MMM Build vs Buy India D2C: ₹5 Lakh Decision Guide

Open-source tools from Google (Meridian) and Meta (Robyn) have brought MMM within reach of brands without data science departments. But "within reach" does not mean "appropriate for every brand." Here is the honest framework for deciding whether to build, buy, or wait.

What MMM Actually Answers

The One Question MMM Is Built For

MMM answers: of all the marketing activities I ran over a period of time, what was each channel's causal contribution to revenue separated from the baseline revenue that would have occurred without any marketing?

It controls for seasonality, external market trends, and lagged effects (spend that drives revenue weeks later). It does not tell you which specific campaign, creative, or audience drove what. MMM works at the channel level, not the campaign level.

For Indian D2C brands, the primary use case is budget allocation across channels: how much should we spend on Meta versus Google versus influencers versus offline, given their respective causal contributions to revenue?

Build vs Buy

The Honest Comparison

	Build (Open-Source)	Buy (Managed Platform)	Wait
Tool cost	Rs 0 (Meridian, Robyn)	Rs 40K – 2L per month	Rs 0
Analyst cost	Rs 80K – 4L per year	Included	Rs 0
Total annual cost	Rs 80K – 4L	Rs 4.8L – 24L	Rs 0
Data required	18–24 months weekly data minimum	18 months minimum	N/A
Right for	Brands with in-house data analyst, above Rs 50L monthly spend	Brands above Rs 1Cr monthly marketing spend	Brands below Rs 20L monthly spend
ROI condition	A 5% allocation improvement = Rs 2.5L+ per month savings at Rs 50L spend	A 5% improvement = Rs 5L+ per month savings at Rs 1Cr spend	Invest in Pulse + spreadsheet MMM first

When Neither Is Right

The Most Common Answer: Wait

If your brand is spending below Rs 20 lakh per month on marketing, MMM is not the right tool for your stage. You do not have enough data volume for statistically reliable model outputs, and the investment required is not proportionate to the insight value.

For brands below Rs 20 lakh monthly spend: invest in better platform-level attribution (Pulse) and the simplified spreadsheet-based channel correlation analysis covered in Blog 26 of the first series. Get to 18 months of clean weekly data across all channels, then revisit the decision.

The Simplified Path to Channel Insights

What Brands Below Rs 20L Can Do Instead

Brands below the MMM threshold are not without options for understanding channel causal contribution. Three approaches provide directional insight without MMM infrastructure.

Channel pause tests: Pause one channel completely for 2 weeks while holding all other channels constant. Measure the revenue change. The revenue difference (after accounting for baseline variance) provides a directional estimate of that channel's incremental contribution. Run this test per channel, once per quarter, on a rotating basis.

Spreadsheet correlation analysis: The simplified 4-step approach in Blog 26 of the first series visual correlation, CORREL function in Sheets, lagged correlation, base revenue identification provides directional channel insights at zero cost. It is not MMM. It is the next-best alternative for brands at a stage where MMM overhead is not justified.

Weekly MER tracking: If your MER (Shopify revenue ÷ total marketing spend) rises significantly when you increase spend on one channel, that channel is likely incremental. If MER stays flat when you increase that channel's spend, the channel may be claiming credit for revenue that would have occurred anyway. Simple. Not statistically perfect. Better than nothing which is what most brands at this stage are currently working with.

"MMM is a powerful tool at the right scale. At the wrong scale, it is an expensive way to learn things your spreadsheet was already trying to tell you."