Purushottam Papatla

Measuring the Dynamic Effects of Promotions on Brand Choice

Promotions are being used with increasing frequency by manufacturers facing highly competitive markets, which is causing concern among some marketers who feel that frequent promotions can hurt a br...

An Investigation of Reference Price Segments

Empirical research on reference price has typically assumed that consumers use either an internal reference price (IRP) or an external reference price (ERP), but not both, in brand choice decisions. In this article, the authors assume that consumers use both IRP and ERP but may consider one of them more salient than the other. The authors develop a model that segments consumers on the basis of the differences in the importance they assign to each type of reference price as well as in their brand preferences and responses to marketing-mix variables. The authors calibrate the model on data for four categories: liquid detergents, ketchup, tissue, and yogurt. In all four categories, the proposed model performs significantly better than the one that assumes that consumers use either IRP or ERP exclusively. The authors discuss the managerial implications of this finding.

Loyalty differences in the use of internal and external reference prices

Recent findings in reference price research suggest that consumer characteristics may affect whether they use an internal reference price (IPR) or an external reference price (ERP) in price judgments. In this paper, we investigate the role of one such characteristic, brand loyalty, in the use of either type of reference price. Specifically, we employ a latent class-type approach to divide consumers on the basis of their brand loyalty into an ERP and an IRP segment. Analysis of the margarine and liquid detergents categories shows that consumers who are highly loyal to a brand are likely to use external reference prices whereas less brand-loyal consumers rely on internal reference prices. We discuss the implications of this finding and suggest directions for future research.

Accounting for heterogeneity and dynamics in the loyalty-price sensitivity relationship

Abstract Marketers have been interested in the relationship between brand loyalty and price sensitivity for many years and have examined whether loyalty reduces consumer price sensitivity. The results, to date, indicate that loyalty does indeed raise the price that consumers are willing to pay for a brand. Other than this broad finding, however, there has been little research in the literature regarding whether and how this relationship varies across consumers and product categories and, within consumers, over time. This is the issue that we investigate in this paper. Specifically, we examine whether the price sensitivity–loyalty relationship is heterogeneous and dynamic. We propose an approach wherein the price sensitivity parameter of a brand choice model is specified as a function of loyalty with three parameters. The first parameter of this function represents the maximum possible reduction in price sensitivity due to loyalty. The second parameter affects the type and shape of the relationship between price sensitivity and loyalty. In particular, depending on the value of this parameter, the relationship could be non-existent, follow a concave shape, indicating decreasing response to increases in loyalty, or be S-shaped to capture the case of increasing response initially followed by decreasing response subsequently. Finally, the third parameter captures the rate at which price sensitivity falls as loyalty increases. We use the proposed approach to investigate the relationship in four frequently purchased categories. In each category, we select a sample of households and calibrate the model on the choices of all the households in the sample. We next employ an Empirical Bayes approach to obtain household-level estimates of all the parameters. These parameters are then used to assign each household in each category to a no response or concave or S-shaped response groups. Within each of these three groups, we assign each household to one of four different response level and rate segments, that is, high response–high rate, high response–low rate, low response–high rate, and low response–low rate. Each of these segments differs in the response level, that is, the maximum reduction in price sensitivity as loyalty reaches a maximum—and the response rate, that is, how quickly price sensitivity falls with increases in loyalty. Following the assignment of each household to a segment in each category, we pool the households across all four categories and calibrate a membership function. This function explains households’ membership in different segments in terms of product category characteristics, household demographics, the household’s responses to price, display, and feature promotions and the evolution of loyalty of the household. Our findings suggest that the nature of the loyalty–price sensitivity relationship does vary across consumers as well as over time. Specifically, the concave response is more likely than the S-shaped response or the absence of a response. We find that the S-shaped response is not related to responsiveness to in-store promotions. It is, however, associated with a slower growth in loyalty to a brand as it is purchased. The concave response, on the other hand, is associated with responsiveness to feature promotions but is unrelated to how loyalty to brands evolves with their purchases. We also find that demographic characteristics are related to the behavior of the concave and S-shaped responses. Specifically, for the S-shaped response, household demographics are related to both the maximum level of the curve as well as its rate of growth. In particular, the curve grows faster with age and its maximum increases with the weekly working hours of the household. In the case of the concave response, high income and more working hours raise the maximum level that the curve achieves. Its rate of growth, however, is unaffected by demographics. We also provide several managerial implications for loyalty and promotional programs based on our findings. Specifically, our first finding—that the loyalty–price sensitivity relationship is dynamic—suggests that, rather than having promotional programs, where the value of the price promotion is fixed and some consumers are targeted with the promotion while others are not, managers should have an entire schedule of price promotions with each level of promotions targeting consumers at a different loyalty level. Our second finding that the nature of the loyalty–price sensitivity relationship is heterogeneous across consumers suggests that designing loyalty programs on the basis of crude classifications such as loyals and non-loyals is not appropriate. Instead, households that are identified as loyal, need to be further divided based on how the loyalty affects their price sensitivity. Promotional programs should then be based on the specific type of relationship that a household exhibits. The third finding that the reductions in price sensitivity to loyalty can exhibit an S-shaped or a concave pattern also has an interesting managerial implication. Specifically, given the differences between the two patterns in how long it might take a consumer to reach a point where s(he) is willing to purchase a brand due to loyalty rather than due to a price promotion, and hence be a profitable customer, it may be preferable for managers to invest more in consumers who exhibit a concave rather than an S-shaped response. Finally, our result that different categories may exhibit different patterns of the relationship between price sensitivity and loyalty implies that each category needs to be analyzed by itself for what the nature of the loyalty–price sensitivity relationship is likely to be so that the most appropriate program for that category can be developed.

Identifying Locations for Targeted Advertising on the Internet

Due to steady erosion in the effectiveness of on-line advertising (e.g., banners and buttons placed at frequently visited sites), on-line businesses need to target their ad campaigns more precisely to reach the segments they are interested in. This paper examines the issue of how to identify ideal paid advertising, banner exchange, or affiliate partner locations, and proposes a model based on consumer search behavior. Calibrated with data obtained from searches for information in 18 different categories, the model allows for heterogeneity by permitting consumers to belong to different segments that have idiosyncratic search points and thresholds. It also includes a segment-membership function, specified in terms of consumer demographics, that can be used to identify the demographics associated with different focal groups.Due to steady erosion in the effectiveness of on-line advertising (e.g., banners and buttons placed at frequently visited sites), on-line businesses need to target their ad campaigns more precisely to reach the segments they are interested in. This paper examines the issue of how to identify ideal paid advertising, banner exchange, or affiliate partner locations, and proposes a model based on consumer search behavior. Calibrated with data obtained from searches for information in 18 different categories, the model allows for heterogeneity by permitting consumers to belong to different segments that have idiosyncratic search points and thresholds. It also includes a segment-membership function, specified in terms of consumer demographics, that can be used to identify the demographics associated with different focal groups.

Choosing the Right Mix of On-line Affiliates: How Do You Select the Best?

Abstract In affiliate advertising, an on-line retailer places a link for its business at a host businesss site. The host earns a commission whenever a visitor clicks the link and consummates a transaction with the sponsor. We offer a framework that on-line retailers can use to find appropriate affiliates. Our approach is based on two assumptions. First, we assume that hosts that carry products related to those of the sponsor will be good affiliates. Second, we assume that the relationships between products are reflected in how consumers search for information. Our model uncovers relationships between products on the basis of these two assumptions and thereby identifies potential affiliates.

Leveraging the Strengths of Choice Models and Neural Networks: A Multiproduct Comparative Analysis*

Choice models and neural networks are two approaches used in modeling selection decisions. Defining model performance as the out-of-sample prediction power of a model, we test two hypotheses: (i) choice models and neural network models are equal in performance, and (ii) hybrid models consisting of a combination of choice and neural network models perform better than each stand-alone model. We perform statistical tests for two classes of linear and nonlinear hybrid models and compute the empirical integrated rank (EIR) indices to compare the overall performances of the models. We test the above hypotheses by using data for various brand and store choices for three consumer products. Extensive jackknifing and out-of-sample tests for four different model specifications are applied for increasing the external validity of the results. Our results show that using neural networks has a higher probability of resulting in a better performance. Our findings also indicate that hybrid models outperform stand-alone models, in that using hybrid models guarantee overall results equal or better than the two stand-alone models. The improvement is particularly significant in cases where neither of the two stand-alone models is very accurate in prediction, indicating that the proposed hybrid models may capture aspects of predictive accuracy that neither stand-alone model is capable of on their own. Our results are particularly important in brand management and customer relationship management, indicating that multiple technologies and mixture of technologies may yield more accurate and reliable outcomes than individual ones.

Shopping Style Segmentation of Consumers

The World Wide Web has rapidly become an alternative means to reach customers and has attracted the attention of many businesses. Unfortunately, however, despite its growth, there is little knowledge of which consumers would be willing to switch to the new format and to what extent. Our paper is aimed at providing some insights into these questions. Specifically, we propose a model to identify segments that differ in their shopping style, i.e., in their preference for which format, or bundle of formats, they like to shop in.Our research question, and model, is similar in spirit to prior research in marketing on how consumers choose assortments. Despite this similarity, our research makes some substantive and methodological contributions to the literature. Substantively, we examine the issue of the choice of channel assortments by consumers across a variety of product categories. We believe this is an important question and one that has not been examined earlier. From a methodological point of view, our model adds to earlier work by specifying the utility of an assortment as a sum of the deterministic and stochastic components of the utilities of its members. This contrasts with previous research where only the deterministic components of the utilities of the component brands of an assortment are added and the relationships between their random components are not accounted for.We calibrate the model on data regarding the format choices of households. In order to control for potentially similar format preferences across purchases of different categories we specify the model to allow for correlation between format preferences over the choice history of each household. Our results suggest that there are four segments of consumers that differ in their preference for different types of formats.

Brand Inequalities in the Loyalty-Effects of Purchases

Choice models in marketing have generally included the effects of loyalty on individual choice behavior.Loyalty has been typically measured as proportion of purchases or as an exponentially smoothed index of past purchases. An underlying assumption of both measures is that all brands gain the same increase in loyalty with a purchase. However, such an assumption may not hold when the competing brands are not comparable. We propose a new exponential smoothing measure which incorporates brand-specific parameters for the loyalty effects of purchases. A choice model, calibrated on individual level data for the detergent category, is used to compare the proposed measure with its traditional version. This comparison reveals that the new approach improves both the fit and predictive performance of the choice model. The results also suggest that the loyalty effects of purchases are likely to be lower for brands which are purchased on price promotions and higher for expensive brands.

A dynamic model of the advertising-price sensitivity relationship for heterogeneous consumers

Abstract With the increase in price competition in many markets, an understanding of the dynamic effects of advertising on price sensitivity has gained added importance. Unfortunately, however, empirical as well as theoretical work in this area has not been able to provide conclusive findings regarding these effects. Whereas some researchers report a reduction in price sensitivity with increased advertising, others conclude the opposite. One explanation for these contradictions is that the results in previous studies were biased by consumer heterogeneity, which was not controlled for in the analyses. In this study, we propose a choice model and estimation procedure to investigate the dynamic effects of advertising exposure on price sensitivity at the individual level while controlling for heterogeneity. The key feature of our model is that household heterogeneity is incorporated through the use of a random effects formulation for the price sensitivity parameter. Furthermore, the parameter is assumed to be a function of the cumulative advertising exposures of each household thereby accounting for heterogeneity in exposures as well. The assumptions that we make regarding the distribution of the random error in utilities result in a heteroskedastic covariance probit specification of the model. We calibrate this model on the A. C. Nielsen scanner database for the liquid detergent and yogurt product categories. Estimates of the parameters indicate that advertising reduces price sensitivity. However, brands differ in how much they gain from increased advertising. Specifically, brands with lower average price sensitivity are found to exhibit stronger reductions with advertising. Whereas our results support the market power hypothesis, additional research is necessary to generalize these findings. For instance, an investigation of markets with different tiers of brands may reveal some asymmetries in the effects of advertising. These and other avenues for future research are also discussed.