Why ecommerce brands are turning to MMM

The shift is real. Five years ago, almost no DTC or pure-play ecommerce business bothered with Marketing Mix Modelling. The economics didn't make sense. Now, the economic logic has flipped.

Three things changed. First, attribution tools became unreliable. iOS privacy changes gutted first-party data tracking. Google's push toward AI-driven optimization forced brands to accept they couldn't trust their conversion funnels anymore. Second, performance marketing matured into a highly competitive space where margins compressed. A 3% improvement in ROAS matters now. Third, ecommerce brands scaled to a point where their media spend moved from hundreds of thousands to millions annually, making sophisticated measurement worth the investment.

MMM solves a specific problem for ecommerce: understanding how your spending across channels drives total revenue when that spending is fragmented across Google, Meta, Shopify, TikTok, YouTube, and a dozen other platforms. It's not about perfect measurement. It's about directionally better decisions than guessing.

What's different about ecommerce MMM

Ecommerce data is fundamentally different from traditional retail or B2B data, and that changes how MMM works.

You have more granular data. A grocery manufacturer gets weekly sales across regions. An ecommerce brand has daily—sometimes hourly—transaction data. This is a blessing and a curse. The blessing: you can build models with short time intervals, which means you can detect channel effects faster and with less noise. The curse: more data means more volatility. A product went viral on TikTok for two days. Was that a channel effect or random variation? The model has to work harder to distinguish signal from noise.

Feedback loops are faster. Traditional MMM works with quarterly data. Ecommerce MMM often works with daily or weekly data. This matters because you can measure an A/B test result in three days instead of three months. The trade-off: you need better data quality. Measurement errors that would be absorbed in quarterly data become obvious in daily data.

You have more channels. An FMCG brand measures TV, radio, print, digital, and perhaps in-store promotions. An ecommerce brand might measure Google Shopping, Google Search, YouTube, Meta Feed, Meta Stories, TikTok organic, TikTok paid, Pinterest, email, SMS, affiliate, direct, organic search, and three different types of retargeting. More channels is theoretically good—better picture of the mix. Practically, it's a nightmare. Attribution overlap becomes severe. Your retargeting audience is built from your previous Google and Meta campaigns. Your email list came from your organic traffic. Disentangling these overlaps requires either sophisticated modeling or honest assumptions about what you don't know.

The biggest ecommerce MMM failure happens when brands treat it like an attribution tool. MMM doesn't tell you which specific purchase was influenced by which campaign. It estimates the marginal contribution of each channel to total revenue. That's a different question—a better question in many cases, but a different one.

The challenges that stop ecommerce MMM from working

Not every ecommerce brand should build an MMM. Some face structural problems that no model can solve.

Short purchase cycles introduce noise. Apparel has a 2-3 week buying window. Athletic shoes, similar. Fashion accessories, tighter. The faster someone converts, the harder it is for MMM to separate the effect of your Monday email campaign from your Tuesday Google ad from your Wednesday TikTok video. When conversion happens in days, not weeks, the model is essentially trying to find signal in daily random variation. It can do it, but it requires significant data and honest modeling about uncertainty.

Heavy discounting creates a dominant variable. Some ecommerce businesses run their entire calendar around promotions: Black Friday, Cyber Monday, seasonal sales, flash sales. When you discount 40% off for two weeks, that event overwhelms every other variable in the model. The model can technically control for discounting, but it does so at the cost of explaining the contribution of other channels. You end up with a model that says "promotions and discounting drive most of your revenue" and "channels matter less than you think." That's not always wrong, but it's not always useful either.

Attribution overlap collapses the model. Your retargeting and first-time buyer buckets exist in the same audience. Your email subscribers came partly from organic, partly from paid, and partly from offline word-of-mouth. Your affiliate partners drive revenue directly, but they also drive incremental revenue from the brand awareness they create. When channels feed each other, the model has to choose: assign all the value to the upper funnel channel that touched them first, or distribute it somehow. Neither choice is perfect. The model won't be wrong—it'll just be uncertain.

Small budgets make models unreliable. An ecommerce brand spending $50k monthly across channels might technically be able to build an MMM. The math works. But the stability won't. A $10k spend reduction in a single channel in a single month could be noise, or it could be a real effect. With enough data and honesty about uncertainty, you can build a model that works. But you'll spend $30k building it to understand a $50k budget. That's not a good trade-off.

What data you need for ecommerce MMM to work

If you're past the point of "should we do this?" and into "can we do this?", you need to audit your data infrastructure.

First, you need clean spend data. Not "we think we spent about this much." Actual export-from-each-platform data. This includes platform fees, which many brands ignore. You should know exactly how much you spent on Google Shopping last week to the dollar.

Second, you need total revenue that's reconciled to your P&L. This is harder than it sounds. If you're using Shopify, Google Analytics, Klaviyo, and an affiliate network, you need to reconcile which revenue is counted where, which revenue is duplicated, and whether you're counting refunds correctly. A brand that thinks they made $2M but actually made $1.8M (after returns and chargebacks) will build a model on a lie.

Third, you need external data for variables you can't measure directly. Seasonality should come from historical data, not guessing. Macro variables (unemployment, inflation, consumer confidence) should come from published sources. Website traffic can be from Google Analytics. Email sends and SMS sends should come from your platform, not estimated. Everything else that you can measure, measure.

Fourth, be honest about what you can't measure. You're not measuring word-of-mouth, Reddit conversation, TikTok organic reach, or Pinterest organic reach. All of those happen. None of them appear in your spend data. A good model tells you what it doesn't know. A bad one pretends data it doesn't have.

When MMM makes sense for ecommerce

Build an MMM if you have all of the following:

Don't build an MMM if:

The practical advice: starting with MMM at your ecommerce company

If you decide to move forward, don't start with a full-blown model. Start smaller. Begin with understanding what a model actually measures and what data you'd need. Most ecommerce brands should run a three-month audit of their data first—checking that spend is cleanly tracked, revenue is accurately measured, and external variables are collected.

Then, build a simple model on your last 18 months of data. Simple is better. A model that you understand beats a black-box model that's theoretically more accurate but that you can't defend to your team. Start with the channels that matter most: whatever drives 80% of your revenue should be in the model. Secondary channels can be grouped or excluded in v1.

Finally, use the model to inform strategy, not replace it. If the model tells you email has a higher ROI than paid social, that's useful—but only if you understand why and if it squares with what you know about your business. If the model tells you something that contradicts your intuition, investigate. Don't just follow the model. The model is a tool for thinking better, not a replacement for thinking.