Research Paper on Long-term Ad Effectiveness

Chief Marketing Officers (CMOs) are under immense pressure to provide quantifiable evidence of the success of their advertising campaigns. Often, the success of advertising campaigns is equated with immediate, short-term sales, ignoring the indirect, long-term effect. This strategy risks underestimating the full impact of brand advertising and can lead to short-sighted, purely performance-based tactics.

In their methodological working paper, Professor Christian Schlereth, Christina Reh, and Manuel Weber from the Chair of Digital Marketing at WHU – Otto Beisheim School of Management, together with Konstanze Fichtner and Torsten Müller-Klockmann from the Marketing Science Department of Meta, address the long-term ad effectiveness measurement using Marketing Mix Models. The paper takes a closer look at the added value of marketing mix models (MMMs) and proposes different ways of capturing the long-term effect.

Using a fictitious case study inspired by real data sets, the authors compare the relative importance of two advertising channels on sales and brand equity in multiplicative regression models. Here, the model with integrated brand equity mindset metrics provides better model prediction and diagnostic ability than simpler models focusing solely on direct sales. With such a model, marketers can examine which ad channels rather affect short-term direct sales and which rather affect long-term sales mediated by brand equity.

For practitioners, we also provide a code of the simplified model that can be adapted to one’s business needs. The code and the exemplary data are available on GitHub.

If you are interested in a cooperation on this topic, companies are invited to contact Professor Christian Schlereth directly (Christian.schlereth[at]whu.edu).

Link to the working paper on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4103941

Link to the code of the simplified model on GitHub:

https://gist.github.com/WHU-Chair-of-Digital-Marketing/ee05482d39317561ac610a8f6ce72026