Don't Let Multicollinearity Mess Up Your Marketing Mix Model
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....
Adstock in Marketing Mix Modeling
Table of Contents What Is Adstock in Marketing Mix Modeling? Why Is Adstock Important In Marketing Mix Modeling? How To Calculate Geometric Adstock Model in Python? How To Calculate Weibull Adstock Model in Python? Which Adstock Models Are Available In Robyn? Geometric Adstock Model Weibull Adstock Model Which Adstock Models Are Available In LightweightMMM? Adstock Hill Adstock Carryover How To Choose The Best Adstock Model?...
Implementing Uber's Marketing Mix Model With Orbit
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....
How To Create A Marketing Mix Model With LightweightMMM
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....
How I Built A Daily Marketing Mix Model For A Product That Doesn’t Sell Every Day
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....
What I Learned Watching 7 Hours of Meta's Marketing Mix Modeling Summits
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.
Table of Contents Granularity Is King If You Have Daily Data, Prefer It Over Weekly Data Prefer Short Modeling Periods And More Recent Data Models Work Better When There Is High Variability In The Inputs Validation With Experiments Marketing Mix Models vs Multi-touch Attribution Modeling Tips Marketing Mix Modeling Is Living a Renaissance Borrowing Methods From Machine Learning Are Making Them Even More Powerful “Always-On” Models Next Steps and Sources Granularity Is King We need to divide and conquer our datasets....
Are Marketing Mix Models Useful? I Spent My Own Money To Find Out
“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”....