Understanding the Characteristics of Price Elasticities for Frequently Purchased Packaged Goods

This study examines category and brand level factors of packaged goods to determine which ones are related to brand price elasticities. A new nonparametric method of elasticity calculation is presented that is suited to situations where detailed feature and display information is not available. To obtain results that lead to strong and reliable generalisations, we examine 26 categories and 110 brands using store level weekly scanner data. We also synthesise our results with four previous studies that have looked at determinants of price elasticities. Three factors consistently emerge as important drivers of elasticity. They are, competitive intensity in the category, whether or not the product is storable and the brand market share. A further, less certain, factor is the frequency of promotion activity, with categories and brands having more frequent promotions exhibiting lower sales increases when one or more of the brands price promotes.     

The impact of location factors on the attractiveness and optimal space shares of product categories

In this paper, we investigate the impact of location characteristics on the relative attractiveness of product categories within a store, and formulate micromarketing implications for the allocation of store space to categories. We present a framework for the impact of store and trading area characteristics on category and store performance, which integrates and extends previous geomarketing research. Building upon this framework, we propose a tractable and flexible procedure for assessing location influences, and derive optimal space allocation rules for different location profiles. Empirical application to national stores of a European retail chain confirms the differential impact of location characteristics on categories' attractiveness. Tailoring the allocation of store space to these location-based differences in category appeal leads to a significant increase in overall chain profitability.     

The fight between store brands and national brands—What's the score?

In the Western world market shares for store brands have increased across all product categories. The competitive position of store brands compared to national brands may depend on the product category and a retail chain's overall brand assortment strategy. In order to investigate these possible chain and category effects we have selected five chains with different store brand strategies and three product categories that differ with respect to the number of strong national brands in a category. The results we report focus on the competitive position of store brands compared to national brands from a consumer point of view. We find that store brands are in a weak competitive position compared to national brands independent of category and retail chain brand assortment strategy.     

Shelf space elasticity: A meta-analysis

This article presents a meta-analysis of 1,268 estimates of shelf space elasticities, that is, the ratio of additional sales to additional space allocated in retail settings. The study finds several new empirical generalizations about shelf space elasticity. The most important generalizations are as follows: The average observed shelf space elasticity is .17, which varies across product categories, with the lowest estimates for commodities, followed by staples, and the highest estimates for impulse buys. Store size moderates the effect of product characteristics on shelf space elasticity: in large stores, the difference between elasticities for brand versus category is greater than in small stores. Furthermore, the shelf space elasticity estimates are less dependent on method characteristics than commonly assumed in the literature. Regarding the influence of the direction of space variation, the study finds that shelf space increases results in greater elasticity estimates than shelf space reduction, a finding that emphasizes the application of shelf space variation as a useful marketing tool. These findings provide several implications for managers and researchers.     

Effect of Store Brand Introduction on Channel Price Leadership: An Empirical Investigation

In this study, we conduct an empirical investigation of the impact of store brand introductions on the price leadership relations in a distribution channel between a retailer and national brand manufacturers. We analyze a multi-product category retail database from a major grocery chain, which captures both a period before and a period after the introduction of a store brand in each product category. By applying the time series approach to this data set, we show that store brand introductions frequently lead to price leadership changes, generally in a more favorable direction for the retailer than for the national brand manufacturer, evidenced by either the decay of the manufacturers’ price leadership or the rise of the retailer’s price leadership. However, such a change is not universal but tends to be concentrated among a certain quality tier of national brands, which is not always the low-tier, but sometimes the top-tier despite the low-price low-quality position of the store brand. The patterns detected in the data suggest that these changes are likely to reflect the retailer’s strategic effort to reshape the price leadership environment in a product category aided by the enhanced bargaining power and managerial sophistication that accompanied the store brand introductions.     

Bridge,Focus, Attack,or Stimulate: Retail Category Management Strategies with a Store Brand

We investigate a monopolist retailer's category management strategy where the main strategic decisions are how to horizontally position a store brand relative to the incumbent national brands and how to price the store and national brands for retail category profit maximization. We analyze a market composed of two consumer segments with differing tastes and heterogeneity with respect to willingness to pay and a product category consisting of two competing national brands and one store brand. We find that contrary to the existing literature, it is not always optimal for a retailer to position its store brand against the leading national brand; instead there are many situations where it is best to position the store brand close to the weaker national brand or to position it in the “middle” so it appeals to both national brands' target segments. In the process we identify four distinct category management strategies that a retailer can use with a store brand. In three of these the optimal store brand price is the brand's monopoly price, while in the remaining one strategy the price is lower. We also suggest an easy to implement means for a retailer to determine which strategy is best to use, depending on the particular competitive environment present before the introduction of the store brand and the relative quality of the store brand. We find that the store brand entry is most beneficial to the retailer when the national brands are moderately differentiated. Finally we show that introducing a store brand not only allows the retailer to garner a higher share of the channel profits through higher retail margins, but also often provides the retailer the benefit of increases in national brand unit sales as well as incremental sales from the store brand. JEL Classification: M310     

Retail network performance evaluation: a DEA approach considering retailers' geomarketing

Stores develop customized assortments based on localization characteristics. In terms of performance analysis, these changes make it more difficult to analyse store performance. This adjustment procedure entails an internal benchmarking which needs small samples to control regional and assortment effects. This paper explores the influence of competitive environment and store neighbourhood characteristics on store efficiency by using three data envelopment analysis (DEA) models. This research is illustrated by using real data from a French supermarket retail chain at the product category level. It is shown that the two-steps DEA model is a relevant analysis tool to take into account location aspects. It presents a discriminant power strong enough to derive managerial implications with small samples.     

The different faces of coupon elasticity

Coupons account for over two-thirds of all consumer promotional efforts initiated by the manufacturers of consumer goods. In this study, the impact of coupons on brand sales is investigated and how that impact decays over the life of the coupon is demonstrated. Specifically, we present an econometric model that can capture coupon effects in terms of equivalent price reduction, account for coupon effects over time, allow inference of coupon effects when retailers decide to double or triple the coupon value, and provide both self-coupon and cross-coupon elasticities at different levels of aggregation. A widely used sales response model is adapted, and an analytical model is proposed to estimate both the self-coupon and cross-coupon (face value) elasticities of sales at the store level. From the store-level elasticity estimates for a given week, the authors analytically derive the coupon elasticities for the chain level by aggregating across stores, and over the life of the coupon by aggregating over time. The proposed sales response model is estimated with the data obtained from three markets for various product categories, and the coupon elasticities are computed. The proposed framework allows one to demonstrate the hypothetical equivalence of a shelf-price reduction for a given coupon face value in each week. Also, the effect of doubling the face value of a coupon results in more than a proportionate increase in elasticity. The authors find that both self and cross-coupon elasticities are much smaller in magnitude than the average self and cross-price elasticity measures reported in the literature.     

Effects of neighbourhood characteristics on store performance supermarkets versus hypermarkets

We investigate the impact of neighbourhood characteristics on the relative attractiveness of product categories within a store, with special attention for the differences between hypermarkets and supermarkets. We consider two questions. Firstly, is the impact of neighbourhood-specific factors on attractiveness of product categories smaller for hypermarkets than for supermarkets? Secondly, is there a difference in relative attractiveness of product categories between supermarkets and hypermarkets and to what extent is this difference dependent on kind of neighbourhood? For the impact of store and trading area characteristics on category and store performance, we use a framework that was originally presented in Campo et al. (J. Int. Res. Market 17 (2000) 225). Empirical application to national stores of a European retail chain confirms the differential impact of neighbourhood characteristics on supermarkets and hypermarkets. The research proves that geomarketing analysis can be useful for developing micromarketing strategies.     

Does Private Label Quality Influence Consumers’ Decision on Where to Shop?

When consumers decide where to shop, they take several criteria into account. It is not yet clear whether private label (PL) quality is one of these criteria. It is the intention of this study to shed light on this issue because many retailers have invested heavily into the quality of their PLs. They assume that PLs differentiate a store's assortment and image, which should attract customers to the store. This study examines this assumption by use of a simultaneous equation model that links PL‐specific quality evaluations to perceptions of the image of the associated store as well as to the category‐specific share of store visits. Empirical results for 10 product categories show that PL quality positively affects store image perception, and influences consumers in the decision of where to shop.     

Predicting Under- and Overperforming SKUs within the Distribution–Market Share Relationship

This research presents a retail analytics application which uses machine learning (ML) to identify and predict under- and overperforming consumer packaged goods (CPGs) using retail scanner data. Essential to measuring market performance at the SKU level is the relationship between distribution and market share (the velocity curve). We validate that ML can reproduce the velocity curve, and ML is further used to predict underperforming, in-line performing, and overperforming SKUs relative to the velocity curve, based on a range of variables (SKU features) at a point in time. Our ML approach can correctly predict 83% of SKUs as under-, in-line-, or overperforming based on their characteristics. The research analyzes 9,321 SKUs of 2,565 brands across seven product categories of CPGs which were sold in 8,117 stores from 49 different retail chains of five different retail channels located in the US states of California, New York, Texas, and Wisconsin. The retail stores comprise convenience stores, drug stores, food stores, liquor stores, and mass merchandise retail stores. The data is Nielsen retail store scanner data for the calendar year 2014. The relationship between distribution and market share is a market-wide proxy for the ratio of relative sales in a category to, for example, aggregate shelf space, a key retail productivity metric. We further find indications that the distribution of SKUs across different store sizes, the stores’ category specialization, the line length of the brands, the overall performance of the parent brand, and sales consistency are the most important characteristics for the prediction of market share performance beyond the velocity curve. The methods and results presented will help CPG marketers (suppliers and retailers) understand which SKUs are under-, in-line-, or overperforming and the potential factors contributing to that performance. Optimizing assortments and portfolios is essential to decrease failure rates of individual SKUs. ML approaches can evolve to complementary support tools for such management problems.     

Consumer learning, switching costs, and heterogeneity: A structural examination

This paper develops and estimates a model of forward-looking consumer learning with switching costs using household level scanner data from a frequently purchased product category. This is novel because current models of consumer purchase behavior assume that only one of these types of dynamics is present, not both at the same time. My model estimates support the presence of both learning and switching costs in this product category. The estimates show that before consuming new products, consumers are unsure of their tastes for them, and subsequently learn their tastes by purchase and consumption of new products. Switching costs are large, comprising roughly 30 percent of the cost of a medium sized package of the product. Additionally, the model incorporates very rich individual level unobserved heterogeneity in price sensitivities, tastes, and switching costs, and the amount by which consumers learn. To show that my model produces different implications than a model with learning or switching costs only, I estimate two more specifications, one without each type of dynamics, and simulate counterfactuals that are of interest to managers and policymakers. I find that intertemporal elasticities are underestimated when either type of dynamics is left out, by as much as 90%. Informative advertising is also affected by the presence of switching costs, although the direction of the bias is not signed. Leaving out dynamics also has a large impact on long-term elasticities, which are used by antitrust policymakers to evaluate the impact of mergers. When learning is ignored, cross elasticities are underestimated by as much as 45%. When switching costs are ignored, both own and cross elasticities are underestimated.     

How category characteristics affect the number of store brands offered by the retailer: a model and empirical analysis

Our research examines why retailers offer, not one, but multiple store brands in some product categories. More specifically, we are interested in how certain product category characteristics affect the number of store brands. We model a product category consisting of two incumbent national brands that may differ in strength. The retailer may introduce one or two store brands depending on which maximizes category profits. Our analysis suggests that the retailer is likely to carry two store brands in categories where (i) the national brands are similar in strength; and (ii) the price sensitivity between the national brands is low. Interestingly, the conditions that support the introduction of more than one store brand are quite different than the conditions that would facilitate the introduction of additional national brands. We provide empirical evidence that support our model-based predictions.     

Impact of different types of in-store displays on consumer purchase behavior

Research on consumer in-store shopping behavior does not account for the existence of different types of display locations (e.g. storefront, store rear, secondary, front end cap, rear end cap, and shelf displays). This article focuses on accounting for and understanding the impact of various displays on consumer purchase behavior based on the Stimulus-Organism-Response (SOR) theory. Specifically, we study how displays closer to and farther from the main location of the focal category influence consumer purchase behavior. Furthermore, within the different types of displays we investigate the impact of specific types of displays on consumer's category purchase and brand choice and the moderating role of price and discounts. A hierarchical Bayesian model is estimated using scanner panel data for a large U.S. grocery chain that contains unique information on the number of product facings at multiple display locations within a store. We find that displays closer to the focal category have a larger impact, with front end cap displays having the largest impact on category purchase and shelf displays having the largest impact on brand choice. We also demonstrate the synergistic impact of price and discounts in enhancing the impact of displays on consumer purchase behavior and brand choice. Equipped with these findings we propose a display allocation optimization that results in an average increase in revenue of about 11.15% and a strategy to distribute displays across all locations in the store rather than letting one location dominate.     

“One-deal-fits-all?” On Category Sales Promotion Effectiveness in Smaller versus Larger Supermarkets

Even within a store chain and format, supermarket outlets often exhibit substantial differences in selling surface. For chain managers, this raises the issue of correctly anticipating the promotion lift, and of profitably managing promotion activities, across these outlets. In this paper, we conceptualize why and how store size influences the category sales effectiveness of four promotional indicators (depth of the promotional discount, display support, feature support, and whether the promotion is quantity-based). We then estimate the net moderating effect on four product categories for 103 store outlets belonging to four chains. For each of the promotion instruments, we find the percentage sales increases to be lower in large stores. For instance, whereas a 10% point increase in feature activity enhances category sales by about 1.64% in a 700 m² store, this figure drops to only 1.03% in a 1300 m² store – a 59% reduction. This moderating effect is especially pronounced for discount depth, the relative sales lift from a typical price cut being about 78% lower in the larger-sized outlet. However, since large outlets also have larger base sales, the picture changes when we consider absolute sales effects. The net outcome is that deeper discounts or quantity-based promotions do not systematically generate larger or smaller absolute sales bumps in large stores, whereas for in-store displays and features, we obtain a clear positive (be it less than proportional) link between store size and absolute category sales lift. When it comes to margin implications, we show that large stores gain higher profit from price cuts than small outlets only as long as the retailer keeps part of the manufacturer discount to himself. Managers can use these insights to improve their promotional forecasts across outlets, as well as to tailor their mix of instruments to store selling surface.     

Leveraging Distribution to Maximize Firm Performance in Emerging Markets

Despite the rise of emerging markets as lucrative destinations for business expansion, marketing literature in this area is largely anecdotal and conceptual. Further, owing to the largely unorganized retail structure in emerging markets, managers tend to make sub-optimal marketing-mix decisions by taking an aggregate view of their distribution network. In this study, we develop an econometric model to help firms develop a multichannel distribution strategy in emerging markets while accounting for (a) own-marketing mix, (b) competitive actions, (c) brand-level heterogeneity, and (d) dependencies that may arise between product offerings. The proposed model is tested on longitudinal data from a large Indian CPG manufacturer. The results indicate that firms must consider store format-specific distribution elasticities (as opposed to aggregate effects), especially in an emerging market, where the role of distribution is critical in brand success. Further, depending on the offering, price (own- and cross-) and advertising elasticities could vary even though the brand is essentially the same. Also, we find that there are significant dependencies between product forms that need to be considered when designing the marketing mix. Finally, we provide re-allocation recommendations to help managers choose the level of store format distribution in order to maximize profits. The proposed distribution re-allocation strategy resulted in an average of 7.7% increase in profits across three product forms for the focal firm.     

Store Brand Quality and Retailer’s Product Line Design

This study analyzes a retailer’s store brand quality decision in vertically differentiated product categories. We analyze a game theoretic model composed of one or two national brand manufacturers and a retailer, who strategically chooses the quality level(s) of its store brand(s) relative to the well-established national brand position(s) to maximize its category profit. Our analysis reveals that the nature of a retailer’s store brand quality positioning is quite different from the manufacturer’s national brand positioning decision, and that the best position for a store brand is not “as close to a national brand as possible” as previous studies suggest. Instead, the optimal quality position of each store brand is remarkably sensitive to the distribution of consumers’ willingness-to-pay. In particular, the relative proportions of quality sensitive consumers and price sensitive consumers determine the balance of three key strategic forces — the market expansion force, the retail margin force, and the consumer profitability force, leading to different optimal product line designs for store brands across different category environments. Interestingly, against multiple incumbent national brands, the retailer’s optimal product line design includes a store brand positioned at the highest quality level in the category only if most consumers are moderately quality conscious. We also analyze the implications of national brands’ brand equity for retailers’ store brand strategy.     

Commoditization in Food Retailing: Is Differentiation a Futile Strategy?

This article addresses commoditization in food retailing whereby competition has a tendency to lead to a continuous addition of new but similar products in a category. This often results in products that are more homogeneous and may make it more difficult for firms to gain unique market positions. In light of this development, we ask whether product differentiation is a futile strategy in food products marketing. We also address how consumers perceive and react to the seemingly ever-increasing number of similar products. These questions are explored through insights from relevant literature and a small-scale study of a seemingly highly differentiated category—smoked salmon—sold at the flagship store of an upmarket UK supermarket chain. It was concluded that no product attributes could be described as truly innovative, unique, or difficult to imitate. Implications are highlighted and discussed.     

Combining sell-out data with shopper behaviour data for category performance measurement: The role of category conversion power

Retailers need to manage a series of complex decisions relating to numerous products. To reduce this complexity, they have introduced category management practices, which consider groups of similar products (categories) that can be managed separately as single business units (SBUs). Although the concept that the store offer should be organised as a category mix and that this strategy allows for better overall store management is already consolidated, retailers still struggle to adopt an approach to the store performance measurement starting from a category level perspective. Nowadays, the available methods for measuring categories’ performance are quite limited. The current trend sees the measurement of category performance mainly based on sell-out data that are ill-equipped to fully address category management issues. Retailers should broaden their field of analysis not only by focusing on the product/sales perspective but also by including other methodologies such as shopper behaviour analysis. In this regard, the use of technology offers the retail sector new perspectives for those analysis. Therefore, we intend to contribute to the ongoing debate on the retail analytics topic by presenting a shopper behaviour analytics system for category management performance monitoring. More in detail, we could derive a new key performance indicator, category conversion power (CCP), aimed at analysing and comparing the single categories organised within the store. The research is based on a unique dataset obtained from a real-time locating system (RTLS), which allowed us to collect behavioural data togheter with sell-out data (from POS scanner). We argue that retailers could exploit this new analytical method to gain more understanding at the category level and therefore make data-driven decisions aimed at improving performance at the store level.     

Patterns of consumer response to retail price differentials

Decomposing price elasticity suggests that the major impact of promotions is on brand switching rather than increased consumption. Consumers may also buy smaller quantities of more expensive brands when compared to cheaper ones (inter-brand elasticity). Using panel data for the purchases of 80 consumers buying nine product categories over a 16-week period, we verified that inter-brand elasticities occur, and report the relative importance of intra- and inter-brand elasticities in determining quantity price elasticity per shopping occasion for the product category. Brands were classified by informational (socially mediated) and utilitarian (product-mediated) benefits. Intra-brand elasticity was higher than utilitarian inter-brand elasticity, which was higher than informational inter-brand elasticity.