Where to launch laundry pickup
Young, busy blocks with few laundries, to launch pickup.
What it does
Every quarter: the blocks of your city with the most people aged 20 to 39 and the fewest laundromats and dry cleaners within 1 km, for where to launch laundry pickup first.
In one sentence: Monthly on the 1st at 08:00, in Seoul, count who lives around, work out new fields, sort them by young and keep the top 60, count the laundromats, dry cleaners and laundry services within 1 km of each, sort them by nearby_count and keep the top 15, then show them on a map and email the list to you.
Why it is useful
Laundry pickup sells to busy young people with no laundry nearby. This finds where they live.
Who it is for
Home services & trades. Cleaning, plumbing, electrical, pest control, moving and repair businesses filling their vans, winning contracts and planning around the weather. Every use case for home services & trades
Best use cases
- A laundry pickup startup
- A dry cleaning chain
- A home services app
What each run gives you
- A map
- An email
The time it saves
Monthly on the 1st at 08:00. Doing the same work by hand takes about 90 minutes each time, so it saves about 6 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 cityWhere · Area of interestSets where the flow looks: countries, cities or specific places, or areas you draw on a map.
- People in every block of the cityxMap 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).
- Young adultsLogic · Calculated fieldAdds fields worked out from the others: a number, a text or a yes/no.
- Best 60 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.
- Laundries within 1 kmxMap 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.
- Show them on a mapAction · Show on a mapShows the results as points on a map, with the area of interest.
- 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
- 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.
- An instant site report of any spot: Paused for now while xMap improves the data behind it. Reports already made still open.
- 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.
- Drive time vs radius: Two ways to draw the area around a site, and when each is good enough.
More for Home services & trades
- Office towers for window and facade cleaning: The biggest office buildings scored for window and facade cleaning.
- Universities at semester end, for student moves and storage: Campuses ranked by the students around them, for moving and storage.
- New apartment buildings for cleaning contracts: New apartment buildings with the property manager, for cleaning contracts.
- Busy restaurant kitchens for deep cleaning: The busiest kitchens graded for deep cleaning, to the sales team.
- Franchise territories by households: Your city split into franchise territories with similar populations.
- Hotels and apartment complexes for pool cleaning: Hotels and apartment complexes scored as pool cleaning clients.