In-Store Analytics in a Cookieless World
Measuring in-store customer behavior has never been more important than it is in 2022. With Covid-19 and the rise of e-commerce, brick and mortar stores are being forced to either rise to the top or suffer a long, slow recovery that may never come. According to McKinsey, 75% of consumers switched to a new store, product, or buying method during the pandemic, and 80% of those intend to continue. If retailers do not take action to attract customers back to the store, their lost business is not likely to return soon, if ever.
The answer is to reinvent the store experience for customers AND employees. Use digital kiosks to supplement the workforce with self-service options. Deliver content with digital signage; personalize the store experience.
But how can retailers provide a personalized experience, in-store? Technology drives these outcomes, but it requires the right infrastructure. To achieve the most personalized, responsive journeys, stores need to be able to understand how customers are interacting with their stores, where they walk, how long they dwell in front of displays, and whether changes in planogram influence that behavior. These analytics require real-time, anonymized location information.
With the fairly recent push to a cookieless mobile world, many of the largest retailers have already begun to deploy in-store digital and analytics
capabilities. The question that this report will focus on: What technology is the best currently available to measure customer behavior?
We look at three main possibilities, all of which have capabilities best suited to specific use cases. However, there is a clear winner when considering
long-term deployments at scale.
In the White Paper:
- Context: The failure of Wi-Fi
- Delivering First-Party Data with Asset Tagging
- Bluetooth AoA BLE (5.1)
- Ultra Wideband (UWB)
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