What is Marketing Mix Modeling?
Marketing Mix Modeling is a sophisticated statistical method that analyzes a company's historical data to determine the impact of various marketing and non-marketing activities on business outcomes.
Why Use Marketing Mix Modeling?
MMM provides a unified view of how all your initiatives contribute to your results. It answers crucial questions like, "What was the return on my investment?" and "What factors drove my KPI?" This knowledge allows you to make informed decisions, optimize your budget, and maximize your returns for sustainable growth. It's important to note that the model doesn't rely on external attribution data; instead, the attribution insights are generated directly from the model's data-driven learning.
How Does Marketing Mix Modeling Work?
The core of our platform's MMM engine is an advanced Bayesian framework. This approach goes beyond traditional methods to provide robust statistical inference and probabilistic predictions. Instead of providing a single, fixed answer, the Bayesian framework calculates a range of possible outcomes and quantifies the uncertainty around them. This allows the model to continuously learn and improve as new data is introduced.
The engine works by analyzing historical time-series data, including media spend, pricing, and external factors like seasonality and economic trends. It applies mathematical transformations to account for key marketing concepts:
Adstock: This models the "carryover" effect of advertising, where the impact of an ad persists over time, affecting KPI long after the initial exposure.
Diminishing Returns: This accounts for the “saturation effect”, where the effectiveness of each additional unit of spend decreases as a campaign reaches its audience limit.
By incorporating these factors, the Bayesian optimization engine can accurately decompose total KPI into a baseline (natural demand) and an incremental portion (driven by marketing activities). The result is a detailed, interpretable model that not only shows what happened in the past, but also provides a foundation for powerful "what-if" scenario simulations and budget optimization, all with a clear understanding of the confidence and risk associated with each recommendation.