Have you ever been excited to dive into understanding marketing mix modeling (MMM) for a project or just to learn, but got stuck trying to find quality datasets? You’re not alone! It can be a real pain trying to sift through endless data sources to find the perfect one for your needs. Whether you’re a student working on a project, a professional testing a new concept, or someone eager to master MMM techniques, I’ve got your back....
One thing you should be aware of when using marketing mix models is something called multicollinearity. This happens when two or more input variables in your model are highly correlated, making it tough to interpret the results. I like to play around with online ads, so I decided to give marketing mix models another try to learn more about them. I don’t actually have anything to sell, but I thought it would be a fun exercise to create an Instagram ad campaign to try and get more followers....
What Is Adstock in Marketing Mix Modeling? Adstock in marketing mix modeling is a way to take into account that the impact of an advertisement on consumer behavior may not be immediate, but rather may build over time. In other words, we need to adjust our model to the fact that changes in how people feel about a product or brand after seeing an ad can last even after the ad is no longer being shown....
There’s a very interesting marketing mix modeling approach published by Uber’s data science team that uses coefficients that vary over time to estimate a media channel’s effects. The modeling approach is called Bayesian Time-Varying Coefficients (BTVC) and it’s available on Orbit, their forecasting package, as Kernel Time-Varying Regression. Instead of getting a single coefficient to understand each media effect, we can see how the effect varied through time with confidence intervals....
The future of advertising attribution is modeled, predicted, estimated, or whatever other word you want. One of the coolest tools (although still under early development) we have to model the impact of advertising campaigns on revenue is LightweightMMM, an implementation of bayesian marketing mix models developed by Google. I talked about using this tool with my course sales data and really liked the results. I was surprised by the number of people that are trying to solve the same problem in their companies....
Last time, I learned it’s very important to have the most granular data possible to build a modern marketing mix model. Usually, this means having daily impressions and conversions (e.g. sales) data. I wanted to find a way to use the power of daily models even with products that don’t sell every day. My course sales data have more days without sales than days with sales, which makes it into a zero vs something prediction instead of a regular regression....
I found about 7 hours of videos of 3 Meta Summits on Marketing Mix Modeling recorded in 2021. They invited experts and companies that used marketing mix models to improve their ROI on the platform. I watched all of it, and here are my learnings. Granularity Is King We need to divide and conquer our datasets. Most of the presentations shared the importance of looking at the data deeper than only at the channel level....
“Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” John Wanamaker (1838 - 1922) Knowing where to spend an advertising budget has always been a big problem for advertisers. The gold standard is testing, which every respectable advertiser does often. It goes back to the 1920s when Claude Hopkins published the still relevant “Scientific Advertising” talking about “measuring keyed returns”....