Knowledge that Transforms

Long-term marketing effectiveness is a high-priority research topic for managers, and emerges from the complex interplay among dynamic reactions of several market players. This paper introduces restricted policy simulations to distinguish four dynamic forces: consumer response, competitor response, company inertia, and company support. A rich marketing dataset allows the analysis of price, display, feature, advertising, and product-line extensions. The first finding is that consumer response differs significantly from the net effectiveness of product-line extensions, price, feature, and advertising. In particular, net sales effects are up to five times stronger and longer-lasting than consumer response. Second, this difference is not due to competitor response, but to company action. For tactical actions (price and feature), it takes the form of inertia, as promotions last for several weeks. For strategic actions (advertising and product-line extensions), support by other marketing instruments greatly enhances dynamic consumer response. This company action negates the postpromotion dip in consumer response, and enhances the long-term sales benefits of product-line extensions, feature, and advertising. Therefore, managers are urged to evaluate company decision rules for inertia and support when assessing long-term marketing effectiveness.

One of the main problems associated with early-period assessment of new product success is the lack of sufficient sales data to enable reliable predictions. We show that managers can use spatial dimension of sales data to obtain a predictive assessment of the success of a new product shortly after launch time. Based on diffusion theory, we expect that for many innovative products, word of mouth and imitation play a significant role in the success of an innovation. Because word-of-mouth spread is often associated with some level of geographical proximity between the parties involved, one can expect “clusters” of adopters to begin to form. Alternatively, if the market reaction is widespread reluctance to adopt the new product, then the word-of-mouth effect is expected to be significantly smaller, leading to a more uniform pattern of sales (assuming that there are no external reasons for clustering). Hence, the less uniform a product's distribution, the higher its likelihood of generating a “contagion process” and therefore of being a success. This is also true if the underlying baseline distribution is nonuniform, as long as it is an empirical distribution known to the firm. We use a spatial divergence approach based on cross-entropy divergence measures to determine the “distance” between two distribution functions. Using both simulated and real-life data, we find that this approach has been capable of predicting success in the beginning of the adoption process, correctly predicting 14 of 16 actual product introductions in two product categories. We also discuss the limitations of our approach, among them the possible confusion between natural formation of geodemographic clusters and word-of-mouth-based clusters.

Buyer search costs for price are changing in many markets. Through a model of buyer and seller behavior, I consider the effects of changing search costs on prices both when product differentiation is fixed and when it is endogenously determined in equilibrium. If firms cannot change product design, lower buyer search costs for price lead to increased price competition. However, if product design is a decision variable, lower search costs for price may also lead to higher product differentiation, which decreases price competition. In this case, the overall effect of lower buyer search costs for price may even be higher prices, lower social welfare, and higher industry profits. The result is especially interesting because recent technological changes, such as Internet shopping, can affect the market structure through lowering buyer search costs.

We use the results of three large-scale field experiments to investigate how the depth of a current price promotion affects future purchasing of first-time and established customers. While most previous studies have focused on packaged goods sold in grocery stores, we consider durable goods sold through a direct mail catalog. The findings reveal different effects for first-time and established customers. Deeper price discounts in the current period increased future purchases by first-time customers (a positive long-run effect) but reduced future purchases by established customers (a negative long-run effect). Overall, the results show evidence of several long-run effects: forward buying, selection, customer learning, and increased deal sensitivity. Short-run metrics that ignore these effects overstate the overall change in demand for established customers. The implication is that if prices are set based on short-run elasticity, then they will be too low. Among first-time customers, the short-run metrics underestimate the total increase in demand. If prices are set based on short-run elasticity, then they will be too high.

While there is a growing literature on investigating the Internet clickstream data collected for a single site, such datasets are inherently incomplete because they generally do not capture shopping behavior across multiple websites. A customer's visit patterns at one or more other sites may provide relevant information about the timing and frequency of his or her future visit patterns at the site of interest. We develop a stochastic timing model of cross-site visit behavior to understand how to leverage information from one site to help explain customer behavior at another. To this end, we incorporate two sources of association in browsing patterns: one for the observable outcomes (i.e., arrival times) of two timing processes and the other for the latent visit propensities across a set of competing sites. This proposed multivariate timing mixture model can be viewed as a generalization of the univariate exponential-gamma model. In our empirical analysis, we show that a failure to account for both sources of association not only leads to poor fit and forecasts, but also generates systematically biased parameter estimates. We highlight the model's ability to make accurate statements about the future behavior of the “zero class” (i.e., previous nonvisitors to a given site) using summary information (i.e., recency and frequency) from past visit patterns at a competing site.

Sales promotions generate substantial short-term sales increases. To determine whether the sales promotion bump is truly beneficial from a managerial perspective, we propose a system of store-level regression models that decomposes the sales promotion bump into three parts: cross-brand effects (secondary demand), cross-period effects (primary demand borrowed from other time periods), and category-expansion effects (remaining primary demand). Across four store-level scanner datasets, we find that each of these three parts contribute about one third on average. One extension we propose is the separation of the category-expansion effect into cross-store and market-expansion effects. Another one is to split the cross-item effect (total across all other items) into cannibalization and between-brand effects. We also allow for a flexible decomposition by allowing all effects to depend on the feature/display support condition and on the magnitude of the price discount. The latter dependence is achieved by local polynomial regression. We find that feature-supported price discounts are strongly associated with cross-period effects while display-only supported price discounts have especially strong category-expansion effects. While the role of the category-expansion effect tends to increase with higher price discounts, the roles of cross-brand and cross-period effects both tend to decrease.

This paper empirically investigates the determinants of retailers' pricing decisions. It finds that competitor factors explain the most variance in retailer pricing strategy. Only in the cases of price-promotion coordination and relative brand price do category and chain factors explain much variance in retailer pricing. These findings are derived from a simultaneous equation model of how underlying dimensions of retailers' pricing strategies are influenced by variables representing the market, chain, store, category, brand, customer, and competition. The optical scanner data base describes 1,364 brand-store combinations from six categories of consumer packaged goods in five U.S. markets over a two-year time period. Our study classifies retailers' pricing strategies based on four underlying dimensions: price consistency, price-promotion intensity, price-promotion coordination, and relative brand price. These four pricing dimensions are statistically related to: (1) competitor price and deal frequency (competitor factors), (2) storability and necessity (category factors), (3) chain positioning and size (chain factors), (4) store size and assortment (store factors), (5) brand preference and advertising (brand factors), and (6) own-price and deal elasticities (customer factors). These findings are useful to retailers profiling alternative pricing strategies, and to manufacturers customizing the levels of marketing support spending for different retailers.

We propose a utility-theoretic brand-choice model that accounts for four different sources of state dependence: 1. effects of lagged choices (structural state dependence), 2. effects of serially correlated error terms in the random utility function (habit persistence type 1), 3. effects of serial correlations between utility-maximizing alternatives on successive purchase occasions of a household (habit persistence type 2), and 4. effects of lagged marketing variables (carryover effects). Our proposed model also allows habit persistence to be a function of lagged marketing variables, while accommodating the effects of unobserved heterogeneity in household choice parameters. This model is more flexible than existing state-dependence models in marketing and labor econometrics. Using scanner panel data, we find structural state dependence to be the most important source of state dependence. Marketing-mix elasticities are systematically understated if state-dependence effects are incompletely accounted for. The Seetharaman and Chintagunta (1998) model is shown to recover spurious variety-seeking effects while overstating habit-persistence effects. Ignoring habit persistence type 1 leads to an underestimation, while ignoring habit persistence type 2 leads to an overestimation of structural state-dependence effects. We find lagged promotions to have carryover effects on habit persistence. Ignoring one or more sources of state dependence underestimates the total incremental impact of a sales promotion. We draw implications for manufacturer pricing.

The authors present a model of free sample effects and evidence from two field experiments on free samples. The model incorporates three potential effects of free samples on sales: (1) an acceleration effect, whereby consumers begin repeat purchasing of the sampled brand earlier than they otherwise would; (2) a cannibalization effect, which reduces the number of paid trial purchases of the brand; and (3) an expansion effect, which induces purchasing by consumers who would not consider buying the brand without a free sample. The empirical findings suggest that, unlike other consumer promotions such as coupons, free samples can produce measurable long-term effects on sales that can be observed as much as 12 months after the promotion. The data also show that the effectiveness of free sample promotions can vary widely, even between brands in the same product category. Application of the model to the data from the two experiments reveals that the magnitude of acceleration, cannibalization, and expansion effects varies substantially across the two free sample promotions. These and other findings suggest that the model can be a useful tool for obtaining insights into the nature of free sample promotions.

For several of the largest supermarket product categories, such as carbonated soft drinks, canned soups, ready-to-eat cereals, and cookies, consumers regularly purchase assortments of products. Within the category, consumers often purchase multiple products and multiple units of each alternative selected on a given trip. This multiple discreteness violates the single-unit purchase assumption of multinomial logit and probit models. The misspecification of such demand models in categories exhibiting multiple discreteness would produce incorrect measures of consumer response to marketing mix variables. In studying product strategy, these models would lead to misleading managerial conclusions. We use an alternative microeconomic model of demand for categories that exhibit the multiple discreteness problem. Recognizing the separation between the time of purchase and the time of consumption, we model consumers purchasing bundles of goods in anticipation of a stream of consumption occasions before the next trip. We apply the model to a panel of household purchases for carbonated soft drinks.