Choosing the right locations with Python and location intelligence
POI, shop attributes and demographics clustered to place customer-care shops, retail outlets and sales teams where they would perform.
Tools
- Python
- MapInfo
- Location APIs
At a glance
- Use case
- Retail + sales siting
- Reach
- Nationwide
Context
Operators needed to place customer-care shops, retail outlets and field sales resources where they would actually perform — not just where space happened to be available.
Approach
I combined points-of-interest data, shop attributes (age, footfall, ratings, distance, product range) and demographic layers, then used Python to cluster opportunities and score locations. The analysis turned a sprawling, judgement-driven siting problem into a ranked, data-backed shortlist.
Outcome
The work informed decisions on new shop placement and the distribution of sales resources nationwide, helping the business put people and outlets where demand and accessibility lined up.
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