Knowledge that Transforms

We propose a model that seeks the optimal timing and depth of retail discounts with the optimal timing and quantity of the retailer's order over multiple brands and time periods. The model is based on an integration of consumer decisions in purchase incidence, brand choice and quantity with the dynamics of household and retail inventory. The major contribution of the model is that it shows how the optimum depth and timing of discount varies with key demand characteristics such as consumer stockpiling, loyalty, response to the marketing mix, and segmentation. In addition, the optima also vary with key supply characteristics such as retail margins, depth and frequency of manufacturer deals, retail inventory, and retagging costs. The most valuable contribution of the model is that it can provide an optimal discount strategy for multiple brands over multiple time periods. The optimization model runs on a user-friendly personal computer program. An application based on UPC scanner data illustrates the model's uses. Sensitivity analyses of the optimization model under alternative scenarios reveal novel insights as to how optimal discounts vary as a function of the key demand and supply characteristics.

We consider two broad categories of incentives by which a manufacturer can motivate its retailers to provide high customer satisfaction: (1) manufacturer assistance that reduces the retailer's cost of providing customer satisfaction (CS assistance); and (2) customer satisfaction index (CSI) bonus. We show that if a retailer has a long-term orientation, CS assistance is a more effective coordination mechanism that induces the retailer to expend more effort at customer satisfaction. However, if the retailer has a short-term orientation, CSI bonus is a more effective coordination mechanism. We then show that a long-term oriented retailer is more valuable to a manufacturer than a short-term oriented one. Finally, we show that the use of CS incentives results in greater profits for both the manufacturer and the retailer.

In this paper we review evidence of a generalized convex cross-sectional relationship between retail distribution and unit market share, i.e., large-share brands have more share points per percentage of distribution than small-share brands. The dynamics and structure of distribution and share can help explain many phenomena in marketing, including this convex shape: (1) market share is both a cause and an effect of distribution, and (2) in the typical convenience goods distribution system there are a few large outlets that stock many brands and numerous smaller outlets that stock the leading brands only. Generally, the observed cross-sectional “curve” relating distribution and share will reflect the retailers' stocking decisions, not the incremental effect of distribution on share. However, a logically consistent model of share based on (1) and (2), when combined with the assumption of low search loyalty, results in customers being willing to switch from preferred to available brands. A further consequence is that the marginal effect of weighted distribution on share is likely to be increasing, i.e., result in convex curves relating distribution and share for a given brand. In some cases, and for some measures of distribution, these convex curves have been observed in time-series data for brands that failed and lost distribution over a relatively short period of time. The implication is that marketers should monitor distribution carefully, as it is the result of combined effects of brand preference, loyalty, and “push” programs. With a better understanding of the market share/distribution relationship, managers should be in a better position to forecast marketplace results for a given level of distribution.

Are marketing efforts able to affect long-term trends in sales or other performance measures? Answering this question is essential for the creation of marketing strategies that deliver a sustainable competitive advantage. This paper introduces persistence modeling to derive long-term marketing effectiveness from time-series observations on sales and marketing expenditures. First, we use unit-root tests to determine whether sales are stable or evolving (trending) over time. If they are evolving, we examine how strong this evolution is (univariate persistence) and to what extent it can be related to marketing activity (multivariate persistence). An empirical example on sales and media spending for a chain of home-improvement stores reveals that some, but not all, advertising has strong trend-setting effects on sales. We argue that traditional modeling approaches would not pick up these effects and, therefore, seriously underestimate the long-term effectiveness of advertising. The paper concludes with an agenda for future empirical research on long-run marketing effectiveness.