07-29, 17:40–17:50 (UTC), Green
"Half the money I spend on advertising is wasted; the trouble is I don't know which half." (J.Wanamaker, 19th-century retailer)
Optimizing marketing spend is still difficult, but this talk introduces a modern marketing analysis: Media Mix Modelling (MMM).
We will combine the strength of Julia with Bayesian decision-making to optimize marketing spend for a hypothetical business.
Find more details in the associated GitHub Repo
This talk requires no previous knowledge.
Media Mix Modelling (MMM) is the go-to analysis for deciding how to spend your precious marketing budget. It has been around for more than half a century, and its importance is poised to increase with the rise of the privacy-conscious consumer.
There are a few key marketing concepts that we will cover, e.g., ad stock, saturation and ROAS.
We will leverage the power of Bayesian inference with Turing.jl to establish the effectiveness of our campaigns (/marketing channels). The main advantage of the Bayesian approach will be the quantification of uncertainty, which we will channel into our decision-making when deciding on the budget allocations.
The "optimal" spend strategy ("budget") will be found with the help of Metaheuristics.jl.
Overall, we will draw on Julia's core strengths, such as composability and speed.
The implementation closely follows the methodology of the amazing Robyn package, but it leverages Bayesian inference for the marketing parameters. While there are many resources available for Python and R, I believe this is the first tutorial for MMM in Julia.
Following the talk, you can use the provided notebook and scripts to replicate this analysis for your marketing budget.
You can find the notebook, presentation and additional resources in the following repository:
- GitHub Repo
- PDF of the presentation
Session photo thanks to Diggity Marketing on Unsplash
I'm Head of Data and Product Insight at LexisNexis.