end up more satisfied because products and services are of greater relevance to them. Additionally, firms should better meet their objectives because products of higher perceived utility can extract higher economic rents, either in terms of increased market share or higher prices. Any successful application of a marketing model must be based on three components; the phenomena in the marketplace, the technical skills of the modeller, and the options open to the manager, including the ques-
tions she needs to answer. We can illustrate a typical example of this nexus using Fig. 1.1 Many researchers base their understanding of markets on a strong foundation in one of these three columns. Most modellers are firmly based in the second box, Statistical Tool Boxes. Research with its roots in modelling has contributed greatly to our understanding of marketplace behaviour. See, for example, the work by Wedel and Kamakura Ž1999. on latent segmentation, which has greatly enriched our view of the nature of consumer heterogeneity. Similarly, modelling has provided many actionable insights to practising managers Žthe right hand box.. See, for example, the reviews of conjoint analysis by
1
)
Tel.: q61-2-9931-9255; fax: q61-2-9662-7621. E-mail address: [email protected] ŽJ.H. Roberts..
Note that marketing models may have extremely valuable purposes other than to focus managerial actions. For example, the desire for understanding of the environment in which we live may constitute an important objective. This paper only focuses on the role of marketing models in so far as they are developed for managerial application.
0167-8116r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 8 1 1 6 Ž 0 0 . 0 0 0 1 2 - 4
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J.H. Robertsr Intern. J. of Research in Marketing 17 (2000) 127–134
Fig. 1. The elements of applying marketing models.
Green and Srinivasan Ž1978. and Shocker and Srinivasan Ž1979., which led to their commercial adoption, documented by Wittink and Cattin Ž1989. and Wittink et al. Ž1994.. Research based on the left hand box of Fig. 1 could be termed phenomenological research. This is the research of someone who is interested in nuts and bolts. The middle box is methodological research and is undertaken by someone who has a wrench. This person walks around looking for nuts that his wrench will do a good job of tightening. The third box contains those people who are interested in fasteners. They are probably not experts in either nuts or wrenches but they are interested in the problem of holding things together. No successful application can be undertaken without a solid foundation in each one of the boxes. The genesis of a marketing modelling application does not matter but the end result must have a balance of all three. A lack of balance in understanding the environment, the appropriate tools to use, or the way in which the results will be applied inevitably leads to a
loss of relevance. Since, as modellers we are primarily methodologists, the danger to us is that we will ignore important marketplace phenomena or that we not focus our problem solving to areas that are actionable by marketing managers. I will argue that these three elements have not always been in balance but that this lack of balance does not emerge in the review paper of Leeflang and Wittink Ž2000.. The framework illustrated in Fig. 1 leads automatically to the desiderata of useful models. These were spelt out by Little Ž1970. in his seminal paper, AModels and Managers: The Concept of a Decision CalculusB. As outlined by Leeflang and Wittink, Little required that marketing models be simple, complete, adaptive, robust and easy to use. That still forms a good set of criteria today. An examination of Fig. 1 shows that to be phenomenologically appropriate the model must be faithful to the processes that are occurring in the marketplace. To be methodologically admissible, it must be rigorous and internally consistent. Finally, for application it must be useful, which includes being accessible to the man-
J.H. Robertsr Intern. J. of Research in Marketing 17 (2000) 127–134
ager as well as addressing the questions that she has to answer in directing her marketing investment and energies. Little’s criteria not only span these three elements, they also span interactions between them. For example, robustness suggests that the model must accurately reproduce marketplace behaviour within all possible ranges of management action considered.
2. Comments on the Leeflang and Wittink perspective Leeflang and Wittink Ž2000. look at the past, present and future of marketing modelling, devoting most attention to the present as a launching pad for the future. I largely agree with their views but I would place a somewhat different emphasis in a number of areas. I would have a different list of names in terms of the profession’s history. I would be less glowing about modelling’s current success rate in influencing management practice. And I would advocate more flexibility and adaptivity in future development. I will explore Leeflang and Wittink’s treatment each of these stages of the profession’s development before elaborating on my own view, using the framework spelt out in Fig. 1. 2.1. Historical eÕolution Leeflang and Wittink give a useful description of how marketing models became more accessible to managers because of the simplicity advocated by scholars such as Little, and the increasing richness of phenomena captured by them as the statistical and econometric foundation on which they are built expanded. They also identify other important trends, such as the movement to modularity. My real surprise with their historical review was with the list of names and references. Work from the strong research centres at M.I.T., Wharton, Stanford and Northwestern barely merits a mention Žwith the exception of Little.. To check if this belief reflected my own prejudices, I went through the list of Paul Converse Award winners over the past 20 years who were modellers. The Converse Award is the top research award of the American Marketing Association for sustained contribution to research. Out of over 100
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references in the Leeflang and Wittink paper, the names of Urban, Hauser, Srinivasan, Staelin, Lehmann, Aaker, and Bagozzi were not listed as first authors. Of the 100q first authors listed, only Little, Wind and Bass were Converse winners. These omissions look all the more critical when one remembers that the article is interested in management practice. The ASSESSOR new product forecasting model won two O’Dell Awards for the most influential piece of research judged 5 years further on ŽSilk and Urban, 1978; Urban and Katz, 1983., had many hundreds if not thousands of applications, and spawned an industry of lookalikes. However, despite this difference in emphasis with respect to contribution, I think that the Leeflang and Wittink review fulfils a useful role in drawing together our history. 2.2. Current practice In terms of current practice, Leeflang and Wittink provide a very extensive overview of what is being done in universities to model marketplace phenomena in a way that can be applied by managers. Their catalogue of existing techniques is valuable. It has a bias towards packaged goods and price promotions and downplays a lot of excellent modelling that has taken place with durability and services, in business markets, on networks, with media planning, and a number of other areas. However, any such review must perforce be selective and so such a criticism may be regarded as severe. Perhaps it is the focus on packaged goods that leads them to conclude that marketing modelling is in a state of maturity. I think that one has to ask, AMaturity where?B There may well be some level of maturity in the technology but I cannot see much evidence in terms of maturity in application. There may well be maturity in using logit models to study promotional effects in packaged goods industries using scanner data, but I see no evidence of maturity in terms of using latent segmentation models to develop mass customisation strategies for example. When Leeflang and Wittink discuss managerial applications, they do so from a supply perspective rather than a demand perspective, in common with most modellers. By this I mean that they look at what we have done, our modelling innovations and their diffusion, rather than managerial needs and the
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level to which we are satisfying them. They look out from the Statistical Tool Box area of Fig. 1 towards the problems that managers face, rather than putting themselves in the shoes of marketing practitioners and looking back to judge the contribution of marketing models. Both perspectives are valid but one asks a totally different set of questions if one uses the second. Questions like, AWhat is the penetration of marketing models in every day managerial decision making?B and AWhat is the average standard of marketing models in industry, as applied?B would leave us feeling a lot less comfortable. They would also change our focus from generating best of breed technology to also include improving quality control and training in application. As an example of their perspective, Leeflang and Wittink’s stages of building a model Žsee their Fig. 1. are well designed to meet the needs of an academic audience. However, for a true managerial application cost benefit considerations Žstage 9. must precede model scope Žstage 3. and time to market dictates that validation Žstage 8. must follow use Žstage 10., not precede it. Another issue that leaves me less enthusiastic than Leeflang and Wittink about the state of marketing modelling applications revolves around the definition of what we call an application. We have been far less rigorous on ourselves in defining what constitutes an application than we have been about what represents an appropriate, grounded model. What percentage of the articles they cite actually had marketing actions based on their findings? What percentage had an application beyond the one used to get the paper published? The impressive list of models in their Section 3.2 is much more an inventory of academic supply than of managerial purchase! In the field of diffusion modelling, for example, Lilien et al. Ž2000. found of the many hundreds of published diffusion models in marketing, they were hard pressed to find evidence of real managerial application of more than about a score. I do not wish to paint a pessimistic view of our profession. Indeed, I am highly optimistic for it. However, if we do not understand the drivers of and constraints to gaining a higher share of leverage in managerial decision making we are unlikely to improve our performance. What is it about conjoint analysis, customer satisfaction models, and discriminant-based segmentation approaches that has led to their managerial adoption while in
relative terms diffusion models, game theoretic competition analysis, and multi-equation econometric systems have languished at the hands of the manager? I would argue that the answer is firstly a focus on management decision variables, secondly, simplicity and ease of implementation, and only lastly, their rigour and sophistication. 2.3. Leeflang and Wittink’s future trends In their evaluation of the future, Leeflang and Wittink point to directions that they expect market modelling to take in response to trends in the marketplace. I was surprised to see a move to routinized modelling applications amongst their forecasts. To me, our failure as modellers is not based on excessive cost but a lack of drawing actionable insights from our marketing analyses. Automation would exacerbate this problem, particularly in a turbulent environment in which great sensitivity is needed in extrapolating from yesterday’s past to tomorrow’s future. Similarly, while undoubtedly artificial intelligence, data, and software availability will increase our ability to undertake analysis, I was also surprised to see increased complexity as a likely trend. While complexity aids our richness of understanding, it does not help in the accessibility of our models to managers. Perhaps the answer lies in the evolutionary model building approach advocated by Urban and Karash Ž1971.. 3. An alternative view 3.1. ImproÕing our current position The Leeflang and Wittink paper undoubtedly provides an extensive and useful review of work that has taken place, is taking place, and could take place in the marketing modelling profession. In this section I add a few thoughts which fit well with the framework of Fig. 1. In looking where to leverage the firm’s resources to greatest effect, we teach our students to apply the two steps of opportunity identification ŽAIs there a gap in the market?B and AIs there a market in the gap?B .. The first looks for areas of potential improvement, the second ensures that they will be valued by our target market if we undertake them. We must swallow our own medicine.
J.H. Robertsr Intern. J. of Research in Marketing 17 (2000) 127–134
In this regard, Bucklin and Gupta’s Ž1999. 2 = 2 classification of problems into resolved and unresolved from the modeller’s and the manager’s perspectives provides a useful framework, not least because it draws our attention to communication gaps between the two groups. Indeed, a valuable further distinction would be to partition unresolved problems into those that are perceived to be unresolvable by both groups and those that are perceived to be tractable. When I look at Fig. 1, I see a better understanding of managers, their problems, and the type of decisions that they will base on our models as the greatest area of leverage for improved performance. The second leverage point is that we become even
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more faithful to the process; that we focus on the critical drivers of marketplace behaviour, not just the ones that we have scanner data to analyse. Last but also least, we need to refine our techniques so that they become sharper and more powerful. Let me address each of the three elements in turn. To increase the value to managers of what we do as modellers, we can use a variety of market-driven or market-driving techniques, as illustrated in Fig. 2. We are already seeing a number of market-driven ways. Managerial interest in brand equity has changed our locus ŽFig. 2 — 1a.. Discrete choice techniques borrowed from econometrics have enabled us to increase our scope ŽFig. 2 — 1b.. And work by authors such as Blattberg and Deighton
Fig. 2. Methods of increasing the managerial applications of marketing models.
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J.H. Robertsr Intern. J. of Research in Marketing 17 (2000) 127–134
Ž1996. has started to change our focus from the marketing mix to account management ŽFig. 2 — 1c.. We have also been somewhat successful in drawing managers closer to what we do Žmarketdriving.. Work in diffusion models and more so hazard rates is moving managers to change their locus and consider customer susceptibility models ŽFig. 2 — 2a.. The electronic world is forcing managers to be interested in CRM Žcustomer relationship management. and thus increase their scope to include discriminant analysis and related techniques ŽFig. 2 — 2b.. Finally, extensive scanner data analysis has influenced manufacturers to focus on the price-promotion trade-off, including important trends like everyday low pricing ŽFig. 2 — 2c.. Improved success in these areas is predicated on understanding that the success of market modelling is contingent. It is contingent on accessibility by the manager, skill by the analyst who is undertaking the application Žnot the developer., and appropriateness of the technique to the actions the manager will take Žas well as fit with the market environment.. If we understand the features of our successes and failures better, we will focus our efforts more effectively. While improved managerial focus will improve the market penetration of our models, we can also gain much leverage by being better interpreters of the market mechanisms that we observe Žthe Marketplace Behaviour box in Fig. 1.. Some of the trends in consumer behaviour research represent the most exciting potential for us as modellers. We no longer need to grimly cling to a limited model of rational economic man as a utility maximizer. We can use some of the behavioural research into heuristics and decision biases. We can look at affect as well as cognition and importantly the interaction between the two in driving behaviour. We can study other aspects of the market environment such as contingent innovations, technology, and regulation. These will all add value and balance to the three pronged solutions that we offer the marketing manager. The final area of improved performance will come from improved techniques. We must focus our models, as detailed above. We must understand and communicate their soft benefits Žunderstanding and insight. as well as their hard ones Žquantified consumer response.. And we must make them more accessible through modular and evolutionary imple-
mentation. In many areas of modelling we are pushing up against the limit of tractability of our analytical approaches. If we are to remain true to the phenomena that we are studying, we may have to be prepared to move from algebraic solution to numerical representation, a trend that is already evident in engineering. Our search for universal empirical generalisations, such as was pursued in the special issue of Marketing Science in 1995 ŽVolume 14, Number 3. may have to share the stage with a more contingent approach where we try to better understand where our truths hold, where there do not, and why. Finally, we may have to be a lot broader about the disciplines from which we borrow to create a step change in the power of our armoury. 3.2. Preparing for the future While I have advocated some changes in emphasis for the current environment, it is also important to analyse what will be necessary to succeed in tomorrow’s environment. There are four trends I would like highlight that will have a major influence on marketing modelling. These are the increasing availability of data to the modeller, greater access to information for consumers, increasing turbulence in the market environment, and a shift in emphasis from the internally held marketing assets of the company to externally held ones. 3.2.1. Increasing aÕailability of data Leeflang and Wittink Ž2000. allude to the increasing availability of data and undoubtedly this is so. As more business processes are automated and more consumer transactions are conducted online, the data that are available will mushroom. Overall, this will allow greater understanding of markets but there are dangers. Because it is primarily behaviour that will be measured by the new data regime, we run the danger of being diverted from understanding market processes in the first box of Fig. 1. Because it is primarily objective, tangible attributes that are input, we run the danger of focusing the marketing manager in the third box away from intangible and perceptual factors. That is, more comprehensive data may increase the fineness of our measurement, but it may also threaten our balance. In this respect, sample survey theory has something valuable to teach us.
J.H. Robertsr Intern. J. of Research in Marketing 17 (2000) 127–134
Stratified sampling tells us to accept all of the information in data rich environments Žthose measured with high precision, having a large number of observations, and incurring a low cost of sampling.. However, it also teaches us to balance our understanding of the overall population by reweighting strata about which we know less Žsee, for example, Hansens et al., 1993.. In this context, we should make sure that we pay due regard to those aspects of the consumer decision process that will not be as well measured, but which still offer leverage to the marketing manager. 3.2.2. Greater customer access to information With the diffusion of the Web for search and purchase, consumers will have access to a much greater range and depth of information. Shaffer and Zettelmeyer Ž1998. provide an interesting analysis of the effects of this trend on power in the channel. The change will also give marketing modellers a variety of new problems to address. The current focus on search engines may well be appropriate for search attributes. For experience and credence attributes, the consumer may well rely on intermediaries. The role of the intermediary changes from a logistics one to trust, quality control and information management with resultant implications for consumer choice and how we represent it. 3.2.3. Increasing turbulence in the market enÕironment As categories converge and new categories emerge, as multicompany product solutions proliferate, and as product portfolio groupings become more prevalent, markets will change more and more rapidly. In a methodological sense, this means that we cannot necessarily assume stationarity of markets and we have to be more interested in the small sample properties of our estimators. Even in markets that are stationary with respect to competitors’ shares, Dekimpe and Hanssens Ž1995. suggest that the marketing actions necessary to maintain those shares will tend to evolve. In a managerial sense, there is the challenge of how to model and respond to these new and fast changing markets. Glaser and Weiss Ž1993. provide experimental evidence that suggests in turbulent times managers may well have to change
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their approach to decision making if they are to avoid poor decisions. Roberts Ž2000. gives a summary of the tools that we have developed to date to calibrate and manage in such environments. 3.2.4. Change in focus from internally held marketing assets to externally held ones Marketing is the study of the exchange between a firm and its products and a customer and his needs. Historically, our emphasis was on the side of that exchange internal to the firm; the marketing mix. More and more, we see the focus moving to customer and what the firm owns in the external environment. Recent work on customer-based brand equity provides a good example of this trend Že.g., Keller, 1998.. Blattberg and Deighton Ž1996. formalise the concept of the customer as a corporate asset with their ACustomer EquityB construct. As this trend continues, models of customer acquisition, retention and maximization are going to become increasingly important.
4. Conclusion Leeflang and Wittink Ž2000. document the fact that marketing modelling has pushed out the efficient frontier in the trade-off between ease of application Žparsimony, simplicity and cost. and insight Žrichness of phenomena explained, completeness and consistency.. This paper has put some caveats on the extent to which that has happened and in doing so has identified where I believe that we, as modellers, can increase our managerial impact. If we immerse ourselves more deeply in the phenomena that we represent with our models and if we improve our communications with the managers who we aspire to guide then our models will have a much greater chance to profoundly impact marketing practice in the twenty first century.
Acknowledgements The author would like to thank the Editor, JanBenedict Steenkamp, and two anonymous reviewers for a number of extremely useful suggestions.
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References Blattberg, R., Deighton, J., 1996. Manage marketing by the customer equity test. Harvard Business Review, 136–144, JulyrAugust. Bucklin, R.E., Gupta, S., 1999. Commercial use of UPC scanner data: industry and academic perspectives. Marketing Science 18 Ž3., 247–273. Dekimpe, M.G., Hanssens, D.M., 1995. Empirical generalizations about market evolution and stationarity. Marketing Science 14 Ž3., G109–G121. Glaser, R., Weiss, A.M., 1993. Marketing in turbulent environments: decision processes and the time sensitivity of information. Journal of Marketing Research 30, 509–521 ŽNovember.. Green, P.E., Srinivasan, V., 1978. Conjoint analysis in market research: issues and outlook. Journal of Consumer Research 5, 103–123 ŽSeptember.. Hansens, M.H., Hurwitz, W.N., Madow, W.G., 1993. Sampling Survey Methods and Theory, Vol. 1, Wiley, New York. Keller, K.L., 1998. Strategic Brand Management: Building, Measuring and Managing Brand Equity. Prentice Hall, Upper Saddle River, NJ. Leeflang, P.S.H., Wittink, D.R., 2000. Building models for marketing decisions: Past, present and future. International Journal of Research in Marketing 17, 105–126 Žthis issue.. Lilien, G.L., Rangaswamy, A., van den Bulte, C., 2000. Diffusion models: Managerial applications and software. In: Mahajan, V., Muller, E., Wind, Y. ŽEds.., New Product Diffusion Models. Kluwer Academic Publishing, Amsterdam.
Little, J.D.C., 1970. Models and managers: The concept of a decision calculus. Management Science 16, B466–B485. Roberts, J.H., 2000. Developing new rules for new markets. Journal of the Academy of Marketing Science 28 Ž1., 31–44. Shaffer, G., Zettelmeyer, F., 1998. The Internet as a medium for marketing communications: Channel conflict over the provision of information. Working paper, University of Rochester, Simon Graduate School of Business, New York. Shocker, A.D., Srinivasan, V., 1979. Multiattribute approaches for concept evaluation and generation: A critical review. Journal of Marketing Research 16, 159–180 ŽMay.. Silk, A.J., Urban, G.L., 1978. Pretest market evaluation of packaged goods: A model and measurement methodology. Journal of Marketing Research 15, 171–191 ŽMay.. Urban, G.L., Katz, G.M., 1983. Pretest market models: Validation and managerial implications. Journal of Marketing Research 20, 221–234 ŽAugust.. Urban, G.L., Karash, R., 1971. Evolutionary model building. Journal of Marketing Research 8, 62–66 ŽFebruary.. Wedel, M., Kamakura, W.A., 1999. Market segmentation: Conceptual and methodological foundations. 2nd edn. Kluwer Academic Publishing, Amsterdam. Wittink, D.R., Cattin, P., 1989. Commercial use of conjoint analysis: An update. Journal of Marketing 53, 91–96 ŽJuly.. Wittink, D.R., Vriens, M., Burhenne, W., 1994. Commercial use of conjoint analysis in Europe: Results and critical reflections. International Journal of Research in Marketing 11 Ž1., 41–43.
The intersection of modelling potential and practice John H. Roberts ) Australian Graduate School of Management, UniÕersity of New South Wales, Sydney, NSW 2052, Australia
Abstract This paper sets up a framework of the environment in which marketing modelling applications take place. It examines the grist to the mill of marketing modelling Žmarketplace consumer behaviour. and the avowed users of its output Žpractising managers.. From this, it establishes the desirable features of effective marketing models, evaluates our performance as modellers, and suggests ways in which we need to go in the future, both to improve our success in the current environment and to ensure success in future ones. In doing so, the paper critiques and builds on the work of Leeflang and Wittink wInternat. J. Res. Marketing 17 Ž2000. 105x. q 2000 Elsevier Science B.V. All rights reserved. Keywords: Marketing models; Management applications
1. The role of marketing modelling Marketing modellers use their tools to help marketing managers focus their actions more finely towards meeting the needs of their target consumers. To the extent that they are able to achieve this consumers are likely to end up more satisfied because products and services are of greater relevance to them. Additionally, firms should better meet their objectives because products of higher perceived utility can extract higher economic rents, either in terms of increased market share or higher prices. Any successful application of a marketing model must be based on three components; the phenomena in the marketplace, the technical skills of the modeller, and the options open to the manager, including the ques-
tions she needs to answer. We can illustrate a typical example of this nexus using Fig. 1.1 Many researchers base their understanding of markets on a strong foundation in one of these three columns. Most modellers are firmly based in the second box, Statistical Tool Boxes. Research with its roots in modelling has contributed greatly to our understanding of marketplace behaviour. See, for example, the work by Wedel and Kamakura Ž1999. on latent segmentation, which has greatly enriched our view of the nature of consumer heterogeneity. Similarly, modelling has provided many actionable insights to practising managers Žthe right hand box.. See, for example, the reviews of conjoint analysis by
1
)
Tel.: q61-2-9931-9255; fax: q61-2-9662-7621. E-mail address: [email protected] ŽJ.H. Roberts..
Note that marketing models may have extremely valuable purposes other than to focus managerial actions. For example, the desire for understanding of the environment in which we live may constitute an important objective. This paper only focuses on the role of marketing models in so far as they are developed for managerial application.
0167-8116r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 8 1 1 6 Ž 0 0 . 0 0 0 1 2 - 4
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J.H. Robertsr Intern. J. of Research in Marketing 17 (2000) 127–134
Fig. 1. The elements of applying marketing models.
Green and Srinivasan Ž1978. and Shocker and Srinivasan Ž1979., which led to their commercial adoption, documented by Wittink and Cattin Ž1989. and Wittink et al. Ž1994.. Research based on the left hand box of Fig. 1 could be termed phenomenological research. This is the research of someone who is interested in nuts and bolts. The middle box is methodological research and is undertaken by someone who has a wrench. This person walks around looking for nuts that his wrench will do a good job of tightening. The third box contains those people who are interested in fasteners. They are probably not experts in either nuts or wrenches but they are interested in the problem of holding things together. No successful application can be undertaken without a solid foundation in each one of the boxes. The genesis of a marketing modelling application does not matter but the end result must have a balance of all three. A lack of balance in understanding the environment, the appropriate tools to use, or the way in which the results will be applied inevitably leads to a
loss of relevance. Since, as modellers we are primarily methodologists, the danger to us is that we will ignore important marketplace phenomena or that we not focus our problem solving to areas that are actionable by marketing managers. I will argue that these three elements have not always been in balance but that this lack of balance does not emerge in the review paper of Leeflang and Wittink Ž2000.. The framework illustrated in Fig. 1 leads automatically to the desiderata of useful models. These were spelt out by Little Ž1970. in his seminal paper, AModels and Managers: The Concept of a Decision CalculusB. As outlined by Leeflang and Wittink, Little required that marketing models be simple, complete, adaptive, robust and easy to use. That still forms a good set of criteria today. An examination of Fig. 1 shows that to be phenomenologically appropriate the model must be faithful to the processes that are occurring in the marketplace. To be methodologically admissible, it must be rigorous and internally consistent. Finally, for application it must be useful, which includes being accessible to the man-
J.H. Robertsr Intern. J. of Research in Marketing 17 (2000) 127–134
ager as well as addressing the questions that she has to answer in directing her marketing investment and energies. Little’s criteria not only span these three elements, they also span interactions between them. For example, robustness suggests that the model must accurately reproduce marketplace behaviour within all possible ranges of management action considered.
2. Comments on the Leeflang and Wittink perspective Leeflang and Wittink Ž2000. look at the past, present and future of marketing modelling, devoting most attention to the present as a launching pad for the future. I largely agree with their views but I would place a somewhat different emphasis in a number of areas. I would have a different list of names in terms of the profession’s history. I would be less glowing about modelling’s current success rate in influencing management practice. And I would advocate more flexibility and adaptivity in future development. I will explore Leeflang and Wittink’s treatment each of these stages of the profession’s development before elaborating on my own view, using the framework spelt out in Fig. 1. 2.1. Historical eÕolution Leeflang and Wittink give a useful description of how marketing models became more accessible to managers because of the simplicity advocated by scholars such as Little, and the increasing richness of phenomena captured by them as the statistical and econometric foundation on which they are built expanded. They also identify other important trends, such as the movement to modularity. My real surprise with their historical review was with the list of names and references. Work from the strong research centres at M.I.T., Wharton, Stanford and Northwestern barely merits a mention Žwith the exception of Little.. To check if this belief reflected my own prejudices, I went through the list of Paul Converse Award winners over the past 20 years who were modellers. The Converse Award is the top research award of the American Marketing Association for sustained contribution to research. Out of over 100
129
references in the Leeflang and Wittink paper, the names of Urban, Hauser, Srinivasan, Staelin, Lehmann, Aaker, and Bagozzi were not listed as first authors. Of the 100q first authors listed, only Little, Wind and Bass were Converse winners. These omissions look all the more critical when one remembers that the article is interested in management practice. The ASSESSOR new product forecasting model won two O’Dell Awards for the most influential piece of research judged 5 years further on ŽSilk and Urban, 1978; Urban and Katz, 1983., had many hundreds if not thousands of applications, and spawned an industry of lookalikes. However, despite this difference in emphasis with respect to contribution, I think that the Leeflang and Wittink review fulfils a useful role in drawing together our history. 2.2. Current practice In terms of current practice, Leeflang and Wittink provide a very extensive overview of what is being done in universities to model marketplace phenomena in a way that can be applied by managers. Their catalogue of existing techniques is valuable. It has a bias towards packaged goods and price promotions and downplays a lot of excellent modelling that has taken place with durability and services, in business markets, on networks, with media planning, and a number of other areas. However, any such review must perforce be selective and so such a criticism may be regarded as severe. Perhaps it is the focus on packaged goods that leads them to conclude that marketing modelling is in a state of maturity. I think that one has to ask, AMaturity where?B There may well be some level of maturity in the technology but I cannot see much evidence in terms of maturity in application. There may well be maturity in using logit models to study promotional effects in packaged goods industries using scanner data, but I see no evidence of maturity in terms of using latent segmentation models to develop mass customisation strategies for example. When Leeflang and Wittink discuss managerial applications, they do so from a supply perspective rather than a demand perspective, in common with most modellers. By this I mean that they look at what we have done, our modelling innovations and their diffusion, rather than managerial needs and the
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level to which we are satisfying them. They look out from the Statistical Tool Box area of Fig. 1 towards the problems that managers face, rather than putting themselves in the shoes of marketing practitioners and looking back to judge the contribution of marketing models. Both perspectives are valid but one asks a totally different set of questions if one uses the second. Questions like, AWhat is the penetration of marketing models in every day managerial decision making?B and AWhat is the average standard of marketing models in industry, as applied?B would leave us feeling a lot less comfortable. They would also change our focus from generating best of breed technology to also include improving quality control and training in application. As an example of their perspective, Leeflang and Wittink’s stages of building a model Žsee their Fig. 1. are well designed to meet the needs of an academic audience. However, for a true managerial application cost benefit considerations Žstage 9. must precede model scope Žstage 3. and time to market dictates that validation Žstage 8. must follow use Žstage 10., not precede it. Another issue that leaves me less enthusiastic than Leeflang and Wittink about the state of marketing modelling applications revolves around the definition of what we call an application. We have been far less rigorous on ourselves in defining what constitutes an application than we have been about what represents an appropriate, grounded model. What percentage of the articles they cite actually had marketing actions based on their findings? What percentage had an application beyond the one used to get the paper published? The impressive list of models in their Section 3.2 is much more an inventory of academic supply than of managerial purchase! In the field of diffusion modelling, for example, Lilien et al. Ž2000. found of the many hundreds of published diffusion models in marketing, they were hard pressed to find evidence of real managerial application of more than about a score. I do not wish to paint a pessimistic view of our profession. Indeed, I am highly optimistic for it. However, if we do not understand the drivers of and constraints to gaining a higher share of leverage in managerial decision making we are unlikely to improve our performance. What is it about conjoint analysis, customer satisfaction models, and discriminant-based segmentation approaches that has led to their managerial adoption while in
relative terms diffusion models, game theoretic competition analysis, and multi-equation econometric systems have languished at the hands of the manager? I would argue that the answer is firstly a focus on management decision variables, secondly, simplicity and ease of implementation, and only lastly, their rigour and sophistication. 2.3. Leeflang and Wittink’s future trends In their evaluation of the future, Leeflang and Wittink point to directions that they expect market modelling to take in response to trends in the marketplace. I was surprised to see a move to routinized modelling applications amongst their forecasts. To me, our failure as modellers is not based on excessive cost but a lack of drawing actionable insights from our marketing analyses. Automation would exacerbate this problem, particularly in a turbulent environment in which great sensitivity is needed in extrapolating from yesterday’s past to tomorrow’s future. Similarly, while undoubtedly artificial intelligence, data, and software availability will increase our ability to undertake analysis, I was also surprised to see increased complexity as a likely trend. While complexity aids our richness of understanding, it does not help in the accessibility of our models to managers. Perhaps the answer lies in the evolutionary model building approach advocated by Urban and Karash Ž1971.. 3. An alternative view 3.1. ImproÕing our current position The Leeflang and Wittink paper undoubtedly provides an extensive and useful review of work that has taken place, is taking place, and could take place in the marketing modelling profession. In this section I add a few thoughts which fit well with the framework of Fig. 1. In looking where to leverage the firm’s resources to greatest effect, we teach our students to apply the two steps of opportunity identification ŽAIs there a gap in the market?B and AIs there a market in the gap?B .. The first looks for areas of potential improvement, the second ensures that they will be valued by our target market if we undertake them. We must swallow our own medicine.
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In this regard, Bucklin and Gupta’s Ž1999. 2 = 2 classification of problems into resolved and unresolved from the modeller’s and the manager’s perspectives provides a useful framework, not least because it draws our attention to communication gaps between the two groups. Indeed, a valuable further distinction would be to partition unresolved problems into those that are perceived to be unresolvable by both groups and those that are perceived to be tractable. When I look at Fig. 1, I see a better understanding of managers, their problems, and the type of decisions that they will base on our models as the greatest area of leverage for improved performance. The second leverage point is that we become even
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more faithful to the process; that we focus on the critical drivers of marketplace behaviour, not just the ones that we have scanner data to analyse. Last but also least, we need to refine our techniques so that they become sharper and more powerful. Let me address each of the three elements in turn. To increase the value to managers of what we do as modellers, we can use a variety of market-driven or market-driving techniques, as illustrated in Fig. 2. We are already seeing a number of market-driven ways. Managerial interest in brand equity has changed our locus ŽFig. 2 — 1a.. Discrete choice techniques borrowed from econometrics have enabled us to increase our scope ŽFig. 2 — 1b.. And work by authors such as Blattberg and Deighton
Fig. 2. Methods of increasing the managerial applications of marketing models.
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Ž1996. has started to change our focus from the marketing mix to account management ŽFig. 2 — 1c.. We have also been somewhat successful in drawing managers closer to what we do Žmarketdriving.. Work in diffusion models and more so hazard rates is moving managers to change their locus and consider customer susceptibility models ŽFig. 2 — 2a.. The electronic world is forcing managers to be interested in CRM Žcustomer relationship management. and thus increase their scope to include discriminant analysis and related techniques ŽFig. 2 — 2b.. Finally, extensive scanner data analysis has influenced manufacturers to focus on the price-promotion trade-off, including important trends like everyday low pricing ŽFig. 2 — 2c.. Improved success in these areas is predicated on understanding that the success of market modelling is contingent. It is contingent on accessibility by the manager, skill by the analyst who is undertaking the application Žnot the developer., and appropriateness of the technique to the actions the manager will take Žas well as fit with the market environment.. If we understand the features of our successes and failures better, we will focus our efforts more effectively. While improved managerial focus will improve the market penetration of our models, we can also gain much leverage by being better interpreters of the market mechanisms that we observe Žthe Marketplace Behaviour box in Fig. 1.. Some of the trends in consumer behaviour research represent the most exciting potential for us as modellers. We no longer need to grimly cling to a limited model of rational economic man as a utility maximizer. We can use some of the behavioural research into heuristics and decision biases. We can look at affect as well as cognition and importantly the interaction between the two in driving behaviour. We can study other aspects of the market environment such as contingent innovations, technology, and regulation. These will all add value and balance to the three pronged solutions that we offer the marketing manager. The final area of improved performance will come from improved techniques. We must focus our models, as detailed above. We must understand and communicate their soft benefits Žunderstanding and insight. as well as their hard ones Žquantified consumer response.. And we must make them more accessible through modular and evolutionary imple-
mentation. In many areas of modelling we are pushing up against the limit of tractability of our analytical approaches. If we are to remain true to the phenomena that we are studying, we may have to be prepared to move from algebraic solution to numerical representation, a trend that is already evident in engineering. Our search for universal empirical generalisations, such as was pursued in the special issue of Marketing Science in 1995 ŽVolume 14, Number 3. may have to share the stage with a more contingent approach where we try to better understand where our truths hold, where there do not, and why. Finally, we may have to be a lot broader about the disciplines from which we borrow to create a step change in the power of our armoury. 3.2. Preparing for the future While I have advocated some changes in emphasis for the current environment, it is also important to analyse what will be necessary to succeed in tomorrow’s environment. There are four trends I would like highlight that will have a major influence on marketing modelling. These are the increasing availability of data to the modeller, greater access to information for consumers, increasing turbulence in the market environment, and a shift in emphasis from the internally held marketing assets of the company to externally held ones. 3.2.1. Increasing aÕailability of data Leeflang and Wittink Ž2000. allude to the increasing availability of data and undoubtedly this is so. As more business processes are automated and more consumer transactions are conducted online, the data that are available will mushroom. Overall, this will allow greater understanding of markets but there are dangers. Because it is primarily behaviour that will be measured by the new data regime, we run the danger of being diverted from understanding market processes in the first box of Fig. 1. Because it is primarily objective, tangible attributes that are input, we run the danger of focusing the marketing manager in the third box away from intangible and perceptual factors. That is, more comprehensive data may increase the fineness of our measurement, but it may also threaten our balance. In this respect, sample survey theory has something valuable to teach us.
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Stratified sampling tells us to accept all of the information in data rich environments Žthose measured with high precision, having a large number of observations, and incurring a low cost of sampling.. However, it also teaches us to balance our understanding of the overall population by reweighting strata about which we know less Žsee, for example, Hansens et al., 1993.. In this context, we should make sure that we pay due regard to those aspects of the consumer decision process that will not be as well measured, but which still offer leverage to the marketing manager. 3.2.2. Greater customer access to information With the diffusion of the Web for search and purchase, consumers will have access to a much greater range and depth of information. Shaffer and Zettelmeyer Ž1998. provide an interesting analysis of the effects of this trend on power in the channel. The change will also give marketing modellers a variety of new problems to address. The current focus on search engines may well be appropriate for search attributes. For experience and credence attributes, the consumer may well rely on intermediaries. The role of the intermediary changes from a logistics one to trust, quality control and information management with resultant implications for consumer choice and how we represent it. 3.2.3. Increasing turbulence in the market enÕironment As categories converge and new categories emerge, as multicompany product solutions proliferate, and as product portfolio groupings become more prevalent, markets will change more and more rapidly. In a methodological sense, this means that we cannot necessarily assume stationarity of markets and we have to be more interested in the small sample properties of our estimators. Even in markets that are stationary with respect to competitors’ shares, Dekimpe and Hanssens Ž1995. suggest that the marketing actions necessary to maintain those shares will tend to evolve. In a managerial sense, there is the challenge of how to model and respond to these new and fast changing markets. Glaser and Weiss Ž1993. provide experimental evidence that suggests in turbulent times managers may well have to change
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their approach to decision making if they are to avoid poor decisions. Roberts Ž2000. gives a summary of the tools that we have developed to date to calibrate and manage in such environments. 3.2.4. Change in focus from internally held marketing assets to externally held ones Marketing is the study of the exchange between a firm and its products and a customer and his needs. Historically, our emphasis was on the side of that exchange internal to the firm; the marketing mix. More and more, we see the focus moving to customer and what the firm owns in the external environment. Recent work on customer-based brand equity provides a good example of this trend Že.g., Keller, 1998.. Blattberg and Deighton Ž1996. formalise the concept of the customer as a corporate asset with their ACustomer EquityB construct. As this trend continues, models of customer acquisition, retention and maximization are going to become increasingly important.
4. Conclusion Leeflang and Wittink Ž2000. document the fact that marketing modelling has pushed out the efficient frontier in the trade-off between ease of application Žparsimony, simplicity and cost. and insight Žrichness of phenomena explained, completeness and consistency.. This paper has put some caveats on the extent to which that has happened and in doing so has identified where I believe that we, as modellers, can increase our managerial impact. If we immerse ourselves more deeply in the phenomena that we represent with our models and if we improve our communications with the managers who we aspire to guide then our models will have a much greater chance to profoundly impact marketing practice in the twenty first century.
Acknowledgements The author would like to thank the Editor, JanBenedict Steenkamp, and two anonymous reviewers for a number of extremely useful suggestions.
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