Does online information drive offline revenues? Only for specific products and consumer segments
with Peter Leeflang, Marije Teerling and Eelko Huizingh, Journal of Retailing, 87 (1) p 1-17, 2011. Winner of the 2013 Davidson Best Paper Award
Full Article
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While many offline retailers have developed informational websites that offer information on products and prices, the key question for such informational websites is whether they can increase revenues via web-to-store shopping. The current paper draws on the information search literature to specify and test hypotheses regarding the offline revenue impact of adding an informational website. Explicitly considering marketing efforts, a latent class model distinguishes consumer segments with different short-term revenue effects, while a Vector Autoregressive model on these segments reveals different long-term marketing response.
We find that the offline revenue impact of the informational website critically depends on the product category and customer segment. The lower online search costs are especially beneficial for sensory products and for customers distant from the store. Moreover, offline revenues increase most for customers with high web visit frequency. We find that customers in some segments buy more and more expensive products, suggesting that online search and offline purchases are complements. In contrast, customers in a particular segment reduce their shopping trips, suggesting their online activities partially substitute for experiential shopping in the physical store. Hence, offline retailers should use specific online activities to target specific product categories and customer segments.
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Mindset Metrics in Market Response Models: An Integrative Approach
with S. Srinivasan and Marc Vanhuele, Journal of Marketing Research, 47 (4), 672-684, 2010, winner of the 2011 Best Paper Award of Syntec (French professional association of consultants) in Marketing and Decision Sciences.
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Demonstrations of marketing effectiveness currently proceed along two parallel tracks: Quantitative researchers model the direct sales effects of the marketing mix, and advertising and branding experts trace customer mind-set metrics (e.g., awareness, affect). The authors merge the two tracks and analyze the added explanatory value of including customer mind-set metrics in a sales response model that already accounts for short- and long-term effects of advertising, price, distribution, and promotion. Vector autoregressive modeling of the metrics for more than 60 brands of four consumer goods shows that advertising awareness, brand consideration, and brand liking account for almost one-third of explained sales variance. Competitive and own mind-set metrics make a similar contribution. Wear-in times reveal that mind-set metrics can be used as advance warning signals that allow enough time for managerial action before market performance itself is affected. Specific marketing actions affect specific mind-set metrics, with the strongest overall impact for distribution. The findings suggest that modelers should include mind-set metrics in sales response models and
branding experts should include competition in their tracking research.
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Do You Want to be my “Friend”? Monetary Value of Word-of-Mouth Marketing in Online Communities
GfK-Marketing Intelligence Review, Vol. 2 No. 1 / 2010.
Short Article
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How large and lasting are the effects of word-of-mouth (WOM) referrals versus paid marketing? What is the $ / € worth of a WOM-referral to an Internet social networking site? This study finds that word-of-mouth referrals have substantially longer carryover effects than traditional marketing actions. The long-run elasticity of WOM on site signups is 0.53; about 20 times higher than that of marketing events, and 30 times that of media appearances. Based on revenue from advertising impressions served to a new member of the site, the monetary value of a WOM referral is about $0.75 per year. By sending out 10 referrals, each network member thus brings in $7.50 to the firm; which represents the maximum reward the firm could consider to incentivize word-of-mouth referrals. Managers can use this approach and findings to benchmark metrics for both WOM and traditional marketing, to test changes in online WOM referral content, and to decide on the appropriate size of financial incentives to stimulate WOM.
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Dashboards as a Service: Why, What, How and What Research is Needed?
with T. Ambler, B. Clark, P. LaPointe, D. Reibstein, B. Skiera, B. Wierenga, T. Wiesel, Journal of Service Research, 12 (2), 175-189, November 2009.
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Recent years have seen the introduction of a “marketing dashboard” that brings the firm’s key marketing metrics into a single display. Service firms across industries have created such dashboards either by themselves or together with a dashboard service provider. This article examines the reasons for this development and explains what dashboards are, how to develop them, what drives their adoption, and which academic research is needed to fully exploit their potential. Overcoming the challenges faced in dashboard development and operation provides many opportunities for marketing to exercise a stronger influence on top management decisions. The article outlines five stages of dashboard development and discusses the relationships among demand for dashboards, supply of dashboards, and the implementation process in driving adoption and use of dashboard systems. Key topics for future research include metrics selection, relationships among metrics, and the ultimate question of whether dashboards provide sufficient benefits to justify their adoption.
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What is Important? Identifying Metrics that Matter
with M. Lautman, Journal of Advertising Research, 49 (3), September, 339-359, 2009.
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How can the importance of consumer wants and needs be quantified? Using data sets from multiple consumer brand and advertising tracking studies, several standard traditional market research techniques are compared to vector autoregression (VAR) modeling. It is demonstrated that by utilizing VAR models and resolving causal ambiguity, key performance indicators can be identified that not only correlate with traditional market research summary metrics, such as overall ratings and purchase interest, but that also drive brand sales/share and thereby qualify as metrics that matter. The analytic philosophy underlying the VAR analytic approach also is shown to be consistent with (and complementary to) market mix modeling analysis. Presented is a procedure for the simultaneous assessment of the relative and absolute impact of multiple marketing initiatives on baseline and incremental sales—including advertising and promotion effects and traditional consumer awareness and attitudinal metrics—facilitating resource-allocation decisions and providing marketers within a single framework for return on marketing investment optimization.
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Effects of Word of Mouth versus Traditional Marketing: Findings for an Internet Social Networking Site
with M. Trusov and R. Bucklin, Journal of Marketing, 73(5), September, 90-102, 2009, Emerald Mgmt Reviews Citation of Excellence 2009, and runner-up for the MSI/H. Paul Root Award for best JM article.
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The authors study the effect of word-of-mouth (WOM) marketing on member growth at an Internet social networking site and compare it with traditional marketing vehicles. Because social network sites record the electronic invitations sent out by existing members, outbound WOM may be precisely tracked. WOM, along with traditional marketing, can then be linked to the number of new members subsequently joining the site (signups). Due to the endogeneity among WOM, new signups, and traditional marketing activity, the authors employ a Vector Autoregression (VAR) modeling approach. Estimates from the VAR model show that word-of-mouth referrals have substantially longer carryover effects than traditional marketing actions. The long-run elasticity of signups with respect to WOM is estimated to be 0.53 (substantially larger than the average advertising elasticities reported in the literature) and the WOM elasticity is about 20 times higher than the elasticity for marketing events, and 30 times that of media appearances. Based on revenue from advertising impressions served to a new member, the monetary value of a WOM referral can be calculated; this yields an upper bound estimate for the financial incentives the firm might offer to stimulate word-of-mouth.
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
Presentation:
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.
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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New Benefits from Adding an Old Channel
Insights from Marketing Science Institute, Spring 2008.
Short Article
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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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