How an emerging brand chose its first store location using POI Data

A fast-growing DTC brand was preparing to open its first physical stores. With limited margin for error, the team needed to make every retail location count.

By leveraging Factori’s POI Data, the brand identified high-potential retail sites based on nearby foot traffic, complementary businesses, and consumer behavior signals. The result? Faster break-even, higher basket sizes, and stronger loyalty—without overextending budget.

• The brand needed to:
  • Select high-potential retail locations with data-backed confidence
    
  • Avoid underperforming areas and costly site selection errors
    
  • Maximize ROI from each new store launch
    
  • Understand customer density, competition, and neighborhood dynamics

Factori delivered a granular POI data feed enriched with spatial, commercial, and behavioral layers.

Key data capabilities:

• 50 M+ curated POIs with commercial and contextual tags
  
• Data attributes: business category, visitation trends, customer density, nearby amenities
  
• Filter by brand affinity, retail co-location patterns, dwell time, and traffic volume
  

Delivered via API or flat file for easy integration into maps and models

With POI data driving the location model, the brand:

• Reduced time to breakeven by 30% through smarter site choices
  
• Increased average transaction value by 25% by selecting high-spend zones
  
• Lifted customer retention rate by 18% due to better neighborhood fit
  
• Accelerated expansion planning while minimizing risk

Factori’s POI data helped the team visualize location viability at a granular level. It enabled:

• Smart filtering for top-performing business clusters
  
• Layering mobility, demographic, and commercial POI signals
• Identifying synergy with complementary brands and services nearby
• Prioritizing neighborhoods with loyal, high-value footfall