Postpromotion Dip Research Articles

Forecast adjustments during post-promotional periods

Sales promotions are a key cause for judgmental adjustments to forecasts, especially in the world of Fast-Moving Consumer Goods (FMCG). Typically, three types of periods are relevant for sales promotions: a normal period as a comparison point, a promotional period, and a post-promotional period. Yet, research on forecasting with sales promotions has focussed specifically on promotional effects and their elevation versus a normal sales period, while generally disregarding adjustments for post-promotional periods. To investigate further into the effects of promotions after their occurrence, we employed an incentivized laboratory experiment focussing specifically on the role of human judgment in sales forecasting after a promotion has occurred. Our study shows that, without guidance, the post-promotional period is considered equal to a normal period in sales numbers. However, a post-promotion dip is commonly observed in reality. We therefore introduced information on the existence of post-promotional dips, in itself (treatment one) or with added average magnitude of such a dip (treatment two). Information provision resulted in increased forecasting accuracy in both treatments, but more so in treatment two. Thus, this study promotes the provision of guiding information. We find that structured support, allows an improvement in judgmental forecasting accuracy after a sales promotion.

Promotion Optimization for Multiple Items in Supermarkets

Promotions are a critical decision for supermarket managers, who must decide the price promotions for a large number of items. Retailers often use promotions to boost the sales of the different items by leveraging the cross-item effects. We formulate the promotion optimization problem for multiple items as a nonlinear integer program. Our formulation includes several business rules as constraints. Our demand models can be estimated from data and capture the postpromotion dip effect and cross-item effects (substitution and complementarity). Because demand functions are typically nonlinear, the exact formulation is intractable. To address this issue, we propose a general class of integer programming approximations. For demand models with additive cross-item effects, we prove that it is sufficient to account for unilateral and pairwise contributions and derive parametric bounds on the performance of the approximation. We also show that the unconstrained problem can be solved efficiently via a linear program when items are substitutable and the price set has two values. For more general cases, we develop efficient rounding schemes to obtain an integer solution. We conclude by testing our method on realistic instances and convey the potential practical impact for retailers. This paper was accepted by Yinyu Ye, optimization.

Buying free rewards: the impact of a points-plus-cash promotion on purchase and reward redemption

While many studies have investigated consumer purchase behavior in reward programs, a better understanding of customer redemption behavior is lacking, particularly when promotions affect a core aspect of reward programs—free rewards. In this paper, we examine the impact of a promotion on purchase and reward redemption in a reward program in which consumers can partially cover the cost of a free reward with their money. The literature on reward programs suggests a positive reinforcement caused by reward redemption, whereas the literature on promotion provides different views regarding the existence of a postpromotion dip. Using data from a major retailer’s reward program, we verify that such a promotion attracted customers with less transaction activity and shorter tenure. Interestingly, consumers using the promotion increased their preference for hedonic rewards compared to their previously observed behavior. This change in preference persisted after the promotion ended. Overall, the promotion significantly increased the number of redemptions but generated a negative impact on subsequent consumer behavior by decreasing purchase incidence and quantity. Our findings point to a need to understand the trade-off between spending money on buying an otherwise free reward and future regular purchases.

Buying Free Rewards: The Impact of a Points-Plus-Cash Promotion on Purchase and Reward Redemption

While many studies have investigated consumer purchase behavior in reward programs, a better understanding of customer redemption behavior is lacking, particularly when promotions affect a core aspect of reward programs—free rewards. In this paper, we examine the impact of a promotion on purchase and reward redemption in a reward program in which consumers can partially cover the cost of a free reward with their money. The literature on reward programs suggests a positive reinforcement caused by reward redemption, whereas the literature on promotion provides different views regarding the existence of a postpromotion dip. Using data from a major retailer’s reward program, we verify that such a promotion attracted customers with less transaction activity and shorter tenure. Interestingly, consumers using the promotion increased their preference for hedonic rewards compared to their previously observed behavior. This change in preference persisted after the promotion ended. Overall, the promotion significantly increased the number of redemptions but generated a negative impact on subsequent consumer behavior by decreasing purchase incidence and quantity. Our findings point to a need to understand the trade-off between spending money on buying an otherwise free reward and future regular purchases.

Optimizing Promotions for Multiple Items in Supermarkets

Promotions are a critical decision for supermarket managers who must decide the price promotions for a large number of items. Retailers often use promotions to boost the sales of the different items by leveraging the cross-item effects. We formulate the promotion optimization problem for multiple items as a non-linear integer program (IP). Our formulation includes several business rules as constraints. Our demand models can be estimated from data and capture the post-promotion dip effect as well as cross-item effects (substitution and complementarity). Since demand functions are typically nonlinear, the exact formulation is intractable. To address this issue, we propose a general class of IP approximations. For demand models with additive cross-item effects, we prove that it is sufficient to account for unilateral and pairwise contributions and derive parametric bounds on the performance of the approximation. We also show that the unconstrained problem can be solved efficiently via a linear program when items are substitutable and the price set has two values. For more general cases, we develop efficient rounding schemes to obtain an integer solution. We conclude by testing our method on realistic instances and convey the potential practical impact for retailers.

The Impact of Linear Optimization on Promotion Planning

Sales promotions are important in the fast-moving consumer goods (FMCG) industry due to the significant spending on promotions and the fact that a large proportion of FMCG products are sold on promotion. This paper considers the problem of planning sales promotions for an FMCG product in a grocery retail setting. The category manager has to solve the promotion optimization problem (POP) for each product, i.e., how to select a posted price for each period in a finite horizon so as to maximize the retailer’s profit. Through our collaboration with Oracle Retail, we developed an optimization formulation for the POP that can be used by category managers in a grocery environment. Our formulation incorporates business rules that are relevant, in practice. We propose general classes of demand functions (including multiplicative and additive), which incorporate the post-promotion dip effect, and can be estimated from sales data. In general, the POP formulation has a nonlinear objective and is NP-hard. We then propose a linear integer programming (IP) approximation of the POP. We show that the IP has an integral feasible region, and hence can be solved efficiently as a linear program (LP). We develop performance guarantees for the profit of the LP solution relative to the optimal profit. Using sales data from a grocery retailer, we first show that our demand models can be estimated with high accuracy, and then demonstrate that using the LP promotion schedule could potentially increase the profit by 3%, with a potential profit increase of 5% if some business constraints were to be relaxed. The online appendix is available at https://doi.org/10.1287/opre.2016.1573

The Impact of Linear Optimization on Promotion Planning

Sales promotions are important in the fast-moving consumer goods (FMCG) industry due to the significant spending on promotions and the fact that a large proportion of FMCG products are sold on promotion. This paper considers the problem of planning sales promotions for a FMCG product in a grocery retail setting. The category manager has to solve the promotion optimization problem (POP) for each product, i.e., how to select a posted price for each period in a finite horizon so as to maximize the retailer's profit. Through our collaboration with Oracle Retail, we developed an optimization formulation for the POP that can be used by category managers in a grocery environment. Our formulation incorporates business rules that are relevant in practice. We propose general classes of demand functions (including multiplicative and additive) which incorporate the post-promotion dip effect, and can be estimated from sales data. In general, the POP formulation has a nonlinear objective and is NP-hard. We then propose a linear integer programming (IP) approximation of the POP. We show that the IP has an integral feasible region and hence, can be solved efficiently as a linear program (LP). We develop performance guarantees for the profit of the LP solution relative to the optimal profit. Using sales data from a grocery retailer, we first show that our demand models can be estimated with high accuracy and then, demonstrate that using the LP promotion schedule could potentially increase the profit by 3%, with a potential profit increase of 5% if some business constraints were to be relaxed.

The dark side of discounts: An inaction inertia perspective on the post‐promotion dip

AbstractAfter a product has been on promotion, the sales of that product may temporarily decrease. This post‐promotion dip is normally explained in terms of forward buying or stockpiling. This article offers a third explanation in terms of brand switching. The results of two experiments show that after missing a discount on their regular brand, consumers may switch to another brand. Switching behavior is much more pronounced when a large discount is missed in comparison to when a small discount is missed. This finding is consistent with recent social psychological research on inaction inertia, and the results are discussed in relation to this phenomenon. © 2005 Wiley Periodicals, Inc.

How Dynamic Consumer Response, Competitor Response, Company Support, and Company Inertia Shape Long-Term Marketing Effectiveness

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.

The Determinants of Pre- and Postpromotion Dips in Sales of Frequently Purchased Goods

The authors empirically study the relationships between pre- and post-promotion dips in weekly store data and Universal Product Code (UPC), category, and store trading-area customer characteristics. Drawing on recent advances in econometric modeling, they estimate 39,441 pre- and postpromotion dip elasticities in 83 stores in ten product categories. They relate these elasticities to 24 characteristics, such as UPC price and market share, category budget share and storability, and store trading-area demographics. The authors find that UPC, category, and store trading-area customer characteristics all explain significant variation in the elasticities. For example, both pre- and postpromotion dips are stronger for high-priced, frequently promoted, mature, high-market-share UPCs. They find that postpromotion dips are not significantly different for private labels but are more prominent for UPCs with less predictable promotion patterns. They also find that stores with trading areas that consist of older customers who live in larger households and own cars are particularly prone to postpromotion dips. The findings potentially deepen the understanding of stockpiling and deceleration and blend two trends in promotion response research: an increased focus on pre- and postpromotion effects and a greater emphasis on examination of cross-sectional differences in promotion response.

The Post-Promotion Dip Puzzle: What do the Data Have to Say?

One of the puzzles of store-level scanner data is the lack of a dip in quantity sold in the weeks following a promotion. Such a dip is predicted by a consumer inventory model. During a promotion consumers buy more, not only for current consumption, but stockpile for future consumption. The predictions of such a model have been confirmed by household-level data yet seem harder to find in aggregate brand- or category-level data. We re-examine this puzzle and reach two conclusions. First, the effects at the household-level are present, but are much smaller than previously found. Our estimates are different because we control for household heterogeneity in a more general way than most previous work. This suggests that since the effects are small they might be harder to spot in aggregate data. Second, we show that the dip is present in the aggregate data, once we control for additional promotional activity, like feature and display. The latter has an opposing dynamic effect that masks the existence of the post-promotion dip.

The Estimation of Pre- and Postpromotion Dips with Store-Level Scanner Data

One of the mysteries of store-level scanner data modeling is the lack of a dip in sales in the weeks following a promotion. Researchers expect to find a postpromotion dip because analyses of household scanner panel data indicate that consumers tend to accelerate their purchases in response to a promotion—that is, they buy earlier and/or purchase larger quantities than they would in the absence of a promotion. Thus, there should also be a pronounced dip in store-level sales in the weeks following a promotion. However, researchers rarely find such dips at either the category or the brand level. Several arguments have been proposed to account for the lack of a postpromotion dip in store-level sales data and to explain why dips may be hidden. Because dips are difficult to detect by traditional models (and by a visual inspection of the data), the authors propose models that can account for a multitude of factors that together cause complex pre- and postpromotion dips. The authors use three alternative distributed lead and lag structures: an Almon model, an unrestricted dynamic effects model, and an exponential decay model. In each model, the authors include four types of price discounts: without any support, with display-only support, with feature-only support, and with feature and display support. The models are calibrated on store-level scanner data for two product categories: tuna and toilet tissue. The authors estimate the dip to be between 4 and 25% of the current sales effect, which is consistent with household-level studies.

Consumer inventory sensitivity and the postpromotion dip

We summarize and critique seven theories that might explain the lack of a postpromotion dip in sales in the weeks following a promotion. We then propose and provide empirical support for a new explanation. We argue that in markets where the consumer category purchase decision is not strongly influenced by inventory levels, the displacement effect of accelerated sales will tend to be distributed fairly uniformly into the future such that clearly defined dips are not observed. We utilize a simulation based on real data to investigate this explanation. The simulation shows that given the degree to which inventory influences the purchase decision, we would not expect to see postpromotion dips, even though promotion influences the purchase decision. However, the simulation shows that if inventory had a greater influence on the purchase decision, we would expect to see postpromotion dips. We conclude with implications for both researchers and managers.

The Normative Impact of Consumer Price Expectations for Multiple Brands on Consumer Purchase Behavior

Empirical research indicates that some consumers form price expectations which may impact their purchase behavior. While literature in operations research has built purchase policy models incorporating uncertain price expectations, these models have been built for commodities. Consumers face an environment with multiple brands. In this paper, we develop a model that incorporates consumer preferences and price expectations for multiple brands as determinants of normative consumer purchase behavior. The model demonstrates how commodity purchase policy models recognizing price uncertainty can be adapted to the study of multi-brand markets. The model is used to analyze the normative impact of changes in price promotion policies and holding costs on individual purchase behavior. It is also used in a Monte-Carlo market simulation that illustrates some scenarios where a post-promotion dip is more or less evident, and provides an explanation for the nonexistent post-promotion dip.