The most common reason retailers hesitate to explore MMM isn’t cost or complexity, it’s usually the belief that their business structure makes it too difficult. That concern is almost always unfounded. Here’s why.
Thought Leadership - 7 min read - MASS Analytics
Grocery and essential retail don’t come in one shape. A buying group might aggregate hundreds of independent operators under a single umbrella. A regional chain might run three or four distinct banner brands serving different customer segments. A wholesaler might be running marketing that influences purchase decisions several steps removed from the end consumer.
Each of these structures raises a genuine question about how marketing measurement works in practice. The short answer is that MMM is designed to work at whatever level of complexity your business operates, and that complexity is usually an asset, not an obstacle.
The Three Structures, and How Measurement Adapts to Each
Structure 01 : Single banner, multiple stores
This is the most straightforward setup: one brand identity, one media plan, many stores. The key insight is that measuring at the banner level only gives you averages, and averages obscure the truth as explained in: The Leaflet Question: What a $14.5M Mistake Teaches Us About Retail Measurement
The recommendation is always to model at store level. Individual stores serve different catchment areas, different demographics, and respond differently to the same marketing activities. A leaflet campaign that drives strong footfall in a suburban location might have almost no measurable impact in a dense urban one. Only store-level measurement surfaces that difference, and only that difference tells you where to concentrate spend and where to pull back.
Store-level data is almost always available. Weekly sales by location, combined with the media data and external factors, gives the model everything it needs. The granularity of the output matches the granularity of the input.
Structure 02: Multi-banner, parent company
Where a parent company operates several distinct banner brands — each with their own positioning, customer base, and potentially their own media agency relationship — the model adapts to support both levels of decision-making simultaneously.
At the top level, the model can inform how total marketing budget should be allocated across banners. If Banner A is generating stronger returns on media investment than Banner B, that’s a signal for how corporate-level budget should be distributed.
At the banner level, each brand can run its own optimisation; understanding which channels are working within that banner’s specific context, and how to allocate media spend across its store estate.
The two levels don’t conflict. They answer different questions for different decision-makers. Corporate gets strategic budget allocation signals; banner marketing teams get operational guidance on channel mix.
Structure 03: Wholesale and independent networks
The wholesale model is genuinely different, and it’s worth addressing directly because it’s where most MMM conversations get derailed.
The fundamental difference is that wholesale marketing operates in a B2B environment, influencing buying decisions made by independent operators rather than end consumers directly. The sales dynamics: volumes, variability, the role of promotional activity, are different from a vertically integrated retail chain.
The good news is that MMM handles this well. The model is adapted to reflect the nature of the buying relationship. Instead of measuring consumer footfall or transaction volume at a retail till, you’re measuring the purchasing decisions of the independent operators in the network. The inputs change; the methodology doesn’t. Customer type becomes a relevant segmentation variable, and the model’s KPIs reflect what matters in a wholesale context.
Wholesale networks have a wide variety of independent operator types from different sizes to formats to buying patterns. That variation is useful to the model. More variation means clearer separation of what’s driving results.
The Granularity Principle
Across all three structures, one principle holds: measure at the lowest level of granularity the data allows, then aggregate upwards. Never measure at the top and assume the findings hold at every level below.
If you’re looking at Meta spend nationally, you’ll end up with a very generic view of how effective it is, too generic to act on at any meaningful level.
The reason is fundamental to how retail works. A media channel’s effectiveness varies by geography, by store format, by customer demographics, and by the competitive environment in each market. A national average hides all of that variation, which means it hides all of the actionable insight. The places where a channel is dramatically over-performing and the places where it’s genuinely wasted both disappear into the middle.
Granular models are not meaningfully harder to build than top-level ones. The data infrastructure required is the same. The analysis simply runs at more specific levels, and the outputs are correspondingly more useful.
Multi-product Analysis: The Halo Effect
Retailers with broad product ranges can extend this further. If a customer comes in for your promoted beverage category, do they also purchase across bakery and deli? MMM can quantify the halo effect of category-level marketing on adjacent categories. That allows you to understand not just whether a campaign drove the targeted category, but what it did to basket size and cross-category behaviour. For businesses running 20–30 product lines, this can change the ROI calculation for entire media channels.
Premium Versus Value: Why Segment-level Analysis Matters
One of the most valuable applications of store-level modeling in retail is the ability to compare performance across store segments, premium versus value locations being the most common example.
Different store formats serve different customer profiles. The marketing that works in a premium urban format may not be what drives footfall in a value-oriented suburban location. Treating them as identical for measurement purposes means optimising for a customer who doesn’t exist which is an average of two very different groups.
Segment-based analysis tells you which channels are most effective at reaching which customer types, which media investments are supporting your premium positioning, and which are driving volume at the value end, and where there are opportunities to shift investment toward higher-margin segments. These are budget decisions with direct profit and loss implications.
Questions structure-specific analysis answers
- Which of our banners is generating the strongest return on media investment?
- Where in our store estate is marketing having the highest impact and where is it being wasted?
- Do our premium and value formats respond to the same channels in the same way?
- In our wholesale network, which operator types are most responsive to our marketing support?
The final article in this series moves from the analytical to the practical: what does it actually take to get started? What data do you need, what does the team structure look like, and what should you expect from the first 90 days of an MMM engagement?
Previous article: Reading The Signals: ROI, Response Curves, and Synergy. Next article: Getting Ready: The Three Roles, The Data, And The First 90 Days
Continue reading the series
Six articles taking you from the measurement problem to practical readiness — written for retail marketing leaders.