CITATIONS 22 READS 165 2 authors: Some of the authors of this publication are also working on the... more CITATIONS 22 READS 165 2 authors: Some of the authors of this publication are also working on these related projects: Customer-based corporate valuation View project ABSTRACT:
M arketers have long struggled with developing forecasts for new products before their launch. We... more M arketers have long struggled with developing forecasts for new products before their launch. We focus on one data source-advance purchase orders-that has been available to retailers for many years but has rarely been tied together with postlaunch sales data. We put forth a duration model that incorporates the basic concepts of new product diffusion, using a mixture of two distributions: one representing the behavior of innovators (i.e., those who place advance orders) and one representing the behavior of followers (i.e., those who wait for the mass market to emerge). The resulting mixed-Weibull model specification can accommodate a wide variety of possible sales patterns. This flexibility is what makes the model well-suited for an experiential product category (e.g., movies, music, etc.) in which we frequently observe very different sales diffusion patterns, ranging from a rapid exponential decline (which is most typical) to a gradual buildup characteristic of ''sleeper'' products. We incorporate product-specific covariates and use hierarchical Bayes methods to link the two customer segments together while accommodating heterogeneity across products. We find that this model fits a variety of sales patterns far better than do a pair of benchmark models. More importantly, we demonstrate the ability to forecast new album sales before the actual launch of the album, based only on the pattern of advance orders.
A criticism of purchase-based brand loyalty measures is that they are confounded by the marketing... more 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 brand's share among the group of households who bought the brand at least once during the time period under consideration.
T his paper develops a model of conversion behavior (i.e., converting store visits into purchases... more T his paper develops a model of conversion behavior (i.e., converting store visits into purchases) that predicts each customer's 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 individual's 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. The purchasing 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.
ABSTRACT Advance purchasing is common in several product markets (e.g., concerts, air travel, etc... more ABSTRACT Advance purchasing is common in several product markets (e.g., concerts, air travel, etc.) but has generally been understudied in the marketing literature. Research to date has focused primarily on the analytical modeling of optimal pricing policies. Our work complements this literature by focusing on the empirical modeling of advance purchasing and the effects of price on consumer purchasing behavior. Since pricing strategies in practice are typically more complex than simply setting a single price point, we consider multiple aspects of price: (1) the existence and use of multiple price tiers (generally based on seat quality), (2) the face value of each ticket, and (3) discounts that result in week-to-week variations in price. We show that failure to account for price tiers can lead to exaggerated inferences about the role of price over time. To sort out these effects, we develop a tier-specific Weibull timing model to describe sales arrivals for event tickets in the advance selling period, using a proportional hazards framework for the time-varying covariates. Additionally, we include a component that captures the role of similar covariates to explain spot market purchasing. Our empirical findings reflect substantial differences across tiers. Purchasers in the high-priced tier tend to buy earlier in the selling period and are influenced by price discounts/premiums in the spot market. Purchasers in the low- and mid-priced tiers tend to delay purchasing and are influenced only by face value prices in the spot market. Advance purchasers are not influenced by any aspect of price (after accounting for differences across price-tiers), and this holds true across all price tiers for our dataset of 22 family events. We discuss the implications of these empirical observations for future modelers.
A busy consumer is pushing her shopping cart down one of the aisles of her favorite supermarket. ... more A busy consumer is pushing her shopping cart down one of the aisles of her favorite supermarket. As she nears the coffee area she scans the brands, looking for special offers. She notices an acceptable brand on promotion, puts a can of that promoted coffee in her cart, ...
... School. WENDY W. MOE and PETER S. FADER* ... GRPs). Figure 1 presents the data for a fairly t... more ... School. WENDY W. MOE and PETER S. FADER* ... GRPs). Figure 1 presents the data for a fairly typical album in our data set (Soup, by Blind Melon), shows the weekly sales for the album, and illustrates the role of the covariates. ...
... Leigh McAlister is Associate Professor of Management Science and Peter Fader is a ... However... more ... Leigh McAlister is Associate Professor of Management Science and Peter Fader is a ... However, this result contradicts the economist's argu-ment that restricting a decision maker's (ie, a channel member's) choices can never make that decision maker better off (Thaler 1980). ...
Many online retailers monitor visitor traffic as a measure of their stores' success. However, sum... more Many online retailers monitor visitor traffic as a measure of their stores' success. However, summary measures such as the number of hits per month provide little insight into individual consumers' behavior. Additionally, behavior may evolve over time, especially in a changing environment like the Internet. Understanding the nature of this evolution provides valuable knowledge that can influence how a web store is managed and marketed.
554 JOURNAL OF CONFLICT RESOLUTION from the agreed price and production guidelines, reaching a cl... more 554 JOURNAL OF CONFLICT RESOLUTION from the agreed price and production guidelines, reaching a climax in 1986 when Saudi Arabia was no longer willing to be the sole cooperator. Early in 1986, the Saudis finally gave up their attempts to maintain ...
CITATIONS 22 READS 165 2 authors: Some of the authors of this publication are also working on the... more CITATIONS 22 READS 165 2 authors: Some of the authors of this publication are also working on these related projects: Customer-based corporate valuation View project ABSTRACT:
M arketers have long struggled with developing forecasts for new products before their launch. We... more M arketers have long struggled with developing forecasts for new products before their launch. We focus on one data source-advance purchase orders-that has been available to retailers for many years but has rarely been tied together with postlaunch sales data. We put forth a duration model that incorporates the basic concepts of new product diffusion, using a mixture of two distributions: one representing the behavior of innovators (i.e., those who place advance orders) and one representing the behavior of followers (i.e., those who wait for the mass market to emerge). The resulting mixed-Weibull model specification can accommodate a wide variety of possible sales patterns. This flexibility is what makes the model well-suited for an experiential product category (e.g., movies, music, etc.) in which we frequently observe very different sales diffusion patterns, ranging from a rapid exponential decline (which is most typical) to a gradual buildup characteristic of ''sleeper'' products. We incorporate product-specific covariates and use hierarchical Bayes methods to link the two customer segments together while accommodating heterogeneity across products. We find that this model fits a variety of sales patterns far better than do a pair of benchmark models. More importantly, we demonstrate the ability to forecast new album sales before the actual launch of the album, based only on the pattern of advance orders.
A criticism of purchase-based brand loyalty measures is that they are confounded by the marketing... more 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 brand's share among the group of households who bought the brand at least once during the time period under consideration.
T his paper develops a model of conversion behavior (i.e., converting store visits into purchases... more T his paper develops a model of conversion behavior (i.e., converting store visits into purchases) that predicts each customer's 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 individual's 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. The purchasing 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.
ABSTRACT Advance purchasing is common in several product markets (e.g., concerts, air travel, etc... more ABSTRACT Advance purchasing is common in several product markets (e.g., concerts, air travel, etc.) but has generally been understudied in the marketing literature. Research to date has focused primarily on the analytical modeling of optimal pricing policies. Our work complements this literature by focusing on the empirical modeling of advance purchasing and the effects of price on consumer purchasing behavior. Since pricing strategies in practice are typically more complex than simply setting a single price point, we consider multiple aspects of price: (1) the existence and use of multiple price tiers (generally based on seat quality), (2) the face value of each ticket, and (3) discounts that result in week-to-week variations in price. We show that failure to account for price tiers can lead to exaggerated inferences about the role of price over time. To sort out these effects, we develop a tier-specific Weibull timing model to describe sales arrivals for event tickets in the advance selling period, using a proportional hazards framework for the time-varying covariates. Additionally, we include a component that captures the role of similar covariates to explain spot market purchasing. Our empirical findings reflect substantial differences across tiers. Purchasers in the high-priced tier tend to buy earlier in the selling period and are influenced by price discounts/premiums in the spot market. Purchasers in the low- and mid-priced tiers tend to delay purchasing and are influenced only by face value prices in the spot market. Advance purchasers are not influenced by any aspect of price (after accounting for differences across price-tiers), and this holds true across all price tiers for our dataset of 22 family events. We discuss the implications of these empirical observations for future modelers.
A busy consumer is pushing her shopping cart down one of the aisles of her favorite supermarket. ... more A busy consumer is pushing her shopping cart down one of the aisles of her favorite supermarket. As she nears the coffee area she scans the brands, looking for special offers. She notices an acceptable brand on promotion, puts a can of that promoted coffee in her cart, ...
... School. WENDY W. MOE and PETER S. FADER* ... GRPs). Figure 1 presents the data for a fairly t... more ... School. WENDY W. MOE and PETER S. FADER* ... GRPs). Figure 1 presents the data for a fairly typical album in our data set (Soup, by Blind Melon), shows the weekly sales for the album, and illustrates the role of the covariates. ...
... Leigh McAlister is Associate Professor of Management Science and Peter Fader is a ... However... more ... Leigh McAlister is Associate Professor of Management Science and Peter Fader is a ... However, this result contradicts the economist's argu-ment that restricting a decision maker's (ie, a channel member's) choices can never make that decision maker better off (Thaler 1980). ...
Many online retailers monitor visitor traffic as a measure of their stores' success. However, sum... more Many online retailers monitor visitor traffic as a measure of their stores' success. However, summary measures such as the number of hits per month provide little insight into individual consumers' behavior. Additionally, behavior may evolve over time, especially in a changing environment like the Internet. Understanding the nature of this evolution provides valuable knowledge that can influence how a web store is managed and marketed.
554 JOURNAL OF CONFLICT RESOLUTION from the agreed price and production guidelines, reaching a cl... more 554 JOURNAL OF CONFLICT RESOLUTION from the agreed price and production guidelines, reaching a climax in 1986 when Saudi Arabia was no longer willing to be the sole cooperator. Early in 1986, the Saudis finally gave up their attempts to maintain ...
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