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Briefing
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- Likelihood to Visit in the Next 30 Days (L30) – Euclid’s newly introduced predictive metrics study shopping patterns at scale and use machine learning to score someone’s likelihood to visit store location over next 30 days
- Predictions Assumptions – Based on visitor data collected through user opt-ins to guest WiFi, such as number of visits, days elapsed since visit, visit duration, number of locations visited, among others
- Better Customer Understanding – Help retailers understand customer behavior, identify likelihood of returning, and tailor-fit marketing strategies and messaging
- Accuracy – Can predict visitation with roughly 80% accuracy
- Market Segmentation – Pre-packaged audience segments available, but retailers can also create new segments using custom visitation data
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Accelerator
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Business Model and Practices
Business Model
and Practices
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Sector
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Information Technology, Wholesale and Retail Trade
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Organization
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Euclid Inc.
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Source
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Original Publication Date
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December 5, 2017
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