Practice Prize Winner: Direct mail to prospects and email to current customers? Modeling and field-testing multichannel marketing 

Multichannel retailers need to understand how to allocate marketing budgets to customer segments and online and offline sales channels.  We propose an integrated methodological approach to assess how email and direct mail effectiveness vary by channel and customer value segment. We apply this approach to an international beauty retailer in six countries and to an apparel retailer in the United States. We estimate multi-equation hierarchical linear models and find that sales responsiveness to email and direct mail varies by customer value segment. Specifically, direct mail drives customer acquisition in the offline channel, while email drives sales for both online and offline channels for current customer segments. A randomized field experiment with the beauty retailer provides causal support for the findings. The proposed reallocation of marketing resources would yield a revenue lift of 13.5% for the beauty retailer and 9.3% for the apparel retailer, compared with the 6.5% actual increase in the field experiment.

Cite as: Valenti, A., Srinivasan, S., Yildirim, G., & Pauwels, K. (2023). Direct mail to prospects and email to current customers? Modeling and field-testing multichannel marketing. Journal of the Academy of Marketing Science, 1-20.

with Ceren Demirci, Gokhan Yildirim and Shuba Srinivasan, International Journal of Research in Marketing, 33(4), 2016, 739-753.

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Rooted in the integrated marketing communication framework, this paper conceptualized how brand familiarity affects online and cross-channel synergies. The empirical analysis uses Bayesian vector autoregressive models to estimate long-term elasticities for four brands. The authors distinguish customer-initiated communication (typically online) from firm-initiated communication (typically offline). Their results indicate that within-online synergy is higher than online-offline synergy for both familiar brands but not for both unfamiliar brands. Managers of unfamiliar brands may obtain substantial synergy from offline marketing spending, even though its direct elasticity pales in comparison with that of online media while managers of familiar brands can generate more synergy by investing in different online media.

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with Evert de Haan and Thorsten Wiesel, International Journal of Research in Marketing, 33(3), 2016, 491-507.

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The Internet has given rise to many new forms of advertising. Scientific studies have focused on individual reactions to specific advertising forms in isolation and have offered little guidance for aggregate-level budget allocation decisions, which are typically based on simple rules. This article compares the long-term effectiveness of nine forms of advertising—seven online and two offline—by means of a structural vector autoregressive model and restricted impulse responses. For five product categories, we investigate how these forms of advertising generate traffic, affect conversion, and contribute to revenue. We find that content-integrated advertising is the most effective form, followed by content-separated advertising and firm-initiated advertising. Although online advertising forms have similar power to drive traffic, content integration dominates content separation in the area of progression toward purchase. Last click attribution underestimates content-integrated activities and suggests online advertising budget allocations that yield 10%–12% less revenue than the status quo, whereas the model’s proposed online advertising budget allocation yields a 21% revenue increase over the status quo. These results highlight the payoffs for companies that integrate content into online media

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with Shuba Srinivasan and Oliver Rutz, Journal of the Academy of Marketing Science, 2015, 1-14.

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This study investigates the effects of consumer activity in online media (paid, owned, and earned) on sales and their interdependencies with the traditional marketing mix elements of price, advertising and distribution. We develop an integrative conceptual framework that links marketing actions to online consumer activity metrics along the consumer’s path to purchase (P2P). Our framework proposes that the path to purchase has three basic stages–learning (cognitive), feeling (affective), behavior (conative)—and that these can be measured with novel online consumer activity metrics such as clicking on a paid search ads (cognitive) or Facebook likes and unlikes of the brand (affective). Our empirical analysis of a fast moving consumer good supports a know–feel–do pathway for the low–involvement product studied. We find, for example, that earned media can drive sales. However, we find that the news is not all good as it relates to online consumer activity: higher consumer activity on earned and owned media can lead to consumer disengagement in the form of unlike. While traditional marketing such as distribution (60%) and price (20%) are the main drivers of sales variation for the studied brand, online owned (10%), (un)earned (3%), and paid (2%) media explain a substantial part of the path to purchase. It is noteworthy that TV advertising (5%) explains significantly less than online media in our case. Overall, our study should help strengthen marketers’ case for building share in consumers’ hearts and minds, as measured through consumer online activity and engagement.

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with Oliver Rutz and Kerstin Reimer, Journal of Interactive Marketing, 28 (4), 2014, 271-284.

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Online commerce gives companies not only a growing global sales platform, but also powerful consumers enjoying 24/7 availability, choice proliferation and the power to opt in and out permission-based communication. Unfortunately, our knowledge is limited on long-term marketing effectiveness in this space and on how it differs across customer segments. Managers appear overwhelmed by the combination of rich online data on hundreds of thousands of customers and the typical aggregate-level data on offline marketing spending.

This paper is the first to investigate the long-term impact of coupon promotions, TV, radio, print, and Internet advertising across customer segments for a major digital music provider with over 500,000 customers. We first segment customers and subsequently analyze how these segments respond in the long run to different marketing activities when purchasing music downloads. Our findings reveal that the effectiveness of marketing differs across segments, while standard segmentation approaches fail to identify the most valuable catches in a sea of consumers. In contrast to empirical generalizations on consumer packaged goods, heavy users of digital music products are least sensitive to price and most sensitive to TV advertising and to multiple touch points. Light users, the majority of consumers, are price-sensitive and tend to opt-out of targeted communication. Our research enables managers in the digital media space to target high-value customer segments with the most effective actions.

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with Pavel Kireyev and Sunil Gupta, International Journal of Research in Marketing, 33(3), 2016, 475-490.

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As firms increasingly rely on online media to acquire consumers, marketing managers feel comfortable justifying higher online marketing spend by referring to online metrics such as click‐through rate (CTR) and cost per acquisition (CPA). However, these standard online advertising metrics are plagued with attribution problems and do not account for dynamics. These issues can easily lead firms to overspend on some actions and thus waste money, and/or underspend in others, leaving money on the table.

We develop a multivariate time series model to investigate the interaction between paid search and display ads, and calibrate the model using data from a large commercial bank that uses online ads to acquire new checking account customers. We find that display ads significantly increase search conversion. Both search and display ads also exhibit significant dynamics that improve their effectiveness and ROI over time. Finally, in addition to increasing search conversion, display ad exposure also increases search clicks, thereby increasing search advertising costs. After accounting for these three effects, we find that each $1 invested in display and search leads to a return of $1.24 for display and $1.75 for search ads, which contrasts sharply with the estimated returns based on standard metrics. We use these results to show how optimal budget allocation may shift dramatically after accounting for attribution and dynamics. Although display benefits from attribution, the strong dynamic effects of search call for an increase in search advertising budget share by up to 36% in our empirical context.

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