Marketing Mix Modeling

Every statistical model lives on a continuum between bias and variance, and navigating that tradeoff is one of the most critical modeling decisions you’ll ever make. Let’s start by getting clear on each term: Bias is the average error a model makes when fitting historical data. A high-bias model is […]

At Recast we spend a lot of time thinking about how to evaluate whether or not a model is “good”. People new to Recast are often surprised when we tell them that while we care a lot about checking to see if a model is good, we generally don’t use

If your model is showing you one of the following scenarios, you’re probably dealing with an identifiability issue: Identifiability answers a simple question: Given the data you have, is there only one realistically plausible explanation for how that data was generated?  If the answer is no – if many different

Most MMMs look great… as long as you don’t change anything.  But that’s the whole point of doing media mix modeling. You need to be able to double your TV spend or cut your branded search and have your MMM forecast accurately what will happen. You don’t need prettier curves

Priors are one of the least understood – and most essential – parts of a Bayesian media mix model. In simple words, a prior is what you know (or believe) about the world before the model sees any data. In marketing, that might include common-sense expectations like: “spending money on

Most media mix models can’t be proven wrong. That’s a problem. You’re supposedly using your model to forecast and allocate millions of dollars on marketing spend… and you can’t tell if it’s right or wrong? The issue is that most models don’t make testable claims – and that needs to

Most marketing mix models break because they assume that marketing effectiveness is constant over time – but that’s not how marketing works in the real world. Marketing effectiveness changes constantly. Sometimes dramatically. It changes when you update creative. It changes when your audience saturates. It changes when new competitors enter

Let’s start with the “bad” news: the true incremental ROI of any campaign is unknown and unknowable. Marketing performance lives in a world of noise, lags, and missing data. We would all love to be able to report numbers like “3.7x ROI” as if they were the objective truth –

You have probably seen R-squared everywhere. It is one of the most widely taught and commonly cited metrics in statistics. It’s taught in every introductory stats course. It’s included by default in nearly all statistical software packages. Run a regression, and R-squared is going to be the metric they base

Let’s say you spent millions last quarter on a brand campaign. Where does that show up in your MMM?  Everyone wants a clean causal brand measurement chain: “we spent $1M on TV → awareness increased → revenue went up.” It’s absolutely not that simple. There are 3 main challenges for