Competitive analysis with retail data, done responsibly

Competitive analysis goes wrong in two ways: it gets sloppy, or it gets personal. Done well, competitive analysis with retail data is a fair, factual comparison of how your products perform against others in the same category, stores and period. It focuses on what the shelf and the register show, uses data obtained properly and leaves opinions about rivals out of it.

The long-run value of competitive analysis comes from its accuracy. Anything that compromises that, whether a biased comparison or a questionable source, costs more than it gains.

What can store-level data show about competitors?

Quite a lot, in aggregate. Scan data covering a category shows each brand's sales, units, prices and distribution across the stores in the dataset. From that, you can compare velocity per store carrying, price positioning, pack-size mix and how each brand's share of the category has moved over time.

It can show where competitors are gaining distribution and where they're losing it. It can show how they price relative to you in different stores. And it can show how the category responded when a competitor launched a new item or ran a promotion.

It can't show why a competitor made a particular decision, or what they plan next. Speculating about intent goes beyond the data.

How do you keep comparisons fair?

Compare like with like. Use the same stores, the same period and the same definitions for every brand. If your item is in 300 stores and a competitor's is in 100, compare velocity per store carrying, not totals.

Match pack sizes and formats. A competitor's multipack compared with your single-serve bottle tells you little. Unit prices and like-for-like sizes make comparisons meaningful.

Account for promotions. If a competitor ran a deep deal during the period, their numbers reflect it. Separate promoted weeks, or note them, before drawing conclusions. Category management practice offers a common framework for these comparisons.

Where are the ethical lines?

Use data you're entitled to use. Aggregated data from a provider, used within the terms of its agreement, is standard practice. Information obtained from a competitor's employees, confidential documents or misrepresentation isn't.

Keep it aggregated. Competitive analysis should compare brands and products, not attempt to identify individual stores' private information beyond what the data provider permits, and never individual shoppers.

Describe competitors factually. Name them when it's useful for clarity, state what the data shows, and avoid characterizing them. "Brand B's velocity per store rose over the period" is a fact. Adjectives about Brand B's strategy are opinion, and they tend to age poorly.

How should you use what you find?

Focus on your own decisions. The point of competitive analysis is to inform your pricing, assortment, distribution and promotion choices. Knowing that a competitor gained distribution in a set of stores is useful if it prompts you to check your own presence there.

Look for patterns over time. A single month's competitive shift can reverse. Sustained changes over several periods are more worth acting on.

One more habit keeps the work honest over time: write down your method before you look at the results. Decide which stores, which weeks and which measures you will compare, and keep that choice fixed for the next read. Changing the comparison set every quarter makes it easy to find whatever story you were hoping for. A stable method also makes it far easier to explain your numbers to a buyer who asks how you got them.

And share findings with appropriate context. When you present competitive analysis to distributors or buyers, frame it around the category's health. That keeps the conversation productive. For category-level context across independent stores, the NRS Insights monthly reports and its news page are useful references.