Know where your next outlet should open, and see why on the map

You get three documents, each for a different reader

Executive summary

One page for the board: what sets your outlets apart, and the five best candidate sites with their districts. See the example.

Technical study

Every figure, the model and its limits, for the team that has to defend the decision. Behind client sign-in.

Candidate-site dossiers

Every candidate site ranked, each compared factor by factor with the outlets it most resembles, with its distance to your nearest outlet.

What the example study found about ayam gepuk

We ran the method on four chains’ public outlet lists, with no sales data.

• It follows fast food on population and income, not on age.30.7% of adult citizens near an outlet are aged 18–29, against 26.8% near fast food.
• It is a shophouse business.2% of outlets are in a mall or retail block, against at least 14% of fast food.
• The chains crowd each other.89 of 237 chain outlets have a rival chain within 1 km; two pairs are 47 m apart.

How a study runs

1. You send your outlet list. Add sales per outlet if you have them: sites are then ranked by what your best outlets share, not by resemblance to all of them.
2. We profile every catchment. Residents, age and ethnicity, income, students, public transport, malls, and fast-food and rival density around each outlet.
3. We compare you with the market. Each outlet is ranked against competitor sites in the same state, so the Klang Valley does not skew the result.
4. We score candidate sites. Your competitors’ sites and any addresses you are weighing, each with its lookalike outlets and its district.
5. You read the results on a private link. Your team signs in with an emailed code; no passwords.

Built on public and aggregate data

No individual’s record is used: age and ethnicity enter only as counts per polling district. Every study states its limits in the same section as the finding: catchments are 2 km circles today, with drive time next.