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Data Insights

Explore our latest publications, from industry insights to in-depth tech analysis

7 Steps to Smarter Business Site Selection

Business site selection helps companies choose stronger locations by aligning expansion goals with real market evidence. By defining clear criteria, analyzing trade areas, assessing competition, comparing candidate sites, and learning from existing locations, businesses can reduce location risk and make more confident growth decisions.

What Is Offline Attribution And Why Digital-Only Measurement Is Costing You

Offline attribution helps businesses measure how digital campaigns influence real-world actions like store visits, branch traffic, dealership visits, and in-person conversions. By using privacy-safe mobility data and visit lift analysis, marketers can connect media exposure to physical outcomes, measure incrementality, and optimize campaigns beyond clicks and online conversions.
High street vs mall_ Retail Opportunities and Risks

High street vs mall: Retail Opportunities and Risks

The high street vs mall decision helps retailers choose locations that best match their customers, store format, cost structure, and growth goals. By comparing footfall quality, visibility, accessibility, surrounding businesses, audience fit, and profitability potential, retailers can reduce site risk and make more confident location decisions.

What Is Cross Visitation Data? How Customer Movement Supports Smarter Planning

Cross visitation data helps businesses understand how customers move between locations, brands, and categories. By revealing shared audiences, competitive overlap, movement patterns, and connected customer journeys, it supports smarter decisions across marketing, expansion planning, customer analysis, partnerships, and demand forecasting.
Real Estate Site Selection_ A Practical Guide to Choosing the Right Location

Real Estate Site Selection: A Practical Guide to Choosing the Right Location

Real estate site selection helps investors choose stronger locations by analyzing demand, accessibility, foot traffic, mobility, nearby businesses, demographics, and competition. By using real-world data instead of assumptions alone, teams can compare sites more confidently, reduce investment risk, and identify locations with stronger long-term potential.
Retail Location Analysis: A Data-Driven Guide to Site Selection

Retail Location Analysis: A Data-Driven Guide to Site Selection

Retail location analysis helps businesses choose stronger store sites by combining foot traffic, mobility, demographics, POI, competitor, and trade area data. By moving beyond static market reports and using real-world behavior signals, retailers can better understand local demand, reduce site selection risk, and make more confident expansion decisions.
Factori Is Now Available on Snowflake Marketplace

Factori Is Now Available on Snowflake Marketplace

Factori is now available on Snowflake Marketplace, giving Snowflake customers direct access to real-world data on people, places, movement, visits, POIs, and audiences. Teams can discover, evaluate, and activate Factori datasets inside Snowflake to accelerate analytics, forecasting, enrichment, and decision-making without complex integrations.
Retail Store Cannibalization

Retail Store Cannibalization: Why New Store Openings Drain Sales and How to Stop It

Retail store cannibalization happens when a new location shifts visits and sales away from nearby stores instead of creating new demand. By using mobility and location intelligence to measure catchment overlap, simulate expansion scenarios, and track post-launch demand shifts, retailers can reduce expansion risk and protect network performance.
Customer Shopping Behavior Across Locations

Customer Shopping Behavior Across Locations: What Transactions Miss and How to Measure It

Customer shopping behavior data helps retailers understand how people visit, move between, and engage with physical locations over time. By combining privacy-safe people and visit data, businesses can uncover visit frequency, dwell time, cross-shopping, repeat behavior, and location-level differences that transaction data alone cannot explain.

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