A common question I often get about media attribution is, “How do you attribute media to sales?” This query always excites my inner data nerd! My typical enthusiastic response is, “We fit a hierarchical Bayesian model that relates adstock saturation transformed media to sales,” which usually leaves everyone more confused.
So, what does that mean in plain English?
Let’s break it down.
Hierarchical means we are stacking similar data together, such as by region, and sharing insights across these groups. This allows us to draw more robust conclusions by leveraging similarities and differences across the data.
Bayesian refers to the statistical approach we use, which not only helps us learn the effect of media on sales but also measures our confidence in these effects. It provides a probabilistic framework, giving us a richer understanding of the data and its uncertainties.
Adstock captures the time-delay effect of media. Most advertising has a delayed impact on sales, and adstock helps us account for this lag.
Saturation accounts for diminishing returns. After a certain point, additional media spend has less and less of an impact. The saturation function helps us adjust for this nonlinear effect.
So, in simpler terms, a better explanation might be “We adjust media for delayed impact and diminishing returns, then relate adjusted media to sales using a model that shares learning and estimates uncertainty.”
By adjusting for these factors, our models provide a more accurate and nuanced understanding of how advertising translates into sales. This approach not only helps in pinpointing the effectiveness of different media channels but also enhances our strategic planning by understanding the varying impacts across regions.
Understanding these concepts can significantly elevate your media attribution strategy, providing actionable insights and driving more informed decision-making in your marketing efforts.
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