Bayesian methods for media mix modeling with carryover and shape effects.
by Jin Y, Wang Y, Sun Y, Chan D, Koehler J.
Media mix models are used by advertisers to measure the effectiveness of their advertising and provide insight in making future budget allocation decisions. Advertising usually has lag effects and diminishing returns, which are hard to capture using linear regression. In this paper, we propose a media mix model with flexible functional forms to model the carryover and shape effects of advertising. The model is estimated using a Bayesian approach in order to make use of prior knowledge accumulated in previous or related media mix models. We illustrate how to calculate attribution metrics such as ROAS and mROAS from posterior samples on simulated data sets. Simulation studies show that the model can be estimated very well for large size data sets, but prior distributions have a big impact on the posteriors when the sample size is small and may lead to biased estimates. We apply the model to data from a shampoo advertiser, and use Bayesian Information Criterion (BIC) to choose the appropriate specification of the functional forms for the carryover and shape effects. We further illustrate that the optimal media mix based on the model has a large variance due to the variance of the parameter estimates.
Challenges and Opportunities in Media Mix Modeling
by David Chan and Mike Perry
Advertisers have a need to understand the effectiveness of their media spend in driving sales in order to optimize budget allocations. Media mix models are a common and widely used approach for doing so. The paper outlines the various challenges such models encounter in consistently providing valid answers to the advertiser’s questions on media effectiveness. The paper also discusses opportunities for improvements in media mix models that can produce better inference.
Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling
by Edwin Ng, Zhishi Wang, Athena Dai
Both Bayesian and varying coefficient models are very useful tools in practice as they can be used to model parameter heterogeneity in a generalizable way.
Motivated by the need of enhancing Marketing Mix Modeling at Uber, we propose a Bayesian Time Varying Coefficient model, equipped with a hierarchical Bayesian structure.
This model is different from other time varying coefficient models in the sense that the coefficients are weighted over a set of local latent variables following certain probabilistic distributions.
Stochastic Variational Inference is used to approximate the posteriors of latent variables and dynamic coefficients.
The proposed model also helps address many challenges faced by traditional MMM approaches.
We used simulations as well as real world marketing datasets to demonstrate our model superior performance in terms of both accuracy and interpretability.
Hierarchical marketing mix models with sign constraints
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Hao Chen, Minguang Zhang, Lanshan Han & Alvin Lim (2021)
Journal of Applied Statistics, 48:13-15, 2944-2960.
Marketing mix models (MMMs) are statistical models for measuring the effectiveness of various marketing activities such as promotion, media advertisement, etc.
In this research, we propose a comprehensive marketing mix model that captures the hierarchical structure and the carryover, shape and scale effects of certain marketing activities, as well as sign restrictions on certain coefficients that are consistent with common business sense.
In contrast to commonly adopted approaches in practice, which estimate parameters in a multi-stage process, the proposed approach estimates all the unknown parameters/coefficients simultaneously using a constrained maximum likelihood approach and solved with the Hamiltonian Monte Carlo algorithm.
We present results on real datasets to illustrate the use of the proposed solution algorithm.
Bayesian Modeling of Marketing Attribution
by Ritwik Sinha, David Arbour, Aahlad Manas Puli
In a multi-channel marketing world, the purchase decision journey encounters many interactions (e.g., email, mobile notifications, display advertising, social media, and so on). These impressions have direct (main effects), as well as interactive influence on the final decision of the customer. To maximize conversions, a marketer needs to understand how each of these marketing efforts individually and collectively affect the customer’s final decision. This insight will help her optimize the advertising budget over interacting marketing channels. This problem of interpreting the influence of various marketing channels to the customer’s decision process is called marketing attribution. We propose a Bayesian model of marketing attribution that captures established modes of action of advertisements, including the direct effect of the ad, decay of the ad effect, interaction between ads, and customer heterogeneity. Our model allows us to incorporate information from customer’s features and provides usable error bounds for parameters of interest, like the ad effect or the half-life of an ad. We apply our model on a real-world dataset and evaluate its performance against alternatives in simulations.
Hidden Markov model for single user response prediction in Digital Advertising Campaigns
by Petrone, Thomas
Online advertising is a multi-billion dollar industry, and for marketers being able to optimize their budget spent is crucial. Today, innovation in technology leads to a wide variety of different digital advertising formats, ranging from search advertising to social media campaigns. Marketing experts struggle to infer which of these campaigns are the most effective, and how to attribute them a weight in driving customers to a conversion event. This thesis addresses the Attribution Problem considering the conversion paths followed by individual users. We model the users’ involvement in an advertising campaign exploiting the Conversion Funnel, and deduct the effect of each advertising event considering a Hidden Markov Model that characterises a user’s behaviour in different positions of the Conversion Funnel. To optimize the advertising campaign, we propose a method based on a customised version of the Policy Gradients with a Parameter-based Exploration algorithm to determine the optimal policy that maximises the conversion rate of a campaign with the same cumulative budget. It is essential to consider that the information on customers’ interactions during an advertising campaign when considering a granularity up to the single exposition event is difficult to obtain. Hence, we propose an Advertising Campaign Simulator to emulate the behaviour of users during a marketing event.