Peter S. Fader

Dynamic Conversion Behavior at E-Commerce Sites

This paper develops a model of conversion behavior (i.e., converting store visits into purchases) that predicts each customers probability of purchasing based on an observed history of visits and purchases. We offer an individual-level probability model that allows for different forms of customer heterogeneity in a very flexible manner. Specifically, we decompose an individuals conversion behavior into two components: one for accumulating visit effects and another for purchasing threshold effects. Each component is allowed to vary across households as well as over time.Visit effects capture the notion that store visits can play different roles in the purchasing process. For example, some visits are motivated by planned purchases, while others are associated with hedonic browsing (akin to window shopping); our model is able to accommodate these (and several other) types of visit-purchase relationships in a logical, parsimonious manner. Thepurchasing threshold captures the psychological resistance to online purchasing that may grow or shrink as a customer gains more experience with the purchasing process at a given website. We test different versions of the model that vary in the complexity of these two key components and also compare our general framework with popular alternatives such as logistic regression. We find that the proposed model offers excellent statistical properties, including its performance in a holdout validation sample, and also provides useful managerial diagnostics about the patterns underlying online buyer behavior.

RFM and CLV: Using Iso-Value Curves for Customer Base Analysis

The authors present a new model that links the well-known RFM (recency, frequency, and monetary value) paradigm with customer lifetime value (CLV). Although previous researchers have made a conceptual link, none has presented a formal model with a well-grounded behavioral ”story.” Key to this analysis is the notion of ”iso-value” curves, which enable the grouping of individual customers who have different purchasing histories but similar future valuations. Iso-value curves make it easy to visualize the interactions and trade-offs among the RFM measures and CLV. The stochastic model is based on the Pareto/NBD framework to capture the flow of transactions over time and a gamma-gamma submodel for spend per transaction. The authors conduct several holdout tests to demonstrate the validity of the models underlying components and then use it to estimate the total CLV for a cohort of new customers of the online music site CDNOW. Finally, the authors discuss broader issues and opportunities in the application of this model in actual practice.

Modeling Hedonic Portfolio Products: A Joint Segmentation Analysis of Music Compact Disc Sales

The authors present a framework that enables researchers to differentiate better among a wide array of hedonic products. Specifically, the authors define and discuss characteristics of hedonic portfolio products and offer a joint segmentation model that is appropriate for understanding the sales dynamics of this class of products. The model offered in this article can accommodate a large degree of product heterogeneity through product clusters and model covariates. The basic premise is that several generic consumer segments exist and remain fixed across all albums, and each album (or each cluster of similar albums) can be viewed as drawing different proportions from each of these underlying segments. The authors also allow explanatory variables to have a differential impact on both components of the model—that is, accelerating purchase rates within a consumer segment and changing the proportions drawn from each consumer segment by each product cluster—thereby expanding or contracting the potential market size. The authors apply this model to music compact disc sales for 20 different albums and discuss the different effects of radio airplay and holiday buying on sales for a sample in the music industry.

An Empirical Comparison of New Product Trial Forecasting Models

While numerous researchers have proposed diAerent models to forecast trial sales for new products, there is little systematic understanding about which of these models works best, and under what circumstances these findings change. In this paper, we provide a comprehensive investigation of eight leading published models and three diAerent parameter estimation methods. Across 19 diAerent datasets encompassing a variety of consumer packaged goods, we observe several systematic patterns that link diAerences in model specification and estimation to forecasting accuracy. Major findings include the following observations: (1) when dealing with consumer packaged goods, simple models that allow for relatively limited flexibility (e.g. no S-shaped curves) in the calibration period provide significantly better forecasts than more complex specifications; (2) models that explicitly accommodate heterogeneity in purchasing rates across consumers tend to oAer better forecasts than those that do not; and (3) maximum likelihood estimation appears to oAer more accurate and stable forecasts than nonlinear least squares. We elaborate on these and other findings, and oAer suggested directions for future research in this area. #1998 John Wiley & Sons, Ltd. Almost every textbook discussion of the new product development process includes a call to conduct some form of market test before actually launching the new product. Such an exercise serves several objectives, including the desire to produce an accurate forecast of the new product’s sales performance over time. These forecasts can help lead to a final go/no-go decision and can also assist in the marketing and production planning activities associated with the product launch. In the case of consumer packaged goods, conducting a market test historically saw the company’s sales force selling the product into retail distribution in one or more markets for one to two years, after which a decision of whether or not to go national with the new product was

Research Note---The Traveling Salesman Goes Shopping: The Systematic Deviations of Grocery Paths from TSP Optimality

We examine grocery shopping paths using the traveling salesman problem (TSP) as a normative frame of reference. We define the TSP-path for each shopper as the shortest path that connects all of his purchases. We then decompose the length of each observed path into three components: the length of the TSP-path, the additional distance because of order deviation (i.e., not following the TSP-order of category purchases), and the additional distance because of travel deviation (i.e., not following the shortest point-to-point route). We explore the relationship between these deviations and different aspects of in-store shopping/purchase behavior. Among other things, our results suggest that (1) a large proportion of trip length is because of travel deviation; (2) paths that deviate substantially from the TSP solution are associated with larger shopping baskets; (3) order deviation is strongly associated with purchase behavior, while travel deviation is not; and (4) shoppers with paths closer to the TSP solution tend to buy more from frequently purchased product categories.

Customer-Base Valuation in a Contractual Setting: The Perils of Ignoring Heterogeneity

The past few years have seen increasing interest in taking the notion of customer lifetime value (CLV) and extending it to value a customer base (with subsequent links to corporate valuation). The application of standard textbook discussions of CLV leads to calculations based on a single retention rate. However, at the cohort level, retention rates typically increase over time. It has been suggested that these observed dynamics are due, in large part, to a sorting effect in a heterogeneous population. We show that failing to recognize these dynamics yields a downward-biased estimate of the residual value of the customer base (compared to an aggregate analysis that ignores these dynamics). We also explore the implications of failing to account for retention dynamics when computing retention elasticities and find that the frequently reported values underestimate the true effect of increases in underlying retention rates in a heterogeneous world.

Uncovering Patterns in Cybershopping

Academics and practitioners alike have been arguing about whether the Internet brings a revolutionary change in the fundamental way we do business or if it simply offers a new distribution channel and communication medium. Regardless of the answer to that debate, one thing is certain: the Internet provides managers with an enormous amount of customer information that was previously unavailable. Thus, the new struggle has been to manage this information and to use it accurately and efficiently to measure customers, trends, and performance. However, the volume of this data has overwhelmed many e-commerce managers. As a result, e-commerce managers have been focusing on aggregate level summary statistics rather than fully leveraging their data. Using commonly available clickstream data, this article discusses the benefits of separating an individual customer9s buying behavior into simple patterns of visits and purchasing conversion. This analysis of the buying process allows us to more carefully examine the relationship between store visits and purchasing behavior.

A Bivariate Timing Model of Customer Acquisition and Retention

Two widely recognized components, central to the calculation of customer value, are acquisition and retention propensities. However, while extant research has incorporated such components into different types of models, limited work has investigated the kinds of associations that may exist between them. In this research, we focus on the relationship between a prospective customers time until acquisition of a particular service and the subsequent duration for which he retains it, and examine the implications of this relationship on the value of prospects and customers. To accomplish these tasks, we use a bivariate timing model to capture the relationship between acquisition and retention. Using a split-hazard model, we link the acquisition and retention processes in two distinct yet complementary ways. First, we use the Sarmonov family of bivariate distributions to allow for correlations in the observed acquisition and retention times within a customer; next, we allow for differences across customers using latent classes for the parameters that govern the two processes. We then demonstrate how the proposed methodology can be used to calculate the discounted expected value of a subscription based on the time of acquisition, and discuss possible applications of the modeling framework to problems such as customer targeting and resource allocation.

A Bayesian lifetime model for the Hot 100 Billboard songs

People have long been enamored by ranked lists of celebrities (e.g., “best-dressed” lists), places (e.g., best cities to live in), things (e.g., most popular songs, books, and movies), and countless other entities. Likewise, people are equally interested in watching these rankings evolve over time and speculating about possible future changes (e.g., who will be ##1 next week?) We focus on a popular, but fairly typical ranked list (the Billboard “Hot 100” songs) to explain and model the simultaneous movement of multiple items (songs) up and down the chart over time. Although our interest in Billboard data partly reflects the glamour of the music industry, these charts provide a very rich and general data structure. Surprisingly little research has been done on time-series models for ranked objects. We further enrich the dataset by adding covariates (e.g., artist history) to capture additional sources of variation across songs and over time. We posit a model for the time series of charts based on a latent lifetime (worth) process. Specifically, the latent popularity of each song is assumed to follow a generalized gamma (GG) lifetime curve with double exponentially (DE) distributed errors. The immense flexibility in the GG family allows the mean of a songs latent worth process to follow an exponential, Weibull, lognormal, or gamma curve (among others), reflecting the many possible paths that it might take through the chart. The DE error structure is used for convenience, as it leads to a well-established “exploding” multinomial-logit likelihood. This framework is embedded in a Bayesian structure in which parameters of the GG curve are song specific, with means related to observed covariates, and assumed to come from a multivariate lognormal prior. Inferences from the model are obtained from posterior samples using Markov chain Monte Carlo techniques.

The Relationship Between the Marketing Mix and Share of Category Requirements

A criticism of purchase-based brand loyalty measures is that they are confounded by the marketing mix variables that affect brand choice. This paper investigates the magnitude and direction of the associations for share of category requirements (SCR), defined as each brands share among the group of households who bought the brand at least once during the time period under consideration. We discuss the theoretical foundations for the relationships between SCR and a set of marketing mix variables (price, promotions, retail distribution) and conduct a latent structure regression analysis of brand-level data to test these relationships. We find that, although the relationship between the marketing mix variables and SCR is statistically significant, in real terms the magnitude of the association is fairly low.