Marketing Science

Sawhney, Mohanbir S.; Eliashberg, Jehoshua

doi: 10.1287/mksc.15.2.113pmid: N/A

The primary objective of this paper is to develop a parsimonious model for forecasting the gross box-office revenues of new motion pictures based on early box office data. The paper also seeks to provide insights into the impact of distribution policies on the adoption of new products. The model is intended to assist motion picture exhibitor chains (retailers) in managing their exhibition capacity and in negotiating exhibition license agreements with distributors (studios), by allowing them to project the box-office potential of the movies they plan to or currently exhibit based on early box-office results. It is also of interest to practitioners in other software industries (e.g., music, books, CD-ROMs) where the distribution intensity is highly variable over the product life cycle and is an important determinant of new product adoption patterns. The model and its extensions are of interest to academic researchers interested in modeling distribution effects in new product adoption, as well as forecasters looking for ways to leverage historical data on related products to forecast the sales of new products.We draw upon a queuing theory framework to conceptualize stochastically the consumer's movie adoption process in two stepsthe time to decide to see the new movie, and the time to act on the adoption decision. The parameter for the time-to-decide process captures the intensity of information intensity flowing from various information sources, while the parameter for the time-to-act process is related to the delay created by limited distribution intensity and other factors. Our conceptualization extends existing new product forecasting models, which assume that consumers act instantaneously on the motivating information they receive about the new product. The resulting model is parsimonious, yet it accommodates a wide range of adoption patterns. In addition, the stochastic formulation allows us to quantify the uncertainty surrounding the expected adoption pattern. In the empirical testing, we focus on the most parsimonious version of the modeling framework. BOXMOD-I, a model that assumes stationarity with respect to the two shape parameters that characterize the adoption process. The model produces fairly accurate early forecasts using at most the first three weeks of data for calibration, and the predictive performance of the model compares favorably with benchmark models. We propose extensions of the basic model that account for more realistic non-stationary distribution intensity patternsincluding a wide release pattern that relies on intensive distribution and promotion, and a platform release pattern that involves a gradual buildup of distribution intensity. Finally, we present an adaptive weighing scheme that combines initial parameter estimates obtained from a meta-analysis procedure with estimates obtained from early data to produce forecasts of box-office revenues for a new movie when little or no box-office data are available.An important finding from the empirical testing is that motion picture box-office revenue patterns display remarkable empirical regularity. We find that there are only three classes of adoption patterns, and these can all be represented within the basic model by using a two-parameter. Exponential or Erlang-2 probability distribution, or a three parameter Generalized Gamma distribution. We also find that cumulative box-office revenues can be predicted with reasonable accuracy (often within 10 of the actual) using as little as two or three data points. However, our attempts to predict revenue patterns without any sales data meet with limited success. While the scale parameter can be estimated reasonably well from a historical database of parameter values, we find that it is considerably more difficult to predict the shape parameters using historical data.The parsimony we seek in developing the model comes at the cost of several limiting assumptions. We assume that the time-to-decide subprocess and the time-to-act subprocess are independent, which may not be the case if decisions on continued exhibition by retailers are endogenously related to box-office revenues over the life cycle. In the basic model formulation, we also assume that the time-to-act process can be represented by an exponential distribution, which may not always be the case. While we provide some empirical evidence to support these assumptions, further research could relax these and other assumptions to enrich the basic model, although this would entail some loss in parsimony.

Lal, Rajiv; Narasimhan, Chakravarthi

doi: 10.1287/mksc.15.2.132pmid: N/A

Our objective in this paper is to explain the relationship between a manufacturer's brand advertising and its impact on wholesale and retail margins in consumer goods markets. We construct a model of re-tailers and manufacturers, and using tools from game theory explain why under some conditions a manufacturer's advertising can squeeze, i.e., lower, the retail margin while simultaneously increasing the wholesale margin. Our paper should be of interest to applied analytical and empirical researchers in marketing as well as managers interested in understanding the strategic impact of brand advertising on margins.The consumer goods retail market is characterized by intense rivalry among retailers competing for a share of the consumer dollar. Retailers carry many products, and on any given purchase occasion a typical consumer buys a subset of the vast number of items a retailer has on its shelf. In general consumers are ignorant about the prices of all the products they want to buy and consequently select a retailer to shop at based on the advertised prices of a subset of the products they intend to buy. Given this, retailers tend to compete more aggressively based on the prices of a selected set of items by advertising these prices to consumers. The items that the retailers select to compete on are those that most consumers desire and value highly. Since the profit from any customer is the sum of profits from advertised and unadvertised items, the intensity of retail competition, as evident from the prices of these items, increases with the amount the consumer will expend on the unadvertised items once at the store. This aggressiveness therefore translates into lower retail margins on these selected items since the retailers expect that consumers, once inside a store, will buy non-advertised products as well on which the retailers make money. Thus manufacturers who are more adept at using pull strategies to enhance the popularity of their product, obtain d significant competitive advantage vis vis others. The positioning of the product and the image conveyed through advertising act as drivers in creating this advantage which results in higher wholesale prices that these manufacturers can charge the retailers.There are several key insights from our analysis. Our model explains why retail and wholesale margins can move in opposite directions and also suggests whenin those retail markets where consumers shop for a basket of goods. Our analysis also reveals that retailers make higher margins on unadvertised products and less on advertised products. Furthermore it shows the power of a popular brand where its popularity can be enhanced through brand advertising. From a managerial standpoint we also show that the effectiveness of advertising should not be narrowly interpreted in terms of increase in share or awareness but should include the ability to charge a higher wholesale price. Finally our analysis sheds light on extant, and provides guidance to future, empirical work in this area.

Kamakura, Wagner A.; Kim, Byung-Do; Lee, Jonathan

doi: 10.1287/mksc.15.2.152pmid: N/A

Consumer heterogeneity is fundamental to the marketing concept, providing the basis for market segmentation, targeting and positioning, as well as micro-marketing. Substantial effort has already been devoted to incorporate heterogeneity in brand choice models. However, most of the research in this area has focused on differences in preferences or tastes across consumers. In contrast, limited attention has been given to the possibility that consumers might also differ in the process they follow when making choices. Failure to account for either form of consumer heterogeneity may lead to misinterpretations of market structure and market segments, as demonstrated in our simulations.Our main research objective in this paper is to account for these two forms of consumer heterogeneity with an integrated model. We develop a choice model that simultaneously identities consumer segments on the basis of their preferences, response to the marketing mix, and choice processes. This choice model is a finite mixture of nested logit models that incorporates the mixture of multinomial logits as a special case. One main limitation of the now popular multinomial logit model is that choices by each consumer are assumed to be Independent from Irrelevant Alternatives. As a consequence, that model predicts that, within each segment, a brand has exactly the same cross-elasticities upon every competitor. In contrast, our mixture of nested logits allows for violations of the IIA property within each segment, leading to more realistic patterns of brand competition, where one brand draws differently from each competitor, depending on the choice process used by members of the segment.In addition to the flexible combination of choice processes and preference structures, our model also incorporates a latent class of hard-core loyal consumers who are expected to always buy the same brand, and are thus insensitive to price and promotions. Application of our model to household scanner data in the peanut butter category led to four types of segments:(a) hard-core loyals, who do not respond to price and promotions.(b) brand-type, for which the brand choice precedes the choice of product form (creamy vs. crunchy).(c) form-type. Members of these segments first choose the product form and then decide for the brand (Peter Pan, Jif, Skippy, or Store brand).(d) IIA-type. Members of these segments make choices according to the IIA property.Our results showed a large (14) hard-core loyal segment. We also identified three segments of the brand-type, one segment of the form-type and three IIA-type segments. All these segments also differ in their preferences for brands and product forms.One useful feature of our model is that it allows for cross-elasticity structures with non-proportional draw, even when computed within a homogeneous segment. Most importantly, the proposed model allows for more complex cross-elasticity structures within a segment, rather than constraining the elasticities to any particular pattern a priori. These cross-elasticity patterns imply distinct competitive environments within each segment, leading to different price and promotion strategies in each segment. For example, a promotion by Jif-crunchy to a brand-type segment is more likely to cannibalize on shares of the creamy form of the same brand, than to draw from competitors. If offered to a form-type segment, the same promotion is more likely to draw shares from competitors.Because of its finite-mixture formulation, our proposed model shares the same limitations of this category of models. For example, maximum likelihood estimation of the model may lead to local optima, thus requiring a multiple number of random starts, in order to increase the chances of finding the model that best fits to the data. Our model also requires specification of the number and type of segments in each particular application. Several criteria are available to select the number of segments. However, the specification of choice processes must be based on a heuristic procedure, or on the basis of prior knowledge.

Lenk, Peter J.; DeSarbo, Wayne S.; Green, Paul E.; Young, Martin R.

doi: 10.1287/mksc.15.2.173pmid: N/A

The drive to satisfy customers in narrowly defined market segments has led firms to offer wider arrays of products and services. Delivering products and services with the appropriate mix of features for these highly fragmented market segments requires understanding the value that customers place on these features. Conjoint analysis endeavors to unravel the value or partworths, that customers place on the product or service's attributes from experimental subjects' evaluation of profiles based on hypothetical products or services. When the goal is to estimate the heterogeneity in the customers' partworths, traditional estimation methods, such as least squares, require each subject to respond to more profiles than product attributes, resulting in lengthy questionnaires for complex, multiattributed products or services. Long questionnaires pose practical and theoretical problems. Response rates tend to decrease with increasing questionnaire length, and more importantly, academic evidence indicates that long questionnaires may induce response biases.The problems associated with long questionnaires call for experimental designs and estimation methods that recover the heterogeneity in the partworths with shorter questionnaires. Unlike more popular estimation methods, Hierarchical Bayes (HB) random effects models do not require that individual-level design matrices be of full rank, which leads to the possibility of using fewer profiles per subject than currently used. Can this theoretical possibility be practically implemented?This paper tests this conjecture with empirical studies and mathematical analysis. The random effects model in the paper describes the heterogeneity in subject-level partworths or regression coefficients with a linear model that can include subject-level covariates. In addition, the error variances are specific to the subjects, thus allowing for the differential use of the measurement scale by different subjects.In the empirical study, subjects' responses to a full profile design are randomly deleted to test the performance of HB methods with declining sample sizes. These simple experiments indicate that HB methods can recover heterogeneity and estimate individual-level partworths, even when individual-level least squares estimators do not exist due to insufficient degrees of freedom.Motivated by these empirical studies, the paper analytically investigates the trade-off between the number of profiles per subject and the number of subjects on the statistical accuracy of the estimators that describe the partworth heterogeneity. The paper considers two experimental designs: each subject receives the same set of profiles, and subjects receive different blocks of a fractional factorial design. In the first case, the optimal design, subject to a budget constraint, uses more subjects and fewer profiles per subject when the ratio of unexplained, partworth heterogeneity to unexplained response variance is large. In the second case, one can maintain a given level of estimation accuracy as the number of profiles per subject decreases by increasing the number of subjects assigned to each block.These results provide marketing researchers the option of using shorter questionnaires for complex products or services. The analysis assumes that response quality is independent of questionnaire length and does not address the impact of design factors on response quality. If response quality and questionnaire length were, in fact, unrelated, then marketing researchers would still find the paper's results useful in improving the efficiency of their conjoint designs. However, if response quality were to decline with questionnaire length, as the preponderance of academic research indicates, then the option to use shorter questionnaires would become even more valuable.

Dhar, Sanjay K.; Morrison, Donald G.; Raju, Jagmohan S.

doi: 10.1287/mksc.15.2.192pmid: N/A

In Raju, Dhar, and Morrison (1994), a paper that appeared earlier in this journal, we developed an analytical model and conducted empirical analyses to examine the effect of package coupons on market share. In this epilogue, we extend the analytical framework in our earlier paper to study the relative impact of package coupons on profits. We also report findings from five quasi-experiments (including two new quasi-experiments) that provide empirical validity to our model-based predictions.Our results have significance for brand managers in packaged goods firms. Our analysis suggest that while selecting among package coupons, brand managers should carefully define which performance criterion to use: market share impact, redemption rate, or profit impact. Package coupons that lead to the highest market share impact (or redemptions) may not lead to the highest profit impact.We compare the relative profit impact of the following package coupons: peel-off, on-pack, and in-pack. Peel-off coupons must be redeemed on the same purchase occasion on which they are obtained. On-pack coupons are obtained at one purchase occasion but can only be redeemed for a discount on the couponed brand at a future purchase occasion. In-pack coupons are similar to on-pack coupons except that the consumer is unaware of the presence of these coupons when the product is purchased (in-pack coupons are printed or placed inside the package). Consumers with an in-pack or on-pack coupon from a previous purchase occasion will have a higher probability of purchasing the couponed brand even if the brand was not currently offering coupons. Consequently, to understand choice behavior, it is not enough to take into consideration the current choice environment; our model therefore must also keep track of whether or not a consumer has a package coupon that was obtained on an earlier purchase occasion. In other words, since choice is dependent on the purchase environment as well as the state of the consumer, we use a Markov model to represent the choice process. Analytical results are derived based on the long run probabilities of the Markov transition matrix.Our analytical and empirical results suggest that by and large, of the various package coupons examined in our research, on-pack coupons lead to the highest impact on profits. Furthermore, while peel-offs lead to a higher market share than in-packs, because in-packs stimulate repurchase among previous buyers, they lead to higher profits than peel-offs; though only for stronger brands.