Modeling heterogeneous effective advertising stock using single-source data

This paper presents a nonlinear modeling of market response between advertising stock and direct utility with heterogeneous parameters using single-source data. We examine advertising threshold effects and measure the effective advertising stock at the individual consumer level. Two kinds of information, i.e., TV advertising exposure data and consumer's purchase history data, are combined for the modeling. The former is used for constructing advertising stock over calendar time via heterogeneous carryover parameters and the latter is applied to the choice model. The Markov chain Monte Carlo (MCMC) method is applied to estimate these heterogeneous parameters. Compared to other possible nonlinear specifications, it is shown that the proposed threshold utility function model with discontinuity at the threshold performs better than other smooth market response models. The empirical results support the existence of an advertising threshold and suggest the pulsing or "on/off" policy for our datasets. In terms of the effective reach, implying the reach after suspending the ad exposure to investigate how it is damping out for a possible "on/off" advertising policy, the optimal "off" interval was measured to be quite short to support a high-frequency pulsing policy, because the carryover parameter as well as the difference of ad stock and threshold are not large enough for our datasets.