Disclaimer: This blog post was written by an external contributor about techniques for validating marketing mix models using cross-validation. The approach laid out in this document is not reflective of how Recast approaches model validation. The actual model specification can be found in the technical model specification. You’ve spent countless …
The right way to cross-validate your Marketing Mix Model Read More »
You can boost revenue by scientifically optimizing your marketing spend. What could go wrong? Statistical mistakes could de-optimize your marketing spend. It will seem very “scientific” while you are doing it. You will feel very good about it until senior management notices the drop in sales – and subjects you …
How confounding variables and company silos can ruin your analysis Read More »
We made this template because honestly it’s a lot of work to do MMM manually if you don’t have the benefit of reusable code or software, and yet nothing beats the humble spreadsheet for developing an intuition for how these things work. We made it free because too many people …
Brand vs. Performance marketing has been a growing battle online since digital advertising grew to be the dominant share of ad spend. Our belief at Recast is that it’s not Brand versus Performance, but that it’s more of a Brand-Performance spectrum, with some channels and campaigns having a longer term …
Measuring the Long Term Effects of Brand with MMM Read More »
The team at Meta did another Marketing Mix Modeling summit last week, and given the popularity of the last one we covered, we thought we’d do it again. This summit covered a lot of familiar themes – e.g. the loss of tracking from iOS14 – but with a particular focus …
The Marketing Science team at Meta set the Marketing Mix Modeling world alight when they released Robyn, their open source solution. Entering the market at a time when everyone was scrambling to solve the iOS14 problem, helped to introduce the method to a wider audience, and validate that it was …
One commonly asked question from clients just getting started with Marketing Mix Modeling (MMM), is how much data they need to build a model. It’s a particularly important question in B2B, where there isn’t always the volume that large scale consumer brands enjoy. In theory there’s no ‘right’ answer to …
How much data do you need for your Marketing Mix Model? Read More »
Disclaimer: This blog post was written by an external contributor about Uber’s open-source Orbit library for time series forecasting and its applications to marketing mix modeling. The approach laid out in this document is not reflective of how Recast approaches time series forecasting and modeling time-varying effects. The actual model …
At Recast I have the privilege and challenge of introducing advanced statistical concepts to people who have a strong desire to be more data driven, but who do not have much of a background in formal statistics. While word has gotten out that “statistical significance” isn’t all it’s cracked up …
Occasionally, prospective customers react with shock when we tell them that we don’t “control for the COVID pandemic”. “The pandemic really impacted our business, so how can your model be accurate if you don’t control for Covid?” they ask. The answer is simple: Recast models changes in the macro-environment, but …
