Site Selection for Retail Teams: Plain-English Queries Replace the Spreadsheet Maze

Site selection for retail expansion has never been a simple task, but the analytical approach that most retail teams use has not changed much in twenty years. A site analyst pulls foot traffic data from one source, competitor locations from another, suburb demographic data from the ABS website, and public transport access points from a council GIS portal. These sit in separate spreadsheets, then get reconciled manually, often in a pivot table that takes days to build and is obsolete by the time it is finished.

The geographic question underneath all of that work is actually quite simple: which available sites have high foot traffic, low competitor density, good transport access, and the right demographic profile for the product? Stated that way, it sounds like one question. Answering it through conventional methods requires about fifteen separate steps and, in most organisations, a GIS specialist or a consulting engagement.

This article describes how that process changes when the analytical layer speaks plain English, and where the limits of natural language site selection sit.

What the spreadsheet maze actually costs

Before getting into how MapAI handles these queries, it is worth being specific about what the conventional workflow costs in practice.

A retail expansion analyst working on a network of potential sites in a new city typically spends two to three weeks on the spatial analysis component alone. Data sourcing takes several days. Building consistent geographic identifiers across datasets (postcode-level ABS data, suburb-level foot traffic data, point data for competitors) takes another day or two. Spatial joins, once the data is clean, require GIS skills or a consultant. Presentation of results requires yet more translation from spatial outputs into a format the commercial team can read.

During those weeks, the list of candidate sites may have changed. New sites may have become available. Competitors may have announced openings. The analysis is a snapshot of a situation that was already moving.

More fundamentally: the analyst is spending cognitive effort on data reconciliation rather than geographic reasoning. The question is "which sites look good?" but most of the time goes to "how do I get the data into a state where I can ask that question?"

Collapsing multiple layers into a single query

Here is a concrete example of how site selection queries work in MapAI.

An expansion analyst for a mid-size specialty retail brand is evaluating potential locations in Brisbane's inner suburbs. They want to shortlist sites based on three criteria: high pedestrian count zones, no existing direct competitor within 800 metres, and a catchment with above-median household income.

In plain English: "Show me retail precincts in inner Brisbane with high foot traffic index, no direct competitor within 800m, and above-median household income in the surrounding 1km catchment."

MapAI parses three distinct spatial conditions. Foot traffic coverage comes from the platform's integrated foot traffic index layer. Competitor locations come from the point-of-interest dataset with category filtering. Household income comes from ABS census data, aggregated to the 1km radius catchment using a population-weighted average. The output is a map layer showing the zones meeting all three conditions simultaneously, with each condition togglable for inspection.

The analyst can then add constraints interactively. "Exclude sites within 200m of a tram stop due to foot traffic already being counted." "Filter to available tenancy addresses only, using our uploaded shortlist." Each follow-up refines the output in seconds. The process of iterating through site criteria, which typically takes days of back-and-forth with a GIS team, compresses into an afternoon of direct querying.

The data layers that matter for retail site selection

Site selection analyses typically draw on several categories of spatial data. Understanding which of these MapAI handles directly, and which require data upload, sets realistic expectations for what is achievable from a plain-English query.

Demographic data by postcode, suburb, SA1, and SA2 is available directly from the connected ABS dataset. This covers household income, age distribution, dwelling type, car ownership, and other variables from the Census. Foot traffic index data is available as a modelled layer for Australian metropolitan areas. Distance to public transport is computable using the included PT network geometry for all major Australian cities. Administrative boundary data (LGA, suburb, postcode, SA1 through SA4) is fully integrated.

What requires data upload: your own existing store locations (for competitor exclusion zones and cannibalisation analysis), your own customer address data (for catchment demand estimation), proprietary tenancy listings (for filtering to available sites), and any custom scoring weights your organisation has developed.

The combination of platform data and uploaded data is where the full site selection workflow comes together. You bring your tenancy list and existing stores. MapAI provides the spatial context layers. The query layer bridges them.

Cannibalisation and network-level analysis

One site selection question that comes up consistently in retail network planning is cannibalisation: how much demand from an existing store will a new site capture? This is a genuine spatial modelling problem that goes beyond proximity analysis. It involves estimating trade area overlap, demand allocation between sites, and the net effect on network revenue.

We want to be straightforward about this: full cannibalisation modelling requires a demand modelling layer that is more complex than what plain-English queries handle natively. Huff model calculations, gravity model calibration, and multi-site demand allocation are specialist operations that produce numerically defensible estimates but require parameter inputs that are specific to each retail category.

What MapAI does well for cannibalisation work is the geographic input stage: visualising the trade area overlap between two sites, identifying shared demographic catchment population, and showing the existing network layout in relation to proposed sites. That geographic context is essential for cannibalisation analysis, and getting it quickly without specialist mediation makes the full modelling process faster even when the modelling itself still requires a specialist.

Where retail teams are finding the most value

In our experience with retail teams during early access, the highest-value use cases have been early-stage candidate screening (producing a shortlist of geographically viable sites before committing to detailed analysis), stakeholder briefing maps (showing the geographic rationale for a shortlisted site to a property team or board), and competitive landscape mapping (visualising competitor density across a market before entering).

These are all situations where the question is clear, the geographic logic is direct, and the value of getting an answer quickly is high. They are also situations where the conventional approach requires specialist involvement even though the underlying analysis is well within the capability of any analyst who can phrase the question.

If your expansion team is spending weeks on spatial analysis for site selection, the diagnostic question is: how much of that time is geographic reasoning versus data preparation and tool overhead? In most cases, the answer reveals that the majority of the time is overhead. Removing that overhead does not replace the commercial judgement of an experienced site selector. It gives that person the geographic evidence they need to exercise their judgement faster.