Store-Level Sales Data for CPG Brands and How to Use It

The question every CPG founder asks at some point is: "How do I know if my product is actually selling?" Getting into a store is one milestone. Knowing whether your product is turning, how your velocity compares to the category average, and which locations are performing best is an entirely different level of visibility.

Store-level sales data is how brands answer those questions. It drives reorder conversations, informs pricing decisions, strengthens retail pitches, and tells you where to invest your trade spend. This guide covers the main ways to access it, what it costs, and when it is worth paying for.

How to Get Sales Data From Retail Accounts Like Natural Grocers

Getting store-level data from a retailer depends on the account. Some make it easy. Others require you to ask directly, with the right contact and the right framing.

Ask your buyer. This sounds obvious, but many founders never ask. Every retail buyer has access to the sales data for their category. Most are willing to share it with vendors, especially when the vendor is actively trying to improve performance. After your product has been on shelf for 60 to 90 days, request a velocity report for your items. Frame it as wanting to understand how the product is performing so you can support it better. Buyers respond to this framing because it signals that you are an engaged vendor, not one who disappears after placement.

What you will get varies by account. Some buyers will email you a clean spreadsheet. Others will share a screenshot of their internal system. Either way, you get real units-sold data at the store or region level, which is enough to understand what is working and what is not.

Retailer vendor portals. Larger chains operate portals where approved vendors can access sales and inventory data directly. Whole Foods uses the Supplier Portal. Walmart uses Retail Link. Target has Partners Online. These systems give you sales by store, by week, inventory on hand, and in some cases, competitive benchmarking within your category.

Getting access to these portals requires an active vendor relationship and sometimes a specific request to your buyer or the retailer's vendor services team. Once you have it, the data is available on demand and you do not need to ask anyone for a report.

Natural Grocers specifically. Natural Grocers is a regional natural grocery chain based in Colorado with stores across the western and central United States. They have a vendor portal that gives approved vendors access to scan data (actual sales at register, not just shipments). Access is typically granted after your product is in their system and active in stores. Your buyer or the category team can provision access. If you are in Natural Grocers and do not have portal access, call your category contact directly and ask how to get it set up.

The data available through Natural Grocers' vendor systems includes unit sales by week, by store, and by UPC. That is the core data set you need to track velocity, identify underperforming doors, and build the case for expansion or TPR support.

Distributor Data vs. Direct Retailer Data

Both data sources are useful, but they measure different things. Mixing them up leads to incorrect conclusions.

Distributor data (sell-in) shows what shipped to retail, not what sold to consumers. When your distributor (UNFI, KeHE, or a regional DSD) sends you a shipment report, that data reflects cases sold out of the warehouse and into the store's back room. It does not tell you whether those cases moved off the shelf. A store can order six cases and sell two. Your distributor data shows the six. That is it.

Distributor data is useful for tracking distribution breadth (how many doors are actively ordering), identifying accounts that have gone dark (stopped reordering), and understanding your overall volume pipeline. It is not useful for measuring consumer demand or product velocity at the shelf level.

Retailer scan data (sell-through) shows what actually sold to consumers. Point-of-sale data, captured when a customer scans your product at checkout, reflects real consumer demand. This is the number that buyers care about, that investors ask about, and that determines whether you get reordered or cut.

Velocity is calculated from scan data: units sold per store per week (USPW). A product moving 5 USPW in natural grocery is average. Moving 10 to 15 USPW in your category is strong. Moving 2 to 3 USPW is at risk of getting cut from the set.

The gap between the two matters. If your distributor data shows strong shipments but your scan data shows slow movement, you have an in-store execution problem. Possibly poor placement, insufficient facings, lack of consumer awareness at the shelf, or a category that is not a fit. If your scan data shows strong velocity but your distributor data shows irregular ordering, you likely have an inventory management or forecasting problem at the retail level.

Look at both together. Neither one alone gives you the full picture.

Reading your scan and shipment data well does more than defend existing accounts; it tells you which kinds of stores your product actually moves in, which is exactly the signal you want guiding where you open next.

When to Invest in Nielsen or SPINS Data

Third-party data from providers like SPINS and NielsenIQ gives you something that buyer portals and distributor reports cannot: category context. It tells you not just how your product is performing, but how it is performing relative to everything else in the set.

What SPINS provides. SPINS specializes in natural, organic, and specialty products across channels including natural grocery, conventional grocery, and specialty retailers. They track scan data from thousands of retailers across the country, aggregated and normalized so you can compare your velocity against category benchmarks, track share of category, identify emerging competitors, and understand which channels are growing or declining in your space.

For natural CPG brands, SPINS is the most relevant data provider. Their coverage of the natural channel is deeper than any other service, and their category definitions are built around the natural grocery world.

What NielsenIQ provides. NielsenIQ covers conventional grocery, drug, and mass channels at scale. Their data is more useful once you are in mainstream retailers (Kroger, Target, conventional regional chains) where SPINS coverage is thinner. For a brand primarily in natural grocery, SPINS is the higher-priority investment. For a brand with meaningful conventional distribution, NielsenIQ becomes more relevant.

How much does it cost? This is the question every founder asks, and the honest answer is that pricing varies significantly based on scope. A full SPINS subscription with category-level access across multiple channels can run $30,000 to $100,000+ per year. That is a real line item, and for most early-stage brands, it is not the right use of capital.

There are more accessible entry points. SPINS offers a brand portal product designed for smaller brands at a lower price point, typically in the $3,000 to $8,000 per year range for limited category access. Some distributors and trade associations include SPINS data access as part of membership benefits. Naturally Chicago, ECRM, and some regional co-op buying groups include data access in their programs.

The practical guidance is this: if you are selling fewer than $500K in retail revenue annually, third-party data is not your most urgent investment. Focus on getting clean data directly from your retail accounts and distributors. Once you cross $500K to $1M in retail revenue and are actively pitching new chains, having category context from SPINS starts to justify the cost because it materially strengthens your sales presentations.

Using Sales Data to Drive Better Retail Strategies

Data is only useful if you do something with it. Here is how to translate what you are seeing in velocity reports and category benchmarks into concrete decisions.

Identify your best and worst-performing doors. Once you have scan data across multiple stores in a chain or region, look for patterns. Are certain regions consistently outperforming others? Are specific store formats (urban vs. suburban, high-income vs. average income demographics) driving the majority of your volume? That pattern tells you where to focus distribution expansion and where to have an honest conversation with your buyer about reducing doors rather than spreading thin inventory across underperforming accounts.

Cutting underperforming doors sounds counterintuitive, but it is often the right move. A brand with 100 doors averaging 3 USPW looks weak to a national buyer. A brand with 60 doors averaging 8 USPW looks like something that belongs in more stores.

Use velocity data in buyer negotiations. When you walk into a buyer meeting asking for expanded distribution, more facings, or incremental shelf space, scan data is the most credible thing you can bring. "Our product averaged 9.2 units per store per week over the last 12 weeks" is a complete argument. "Our product is doing really well and customers love it" is not.

If your velocity is strong, lead with the number. If it is below category average, come with a plan to fix it: a specific promotional investment, a demo schedule, a social campaign driving traffic to their stores. Buyers respect founders who understand their own numbers and have a plan.

Benchmark before you pitch new accounts. When you are prospecting new retail accounts, knowing how your product performs in comparable stores gives you a credible comp. If you are pitching a natural grocery chain in the Midwest and you have solid velocity data from a comparable chain in the Southeast, that data transfers as a proxy for what you can do in the new account.

Category data from SPINS strengthens this even further. If you can show that your product outperforms the category average in the accounts where you have distribution, and you can back that claim with third-party data, the conversation with a new buyer changes. You are no longer asking them to take a chance on your brand. You are showing them what the category data says.

Track the competition. Category data lets you see what else is happening in your segment. If a competitor launched a new SKU that is taking share, you want to know that before your buyer brings it up. If your category is growing at 15% year-over-year but your brand is growing at 40%, that is a story worth telling. If your category is declining and your brand is holding flat, that is a different story, but you need to know it and have a point of view on it.

Building a Sustainable Data Practice Without a Team

Most early CPG brands do not have a category management person or a data analyst. The founder is doing everything, including tracking retail performance. The goal is to build a data practice that is simple enough to maintain consistently without it becoming a full-time job.

Build a simple velocity tracker. A spreadsheet with your stores listed by account, current door count, and weekly scan velocity is enough to run a competent retail operation. Update it monthly. Track the trend, not just the snapshot. A store that was moving 5 USPW and is now moving 8 USPW is a very different story than one that was at 8 and is now at 5.

Set a data review cadence. Put a monthly calendar block for reviewing your retail performance data. At minimum: pull velocity from any portals you have access to, check distributor shipment reports for active accounts, and flag any accounts that have stopped ordering. This 90-minute monthly review prevents you from being blindsided in buyer meetings and keeps you on top of your retail health.

Know which accounts need attention before your buyer calls you. The worst version of a buyer conversation starts with the buyer telling you your product is not moving. The best version starts with you walking in having already identified the problem and a plan to fix it. Data gives you the heads-up you need to have the proactive conversation instead of the reactive one.

The brands that grow the fastest in retail are not necessarily the ones with the most sophisticated data infrastructure. They are the ones who use the data they have, consistently, to make better decisions than their competition.