--- title: "Brand Comparison" description: "Compare brands with location insights (scale, typical store performance, and development over time) in support of competitive intelligence and market understanding." slug: "/docs/guides/brand-comparison" last_updated: "2026-08-31" status: "published" industry: ["retail"] location_types: ["place"] tags: ["competitor-intel", "benchmarking", "brand-analysis"] is_case_study: false locked: false related_metrics: ["foot-traffic", "visitor-demographics", "visitor-profile"] related_datasets: [] example_count: 1 example_tools: ["node"] --- # Brand Comparison Compare brands with location insights (scale, typical store performance, and development over time) in support of competitive intelligence and market understanding. ## Metadata - Industry: retail - Location types: place - Tags: competitor-intel, benchmarking, brand-analysis Our Places metrics let you compare brands on measured visitation: how much presence each brand commands, how well its typical store performs, how that develops over time, and who its visitors are. This guide covers the comparisons that hold up and how to read them. To go deep on one location, see [Store Profiling](/docs/guides/store-profiling). --- ## 1. Define the brand set Decide the scope first: which brands (direct competitors, the leaders of a category, or a whole category with no names attached), which market (nationwide, a region, a set of metros), and which period. Keep the scope constant across the set, so that differences come from the brands and not from the framing. A regional chain against a national one is best compared on the markets they share; if you compare full footprints instead, say so. ## 2. Overall vs. typical performance Summing visits across a fleet and averaging them per store answer different questions. Keep them apart: - **The network total** (visits summed over all locations) measures overall presence. It scales with fleet size: a brand with four times the stores will usually lead on total visits whatever its individual stores do. - **The typical store** (visits per store, preferably the median) measures how well one location performs. Prefer the median: store distributions are skewed, and a few flagship sites pull a mean well above what a typical store does. A brand can lead on total presence while its typical store trails the challenger's. That is two findings, not a contradiction. Say which one each claim rests on. ## 3. Per-store development Levels carry everything that differs between brands besides performance: location mix, geography, how well each brand's sites are captured. Development, each store's change against its own past, cancels most of it. That makes per-store development the sturdiest brand comparison there is. Three rules make it work: - **Same-store basis.** Compute development only on locations present in both periods. Openings and closings are a separate number (fleet growth), not part of the trend. A brand can grow its total while its same-store development is negative; that combination is the finding. - **Identical windows.** Compare the same months for every brand, year over year. Season and market conditions then hit all brands equally and drop out of the difference. - **Distribution, not just the average.** Two brands with the same average development can differ completely in spread: one lifted evenly, the other carried by a few winners while the tail declines. The share of stores growing is often the more honest headline than the mean. ## 4. Visitor demographics and profiles Visitor demographics and profiles serve brand comparison in two ways: - **As a fingerprint.** A brand's visitor mix (age and income bands, lifestyle personas, as shares) is a stable signature of who the brand is for. Compare fingerprints to see whether two brands contest the same audience or coexist on different ones. Two brands can trade at similar volumes with little audience overlap, which changes what "competitor" means. - **As a trend.** Movement in the fingerprint leads movement in volume. A brand whose visitor mix is aging, moving up-market, or converging on yours shows it in the audience first. Like per-store development, this is each brand against its own past, the robust kind of comparison. Between-brand differences are often a point or two. Read the shape and the direction, not the raw gaps. These attributes are modeled through visitors' inferred home areas: traits that follow where people live (income, age) carry well; treat differences as directional. --- **What to trust, in order:** per-store development on a same-store basis, then typical-store medians, then the audience fingerprint and its trend, then network totals, which are context rather than verdicts. ## Examples ### Grocery brand comparison _(Node.js ยท USA)_ Four grocery chains head to head in Node.js: overall vs. typical store, same-store development with the share of stores growing, and the audience fingerprint. Full worked example: [/docs/guides/brand-comparison/us-grocery-brand-comparison.md](/docs/guides/brand-comparison/us-grocery-brand-comparison.md) ## Related metrics - Foot Traffic (place) - Visitor Demographics (place) - Visitor Profile (place)