Mobile Location Analytics (MLA)
|“||provides retailers with insights into the in-store behavior of their customers by tracking the number, location, and patterns of smart mobile devices that enter and exit their stores. While this technology provides retailers and customers with many benefits — generally speaking, it allows retailers to adapt more efficiently and effectively to the demands of their customers — it also raises many privacy concerns. In order for MLA to maintain the trust and confidence of consumers while improving retailers' understanding of them, these privacy concerns must be addressed in a manner that simultaneously allows for the generation of effective retail analytics.||”|
"MLA refers to a set of technologies that capture and analyze radio signals, such as Bluetooth and Wi-Fi signals, from nearby mobile devices to generate aggregate reports about consumer behavior for retailers."
"By combining and analyzing the MAC addresses and signal strengths of nearby smart mobile devices, MLA is able to enhance retail metrics by generating aggregate reports on window conversion, walking paths, heat maps depicting busy areas, repeat visits, sequential visits, dwell times, sales intercepts, product interaction, queue wait times, zone traffic counts, dwell-to-traffic ratios, personnel location, employee to customer ratio, and public safety. Inclusion of these metrics allows for a better understanding of consumers' in-store behavior, enabling stakeholders to optimize their processes and provide a better overall shopping experience. For example, using MLA a retailer would be able to optimize store layout, measure key metrics before and after the rollout of different sales campaigns, and minimize checkout times."
- ↑ Building Privacy Into Mobile Location Analytics (MLA) Through Privacy by Design, at 1.
- ↑ Id. at 2.
- ↑ Id.