Multicollinearity is one of the hardest problems in marketing analytics. If you took a statistics course in undergrad, you might have covered it; it describes a phenomenon where two or more variables in a model are correlated. Let’s bring it to marketing to make it more tangible. Imagine we’re trying …
How D2C Brands Can Deal with Multicollinearity in their Marketing Mix Modeling Read More »
With digital tracking breaking and privacy concerns on the rise, more consumer brands are looking for alternatives to multi-touch attribution. While MTA is still the standard measurement method, especially in the early growth stages, marketing mix modeling is quickly growing in usage across top brands because it only needs aggregate …
There are three main ways that consumer brands measure marketing effectiveness: digital tracking (MTA), marketing mix modeling (MMM), and testing/conversion lift studies (CLS). For the last few years, we’ve lived in a world where most consumer brands didn’t have to think too much about this – they got away with …
Top consumer brands don’t pick one measurement method: they triangulate. Read More »
Marketing Mix Modeling’s main goal is to find the incremental return of every marketing investment – how many sales did each channel drive that we wouldn’t have gotten otherwise? If you can find true incrementality, you can use it to optimize your budgets, get the right channel mix, and get …
3 Methods To Validate Your Marketing Mix Modeling for True Incrementality Read More »
Most small e-commerce brands probably don’t need anything more complicated than digital tracking tools for measuring marketing performance. But when the channel mix gets more complex and their marketing grows, digital tracking is simply not enough. After $100k+ / month in paid media budget, it’s a good time to start …
From Digital Tracking To MMM: How Brands Scale Through Marketing Experimentation Read More »
MMM is hard – we totally get that, but there are common mistakes made by consultants in the MMM space that need addressing. Here’s 10 of them: 1 – Focusing on the in-sample R-squared metric for MMM models. The standard definition of R-squared is “the amount of variation in the …
People who have been around the world of marketing mix modeling (MMM) for a while are often shocked that Recast is designed to be refreshed on a weekly cadence. “How are you able to do in a week what it takes many other vendors months to do?” they might ask. …
Marketers hate planning season. It can often feel like an impossible task: you’re being asked to predict the future in an industry where consumer sentiment (not to mention channel performance) can change suddenly and where multi-touch attribution tools (MTA) can’t be used for reliable predictions. Over the last few years …
MMM is hard enough — but it’s just impossible if you don’t have the right data. Getting good, clean, and ideally automated data is critical to run a really good model because, without it, your model will take so much longer to set up and train – and it won’t …
What Data is Needed for Marketing Mix Modeling (MMM)? Read More »
Marketing Mix Models (MMM) are very complex, and the choice of the parameters your data scientists choose can really influence the results you will get. If the model allows for your analyst to guess, check, and set parameters until things look right to them, there’s a high chance the end …
How to reduce bias in your marketing mix modeling (MMM) with better parameter settings Read More »
