Competitor density around each site
People per competitor around each of your sites, ranked.
What it does
Every month: for each site in your list, the competitors and the people within 1 km, people per competitor worked out, scored by xMap AI, ranked and saved to a sheet.
In one sentence: Monthly on the 1st at 08:00, take your table, count the restaurants, coffee shops and fast food restaurants within 1 km of each, count who lives around, work out new fields, score and rank them with xMap AI, sort them by fit score and keep the top 50, then add row in Google Sheets and email the list to you.
Why it is useful
Competition only matters relative to demand. People per competitor gives one comparable number per site.
Who it is for
Investors, analysts & consultants. Investors, analysts and consultants who need location numbers they can trust, on a schedule. Every use case for investors & consultants
Best use cases
- A site selection consultant
- A franchise investor
- A retail analyst
What each run gives you
- A table in a sheet
- An email
The time it saves
Monthly on the 1st at 08:00. Doing the same work by hand takes about 240 minutes each time; it runs about 1 time a month, so it saves about 4 hours a month (an estimate). It is medium: 9 blocks.
The flow, block by block
- On the 1st of every monthTrigger · ScheduleStarts the flow on a timetable.
- My sitesYour data · My dataBrings in a table you uploaded or a list you saved on the My data page: your stores, customers, target accounts or sites.
- Competitors around eachxMap data · Nearby placesPlaces around a point from xMap’s own places database (300M+ places, refreshed every month): pick the types of place (more than 5,000, such as Restaurant, Cafe, Pharmacy, Hospital or Gas station, or a whole group of them) and how far to look.
- Who lives aroundxMap data · DemographicsWho lives in an area: the number of residents, men and women, and nine age groups, from xMap’s population grid (hexagons of about 0.1 km², about 100 countries).
- People per competitorLogic · Calculated fieldAdds fields worked out from the others: a number, a text or a yes/no.
- Score each sitexMap AI · xMap LLMTell the AI what to do in plain words.
- Best 50 firstLogic · Sort and limitPuts the items in order, and can keep only the first ones: the 10 best rated, the 20 nearest, the highest fit scores.
- Save the tableApp · Google SheetsCreate, edit, and collaborate on spreadsheets online
- Email me the listAction · EmailSends one email per run with all the items, or one email per item.
Every block can be changed after you use the template: the city, the type of place, the score, where the results go.
Use it now
Answer a few questions, enter your work email, and it runs in your own account, kept running until you pause it. Use this template.
How to set it up
- Build your first flow in ten minutes: From a blank page to a flow that emails you a ranked list every morning.
- Ask the map a question: How many of a kind of place, how many people for each, and where the gaps are, answered on the spot.
- Count what is around each of your places: Competitors, footfall magnets or services within a distance of each site.
- Compare sites side by side: Two or three spots measured the same way, one column each, the best of every row marked and the best site and why.
- How to score candidate store sites: Compare sites on residents, competitors and neighbours, with an AI score and a reason.
- Location intelligence: Using where things are, and what is around them, to make business decisions.
- Site selection: Choosing where to open a store, branch, clinic or facility.
More for Investors, analysts & consultants
- Market entry study, refreshed monthly: A market entry study refreshed every month: size, chains, demand, the best areas and the risks.
- Store visit route for a due diligence trip: The branches of a chain you are studying, the strongest and the weakest marked, in the best order to visit them.
- Who lives around new places: The new cafes in your city with the people living around each.
- Market size of a category across cities: Every coffee shop in five cities, counted and summarised every month.
- New openings into my database: New openings saved to your own database every week, classified.
- Monthly hiring index for a sector: A monthly hiring index for a sector across cities.