Malls where my category is missing
Malls with the fewest stores of your category.
What it does
Every quarter: shopping malls in your country with how many stores of your category are inside or next to them, the ones with the fewest first, as a list for the leasing team.
In one sentence: Monthly on the 1st at 08:00, in Seoul, Busan and 2 more, find shopping malls in the area, count the shoe stores within 250 m of each, sort them by nearby_count and keep the top 20, then add row in Google Sheets and email the list to you.
Why it is useful
A mall without your category is a gap you can fill. The leasing team sees them ranked.
Who it is for
Retail & franchise expansion. Expansion, real estate and network planning teams deciding where the next store or franchise goes. Every use case for retail & franchise
Best use cases
- A shoe chain
- A retailer’s leasing team
- A franchise developer
What each run gives you
- A Google Sheet
The time it saves
Monthly on the 1st at 08:00. Doing the same work by hand takes about 120 minutes each time, so it saves about 2 hours a month (an estimate). It is medium: 7 blocks.
The flow, block by block
- On the 1st of every monthTrigger · ScheduleStarts the flow on a timetable.
- My citiesWhere · Area of interestSets where the flow looks: countries, cities or specific places, or areas you draw on a map.
- Shopping mallsxMap 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.
- Shoe stores inside or next doorxMap 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.
- The fewest nearby 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.
- Update the leasing listApp · Google SheetsCreate, edit, and collaborate on spreadsheets online
- Email me the resultsAction · 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
- The xMap Flows browser extension: what is around this business: On a company’s website or a place on a map, one click shows its market and the AI employees to hire for it.
- Build your first flow in ten minutes: From a blank page to a flow that emails you a ranked list every morning.
- What xMap remembers about your company: Your city, industry and what you sell, filled in for you and always yours to change.
- 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.
- Location intelligence: Using where things are, and what is around them, to make business decisions.
- Catchment area: The area a store or service draws its customers from.
- Site selection: Choosing where to open a store, branch, clinic or facility.
More for Retail & franchise expansion
- Expansion committee pack, every month: A monthly expansion committee pack: every block scored, the best 20 investigated, approved, mapped.
- Score every neighbourhood for a new branch: Every block of your city scored for a new supermarket, the 20 best on a map.
- Where my rival is and I am not: Every rival branch with none of yours within 2 km, with who lives around it.
- New homes with no supermarket yet: New apartment buildings with no supermarket nearby, scored as places for your next store.
- Busy stations with no convenience store: The train and metro stations with the fewest convenience stores around them, scored for a new one.
- Stores competing with each other: Your stores that sit within 1 km of each other, with the people around them.