An education company was sending sales teams to districts without knowing which schools could buy. Every school was assessed on its ability to buy and ranked, so sales teams went straight to the schools most likely to convert.
Thirty-four client-selected expansion districts, every private school assessed.
Leadership selected the districts. Nobody could say how many schools in each one could realistically become customers.
The national education registry names every school. It does not say which ones have the fee base, scale, or infrastructure to buy the product.
Representatives entering an unfamiliar district had no ranking to decide which schools to visit first or which districts to staff.
EPIC screened every private school in the 34 expansion districts against the ideal customer profile. The output is a per-district target list, scored and ordered so field teams work from the top.
Before the 34-district engagement, EPIC ran the same screening across ten postcodes in two districts near a state capital. Of 1,073 private schools, 77 cleared the profile. Five of those were already partner schools, giving the company a measured 6.5% share of its own addressable market. The pilot confirmed that registry data, catchment signals and public reviews could reliably separate qualified schools from the rest.
Roughly 27,000 schools across 34 districts, each assessed on school-level registry data, neighbourhood signals and district-level economics.
Affiliation, student numbers, teacher counts and classroom counts from the national education registry.
Registered physical infrastructure at each school, used as a proxy for the fee base.
Points of interest and household density around each school, from mapping data and population estimates.
Review volume and average rating from mapping platforms.
District-level annual household expenditure on education, Z-scored across the national dataset to normalise for regional variation.
Districts were ranked by household education spend, private school density and the number of qualified targets. The ranking sets where the company staffs first.
One scored dataset, abstracted for each level of the organisation.
Which of the 34 districts to resource first, based on the size of the qualified pool and the economic profile of the catchment.
Per-district target counts and a staffing model benchmarked at 200 schools per salesperson, so territory plans are built from the qualified list.
Named schools clearing the profile, scored and ordered within each district. The representative works from the top and stops when the territory is covered.
Three questions the expansion plan could not answer without scored data.
The question that turns a scored list into a system. Verified fees, confirmed infrastructure and visit outcomes collected in the field are the same signals the registry approximates. Each visit either confirms the profile or sharpens it for the next district.
Give us a district list and your ideal customer profile. We will return a ranked target list your field team can work from day one.
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