One of the complaints many have with respect to MMM is that it does not provide granular insights. While there are multiple solutions to this problem (will talk about this in future posts), let me unpack the granularity problem. So what is the Granularity problem? Lets take the example of TV. Many brands spends lot […]
Unpacking the granularity problem in MMM Read More »
Did you know MMM has inbuilt Incrementality testing? Let me elaborate. Incrementality is defined as the additional impact of a marketing on the KPI (sales, TOMA, CAC etc..) over and above what would have been generated organically. Recently, we had an interesting conversation with a brand that ventured into TV ads since past one year.
What? MMM has inbuilt Incrementality testing? Read More »
There are some parallels between efficient and quick MMM delivery and that of a F1 pit stop. Much like a F1 Pit stop, a lot of things needs to come together for an efficient and quick MMM delivery. They are: ◾ Data Analysis Skills: One of the biggest roadblock I have seen in getting MMM
How to deliver robust MMM models quickly Read More »
During the Black Friday – Cyber Monday (BFCM) period, we often notice that brands increase their marketing spends, roll out discounts, try new media channels and launch new campaigns. The resultant sales for some of these brands during this period is higher than any other period in the year. But most marketers still don’t have
Boost your incrementality testing through Causal Experiments Read More »
MMM is for enterprises of all sizes. Of late I came across a lot of posts proclaiming that MMM is not ideal for SMBs. I want to dispel some myths on this. 📌Small Marketing Spends Small marketing spends is not a show stopper. Not statistically and not marketing wise either. There are various statistical techniques
You can do MMM with small marketing spends Read More »
MMM is something that always leads you to reminisce about the learning one had or hadn’t during their statistics course. 15 years ago, when I was a student, I attended one statistics seminar. In it the professor told “Always be testing your assumptions”. That statement rings true even now, especially in MMM. In MMM, inference
Why Heteroscedasticity matters in Marketing Mix Modeling Read More »
1. Spends are more actionable and transactional, as you can measure direct impact on sales. Spends data directly reflects the financial investment in marketing (your CFO would be happy 😅). 2. Spends are more accurate than impressions. 3. Cross-Channel Comparisons Spend data enables easier comparisons between different marketing channels or campaigns in terms of their
MMM pro tip: Use spends instead of Impressions Read More »
In Marketing Mix Modeling, I often come across explanations that conflate Halo effect and Interaction effect. The two are not the same !! 📌 So what is Halo Effect? Halo effect is the positive ‘rub-off’ effect of a larger brand on its sister brands or products. This rub-off effect means that the customer buys the
Halo Effect ≠ Interaction Effect Read More »
Market Mix Modeling (MMM) is a technique which helps in quantifying the impact of several marketing inputs on sales or Market Share. The purpose of using MMM is to understand how much each marketing input contributes to sales, and how much to spend on each marketing input. MMM helps in the ascertaining the effectiveness of
Market Mix Modeling (MMM) -101 Read More »
In this article, I would like to explain — How to interpret Contribution charts and what are the common pitfalls to avoid. So, what is a Contribution Chart? Contribution Chart is a visual way of representing what marketing inputs drive sales and how much is the impact of each marketing input. It always helps to ease the
Market Mix Modeling 101 — Part 2 (Contribution Charts) Read More »
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