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.
Summary
We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value‐at‐risk forecasts.
We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defined over a class of plausible predictive models. After observing data, we update the prior to a posterior over these models, via a criterion that captures a user-specified measure of predictive accuracy. Under regularity, this update yields posterior concentration onto the element of the predictive class that maximizes the expectation of the accuracy measure. In a series of simulation experiments and empirical examples, we find notable gains in predictive accuracy relative to conventional likelihood-based prediction.
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