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Retailer Pricing and Competitive Effects

with P. Kopalle, D. Biswas, P. Chintagunta, J. Fan, B. Ratchford and J. Sills, Journal of Retailing, 85 (1), 56-70, 2009.

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Until recently, retailers have taken an either/or approach to competition: either reacting fiercely to competitive price changes or ignoring them altogether. Today, however, firms make a concerted effort to determine and quantify competitive effects. In this paper, we focus on how pricing and competitive effects interact as a general phenomenon, particularly as it applies to retailing. We attempt to construct a general framework that enhances our understanding of the emerging research issues in the area of pricing and competitive effects, and we examine their implications for practice. The areas that show high promise/opportunity are in the online setting for all types of goods—fashion, perishable and packaged staples, and durables—particularly with respect to pricing for profitability and understanding the impact of competition. Other opportunities include understanding the pricing and competitive effects in the perishable goods category sold in specialty, discount, and convenience stores.

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Product innovations, marketing investments and stock returns

with J.Silva-Risso, S.Srinivasan, D.M. Hanssens, Journal of Marketing, 73(1), 24-43, 2009.

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Under increased scrutiny from top management and shareholders, marketing managers feel the need to measure and communicate the impact of their actions on shareholder returns. In particular, how do customer value creation (through product innovation) and customer value communication (through marketing investments) affect stock returns? This article examines, conceptually and empirically, how product innovations and marketing investments for such product innovations lift stock returns by improving the outlook on future cash flows. The authors address these questions with a large-scale econometric analysis of product innovation and associated marketing mix in the automobile industry. They find that adding such marketing actions to the established finance benchmark model greatly improves the explained variance in stock returns. In particular, investors react favorably to companies that launch pioneering innovations, that have higher perceived quality, that are backed by substantial advertising support, and that are in large and growing categories. Finally, the authors quantify and compare the stock return benefits of several managerial control variables. The results highlight the stock market benefits of pioneering innovations. Compared with minor updates, pioneering innovations have an impact on stock returns that is seven times greater, and their advertising support is nine times more effective as well. Perceived quality of the new car introduction improves the firm’s stock returns, but customer liking does not have a statistically significant effect. Promotional incentives have a negative effect on stock returns, indicating that price promotions may be interpreted as a signal of demand weakness. Managers can combine these return estimates with internal data on project costs to help decide the appropriate mix of product innovation and marketing investment.

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Private-Label Use and Store Loyalty

with Kusum Ailawadi and Jan-Benedict Steenkamp, Journal of Marketing, 72 (6), 19-30, 2008, Emerald Mgmt Reviews Citation of Excellence 2008

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The authors develop an econometric model of the relationship between a household’s private-label (PL) share and its behavioral store loyalty. The model includes major drivers of these two behaviors and controls for simultaneity and nonlinearity in the relationship between them. The model is estimated with a unique data set that combines complete purchase records of a panel of Dutch households with demographic and psychographic data.The authors estimate the model for two retail chains in the Netherlands—the leading service chain with a well-differentiated high-share PL and the leading value chain with a lower-share PL. They find that PL share significantly affects all three measures of behavioral loyalty in the study: share of wallet, share of items purchased, and share of shopping trips. In addition, behavioral loyalty has a significant effect on PL share. For the service chain, the authors find that both effects are in the form of an inverted U. For the value chain, the effects are positive and nonlinear, but they do not exhibit nonmonotonicity, because PL share has not yet reached high enough levels. The managerial implications of this research are important. Retailers can reap the benefits of a virtuous cycle; greater PL share increases share of wallet, and greater share of wallet increases PL share. However, this virtuous cycle operates only to a point because heavy PL buyers tend to be loyal to price savings and PLs in general, not to the PL of any particular chain.

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Challenges in Measuring Return on Marketing Investment: Combining Research and Practice Perspectives

Review of Marketing Research, 6, Naresh Malhotra, 2009

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Return on Marketing Investment (ROMI) is defined as the incremental margin generated by a marketing program divided by the cost of that program at a given risk level (Powell 2002). The typical formula is displayed in equation 1:

Return on Marketing Investment = [Incremental Margin – Marketing Investment] / Marketing Investment

Use of this metric promotes accountability for marketing spending, enables comparison across alternatives to decide on the best action and furthers organizational learning and cross-functional team work. Unfortunately, managers are struggling to define and calculate ROMI (Woods 2004), especially outside the price/promotions domain (Bucklin and Gupta 1999). A survey of over 1000 C-level managers (CMO Council 2004) revealed that over 90% of marketing executives viewed marketing performance metrics as a significant priority, but that over 80% were unhappy with their current ability to measure performance. Only 17% of marketing executives have a comprehensive system to measure marketing performance. The companies they work for outperformed other firms in revenue growth, market share and profitability. Thus, most organizations experience considerable roadblocks to fulfill the appealing promise of measuring ROMI and using it to enable better marketing decisions and higher performance. Since financial decisions within the firm in non-marketing domain are evaluated, at least in part, if not primarily, based on their return on investment, it makes investing in marketing activities more difficult by not having their comparable measure.

Several reasons underlie these difficulties, from the improper use of the term ‘return on investment’ for measures that do not include profits/margins nor investment costs (Lenskold 2003), to the lack of research into how return on marketing investment can be measured and how it can be used to enhance performance (Pauwels et al. 2008). Indeed, while many marketing practitioners and academics have expressed concern about marketing accountability and return on investment, the current push largely has come from outside the field, notably top management and finance (Lehmann and Keller 2006). Unfortunately, CEOs and CFOs have been disappointed by the most common responses of the marketing field, from ‘it is hard to judge the impact of marketing spend since so many factors come into play between the spending and the ultimate financial result’ (marketing practice), to ‘we already show it through our sales response functions’ (marketing academia). The authors’ experience in recent years demonstrates that such positions are of little help in bridging the gap between marketing and finance fields, enabling joint understanding and trust in ROMI calculations and ROMI-based decisions and building the standing of marketing in the C-suite.

Previous authors have already laid the conceptual frameworks for return on marketing investment (Lehmann 2005, Lehmann and Reibstein 2006, Rust et al. 2004, Sheth and Sisodia 2002, Srivastava et al. 1998). True to the focus of Review of Marketing Research on “implementing new marketing research concepts and procedures”, the current paper discusses 10 conceptual and implementation issues that complicate measurement and use of return on marketing investment. First, the ‘incremental margin’ in equation 1 (hereafter ‘return’) needs to be forecasted, in terms of magnitude but also timing and associated risk. Second, the investment could involve a combination of marketing actions and needs to be considered from the point of decision perspective. Once the components of returns and investment are measured, it is still unclear whether they should be combined for a focus on (7) impact versus efficiency and (8) realized versus potential return on marketing investment. Finally, acting upon measured return on marketing investment requires (9) clarity on how to weigh multiple objectives and (10) an understanding of whether high ROMI means the marketing action should get more or less investment in the future Often, spending more on programs with a high ROI will lower the ROI percentage but raise the total return, given we are generally at the diminishing returns stage of the response curve.

The remainder of this paper discusses all 10 challenges in detail, giving examples and critically examining how research has addressed and should further address these issues.

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Winners and Losers in a Major Price War

with E. Gijsbrechts and H. van Heerde, Journal of Marketing Research, 45(5), October, 499-518, 2008, leading articleand finalist for the 2009 Paul Green Award

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Although retail price wars have received much business press and some research attention, it is unclear how they affect consumer purchase behavior. This article studies an unprecedented price war in Dutch grocery retailing that started in fall 2003, initiated by the market leader to halt its sliding market share. The authors investigate the short- and long-term effects of the price war on store visits, on spending, and on the sensitivity of these decisions to weekly prices and price image. They use a unique data set with consumer hand-scan and perceptual data for a national panel of 1821 households, covering two years before and two years after the price war started. Although the price war initially entailed more shopping around and increased spending, spending per visit ultimately dropped because consumers redistributed their purchases across stores. The price war made consumers more sensitive to weekly prices and price image, which helped both the chain that showed an improvement in price image (the price war initiator) and the chains that already had a favorable price image (hard discounters). The price war initiator managed to halt the slide in its market share, and its stock price improved. The losers were the rival mid-level and high-end chains. Unlike the initiator, their price image did not improve, and they suffered from increased price image sensitivity. The authors provide managerial implications for firms that are (or about to be) involved in a price war.

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The impact of brand equity and innovation on the long-term effectiveness of promotions

with Rebecca Slotegraaf, Journal of Marketing Research, 45(June), 293-306, 2008

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Although managers often hope to obtain long-term benefits with temporary marketing actions, academic studies imply that their chances are slim. Extant research has implicitly assumed that the brand itself carries no influence over whether marketing promotions have the power to lift sales permanently. Using panel data for seven years from 100 brands across seven product categories, the authors employ a two-stage approach in which long-term promotional effectiveness is first estimated with persistence modeling and then these effectiveness estimates are related to brand equity and new product introductions. By examining a broad range of brands in each category, the authors find that positive sales evolution from promotional efforts is fairly common, especially for small brands. Moreover, the authors find that both permanent and cumulative sales effects from marketing promotions are greater for brands with higher equity and more product introductions, whereas brands with low equity gain greater benefits from product introductions. These results offer new research and managerial insights into the presence and conditions for persistent benefits from marketing promotions.

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Moving from Free to Fee: How Online Firms Market to Successfully Change the Business Model

with A. Weiss, Journal of Marketing, 45(May), 14-31, 2008, finalist for the 2008 MSI / H. Paul Root Award.

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Moving from free to “free and fee” for any product or service represents a challenge to managers, especially when consumers have plenty of free alternatives. For one online content provider, this article examines (1) the sources of long-term revenue loss (through attracting fewer free subscribers) and (2) how the firm’s marketing actions affect its revenue gains (through attracting paid subscribers). The authors quantify revenue loss from several sources, including the direct effects of charging for part of the online content and the reduced effectiveness of search-engine referrals and e-mails. The analysis suggests several managerial implications. Managers should focus their price promotions on stimulating new monthly subscriptions, rather than the current promotional focus on stimulating new yearly contracts. In contrast, e-mail and search-engine referrals appear to be effective at generating yearly subscriptions. Meanwhile, free-to-fee conversion e-mail blasts are a double-edged sword; they increase subscription revenue at the expense of advertising revenue. Finally, further analysis shows that the move was preceded by the buildup of momentum in new free subscriptions, which appears to be beneficial for the move’s success. The decomposition and comparison of the sources of revenue loss versus gains reveals several trade-offs facing companies moving from free to free and fee.

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Demand-Based Pricing Versus Past-Price Dependence: A Cost-Benefit Analysis

with Vincent Nijs and Shuba Srinivasan, Journal of Marketing, 72 (March), 15-27, 2008

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The authors develop a conceptual framework of the factors that motivate a retailer’s decision to rely on demand conditions and past prices in setting current and future prices. Specifically, they examine the circumstances under which retailers choose demand-based pricing versus past-price dependence for different brands and categories.  Given scarce resources and costs of price adjustments, demand-based pricing is more likely when the customer-driven and firm-driven costs of adjusting pricing patterns are low or when the benefits of such adjustments are high. First, the customer-driven benefits of demand-based pricing are expected to be greater in categories with higher penetration and for brands with higher market share and higher demand sensitivity to price. Second, the firm-driven benefits are greater for categories with higher private-label share. Finally, the customer-driven costs are greater for expensive categories, whereas the firm driven costs are greater for categories with many stock-keeping units. The empirical findings support the conceptual framework, implying that customer-driven and firm-driven benefits are the main stimulants in the retailer’s choice of demand-based pricing. In contrast, customer-driven and firm-driven costs significantly hinder retailer implementation of demand-based pricing. These insights enable retailers to identify problem areas and opportunities to improve the allocation of scarce pricing resources. The results also contribute to the ongoing debate in economics and marketing on the rationality of observed past-price dependence. Whereas previous research points to the negative impact on gross margins of this practice, the authors find that retailers weigh the costs and benefits of demand-based pricing rather than adhere to past-pricing patterns.

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How retailer and competitor decisions drive the long-term effectiveness of manufacturer promotions for fast-moving consumer goods

Journal of Retailing, 83(3), 297-308, 2007, Winner of the 2009 Davidson Best Paper Award.

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While both retailer and competitor decisions contribute to long-term promotional effectiveness, their separate impact has yet to be evaluated. For 75 brands in 25 categories, the author finds that the long-term retailer pass-through of promotions is 65 percent, yielding a long-run wholesale promotional elasticity of 1.78 before competitive response. However, competitors partially match the wholesale price reduction by 15 percent, which decreases promotional elasticity by 10 percent. The range of retailer and competitor response across the analyzed cases is very wide, and is affected by category and brand characteristics. As to the former, large categories yield stronger retailer response, while concentrated categories yield stronger competitor response. As to the latter, smaller brands face a fourfold disadvantage compared to leading brands: they obtain lower retail pass-through, lower retail support, and lower benefits from competing brand’s promotions, while their promotions generate higher benefits to competitors. Interestingly, the mid-1990s move from off-invoice allowances towards scan back deals only partially improves their promotional effectiveness compared to that of leading brands.

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Retail-Price Drivers and Retailer Profits

with Shuba Srinivasan and Vincent Nijs, Marketing Science, 26(4), 473-487, 2007

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What are the drivers of retailer pricing tactics over time? Based on multivariate time-series analysis of two rich data sets, we quantify the relative importance of competitive retailer prices, pricing history, brand demand, wholesale prices, and retailer category-management considerations as drivers of retail prices. Interestingly, competitive retailer prices account for less than 10% of the over-time variation in retail prices. Instead, pricing history, wholesale price, and brand demand are the main drivers of retail-price variation over time. Moreover, the influence Of these price drivers on retailer pricing tactics is linked to retailer category margin. We find that demand-based pricing and category-management considerations are associated with higher retailer margins. In contrast, dependence on pricing history and pricing based on store traffic considerations imply lower retailer margins.

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Performance Regimes and Marketing Policy Shifts

with Dominique M. Hanssens, Marketing Science, 26 (3), 293-311, 2007, leading article.

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Even in mature markets, managers are expected to improve their brands’ performance year after year. When successful, they can expect to continue executing on an established marketing strategy. However, when the results are disappointing, a change or turnaround strategy may be called for to help performance get back on track. In such cases, performance diagnostics are needed to identify turnarounds and to quantify the role of marketing policy shifts in this process. This paper proposes a framework for such a diagnosis and applies several methods to provide converging evidence for two main findings. First, contrary to prevailing beliefs, the performance of brands in mature markets is not always stable. Instead, brands systematically improve or deteriorate their performance outlook in clearly identifiable time windows that are relatively short compared to windows Of stability. Second, these shifts in performance regimes are associated with the brand’s marketing actions and policy shifts, as opposed to competitive marketing. Promotion-oriented marketing policy shifts are particularly potent in improving a brand’s performance outlook.

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When do price threshold matter in retail categories?

with Shuba Srinivasan and Philip-Hans Franses, Marketing Science, 26(1), 83-100, 2007.

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Marketing literature has long recognized that brand price elasticity need not be monotonic and symmetric, but has yet to provide generalizable market-level insights on threshold-based price elasticity, asymmetric thresholds, and the sign and magnitude of elasticity transitions. This paper introduces smooth transition regression models to study threshold-based price elasticity of the top 4 brands across 20 fast-moving consumer good categories. Threshold-based price elasticity is found for 76% of all brands: 29% reflect historical benchmark prices, 16% reflect competitive benchmark prices, and 31% reflect both types of benchmarks. The authors demonstrate asymmetry for gains versus losses on three levels: the threshold size and the sign and the magnitude of the elasticity difference. Interestingly, they observe latitude of acceptance for gains compared to the historical benchmark, but saturation effects in most other cases. Moreover, category characteristics influence the extent and the nature of threshold-based price elasticity, while individual brand characteristics impact the size of the price thresholds. From a managerial perspective, the paper illustrates the sales, revenue, and margin implications for price changes typically observed in consumer markets.

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Modeling Marketing Dynamics by Time Series Econometrics

with Imran Currim, Marnik G. Dekimpe, Eric Ghysels, Dominique M. Hanssens, Natalie Mizik and Prasad Naik, Marketing Letters, 15:4, 167-183, 2005, leading article

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This paper argues that time-series econometrics provides valuable tools and opens exciting research opportunities to marketing researchers. It allows marketing researchers to advance traditional modeling and estimation approaches by incorporating dynamic processes to answer new important research questions. The authors discuss the challenges facing time-series modelers in marketing, provide an overview of recent methodological developments and several applications, and highlight fruitful areas for future research. This discussion is based on the First Annual Conference on ‘Modeling Marketing Dynamics by Time Series Econometrics’ at the Tuck School of Business at Dartmouth, Hanover, New Hampshire, USA on September 16–17, 2004.

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New Products, Sales Promotions, and Firm Value: The Case of the Automobile Industry

with J.Silva-Risso, S.Srinivasan and D.M. Hanssens, Journal of Marketing, 68 (October), 142-156, 2004

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Year after year, managers strive to improve financial performance and firm value through marketing actions such as new product introductions and promotional incentives. This study investigates the short- and long-term impact of such marketing actions on financial metrics, including top-line, bottom-line, and stock market performance. The authors apply multivariate time-series models to the automobile industry, in which both new product introductions and promotional incentives are considered important performance drivers. Notably, whereas both marketing actions increase top-line firm performance, their long-term effects strongly differ for the bottom line. First, new product introductions increase long-term financial performance and firm value, but promotions do not. Second, investor reaction to new product introduction grows over time, indicating that useful information unfolds in the first two months after product launch. Third, product entry in a new market yields the highest top-line, bottom-line, and stock market benefits. Managers may use these results to justify new product efforts and to weigh short- and long- term consequences of promotional incentives.

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How dynamic consumer response, competitor response, company support, and company inertia shape long-term marketing effectiveness

Marketing Science, 23 (4), 596-610.

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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.

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Who benefits from store brand entry?

with S. Srinivasan, Marketing Science, 23 (3), Summer, 364-390, 2004

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Store brand entry has become a key issue in marketing as it may structurally change the performance of and the interactions among all market players. Based on their multivariate time-series analysis, the authors demonstrate permanent performance effects of store brand entry, typically benefiting the retailer, the consumers, and premium-brand manufacturers, while harming second-tier brand manufacturers. For the retailer, they consistently find two beneficial effects of store brand entry: high unit margins on the store brand itself and higher unit margins on the national brands. This increase in unit margins implies that the retailer strengthens its bargaining position vis-à-vis national brand manufacturers. However, store brand entry only rarely yields category expansion and does not create store traffic or revenue benefits. Second, consumers do not obtain lower prices on all national brands, only on some second-tier brands. However, they benefit from enlarged product assortment and intensified promotional activity that lowers average price paid for two out of four categories. For the manufacturers, store brand entry is typically beneficial for premium-price national brands, but not for second-tier national brands. Often, premium brands experience lower long-term price sensitivity and higher revenues, whereas second-tier brands experience higher long-term price sensitivity and lower revenues.

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Do Promotions Benefit Retailers, Manufacturers, or Both

with S. Srinivasan, D.M. Hanssens and M. Dekimpe, Management Science, 50 (5), 617-629, 2004.

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Do price promotions generate additional revenue and for whom? Which brand, category, and market conditions influence promotional benefits and their allocation across manufacturers and retailers? To answer these questions, we conduct a large-scale econometric investigation of the effects of price promotions on manufacturer revenues, retailer revenues, and total profits (margins). A first major finding is that a price promotion typically does not have permanent monetary effects for either party. Second, price promotions have a predominantly positive impact on manufacturer revenues, but their effects on retailer revenues are mixed. Moreover, retailer category margins are typically reduced by price promotions. Even when accounting for cross-category and store-traffic effects, we still find evidence that price promotions are typically not beneficial to the retailer. Third, our results indicate that manufacturer revenue elasticities are higher for promotions of small-share brands, for frequently promoted brands and for national brands in impulse product categories with a low degree of brand proliferation and low private-label shares. Retailer revenue elasticities are higher for brands with frequent and shallow promotions, for impulse products, and in categories with a low degree of brand proliferation. Finally, retailer margin elasticities are higher for promotions of small-share brands and for brands with infrequent and shallow promotions. We discuss the managerial implications of our results for both manufacturers and retailers.

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The Long-Term Effects of Price Promotions on Category Incidence, Brand Choice and Purchase Quantity

with D.M. Hanssens and S. Siddarth, Journal of Marketing Research, vol. 34 (November), 421-439, 2002. Winner of the O’Dell Award 2007.

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To what extent do price promotions have a long-term effect on the components of brand sates, namely, category incidence, brand choice, and purchase quantity? The authors answer this question by using persistence modeling on weekly sales data of a perishable and a storable product derived from a scanner panel. Their analysis reveals, first, that permanent promotion effects are virtually absent for each sales component. Next, the authors develop and apply an impulse response approach to estimate the promotional adjustment period and the total dynamic effects of a price promotion. Specifically, they calculate the long-term equivalent of Gupta’s (1988) 14/84/2 breakdown of promotional effects. Because of positive adjustment effects for incidence but negative adjustment effects for choice, the authors find a reversal of the importance of category incidence and brand choice: 66/11 /23 for the storable product and 58/39/3 for the perishable product. The authors discuss the implications of the findings and suggest some areas for further research.

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