“We don’t have the data to do category management.” If that sounds familiar, I’d start with a question. What decision are you trying to make—and what information do you already have that could help? You may not have item-level Retailer sales, consumer panel data, or a complete view of the market. You may have shipment reports, a few customer scorecards, store observations, and people who know the business well. That isn’t the same as having a full category data set. But it doesn’t mean there’s nothing useful you can do. The opportunity is to work with what you have, understand what it leaves out, and make recommendations that fit the evidence. Waiting for perfect data won’t build that capability for your team. What decision are you trying to make? “Understand the category” can turn into a long list of reports you wish you had. A more specific question helps you decide where to start. Are you trying to understand why a customer is ordering less? Whether an item deserves a wider listing? Where the assortment may be missing a Shopper need? Each question needs different information. Before asking for another data source, write down the decision, what you already know, and what you would need to check. For example, declining shipments might give a supplier a reason to investigate a customer’s business. They don’t prove that Shoppers are buying less. The customer may have changed its ordering pattern, reduced inventory, or bought ahead in an earlier period. That distinction matters. It changes both your explanation and the conversation you should have next. Know what your data can tell you A useful starting point is to sort your information by the question it can help answer—not just by the name of the report. Shipment and order data: What did we sell to the customer, when, and in what quantities? Check returns, timing, and inventory changes before treating that as Shopper demand. Retailer sales and scorecards: What happened in the business covered by the report? Confirm the measures, periods, stores, and channels included. Store and online observations: What can Shoppers find, compare, and buy at the locations or pages you checked? A store visit can reveal a question worth investigating, but it doesn’t establish how common the issue is. Customer discussions and internal knowledge: What do people closer to the work know about changes in listings, promotions, availability, or execution? Use that knowledge to develop explanations you can check. Market or Shopper research, where available: What wider context does it add? Make sure its category definitions, coverage, and timing fit your question. Then ask: Where is the gap that could change our decision? You don’t need to fill every gap at once. If you’re investigating an availability issue, confirming whether stock reached the shelf may be more useful than purchasing a broad market report. Match the recommendation to the evidence Imagine a supplier sees lower shipments for a range over the last quarter. The team also visits a few stores and notices empty shelf positions for two items. Would you recommend cutting the range? I wouldn’t start there. The shipment decline and store observations give you reasons to ask questions, not enough evidence for a broad assortment change. A more useful recommendation might be to review customer inventory, confirm the current listings, and check availability across a defined group of stores. If the customer can share sales for those items, you can compare what sold with what was ordered and delivered. You’ve moved the discussion forward without pretending you know more than you do. When presenting your findings, separate three things: What we know. State the finding and the period or locations it covers. What might explain it. Identify possible explanations without presenting them as established causes. What we recommend next. Propose a focused check, a small test, or an action supported by the evidence. Limited data should influence the scope and confidence of your recommendation. It doesn’t mean you have to abandon a fact-based approach. Build capability alongside your data It can be helpful to think about category management as a continuum. As access to information, team skills, resources, and collaboration develops, the questions you can answer may become more detailed. But I’d be careful about putting an entire organization into one tier based on its data access. A team may have detailed sales data for one customer and very little for another. It may have strong analytical skills but limited time, or useful information that different functions haven’t connected. More data, more capability, and more collaboration don’t always arrive together. Ask your team: Can we interpret the measures and comparisons we already use? Do we understand the Retailer’s strategy and the Shopper needs behind the category? Are we connecting information across sales, category management, marketing, finance, and supply chain? Can we explain what our recommendation could mean for sales, profit, and execution? A new dashboard won’t answer those questions for you. Neither will AI. Tools can help organize information and explore possible explanations, but they can’t make missing evidence appear. Your team still needs to check the inputs and judge what the output supports. Before investing in more data, identify what it would help you decide and whether your team is ready to use it. Sometimes the priority is a new source. Sometimes it’s learning how to get more value from the information already available. Take the next step with your team At your next category discussion, bring one business question and the information you have today. Work through what it tells you, what it doesn’t, and which gap matters most to the decision. Agree on a next step, who will own it, and when you’ll review what you learned. That is category management work. It may be a more focused analysis than a team with access to a full range of data could complete, but it can still help you ask better questions and make a more informed recommendation. Through CMKG’s Learn → Connect → Apply approach, we can create learning using your own data, reports, and business examples. Pair online learning with a virtual session focused on interpreting the information you have, identifying its limitations, and turning findings into practical next steps. Explore Our Learning Approach to see how we can support your objectives, whether you’re developing your own analytical skills, working with a small team, or building a shared approach across your commercial organization. Happy learning! Sue
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