Advances in data collection have made it increasingly easy to collect information on advertising exposures. However, translating this seemingly rich data into measures of advertising response has proven difficult, largely because of concerns that advertisers target customers with a higher propensity to buy or increase advertising during periods of peak demand. We show how this problem can be addressed by studying a setting where a firm randomly held out customers from each campaign, creating a sequence of randomized field experiments that mitigates (many) potential endogeneity problems. Exploratory analysis of individual holdout experiments shows positive effects for both email and catalog; however, the estimated effect for any individual campaign is imprecise, because of the small size of the holdout. To pool data across campaigns, we develop a hierarchical Bayesian model for advertising response that allows us to account for individual differences in purchase propensity and marketing response. Building on the traditional ad-stock framework, we are able to estimate separate decay rates for each advertising medium, allowing us to predict channel-specific short- and long-term effects of advertising and use these predictions to inform marketing strategy. We find that catalogs have substantially longer-lasting impact on customer purchase than emails. We show how the model can be used to score and target individual customers based on their advertising responsiveness, and we find that targeting the most responsive customers increases the predicted returns on advertising by approximately 70% versus traditional recency, frequency, and monetary value鈥揵ased targeting. This paper was accepted by Pradeep Chintagunta, marketing.
Dynamic partition models are used to predict movements in the term structure of interest rates. This allows one to understand historic cycles in the performance of how interest rates behave, and to offer policy makers guidance regarding future expectations on their evolution. Our approach allows for a random number of possible change points in the term structure of interest rates. We use particle learning to learn about the unobserved state variables in a new class of dynamic product partition models that relate macro-variables to term structures. The empirical results, using data from 1970 to 2000, clearly identifies some of the key shocks to the economy, such as recessions. We construct a time series of Bayes factors that, surprisingly, could serve as a leading indicator of economic activity, validated via a Granger causality test. Finally, the in-sample and out-of-sample forecasts from our model are quite robust regardless of the time to maturity of interest rates.
In recent years, Markov chain Monte Carlo (MCMC) methods have been used to provide a full Bayesian analysis both when the posterior distribution of interest is analytically intractable, and it is not known how to draw independent samples. In this article, a non-MCMC approach to sampling from posterior distributions is developed and illustrated. Some sampling problems, now thought to be best handled by MCMC methods alone, are tackled efficiently via independent samples. This article has supplementary material online.
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