Marketing mix modeling for Indian businesses: when attribution tools are not enough
MMM gives you a statistical view of how your entire marketing budget is working, including the offline and unmeasurable parts.
Attribution tools tell you what happened in your trackable digital channels. Marketing mix modeling tells you how all your spending, digital and offline together, affected your revenue. If you spend on TV, hoardings, influencer gifting, radio, WhatsApp broadcasts, and Google Ads simultaneously, no attribution tool can separate their effects. MMM can.
This used to be only accessible to large FMCG companies with statisticians on payroll. That has changed. Lightweight open-source tools and newer SaaS products have brought MMM within reach of mid-size Indian businesses.
What MMM actually does
MMM is a statistical regression model. You feed it historical data: weekly or monthly revenue (the dependent variable) alongside your weekly spending on each channel, plus external factors like seasonality, holidays, and competitor activity. The model estimates the incremental revenue contribution of each rupee spent on each channel.
It handles things attribution cannot. If your hoarding in Connaught Place ran during the same month your Google Ads spend doubled, MMM can statistically separate which drove more lift. Attribution tools would give all credit to the digital touchpoint because that is what they can measure.
Why Indian businesses need MMM more than most
Indian marketing budgets are unusually split between trackable and untrackable channels. Even mid-size brands spend on WhatsApp bulk messages that do not have reliable open tracking, outdoor hoardings in tier-2 cities, local newspaper inserts, and events or activations. At the same time, they run Google Ads, Meta, and email. Standard digital attribution ignores the offline half.
Seasonality is also extreme in India. Diwali, Eid, Holi, wedding season, and IPL create spending spikes that dwarf normal periods. A naive read of monthly performance data will attribute Diwali revenue to whatever channel happened to be running then. MMM controls for these seasonal effects explicitly.
Getting started without a data science team
The most accessible entry point is Meta's open-source Robyn, which runs in R. It is free, well documented, and several Indian digital agencies now have people who can run it. You need at minimum 104 weeks of data for a reliable model, though 52 weeks is workable.
If you do not have R skills in-house, Google has Meridian (also open source, in Python), and there are paid SaaS options like Northbeam and Recast that offer simpler interfaces. Recast specifically has good documentation on using it for DTC brands, which is applicable to Indian e-commerce.
What you need to collect: weekly revenue (or orders), weekly spend by channel, and ideally a variable for each major Indian holiday that overlapped your data period. If you have offline channels, even rough estimates of weekly impressions or spend help.
Interpreting and acting on MMM results
The key output is the response curve for each channel. This shows how incremental revenue changes as you increase spend. Most channels follow a saturation curve: early spend is highly efficient, but returns diminish as you push more budget through the same channel.
If your Google Ads response curve shows saturation at ₹3 lakh per month but you are spending ₹5 lakh, the model suggests you are wasting ₹2 lakh. If your Meta response curve has not yet hit saturation at current spend, there may be room to grow. This guides budget reallocation.
Run MMM quarterly rather than monthly, since monthly data has too much noise. The model is a planning tool, not a real-time optimisation tool. Use it to make quarterly budget decisions, then use GA4 and ad platform data for week-to-week optimisation.
Frequently asked questions
How is MMM different from multi-channel attribution?
Attribution tracks individual customer journeys through measurable digital touchpoints and assigns credit to each. MMM uses aggregate statistical analysis of spending and revenue over time, which means it can include offline channels and control for external factors like seasonality. They are complementary, not competing.
What data do I need to run an MMM model in India?
At minimum: 52 weeks of weekly revenue or sales data, weekly spend on each marketing channel, and a list of major holidays and promotional events. More data and more channels give better results. Offline spend estimates work even if they are not perfectly precise.
Is MMM worth it for a business doing less than ₹1 crore per year in revenue?
Probably not at that scale. The setup cost and complexity outweigh the benefits unless you have a complex multi-channel mix. MMM becomes genuinely useful when you are spending ₹15 lakh or more per month across four or more channels, some of which are offline.
Can MMM work for service businesses, not just product companies?
Yes. Any business that has revenue data and marketing spend data can run MMM. For service businesses, use monthly revenue or leads generated as the dependent variable rather than product sales.