--- title: "Store Profiling" description: "Build a complete picture of a single location: visit volume against the chain and category, weekly rhythm, who visits, and where they come from." slug: "/docs/guides/store-profiling" last_updated: "2026-08-31" status: "published" industry: ["retail", "real-estate"] location_types: ["place"] tags: ["competitor-intel", "site-analysis", "profiling"] is_case_study: false locked: false related_metrics: ["foot-traffic", "popular-times", "visitor-demographics", "visitor-profile", "trade-area", "visitor-journey"] related_datasets: [] example_count: 1 example_tools: ["python"] --- # Store Profiling Build a complete picture of a single location: visit volume against the chain and category, weekly rhythm, who visits, and where they come from. ## Metadata - Industry: retail, real-estate - Location types: place - Tags: competitor-intel, site-analysis, profiling Our Places metrics let you profile a single location: how its visits trend against its chain, competitors and category, where its visitors live and work, who they are, what journey they are on, and when they come. This guide covers each of those reads and what to set them against. --- ## 1. Foot traffic trends A raw visit count for one store means little on its own. The value comes from what you set it against: - **Its own history**: the monthly trend, read year-over-year on identical months so seasonality cancels out. Laying the years over each other in one table gives the seasonal shape and the change at once. - **Its chain**: the store vs. the brand's other stores in the same market, or vs. the brand's typical store (median footfall). Is this a top-quartile location or a laggard? - **Its competitors**: the store vs. a named set of competitor brands in the same market. - **Its category**: the store vs. the same category in the same city or metro. A store can lag its chain and still be the local category leader. Compare on the same observation ticks for all series, so market-wide movement hits every series equally and drops out of the difference. When the chain's store count changes over the window, compute the chain on the stores present throughout as well: openings nearby are one of the most common explanations for a single store's change, and the fleet count is where they show first. These comparisons are the profile's spine. Everything else explains *why* they look the way they do. ## 2. Trade area The **[trade area](/docs/concepts/metrics/places_trade_area)** lists the origin areas the store's visitors call home, with shares. The catchment's size and shape tell you what the store's real market is: a few origins supplying most visitors is a local store, a long flat tail is a destination. Comparing the catchment with a year earlier shows where a change in volume came from: a shared home area losing share while others hold is what cannibalization by a sibling or competitor looks like. ## 3. Visitor demographics and profiles Visitor demographics and profiles describe the store's audience as fractional shares: age, income and education mix, lifestyle personas. These are modeled through visitors' inferred home areas, so how sharply a trait comes through depends on how strongly it correlates with where people live. Treat differences as directional: "this store skews more affluent than the chain" is the reliable kind of finding, and usually the decision-relevant one. Set the store against the same comparison groups as in section 1. ## 4. Visitor journeys **[Visitor journeys](/docs/concepts/metrics/places_visitor_journey)** show where visitors go directly before and after, on the same trip or day. That is the store's real local context: the trip-chain partners it shares visits with (fuel, pharmacy, quick service), and the competitors it appears next to in the same journey. The journey field is also worth seeing on a map: weighted by journey share, it draws where the store's gravity actually reaches, corridor by corridor. ## 5. Popular times Popular-times data gives the location's hour-of-week visitation pattern: which days carry the week, when the peaks land, how sharp they are. The rhythm often explains a volume story on its own: a store that underperforms its chain overall but matches it at weekends has a weekday problem, not a location problem. The sections together cover: how much and how it trends, where from, who, what journey, and when. When a profile reveals a store that performs, the natural next question is where else the same situation exists: that is [analog modeling](/docs/guides/analog-modeling). ## Examples ### Profiling an H-E-B store in San Antonio _(Python ยท USA)_ One store, full picture in lean Python: trend vs. chain, competitors and category, home and work catchment, audience, journeys, and the weekly rhythm. Full worked example: [/docs/guides/store-profiling/us-heb-san-antonio-store-profile.md](/docs/guides/store-profiling/us-heb-san-antonio-store-profile.md) ## Related metrics - Foot Traffic (place) - Popular Times (place) - Visitor Demographics (place) - Visitor Profile (place) - Trade Area (place) - Visitor Journey (place)