What data to bring to a retailer meeting

Why most retailer meetings underdeliver

The issue rarely comes down to missing data. Trade managers and category managers have access to more data than ever: syndicated market data from Nielsen or Circana, POS data from platforms like SIS or 7EVEN, promotional data from IPV or Superscanner, and internal sales data from their own systems.

The challenge is combining them. Each source uses different categorizations, different EAN structures, and different update rhythms. Matching them manually takes hours, sometimes days. By the time a presentation is ready, some figures are already a week old. And even then, the insights only cover the handful of key accounts that had enough data to work with.

Our experience with over 15 category teams confirms that data crunching eats between 25% and 80% of a category manager's working week, depending on the number of retail channels. That is time not spent on analysis, strategy, or building the retailer relationship.

The fix is not working harder. It is knowing exactly which data sources to bring, in what combination, and with what level of freshness.

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The three data sources that convince retailers

A strong category story for a retailer meeting is built on three layers: market context, in-store performance, and your own numbers. Each layer answers a different question. Together they tell a complete story.

1. Syndicated data: the market picture

Syndicated data from Nielsen or Circana gives you the category view that the retailer cannot build themselves from POS data alone. It shows how your category is performing across the total market, how your products compare to competitors in distribution and share, and where consumer trends are heading.

This is the layer that earns you credibility as a category advisor rather than just a supplier. When you can tell a retailer that a subcategory is growing 12% in the total market but only 4% in their stores, you immediately frame a conversation about opportunity, not about your product, but about their category.

The limitation of syndicated data is that it is always a few weeks behind. It also does not show you what is happening in that specific retailer's stores right now. That is where the second layer comes in.

2. POS data: what is actually happening in-store

Point-of-sale data from platforms like SIS (Albert Heijn), 7EVEN (Jumbo), or similar retailer portals gives you near real-time visibility into what is actually selling, where, and at what velocity. This data is specific to that retailer and that category. It is the most relevant number in the room when you are sitting across from their buyer.

POS data tells you which SKUs are over- or underperforming, where you are losing shelf space to competitors, and how promotions are actually landing versus what was planned. If weekly POS data is available, you can show trends that are still developing. If daily PoS data is available, you can respond to shifts in real time.

The challenge with POS data is that it comes in different formats from every retailer. SIS exports look different from 7EVEN. Matching them to your internal product master takes work, unless that process is automated.

3. Internal data: your own performance layer

Internal data, your ex-factory sales, forecast numbers, stock levels, is the third piece. It closes the loop between what you shipped and what actually sold through. There is also data in the room that the retailer cannot see themselves.

Used well, internal data lets you connect your supply chain realities to the category conversation. If a promotion underdelivered, you can show whether it was a sell-in issue or a sell-out issue. If a new SKU is scaling slowly, you can show forecast versus actual and frame what support would accelerate it.

The combination of all three layers: syndicated market view, near real-time POS performance, and your own internal numbers, is what separates a category presentation from a sales pitch.

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What is velocity and why it matters in a retailer meeting

Velocity is one of those terms that sounds technical but is actually simple and incredibly powerful in a retailer conversation.

Velocity measures how fast a product sells per distribution point over a given time period. In practice: units sold divided by the number of stores carrying the product, divided by the number of weeks. It tells you not just how much a product sells in total, but how well it sells wherever it is listed.

Why does this matter? Because total sales volume can be misleading. A product might show strong absolute sales simply because it has very wide distribution. Velocity strips that out. It shows whether the product is actually performing on shelf, regardless of how many stores it is in.

Velocity as a conversation starter

Velocity data opens three types of conversations that trade marketeers struggle to start otherwise:

• Distribution gaps: if a product has high velocity in the stores where it is listed but low total distribution, that is the argument for expanding the listing. The product works, it just needs more shelf presence.
• Assortment rationalization: if a SKU has low velocity across all stores, the retailer has a legitimate reason to consider removing it. Coming to this conversation with your own data builds trust, it shows you are thinking about the category, not just protecting your range.
• Promotion effectiveness: comparing velocity before, during, and after a promotion shows whether the uplift was real or just forward buying. Price elasticity per SKU, calculated from this data, tells you which products respond well to price activation and which ones do not.

Our experience across category teams shows that velocity is the metric that most reliably shifts a retailer meeting from a general conversation to a specific, actionable one. It gives both sides a shared number to work from.

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How to combine these sources without spending days on it

The reason most retailer meetings rely on incomplete data is not lack of access, it is the time it takes to bring everything together. Matching EAN codes across sources, aligning category definitions, correcting errors and then building a presentation from the result is a process that easily takes two to three days per account.

At that pace, a team managing 20 retail accounts simply cannot prepare equally well for every meeting. The result is that smaller accounts get generic presentations, and larger accounts get presentations that are already outdated by the time they are delivered.

Automation changes this. When data from SIS, 7EVEN, Nielsen, Circana, IPV, Superscanner, and internal systems is harmonized automatically, with EAN matching, category alignment, and error correction running in the background, the preparation time collapses from days to hours. Near real-time data means the numbers in your presentation reflect what happened last week, not last month.

That is exactly what happened at Elho, a supplier of sustainable plant pots operating across 57 countries and 33 retail channels. Before automating their data process, only 10 key accounts received properly substantiated category plans. After implementing Captain, that number grew to 25+, with all retail channels receiving insight-driven plans. Read the full story in the Elho client case.

Having a human in the loop remains essential throughout this process. Automation handles the data crunching, but the category insight, what the data means for this retailer, in this category, right now, still requires a category manager to interpret and act on it. The ability to adjust or improve data quality right away is key. The data foundation has to be right.

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From data to a category plan that wins retailer meetings

Bringing the right data to a retailer meeting is the starting point. The goal is a category plan that moves from gut feel to fact based decision making for both the supplier and the retailer.

A strong category plan built on combined data sources enables three things that matter commercially. First, it enables forecasting: with price elasticity calculated per SKU and near real-time POS trends, you can project what will happen with a promotion or a distribution change before it goes live. Second, it enables proactive steering: instead of reacting to what already happened, you can signal underperformance early and propose corrections before the damage shows up in quarterly reviews. Third, it enables a genuine win-win conversation: more revenue and less waste for the retailer, more deals and stronger shelf presence for the supplier.

That is the shift from being a supplier who asks for shelf space to being the strategic partner who helps the retailer grow the category.

Find out what data Captain can prepare for your next retailer meeting

Captain is an AI-driven platform that connects your retail data sources. From SIS and 7EVEN to Nielsen, Circana, and your own internal systems and harmonizes them automatically so you always walk into a retailer meeting with near real-time, insight-ready data.

Request a strategic call to see how it works in practice. We will walk through a real category scenario and show you exactly what your data story could look like.

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