Smart Advertising Decisions
Project Summary
| Category | Marketing |
|---|---|
| Customer | Omnicom Media Group |
| Period | 2016-12-01 to 2018-09-30 |
Overview
Machine learning for precision marketing.
Projects and Contracts
- Contract Art. 83 between Omnicom Media Group S.A. and Univ. Complutense de Madrid. PI: David Gómez-Ullate (UCM-ICMAT), 01/12/2016 - 30/11/2017, Sum: 27.600 EUR.
- AI on geodata applied to conversion and CTR prediction in precision marketing. Contract Art. 83 between Omnicom Media Group S.A. and Univ. Complutense de Madrid. PI: David Gómez-Ullate (UCM-ICMAT), 01/12/2017 - 30/09/2018, Sum: 20.400 EUR.
One of the central problems in marketing and advertising is to answer the following question:
How much do advertising campaigns actually increase sales? And which channels are worth the investment?
In this project, we study real sales and advertising data from a large fast-food franchise to understand how different advertising channels (TV, online, radio, outdoor, etc.) influence weekly sales. Instead of relying on simple correlations, we use a data-driven approach that separates long-term trends, seasonal effects (such as holidays), and external factors like weather or major events.
The result is a practical decision-support tool that helps managers answer questions such as “Where should I spend my advertising budget next week?” and “What trade-off am I making between expected sales and risk?” The model not only forecasts future sales accurately, but also provides clear guidance on how to allocate advertising budgets more effectively across channels.
From a methodological perspective, the analysis is based on a Bayesian dynamic linear (state-space) model that builds on the classic Nerlove–Arrow framework for advertising response. This approach models advertising as a stock with delayed and decaying effects over time, while naturally incorporating trends, seasonality, and uncertainty. The Bayesian formulation allows prior information to be included and provides full predictive distributions, making it especially well suited for forecasting and risk-aware budget allocation.
This work shows how modern statistical methods can turn complex business data into actionable insights for real-world decision making. In addition, we explored how different advertising plans can be compared using multi-objective optimization, highlighting trade-offs between expected sales, risk, and innovation.
This project was an Art.83 collaboration between my group at Universidad Complutense and Omnicom Media Group. Besides providing an actionable model for the advertising company, it led to a couple of academic publications:
