Marketing Mix Modeling
What is MMM?
Marketing Mix Modeling (MMM) is a technique which helps in quantifying the impact of several marketing inputs on sales or other KPI. The purpose of using MMM is to understand how much each marketing input contributes to sales (or other KPI), and how much to spend on each marketing input. MMM helps in the ascertaining the effectiveness of each marketing input in terms of Return on Investment.
Why MMM now? – The Rennaisance of MMM
MMM is an ideal solution for a privacy first world.
Our MMM Process
Built With Statistics, Enhanced with AI
We Innovate Everyday - Animated MMM Contribution Charts
Because Marketing reality is not a snapshot but a Movie
Why Frequentist MMM is better for you
Measure your ROI, without eating into your ROI
Maximize your ROI with our privacy proof solutions
Cut down wasted marketing spends
Understand marginal returns across different social media platforms/campaigns
Prove value of your marketing efforts
Unbiased view across multiple platforms like Google, Facebook ,Instagram, Tik Tok
What KPIs you can model with MMM?
The magic of MMM is that it can help you model diverse KPIs like Sales, Leads, CAC, CLTV etc.
Don’t find your KPI in the list?
FAQs
Cookie deprecation is planned for 2024. Also, with data privacy regulations in place, most brands have been looking at other methods to measure their marketing effectiveness. MMM doesn't need any PII or cookie data. All it needs is your marketing spends information and other macro-economic factors.
Since MMM is an econometric model, we generally recommend 2-3 years of data. However, this depends on the data granularity too. For digital first brands, that collect data at a daily/weekly level, the data requirement could be 1 year.
Through years of experience and R&D, we have been able to lower the time to model MMM. Post getting all the data and signing off on the data quality, it takes around a week to model the results. Having delivered 300 + models across 8 industries, over a span of a decade, our learning curves with regards to MMM and its challenges is less steep as compared to others.
MMM is mix of art and science. It involves capturing various domain nuances while maintaining statistical robustness. It is a tricky proposition to maintain a balance between the two. A model built in a day might not be specified correctly and we do not want our clients taking wrong business decisions for their brands.
A MMM model or for that matter any model is a snapshot of reality. The problem is, a snapshot can tell you only so much. More specifically it can only tell you about the things that took place at the time of taking the snapshot. Market reality is very dynamic. Things evolve constantly. Your marketing spends does not remain static. Its effect on the KPI is not static. Your competitors are not static either. Hence it just makes sense to get frequent updates about the reality.
It depends on your advertisement efforts. If you are launching short term digital campaigns, it makes sense to update weekly. But if you advertise on channels like TV and launch more brand building campaigns, it makes sense to update monthly/quarterly.
We use both statistical, econometric and evolutionary algorithms to model MMM.
We handle Endogeneity through multiple ways. For example, we utilize 2SLS and Instrumental variables. For Multicollinearity, we use a mix of regularization techniques and other information theoretic methods. Not to mention, we also heavily rely on our experience of a decade building and delivering 300+ models.
MMM is agnostic to the quantum of marketing budget. If you are spending consistently every month, you can utilize MMM to optimize your marketing spends. With cookie deprecation happening soon, it will be useful to utilize MMM to answer questions on sales lift, campaign effectiveness, CAC and cost per order across different platforms.
We accept the data in many formats like APIs, CSV, etc We set up a secure S3 bucket for every client to drop their data there. Alternatively we also accept data via secure emails and dropbox.
