Bayesian Cointegrated Panels in Digital Marketing

TL;DR

This paper introduces a Bayesian cointegrated panel model tailored for digital marketing, enabling analysis of how investment strategies influence key ROI metrics like clicks and impressions, validated through real data and simulations.

Contribution

It presents a novel Bayesian cointegrated panel modeling approach specifically designed for digital marketing ROI metrics, with demonstrated effectiveness in real and simulated data.

Findings

Clicks and impressions significantly impact session generation.

The proposed model shows high estimability and accuracy.

Simulation results confirm robustness across different processes.

Abstract

In this paper, we fully develop and apply a novel extension of Bayesian cointegrated panels modeling in digital marketing, particularly in modeling of a system where key ROI metrics such as clicks or impressions of a given digital campaign considered. Thus, in this context our goal is evaluating how the system reacts to investment perturbations due to changes in the investment strategy and its impact on the visibility of specific campaigns. To do so, we fit the model using a set of real marketing data with different investment campaigns over the same geographic territory. By employing forecast error variance decomposition, our findings indicate that clicks and impressions have a significant impact on session generation. Also, we evaluate our approach through a comprehensive simulation study that considers different processes. The results indicate that our proposal has substantial capabilities in terms of estimability and accuracy.