Puneet Manchanda

Response Modeling with Nonrandom Marketing-Mix Variables

Sales response models are widely used as the basis for optimizing the marketing mix. Response models condition on the observed marketing-mix variables and focus on the specification of the distribution of observed sales given marketing-mix activities. The models usually fail to recognize that the levels of the marketing-mix variables are often chosen with at least partial knowledge of the response parameters in the conditional model. This means that contrary to standard assumptions, the marginal distribution of the marketing-mix variables is not independent of response parameters. The authors expand on the standard conditional model to include a model for the determination of the marketing-mix variables. They apply this modeling approach to the problem of gauging the effectiveness of sales calls (details) to induce greater prescribing of drugs by individual physicians. They do not assume a priori that details are set optimally, but instead they infer the extent to which sales force managers have knowledge of responsiveness, and they use this knowledge to set the level of sales force contact. The authors find that their modeling approach improves the precision of the physician-specific response parameters significantly. They also find that physicians are not detailed optimally; high-volume physicians are detailed to a greater extent than low-volume physicians without regard to responsiveness to detailing. It appears that unresponsive but high-volume physicians are detailed the most. Finally, the authors illustrate how their approach provides a general framework.

Asymmetric Social Interactions in Physician Prescription Behavior: The Role of Opinion Leaders

The authors quantify the impact of social interactions and peer effects in the context of physicians’ prescription choices. Using detailed individual-level prescription data, along with self-reported social network information, the authors document that physician prescription behavior is significantly influenced by the behavior of research-active specialists, or “opinion leaders,” in the physicians reference group. The authors leverage a natural experiment in the category: New guidelines released about the therapeutic nature of the focal drug generated conditions in which physicians were more likely to be influenced by the behavior of specialist physicians in their network. The authors (1) find important, statistically significant peer effects that are robust across model specifications; (2) document asymmetries in response to marketing activity across nominators and opinion leaders; (3) measure the incremental value to firms of directing targeted sales force activity to these opinion leaders; and (4) present estimates of the social multiplier of detailing in this category.

The Role of Targeted Communication and Contagion in Product Adoption

The two main influences leading to adoption at the individual consumer level are marketing communication and interpersonal communication. Although evidence of the effect of these two influences is abundant at the market level, there is a paucity of research documenting the simultaneous effect of both influences at the individual consumer level. Thus, the primary objective of this paper is to fill the gap in the literature by documenting the existence and magnitude of both influences at the customer level while controlling for unobserved temporal effects. The pharmaceutical industry provides an appropriate context to study this problem. It has been conjectured that adoption and usage patterns of a new drug by physicians—“contagion”—acts as a “consumption externality,” as it allows a given physician to learn about the efficacy and use of the drug. In addition, pharmaceutical companies target individual physicians via marketing activities such as detailing, sampling, and direct-to-consumer advertising. Our data contain the launch of a new drug from an important drug category. We chose two unrelated markets (Manhattan and Indianapolis) for our empirical analysis. We model an individual physicians decision to adopt a new drug in a given time period as a binary choice decision. This decision is modeled as a function of temporal trends (linear and quadratic) and individual physician-level contagion and marketing activity (both individual level and market level). Our contagion measure aggregates the adoption behavior of geographically near physicians for each physician in our sample. Our results from the Manhattan market indicate that both targeted communication and contagion have an effect on the individual physicians adoption decision. A major challenge is to rule out alternative explanations for the detected contagion effect. We therefore carry out a series of tests and show that this effect persists even after we control for the effects of time, individual salespeople, other marketing instruments, local market effects, and the effects of some institutional factors. We believe that our contagion effect arises because the consumption externality is stronger for geographically close physicians. We discuss some underlying processes that are probably giving rise to the contagion effect we detected. Finally, we compute the social multiplier of marketing and find it to be about 11%. We also use the estimated parameters to compare the relative effect of contagion and targeted marketing. We find that marketing plays a large (relative) role in affecting early adoption. However, the role of contagion dominates from month 4 onward and, by month 17 (or about half the duration of our data), asymptotes to about 90% of the effect.

An Empirical Model of Advertising Dynamics

This paper develops a model of dynamic advertising competition, and applies it to the problem of optimal advertising scheduling through time. In many industries we observe advertising “pulsing”, whereby firms systematically switch advertising on and off at a high-frequency. Hence, we observe periods of zero and non-zero advertising, as opposed to a steady level of positive advertising. Previous research has rationalized pulsing through two features of the sale response function: an S-shaped response to advertising, and long-run effects of current advertising on demand. Despite considerable evidence for advertising carry-over, existing evidence for non-convexities in the shape of the sales-response to advertising has been limited and, often, mixed. We show how both features can be included in a discrete choice based demand system and estimated using a simple partial maximum likelihood estimator. The demand estimates are then taken to the supply side, where we simulate the outcome of a dynamic game using the Markov perfect equilibrium (MPE) concept. Our objective is not to test for the specific game generating observed advertising levels. Rather, we wish to verify whether the use of pulsing (on and off) can be justified as an equilibrium advertising practice. We solve for the equilibrium using numerical dynamic programming methods. The flexibility provided by the numerical solution method allows us to improve on the existing literature, which typically considers only two competitors, and places strong restrictions on the demand models for which the supply side policies can be obtained. We estimate the demand model using data from the Frozen Entree product category. We find evidence for a threshold effect, which is qualitatively similar to the aforementioned S-shaped advertising response. We also show that the threshold is robust to functional form assumptions for the marginal impact of advertising on demand. Our estimates, which are obtained without imposing any supply side restrictions, imply that firms should indeed pulse in equilibrium. Predicted advertising in the MPE is higher, on average, than observed advertising. On average, the optimal advertising policies yield a moderate profit improvement over the profits under observed advertising. Copyright Springer Science + Business Media, Inc. 2005

Responsiveness of Physician Prescription Behavior to Salesforce Effort: An Individual Level Analysis

Firms in many industries, e.g., pharmaceuticals, spend a significant amount of marketing dollars on salesforce effort. However, there exists very little research examining customer response to salesforce effort at the disaggregate level.We use data from a pharmaceutical category to examine the response in prescriptions written to salesforce effort (detailing). We use a hierarchical Bayesian count data model that allows us to estimate individual physician-level response parameters.We find that while detailing has positive impact on prescriptions written, there are diminishing returns to detailing for most physicians in our sample. We use our results to show how detailing reallocation can increase revenues.

The Effect of Signal Quality and Contiguous Word of Mouth on Customer Acquisition for a Video-on-Demand Service

This paper documents the existence and magnitude of contiguous word-of-mouth effects of signal quality of a video-on-demand (VOD) service on customer acquisition. We operationalize contiguous word-of-mouth effect based on geographic proximity and use behavioral data to quantify the effect. The signal quality for this VOD service is exogenously determined, objectively measured, and spatially uncorrelated. Furthermore, it is unobserved to the potential subscriber and is revealed postadoption. For a subscriber, the signal quality translates directly into the number of movies available for viewing, thus representing a part of the overall service quality. The combination of signal quality along with location and neighborhood information for each subscriber and potential subscriber allows us to resolve the typical challenges in measuring causal social network effects. We find that contiguous word of mouth affects about 8% of the subscribers with respect to their adoption behavior. However, this effect acts as a double-edged sword because it is asymmetric. We find that the effect of negative word of mouth arising from poor signal quality is more than twice as large as the effect of positive word of mouth arising from excellent signal quality. Besides contiguous word of mouth, we find that advertising and the retail environment also play a role in adoption.

Response Modeling with Non-random Marketing Mix Variables

Sales response models are widely used as the basis for optimizing the marketing mix or for allocation of the sales force. Response models condition on the observed marketing mix variables and focus on the specification of the distribution of observed sales given marketing mix activities. These models fail to recognize that the levels of the marketing mix variables are often chosen with at least partial knowledge of the response parameters in the conditional model. This means that, contrary to standard assumptions, the marginal distribution of the marketing mix variables is not independent of response parameters. We expand on the standard conditional model to include a model for the determination of the marketing mix variables. We apply this modeling approach to the problem of gauging the effectiveness of sales calls (details) to induce greater prescribing of drugs by individual physicians. We do not assume, a priori, that details are set optimally but, instead, infer the extent to which sales force managers have knowledge of responsiveness and use this knowledge to set the level of sales force contact. We find that physicians are not detailed optimally; high volume physicians are detailed to a greater extent than low volume physicians without regard to responsiveness to detailing. In fact, it appears that unresponsive but high volume physicians are detailed the most.

Quantifying the Benefits of Individual-Level Targeting in the Presence of Firm Strategic Behavior

The authors develop a method to quantify the benefits of individual-level targeting when the data reflect firm strategic behavior—that is, when firms (1) are engaged in targeting and (2) take into account the actions of competing firms. This article studies a pharmaceutical firms decision on the allocation of detailing visits across individual physicians. For this analysis, the authors develop, at the individual level, a model of prescriptions and a model of detailing. Using physician panel data, they estimate, at the physician level, the parameters of the prescription and detailing models jointly using full-information Bayesian methods. The results suggest that accounting for firm strategic behavior improves profitability by 14%–23% compared with segment-level targeting. In addition, ignoring firm strategic behavior underestimates the benefit of individual-level targeting significantly. The authors provide reasons for this finding. They also carry out several robustness checks to test the validity of the modeling assumptions.

Perspectives on Multiple Category Choice

Multiple category choice is a decision process in which an individualselects a number of goods, all of which are nonsubstitutable with respect toconsumption. Choices can be made either simultaneously or sequentially. Thekey feature of multiple category choice is the treatment of the choices asinterrelated because each item in the final collection of goods contributesto the achievement of a common behavioral goal. We discuss current andpotential applications of psychology, economics and consumer choice theoryin developing models of multiple category choice.

An Empirical Analysis of Individual Level Casino Gambling Behavior

Gambling and gaming is a very large industry in the United States with about one-third of all adults participating in it on a regular basis. Using novel and unique behavioral data from a panel of casino gamblers, this paper investigates three aspects of consumer behavior in this domain. The first is that consumers are addicted to gambling, the second that they act on “irrational” beliefs, and the third that they are influenced by marketing activity that attempts to influence their gambling behavior. We use the interrelated consumer decisions to play (gamble) and the amount bet in a casino setting to focus on addiction using the standard economic definition of addiction. We test for two irrational behaviors, the “gambler’s fallacy” and the “hot hand myth”—our research represents the first test for these behaviors using disaggregate data in a real (as opposed to a laboratory) setting. Finally, we look at the effect of marketing instruments on the both the decision to play and the amount bet. Using hierarchical Bayesian methods to pin down individual-level parameters, we find that about 8% of the consumers in our sample can be classified as addicted. We find support in our data for the gambler’s fallacy, but not for the hot hand myth. We find that marketing instruments positively affect gambling behavior, and that consumers who are more addicted are also affected by marketing to a greater extent. Specifically, the long-run marketing response is about twice as high for the more addicted consumers.