Finding where families live, for nurseries, tutoring centres and schools

Education businesses find families by looking at where children live. The count of residents aged 0 to 9 points to demand for nurseries, early learning and primary-age activities; the count aged 10 to 19 points to tutoring, exam preparation and secondary schools. Set those counts against the centres already operating nearby and the districts with many children and few places to send them stand out.

This article is for nursery operators, tutoring and language centres, private schools, after-school clubs and the people who market them. It explains which age figures to use, what they can and cannot say about families, an example of ranking districts, and how to have an AI employee find and refresh the list.

Which age figures to use

The Demographics block counts residents in ten-year bands. For education, two matter most:

Fields of the Demographics block.

What makes a district promising

• Many children of the right age, as a count, not only as a share.
• A high share of children, which suggests a family neighbourhood where word of mouth travels fast.
• Few providers nearby, counted with Nearby places: nurseries, tutoring centres or schools of your kind.
• A short journey: parents rarely cross a city twice a day for a nursery. A 10 to 15-minute drive or walk is a sensible catchment to test.

An example: tutoring in Vancouver

Suppose a tutoring company in Vancouver wants to open a second centre. It draws six districts it could reach from one location, counts residents aged 10 to 19 in each, and counts tutoring and learning centres around each. The figures below are made up to show how the ranking works.

Invented figures for illustration; not real figures for Vancouver.

District C has the most teenagers but is already crowded with centres. Districts B and D have the most teenagers for each centre; D has nearly twice as many in absolute terms and a family-heavy profile, so it goes to the top of the visit list. Before deciding, the company would check rents, transport and the reputation of the two centres already there.

The same reading works for a nursery with age_0_9, or for a private school looking at both bands across a wider drive-time area. In cities with large populations, such as Delhi or Cairo, the counts in each district can be big enough that even a crowded district has room: look at children for each provider rather than the count of providers alone.

Schools and childcare as public services

Public bodies use the same counts the other way round: not to find pupils, but to check that places exist where children live. A city education team can count children aged 0 to 9 in each district, count the primary schools and nurseries in it, and see which districts have the most children for each school. A district that has grown quickly, with new apartment blocks full of young families, often shows up here years before waiting lists make the gap obvious.

Mistakes that cost a term's enrolment

• Choosing on share alone. A small suburb with 30% children may have fewer of them than a dense district at 15%.
• Counting the wrong rivals. Decide which kinds of place compete with you (tutoring centres only, or also language schools and libraries with homework clubs) and count the same list everywhere.
• Ignoring the school run. Families often choose activities near the school, not near home. A district next to several schools can draw children from well beyond it.
• Using a country without figures. Population data covers about 100 countries, among them Canada, India, Egypt, Thailand, France and Australia, but not the United Kingdom or the United States.

Having an AI employee do it

1. Start with a Schedule; once a term is plenty.
2. Add an Area of interest with the districts you could serve, drawn on the map.
3. Add Demographics with Totals for each area, so each district comes back with age_0_9, age_10_19 and the shares.
4. Add Nearby places counting your rivals in each district.
5. Add a Calculated field such as kids_per_centre = round(age_10_19 / max(nearby_count, 1)), then Sort and limit, highest first.
6. End with an Email and a map, or an Approval step if a manager should sign off the shortlist.

Templates that already work this way: Where to open a nursery, Where families need after-school activities, Best neighbourhoods for families and Children per school, block by block, which public bodies use to check school places against where children live.