Retail

Know what makes your best stores work

Censible profiles the people around every store in your network, shows what your strongest stores have in common, and finds the places that look like them. It's built on the ABS Census, with every figure traced to its source.

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618
Census figures for every catchment
1–10 km
A circle, drive time or walk time around each store
G-NAF
Store addresses matched to Australia's address file
Context

Same format, different results

A store can trail the rest of a network for reasons the P&L doesn't explain when the fit-out, the range and the team are the same as elsewhere. One place to look is the people who live around it: their age, their income, whether they rent, and how many of them live alone.

The Census has all of that for every block in Australia, but it comes as thousands of coded tables on boundaries that don't match a store's catchment. Censible does that work once, so a retail analyst can see a catchment's profile without starting a data project.

An example

Who lives within 3 km of Sydney Road

About 115,000 people live within 3 km of Sydney Road in Brunswick, Melbourne. Compared with Greater Melbourne, 45% of households rent (against 31%), 52% of people aged 15 and over have a bachelor degree or higher (against 33%), and a third of households are one person (against a quarter).

Ask Censible for other places with the same profile and it finds Kensington in Melbourne, Petersham and Stanmore in Sydney's inner west (an 87% match), and Fairfield and Dutton Park in Brisbane. If a format works on Sydney Road, those places have the closest match on the figures Censible compares.

Limitations

Census figures, not sales data

Censible tells you who lives around a store. It doesn't know your sales, foot traffic or where your competitors are, so it works best alongside your own figures. Catchments are a circle of 1 to 10 km, a drive or walk time on OpenStreetMap's roads without traffic, or a shape you draw.

When Censible compares your best stores with the rest, it shows what goes with strong performance, not what causes it, and a small network can make a pattern look stronger than it is. The latest figures are from the 2021 Census. Its work and travel figures were collected during lockdowns in Sydney and Melbourne, and Censible says so wherever they appear. Results from the 2026 Census arrive from June 2027.

Use cases

What retail teams use it for

From one store's catchment to the shape of the whole network.

  1. 01Profile every store's catchmentUpload your stores as a spreadsheet with addresses or coordinates. Each one is placed on the map, given a catchment of 1 to 10 km or a drive or walk time, and profiled: population, age, household income, renting, household types and more.
  2. 02Explain why a store trailsOpen any store and compare its catchment with your network's average or with your best stores. Censible describes what stands out, such as more young renters or fewer families with children.
  3. 03See what your best stores shareAdd one performance figure per store, such as weekly sales. Censible compares your strongest stores with your weakest and shows the few catchment figures that differ most between them.
  4. 04Find where to open nextSearch for suburbs, postcodes or statistical areas that look like your best stores' catchments, leaving out places where you already have a store nearby.
  5. 05Use the figures in your own toolsThe same figures are available through an API for your BI tools and models, and through an MCP server for AI assistants, so you can ask questions like "which of my stores is most like Essendon?"

Questions people ask

Start with the stores you already have

Upload a spreadsheet of your stores and see every catchment side by side.

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