A Decision Support System for Planning Manufacturers’ Sales Promotion Calendars

A common event in the consumer packaged goods industry is the negotiation between a manufacturer and a retailer of the sales promotion calendar. Determining the promotion calendar involves a large number of decisions regarding levels of temporary price reductions, feature ads, and in-store displays, each executed at the level of individual retail accounts and brand SKUs over several months or a year. Though manufacturers spend much of their marketing budget on trade promotions, they lack decision support systems to address the complexity and dynamics of promotion planning. Previous research has produced insights into how to evaluate the effectiveness of promotional events, but has not addressed the planning problem in a dynamic environment. This paper develops a disaggregate-level econometric model to capture the dynamics and heterogeneity of consumer response. By modeling the purchase incidence (timing), choice and quantity decisions of consumers we decompose total sales into incremental and nonincremental (baseline plus borrowed). The response model forms the basis of a market simulator that permits us to search for the manufacturer’s optimal promotion calendar (subject to a set of constraints, some of them imposed by the retailer) via the simulated annealing algorithm. Calendar profits are the net result of the contribution from incremental sales minus the opportunity cost from giving away discounts to nonincremental sales and the fixed costs associated with implementing promotional events (e.g., retagging, features, displays). Incremental sales result from promotion-induced switching, the acceleration and quantity promotion effects on those switchers, increased consumption and the carryover effect from purchase event feedback. We applied our approach to the promotion-planning problem of a large consumer-packaged goods company in a nonperishable, staple product category suggested by company executives (canned tomato sauce). Subject to a retailer pass-through constant rate of 80%, provided to us by the collaborating firm, the optimal promotion calendar produced by the modeling system followed a pattern of frequent and shallow temporary price reductions with no feature or display activity. We also analyze how that result would change under different retailer pass-through scenarios. Our findings indicated that the manufacturer could substantially improve the profitability of its sales promotion activity and that there would be a concurrent positive effect on retailer profit and volume levels. Management reported to us that the insights from the use of the system were implemented in their promotion-planning process and produced positive results. A validation analysis on follow-up data for one market showed that promotion activity could be significantly reduced, as recommended, with no adverse effect on the brand’s market share, as predicted. To generalize the model beyond the specific category where it was implemented, we conducted a sensitivity analysis on the profile of the calendar (i.e., frequency, depth, and duration of deals) with respect to changes in market response, competitive activity, and retailer pass-through. First, we found that the optimal depth, frequency, and timing of discounts is stable for price elasticities ranging from near zero to around four (in absolute magnitude). We also found no systematic impact of competitive promotions on the profile of the optimal calendar. For example, variation in competitive activity did not affect the optimal depth or frequency of discounts. Lastly, we found changes in retailer passthrough to have a significant effect on the optimal depth and number of weeks of trade promotion that a manufacturer should offer. This emphasizes the importance to manufacturers of having accurate estimates of pass-through for purposes of promotion budgeting and planning. (Trade Promotion; Brand Management; Decision Support Systems; Scanner Data) A DECISION SUPPORT SYSTEM FOR PLANNING MANUFACTURERS’ SALES PROMOTION CALENDARS Marketing Science/Vol. 18, No. 3, 1999 275 Introduction Trade promotion continues to be the largest single spending category in the marketing mix budget of U.S. packaged goods companies (e.g., Kotler 1997). Improving the productivity of trade promotion dollars therefore remains a high priority in the consumer products industry. A significant opportunity to enhance the efficiency and effectiveness of marketing spending lies in the implementation of trade promotion policies. This process involves a very large number of tactical decisions regarding desired levels of temporary price reductions, feature ads, and in-store displays, each executed at the level of individual retail accounts and brand SKUs. When viewed over several months or a year, these decisions collectively make up what is known as the sales promotion calendar. The promotion calendar reflects not only numerous decisions but inherently complex ones. Each should take into account a variety of factors, including marketingmix effects, the dynamics of consumer response, competition, and retailer behavior. In this environment, a decision support system (e.g., Little 1979) offers the potential to improve decisionmaking (cf. Hoch and Schkade 1996) and, of course, to save an enormous amount of time. By programming the system to produce “win-win” promotion calendars (i.e., where both manufacturer and retailer come out ahead), a manufacturer’s gains need not come at the expense of the retailer. By presenting “win-win” calendars, backedup by forecasts of category profitability, sales representatives should be able to streamline trade promotion discussions with retailers and devote more time to brand-building activities. Our paper shows how scanner modeling technology and optimization methods can make it possible to begin to automate the process of planning the promotion calendar. First, we develop and demonstrate the benefits of an implementable decision support system for the tactical decisions that comprise the sales promotion calendar. Second, we provide insight about the profile of the promotion calendar and what is robust with respect to variations in the marketing environment and rates of retail pass-through. Finally, we show how to apply simulated annealing (Kirkpatrick et al. 1983) to solve the complex optimization problem (large number of control variables, nonlinear function, and multiple local optima) involved in determining the promotion calendar over a one year time horizon. During the development of the decision support system, weworked closely with seniormanagement at the firm with whom we collaborated. The extensive interaction allowed us to develop a system that was compatible with the trade promotion practices of the firm, thereby enhancing management understanding and acceptance of the system. Based on the insights provided by the model, the collaborating firm made significant changes to its trade promotion policy in the product category we analyzed and obtained positive results. (In a subsequent section, we present follow-up data that validates our model.) A stated long-term goal of management was to have a portable version of the system installed in the laptops of the sales representatives to support the sales force in its negotiations with retailers. Sales Promotion Planning Our model is based on a dynamic view of the promotion decision-making process. Manufacturers’ current practice often pays little consideration to the future impact of promotional offerings. For example, at the packaged goods company with which we collaborated, planners based their promotion budget allocations on response elasticities that were assumed to be fixed over time and did not take into account the effects of previous marketing activity. Our approach allows manufacturers to consider the dynamic effects of sales promotion on consumer response (e.g., household inventory, increased category usage or consumption, and purchase event feedback) and to adopt a longer-run view of the promotion calendar. This paper focuses on what promotions the manufacturer would like to see in front of the consumer over a prespecified time horizon. What the consumer sees, however, is the result of the way the retailer implements the manufacturer’s promotional offerings. Thus, a decision-support system for this problem must include the role of the retailer. Our approach assumes that the manufacturer has good knowledge of the nature of retailer response. Managers at our collaborating firm had historical data on how each retailer had responded to different promotional offerings, from SILVA-RISSO, BUCKLIN, AND MORRISON Planning Manufacturers’ Sales Promotion Calendars 276 Marketing Science/Vol. 18, No. 3, 1999 which the retailer’s response function could be approximated. Our model is designed to work by then searching for the promotional offering to the trade that would result in the desired calendar in front of the consumer at the point of purchase. (We also show how variable rates of retail pass-through can be incorporated into the decision support system.) Our system is intended to be used as a planning and negotiating tool by a manufacturer’s sales representatives in the field. To avoid inefficiencies in the supply chain, such as inventory build-ups by retailers, manufacturers need to design promotional offerings that reward the retailer for execution of the program rather than just forward buying. Our model is based on a “pay-for-performance” environment where the retailer has no incentive to forward buy. In this arrangement, the retailer is paid a promotional allowance based on the volume sold during the promotion period. This is how the collaborating firm operated in the product category we analyzed. This type of agreement is already a significant part of trade dealing, in part because it places the focus on consumer demand as the driving force for promotional decisions and thereby attempts to minimize inventory held in the channel. One key premise of our approach is that the manufacturer wants the right calendar in front of the consumer. We therefore optimize manufacturer profit over sales to the consumer, taking into account the pass-through response function of the retailer. Our focus is to determine how to structure the offer to the retailer so as to obtain the desired effects at the consumer level. A second key premise is that the market response model should be based on disaggregate data. This enables the “truly incremental” sales due to promotion to be separated from not only baseline sales, but also from borrowed sales (i.e., purchases that would have been made in the future but were accelerated due to promotion). Such a capability provides a critical distinction from promotion evaluation models that are based on aggregate-level data. By using a disaggregate model of demand, we can also naturally incorporate consumer heterogeneity into the planning of the promotion calendar. We note that extensive previous research (e.g., Stiglitz 1977, Varian 1980, Narasimhan 1984, Jeuland andNarasimhan 1985, Narasimhan 1988, Raju et al. 1990) has shown that sales promotion can work as a price discrimination device, i.e., a marketing tool that takes advantage of consumer heterogeneity. Literature A number of recent articles are closely related to our work. Abraham and Lodish (1993) described a method to measure the effectiveness of promotional events using store-level data. Because the approach uses storelevel data, it does not decompose the promotional lift (i.e., the volume of sales above baseline) into sales that are truly incremental versus those that are borrowed. (Our approach uses panel data to estimate the purchase acceleration and/or stockpiling induced by a promotion.) Their approach also does not incorporate dynamics in consumer response or the effects of previous marketing activity on future promotional events. Midgley et al. (1997) use genetic algorithms to analyze marketing strategies under oligopolistic competition. In their approach, demand is represented by an aggregate or market-level model. Again, this does not permit a decomposition of the promotional “bump” into incremental and borrowed sales. Neslin et al. (1995) develop an aggregate model with three players: the manufacturer, the retailer, and a set of consumers. The manufacturer’s profit is maximized over sales to the retailer, not over sales to the consumer. A potential drawback of this approach is that sales could end up in inventory build-ups in the channel, potentially overstating the true profitability of a promotion. In contrast to the work just described, Tellis and Zufryden (1995) develop a retail promotion planning model that is based on a disaggregate consumer response model. Their work differs from our approach on several dimensions. First, they address the retailer’s problem, taking the manufacturer’s behavior as given, i.e., the objective function is retailer category profits given full knowledge of manufacturers’ trade promotions. In contrast, the goal of our system is to determine manufacturers’ promotion programs, taking into account the managerially calibrated response of the retailer. Second, their demand model does not segment consumers in terms of their responsiveness to marketing activity. This omits a key driver of price promotions, i.e., the ability to identify consumer segments with different demand functions (cf. Stiglitz 1977). SILVA-RISSO, BUCKLIN, AND MORRISON Planning Manufacturers’ Sales Promotion Calendars Marketing Science/Vol. 18, No. 3, 1999 277 Figure 2 Truly Incremental and Borrowed Sales Figure 1 Overview of Decision Support System Lastly, their optimization and sensitivity analyses are based on mean household values. While this greatly simplifies the optimization problem, it implies that the procedure does not take into account any preference or response heterogeneity among panelists. Overview of Decision Support System Our system (see Figure 1) combines a disaggregate market response model with an optimization procedure that searches for the promotion calendar providing the greatest increment to the manufacturer’s profit. To estimate a promotion’s incremental impact on profit, the consumer response model computes expected sales and the proportion that is truly incremental from the promotion. Truly incremental sales are (1) units sold to consumers who bought the brand as a consequence of its promotional status and who would not have bought it otherwise (now or in the future), (2) any promotionally-induced consumption increase, and (3) any positive carryover effect from purchase event feedback. The sales promotion “bump” (Figure 2) that is routinely observed in sales data also contains sales to consumers who accelerated their purchases or bought more units than usual (stockpiled), but would have bought the brand at the regular price (now or in the future) had the promotion not been run. These units (less any that may be attributed to a consumption increase; see Ailawadi and Neslin 1998) are sales borrowed from the future, and are not truly incremental for the manufacturer. To decompose the promotional “bump,” we require a modeling approach that captures the source of consumer response: switching, acceleration and/or stockpiling. Previous research (e.g., Neslin et al. 1985, Krishnamurthi and Raj 1988, Currim and Schneider 1991, Bucklin and Gupta 1992, and Bell et al. 1999), has shown that acceleration and stockpiling play significant roles in consumer response to promotional activity. Our responsemodel captures consumers’ purchase incidence, choice and quantity decisions and handles consumer heterogeneity by using latent-class analysis. The system then uses parameter estimates from the response model, together with household specific variables (e.g., brand loyalty, consumption, and purchase rates) and environment variables (e.g., competitive activity, retailer pass-through, and mark-up) to simulate the purchase decisions made by a large sample of panelists. We link the market response model that simulates household decisions to an optimization module that uses the simulated annealing algorithm (Kirkpatrick et al. 1983) to search for the set of decisions over the planning horizon that maximizes manufacturer profit. Those decisions include when, for how long, and, in the case of temporary price reductions, how deep to run promotional events. The optimization procedure is constrained to the set of schedules that would produce acceptable levels of expected category profit for the retailer. Comparative statics analyses can be performed by simulating different market characteristics (e.g., by varying response parameter values, segment sizes, competitive activity, retailer pass-through and mark-up) and examining the changes, if any, in the optimal promotion calendar. This feature of the model SILVA-RISSO, BUCKLIN, AND MORRISON Planning Manufacturers’ Sales Promotion Calendars 278 Marketing Science/Vol. 18, No. 3, 1999 also may be used bymanufacturers to search for robust strategies. Market Response Model and Incremental Sales Estimation Using purchase histories, we compute for each household the probability of visiting each store in themarket area. Conditional on a store visit, the consumer then decides whether to buy in the target category. Given a decision to purchase in the category, the consumer then chooses a brand-size alternative. (If the promotion planning is to be performed at the UPC level, the model could be modified with the procedure developed by Fader and Hardie 1996.) Finally, given a category purchase and a brand-size choice, the consumer decides how many units of the brand-size alternative to purchase (cf. Ailawadi and Neslin 1998, Bucklin et al. 1998). The household-level demand model, conditional on a store visit, is given by h h h E(Q ) E(Q |Q 0) it it it h h P (i|inc) P (inc), where (1) t t 0) the expected number of units that h h E(Q |Q it it household hwill buy of brand-size alternative i at time t given that household h has decided to buy brand-size alternative i at time t (i.e., given that h Q 0), it the probability that household h chooses h P (i|inc) t brand-size alternative i at time t, given that it has decided to purchase in the product category (i.e., given purchase incidence), and the probability that household h decides h P (inc) t to make a category purchase at time t, given a store visit. The Appendix gives details of the response model specification. We now describe how the response model can be used to estimate incremental vs. nonincremental sales from promotions, thereby producing the inputs needed for the calendar optimization. Using the Response Model to Measure a Manufacturer’s Incremental Sales A promotion calendar is composed of a sequence of events that result in “bumps” in sales volume (see Figure 2). The challenge is to estimate how much of the observed “bump” is truly incremental for the manufacturer. A traditional approach (e.g., Abraham and Lodish 1987, 1993) is to estimate the volume of sales that would have been achieved had the promotional event not been run (baseline sales) and define as incremental all the volume above the baseline. As we illustrate below, this approach has several limitations (cf. Abraham and Lodish 1993, p. 250, para. 2). The Loyal Consumer. Consider the case of a hardcore loyal (Colombo and Morrison 1989) consumer of brand A. Let us refer to that consumer as household 1. Household 1 only buys brand A and will not consider buying any competitive brands, even if they are on promotion. Assume that brand A is on promotion in period t. Household 1 may take advantage of that promotion by accelerating the timing of its purchase (incidence effect) and/or buy more units than usual (quantity effect). Thus, brand A’s promotion would result in household 1 buying more units in period t than it would had the promotion not been run. Following Abraham and Lodish’s approach, those additional units bought by the hard-core loyal consumer of brand A would be computed as incremental volume for the manufacturer. Consider now that household 1 has a constant consumption rate, regardless of its inventory level. For example, household 1 always consumes one unit of brand A per week, regardless of howmany units it has in its pantry. In that case, the additional units bought by household 1 in period twill cannibalize future sales of brand A and will not be truly incremental for the manufacturer in the long run. Therefore, those units should be considered “borrowed” and not incremental. Assume that household 2 is also a hard-core loyal of brand A. It follows a purchase behavior similar to household 1, with the exception that holding additional inventory motivates household 2 to increase its consumption rate. To simplify the example, assume that all of the incremental household inventory becomes incremental consumption for household 2. In that case, the extra units bought when brand Awas on promotion will be incremental for the manufacturer. Between household 1 and household 2, we can think of a continuum of hard-core loyal households for SILVA-RISSO, BUCKLIN, AND MORRISON Planning Manufacturers’ Sales Promotion Calendars Marketing Science/Vol. 18, No. 3, 1999 279 whom the extra inventory will result in some increase in consumption but will also result in some cannibalization of future sales. The computation of incremental sales should capture these effects. The Brand Switching Consumer. Let us now consider the case of a brand switching consumer. In the absence of any promotional activity, household 3 (a hypothetical brand switcher) has a 50-50 chance of buying brand A or a competitor’s brand. When brand A is on promotion the choice probabilities shift to 1 for the promoted brand A and 0 for the unpromoted competitor’s brand (and vice-versa when the competitor’s brand is on promotion). Assume that when no brand is on promotion, household 3 would make a category purchase of 4 units when its inventory reaches 0.When a brand is on promotion, household 3 would buy enough units of the promoted brand to reach a household inventory level of 8 units (storage constraint). Consider the scenario where household 3’s category inventory reaches 0 at time t and no brand is on promotion. In this instance, the household would make a category purchase of 4 units. With 0.50 choice probabilities for both brands, the expected quantities are 2 units for brand A and 2 units for the competing brand. These units would be baseline sales (i.e., they occur in the absence of promotional activity). Now consider the scenario where brand A is on promotion and household 3’s inventory reaches 0. Here, household 3 would buy 8 units of brand A and 0 units of the competing brand. The Abraham and Lodish (1987, 1993) approach would compute incremental units for brand A by subtracting the baseline sales (2 units) from the total sales on promotion (8 units), giving incremental sales of 6 units. Truly incremental sales, however, are likely to be less than 6 units. To see this, begin by noting that those 6 units can be decomposed into 2 units due to switching (0 units for the competitor’s brand vs. 2 units in the baseline case) and 4 units due to stockpiling (a category purchase of 8 units versus 4 units in the baseline case). Of the 4 units stockpiled, 2 are incremental for brandA, because they can be attributed to the promotion’s effect on brand choice. (With a 0.50 baseline choice probability the inventory is expected to be 50% brand A and 50% the competitor’s brand.) Thus, 4 out of the 6 units making up the sales bump for brand A are clearly incremental. Of the remaining 2 units, some, all, or none will be incremental, depending on how many become extra household consumption and how many cannibalize future sales. Our approach initially classifies these two units as borrowed sales. All, none, or part of the borrowed units will ultimately be incremental for the manufacturer, depending upon the extent to which the additional household inventory prompts an increase in consumption. We summarize below the preceding sales decomposition according to how it would be computed from the incidence, choice, and quantity response models at the level of an individual household. To simplify this example, we take the household’s initial inventory to be 0. Thus, the purchase incidence probability will be 1 regardless of the promotional status of brand A. Brand A Total Sales (Brand A on Promotion) Incidence Probability 1 Choice Probability 1 Quantity Model 8 Expected Total Quantity 8 Brand A Baseline Sales (Assuming No Promotions on Brand A) Incidence Probability 1 Choice Probability 0.5 Quantity Model 4 Expected Baseline Quantity 2 Brand A Baseline Borrowed Sales (Assuming No Choice Effect) Incidence Probability 1 Choice Probability 0.5 Quantity Model 8 Expected Baseline Borrowed Quantity 4 The Abraham and Lodish (1987, 1993) approach would estimate an incremental volume of 6 units (total sales minus baseline sales). This volume, however, is truly incremental if and only if the 2 units of “borrowed sales” all end up in incremental consumption for household 3 (i.e., there is no cannibalization of future sales). At the time of a promotion, we do not know how many (if any) of those 2 borrowed units (4 baseline and borrowed minus 2 baseline) will become incremental consumption for the household. (Note that SILVA-RISSO, BUCKLIN, AND MORRISON Planning Manufacturers’ Sales Promotion Calendars 280 Marketing Science/Vol. 18, No. 3, 1999 baseline plus borrowed sales include baseline sales plus those sales that will turn out to be either stockpiled or extra consumption.) Therefore, our approach takes the difference between total sales (8 units) and baseline plus borrowed sales (4 units) to be incremental at that time. We then determine how much, if any, of the 2 borrowed units to classify as incremental by estimating whether there is additional consumption by the household in future periods (see the Appendix, Equation (A10)). Those units are then credited as incremental to the period in which the added consumption is realized. The response model in Equation (1) provides an estimate of total sales, E( ). To obtain an estimate of h Qit incremental sales, we simulate baseline plus borrowed sales, E( ). Incremental sales are then given by the h BBQit difference between these two quantities. We note that simulated baseline sales, , should be a function h E(BQ ) it of the inventory and consumption levels that would have been realized in the absence of promotional activity for the target brand (brand-size alternative i). Similarly, simulated baseline plus borrowed sales should be a function of the inventory and consumption levels that would have been realized if promotions on the target brand resulted only in borrowed sales (via purchase acceleration and/or stockpiling). Thus, in computing simulated baseline plus borrowed sales, we remove the choice effect of brand-size i’s promotions. We then compute how many of those borrowed units result in incremental consumption in future periods. For this computation we use the difference between the consumption rate for the simulated baseline plus borrowed sales, , and the consumption rate perh CRBBt taining to baseline sales, . Thus, the borrowed volh CRBt ume that results in incremental consumption can be estimated period by period by the difference between the following two consumption rates, . h h CRBB CRB t t Those units are then added to the incremental sales for period t. Thus, our model captures the incremental component, if any, of the “borrowed” sales from a promotion by summing the consumption carryover effect across future periods. This is given by . h h CRBB CRB s t 1 s s Our model also includes the carryover effects of purchase feedback. These effects result in an increase or decrease in choice probabilities in future periods as a consequence of previous choice decisions. This is captured via the last brand purchased (LBP) variable in the choice model (see the Appendix, Equation (A2)). Thus, a promotion for brand A which results in a purchase of brand A will increase the choice probability for brand A at the subsequent choice occasion. We compute this carryover effect by simulating baseline plus borrowed sales with the LBP variable set to the value it would have had, had no promotions for brand A been offered (see the Appendix, Equations (A13) and (A14)). Putting all of the above together, from the manufacturer’s perspective, a promotional event can produce truly incremental sales under three conditions: (1) consumers switch to the target brand (choice effect), (2) a temporary increase in consumption takes place (consumption effect), or (3) future sales increase due to purchase event feedback. Nonincremental volume (baseline plus borrowed) is simulated by setting promotional variables to zero in the choice model, incremental consumption to zero, and removing purchase feedback effects from the choice probabilities. Mathematically, this is expressed as h h h h h E(BBQ ) E(Q |Q 0)| it it it INV INVBB t t h h h h P (i|inc)| P (inc)| (2) NO-PROMO t t INV INVBB t t NO-PURCH.FEEDBACK where BBQ is baseline plus borrowed volume, is the household’s inventory given that the h INVBBt choice effect of promotions for brand-size i is removed, NO-PROMO sets promotional variables (e.g., temporary price reduction, feature, display) to zero, andNOPURCH.FEEDBACK removes purchase event feedback effects from the choice model. For example, the three factors in Equation (2) respectively correspond to the magnitudes 8, 0.5, and 1 in the computation of the 4 baseline plus borrowed units in our previous example. Note that this number may be significantly higher than the expected baseline sales given by: h h h E(BQ ) E(Q |Q 0)|NO-PROMO, it it it h h INV INVB t t h h P (i|inc)| P (inc)| , (3) NO-PROMO NO-PROMO, t t h h NO-PURCH.FEEDBACK INV INVB t t where BQ refers to baseline volume and is an h INVBt SILVA-RISSO, BUCKLIN, AND MORRISON Planning Manufacturers’ Sales Promotion Calendars Marketing Science/Vol. 18, No. 3, 1999 281 estimate of what the household’s inventory level would be if no promotions on brand-size i had been run. (The three factors in Equation (3) would respectively be the magnitudes 4, 0.5, and 1 in the computation of the 2 baseline units in our previous example.) The expected incremental number of units (for the manufacturer) of brand-size alternative i sold to household h at time t is then obtained by (1) subtracting baseline plus borrowed sales from total expected units, and (2) adding back any previously “borrowed” sales that resulted in incremental consumption in period t ( ). Incremental units are h h h DCR CRBB CRB t t t