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 to extreme scrutiny. One way …
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 we know were getting ripped …
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 effect, and others getting quicker …
Measuring the Long Term Effects of Brand with MMM Read More »
One of the core problems to solve when doing marketing mix modeling (MMM) is handling how the results from a previous period ‘carry over’ into the next period. In marketing we know that the full effect of advertising isn’t felt on the same day or week in which the campaign ran. For example a memorable …
Adstocks: Accounting for the Lagged Effect of Advertising 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 on the mobile gaming industry. …
If you’re in the marketing world, you’re probably familiar with marketing mix modeling (MMM), one of the attribution methods popularized since Apple limited tracking in iOS14. MMM is a statistical technique that helps marketers allocate resources across different channels based on past performance. However MMM works based on historical data, which you don’t have when …
Why Marketing Mix Modeling doesn’t work for new channels (and how to fix it) Read More »
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 a reliable approach to use. …
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 this question, because like most …
How much data do you need for your Marketing Mix Model? Read More »
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 to be, and best practices …
Forecasting isn’t a hobby at Uber, it’s core to their business. Time series models help Uber predict demand so they know where to send their drivers, forecast hardware and computation requirements so their servers don’t go down, and allocate billions of dollars in annual marketing budget, the use case we are most interested in. Marketing …
