Joint Category Development: the model that fixes how FMCG and retail work together

Why the current model is broken

The European FMCG market is worth approximately 700 billion euros. It still primarily runs on Excel, email and periodic meetings where supplier and retailer bring their own version of the data and try to reach alignment on decisions that affect both of them.

That model has a structural problem. Retailers own roughly 60% of the relevant data in the value chain: POS data, inventory, basket composition, shopper behavior. Suppliers own around 20%: ex-factory figures, trade spending, product information, upstream logistics. Together, with shared data, they have access to 90% of what is needed for good category decisions. Separately, both parties are working with an incomplete picture.

The result is predictable. Suppliers give generic category advice because they lack the store level data to make it specific. Retailers make assortment and promotion decisions without the product expertise and cross retailer perspective that suppliers could contribute. Both parties see the same problems, for instance a luxury cheese range selling out in one store cluster and collecting dust in another, but without a shared data foundation, it stays at signaling. Nobody fixes it.

As AI becomes more central to retail decision making, that gap is growing. Retailers are investing heavily in AI tools that make their own data more powerful. Suppliers who cannot match that pace with their own data capability become less relevant as partners. The advice they give is not grounded in enough data to be useful. Read more about this dynamic in our article on the data power gap between supplier and retailer.

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What joint category development actually means

Joint category development is the model where supplier and retailer work from a single shared source of truth toward shared category goals. Not just aligned on outcomes, but actively collaborating on the analysis and decisions that get there.

In practice, that means the supplier brings harmonized data from all their retail sources into one platform. That data is mapped against a consistent product structure, updated continuously, and accessible to both parties. The retailer can see the category picture the supplier is working with. The supplier can validate their recommendations against real store-level performance. Together, they build category plans, promotion strategies and assortment proposals on the basis of the same data, not two competing versions of it.

The shift this creates is significant. A supplier who operates from a shared data foundation can give advice that is specific to a retailer's actual situation, not generic market guidance. They can flag a promotion that is underperforming before the retailer notices it. They can propose an assortment change that is grounded in that retailer's shopper data and velocity patterns. That is what makes a supplier a strategic partner rather than a vendor asking for more shelf space.

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What joint category development delivers

+5% category growth and less waste

The results of proper joint category development are concrete. When supplier and retailer work from shared data to optimize assortment at store cluster level, the outcomes are measurable. A national supermarket chain selling the same cheese assortment in every store, regardless of whether it is in a student neighborhood or an affluent suburb, is leaving significant value on the table. Luxury items pile up in stores where they do not sell. Budget options run out in stores where demand is higher. Both the retailer and the supplier see it happening, but without shared data, neither can fix it efficiently.

When that data is shared and the assortment is optimized per store cluster, the result is more revenue and less waste simultaneously. Category growth of 5% or more alongside a reduction in waste of comparable magnitude. That is the win-win that joint category development makes possible, and it is available in every category where supplier and retailer are currently working from separate data.

Promotion margins growing up to 4%

Promotion planning is one of the highest-stakes areas in category management. At some FMCG suppliers, 60% of volume is sold in promotion. The difference between a promotion that grows the category and one that simply shifts volume, cannibalizes other SKUs and erodes margin is often invisible until weeks after the promotion has run.

Joint category development changes the economics of promotion planning. When the supplier's AI models for price elasticity per SKU and cannibalization risk are validated against the retailer's actual shopper and basket data, promotion plans become significantly more accurate. The result is promotions that genuinely grow the category, with promo margins growing up to 4% and derving reduced for both parties.

From 10 to 25 data backed category plans

The capacity constraint in category management is real. When 60% of available time goes to data preparation, only the largest retail accounts get properly substantiated category plans. Smaller retailers receive generic approaches. That is not a reflection of the team's capability. It is a reflection of the tools they are using. At elho, a leading supplier of sustainable plant pots operating across 33+ retail channels, the team was spending 60% of their time harmonizing data before they could do any analysis. After automating that foundation with Captain, the number of data-backed category plans grew from 10 to 25+. Every retail channel received a properly substantiated plan. Read the full case here.

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The three things joint category development requires

One shared truth

Joint category development starts with data that both parties recognize as accurate. That means all retail sources harmonized against a single product structure. SIS, 7EVEN, Nielsen, Circana, IPV, Superscanner and internal data all mapped consistently, with EAN changes processed automatically so historical trend lines stay intact. Without that foundation, every category conversation starts with a debate about whose numbers are right rather than what the data means.

Having a human in the loop remains essential throughout. Data quality is the foundation of everything that follows. If the underlying data is wrong, every recommendation built on top of it is wrong too. The platform must make it easy to see where data comes from and where issues exist, so the category manager can correct problems quickly rather than discovering them in a retailer meeting.

Actionable insights, not just dashboards

A shared data foundation is necessary but not sufficient. Joint category development requires that the insights surfaced from that data are specific enough to act on. Not market trends from six weeks ago, but near real-time signals from this week's sell-out data. Not category averages, but velocity by store cluster. Not generic promotion guidance, but price elasticity per SKU calculated from the actual promotional history of that product at that retailer. That is what AI in category management makes possible: moving from gut feel to fact-based decision making, at a speed and scale that manual analysis cannot match.

A platform both parties can trust

Joint category development only works if both supplier and retailer are willing to work from the same data. That requires trust: confidence that the data is accurate, that the insights are objective, and that the recommendations serve the category rather than just one party's commercial interest.

A category manager who approaches the retailer relationship with objective, data-grounded category advice, who flags underperformance proactively and brings proposals that clearly benefit the category as a whole, builds that trust over time. The platform is the infrastructure. The relationship is still built by the people.

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How agentic AI accelerates joint category development

The current model of joint category development, where supplier and retailer meet periodically and align on category plans, is already a significant improvement on the status quo. But it is not the endpoint. Agentic category management is the next step: AI agents on both sides of the relationship that monitor category performance continuously, surface signals proactively, and prepare recommendations without waiting for a human to ask.

In that model, the supplier agent monitors all retail accounts in near real-time. It flags a velocity drop, models the probable cause, and prepares a proposal before the category manager has even opened their laptop. The retailer agent validates that proposal against shopper data. The category manager on each side reviews the recommendation, applies judgment, and approves the action. The cycle that currently takes weeks takes days.

That is what Europe's first Agentic joint category development Platform is built to enable. Not just a better dashboard, but a fundamentally different operating model for how suppliers and retailers work together on category growth.

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Why this matters now

The FMCG industry is at an inflection point. Retailers are investing heavily in AI and data infrastructure. The suppliers who can match that capability, who can arrive at every retailer meeting with current, harmonized, actionable data and proposals grounded in shared category goals, will win stronger shelf positions, better deal terms and longer-term commercial partnerships.

The suppliers who cannot will find that the gap between their advice and what the retailer already knows from their own data keeps growing. At some point, that gap becomes large enough that the retailer stops asking for input and starts making category decisions independently. That is not a theoretical risk. It is already happening in categories where the data capability gap between supplier and retailer has become too large to bridge with periodic meetings and Excel prepared presentations.

Joint category development is the answer. Not because it is a nice concept, but because it is the only model that allows suppliers and retailers to realize the full value of the 90% of data they have access to when they work together. That value is real: more category growth, less waste, better promotion performance, stronger retail relationships. It just requires the right foundation to unlock it.

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Ready to build the foundation for joint category development?

Request a demo to see how Captain helps your team move from fragmented data and periodic meetings to a shared data foundation that enables real Joint Category Development, and come away with practical tips for your specific situation.