Publication | Open Access
Advertising Effectiveness for Multiple Retailer-Brands in a Multimedia and Multichannel Environment
74
Citations
37
References
2020
Year
Marketing AnalyticsDigital MarketingRelative ImportanceTargeted AdvertisingTobit ModelConsumer ResearchMultichannel MarketingCommunicationManagementMarketing CommunicationMultiple Retailer-brandsMultichannel EnvironmentStatisticsBrand AwarenessConsumer AppealMarketingAdvertisingInteractive MarketingBusinessMultichannel ManagementAdvertising EffectivenessBrand EquityMultimedia Ad Exposures
Customer-level analysis of how each advertising medium influences sales across multiple retailer‑brands in a product category has been largely unexplored, but recent customer databases now enable such studies. The study tracks 4,000 customers over two years, linking exposure to email, catalogs, and paid search with in‑store and online purchases for three clothing retailer‑brands, and estimates a Tobit model with individual‑level response parameters using variational Bayes to handle 2.4 million observations and 60 random effects. Email and catalogs from a focal retailer‑brand can suppress sales of other brands, paid search affects only the focal brand, competitor catalogs boost focal brand sales among omnichannel shoppers, and a sizable cross‑brand, cross‑channel customer segment is the most responsive to multimedia advertising.
An important aspect of multimedia advertising effectiveness that remains unexplored is a customer-level analysis of the relative importance of each medium for multiple retailer-brands within a product category. The increasing availability of customer databases for parent companies containing multimedia ad exposures, sales transactions in several purchase channels, and information across multiple retailer-brands now allows for a broader examination of advertising effectiveness. In this research, the authors monitor 4,000 customers over two years, linking their exposure to three media (email, catalogs, and paid search) to their in-store and online purchases for three retailer-brands in the clothing category. They develop a Tobit model for sales response to multimedia advertising that captures within-brand and within-channel correlations and accommodates individual-level advertising response parameters. Due to the very large number of observations (2.4 million) and random effects (60), the authors employ an emerging machine learning technique, variational Bayes, to estimate the model parameters. They find that email and sometimes catalogs from a focal retailer-brand have a negative influence on other retailer-brands in the category, whereas paid search influences only the focal retailer-brand. However, competitor catalogs often positively influence focal retailer-brand sales, but only among omnichannel customers. They segment customers by retailer-brand and channel usage, revealing a sizeable group of customers who shop across multiple retailer-brands and both purchase channels. Moreover, this segment is the most responsive to multimedia advertising.
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