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In-Store Marketing eller Analysis er en effektiv måde at hverve nye kunder og tilbyde dem et fremtidigt tilhørsforhold til forretningen

Beskeder kan tilpasses den enkelte profils interesseområder og en nem måde at ramme den enkelte kundes interesse område. Selv kunder der passerer udenfor en butik kan kenkendes og tilbydes relevante tilbud. Nok så væsentligt analyseres data så bevægelsesmønstre og besøgsfrekvens direkte kan aflæses.

Wi-Fi analytics explained
As all this is very new and emerging, many people are eager to understand how do you obtain and analyse visitor patterns using Wi-Fi. With almost a decade of experience of Wi-Fi based indoor positioning and retail store analytics, we wanted to explain as simply as possible how Wi-Fi analytics works:

1. Our handhelds are making a noise of themselves

Even if not connected, all Wi-Fi enabled devices continually transmit signals to detect and connect to available networks. This typically takes place in 15-30 second intervals, depending on the device hardware. Let’s call these signals “pings”.

2. Wi-Fi access points and sensors detect the signals sent by devices

Existing Wi-Fi access points and/or dedicated Wi-Fi sensors listen to the pings sent by smart devices, and also the signal strengths of those pings. By listening to those pings from multiple locations in the store, we can start approximating where the pinging device is located.

3. From signals into observations of customer’s in-store location

Robust Wi-Fi analytics solutions have built-in advanced algorithms that use signal strength and other parameters to accurately detect the presence and location of all active Wi-Fi devices. Additional filtering algorithms are used for cleaning out static and staff devices and to correct any deviations and errors in the observations.

4. From observations into behavioural patterns

With a series of observations on visitors’ in-store location, it is possible to analyse entire visits to retail stores by customers — which locations they visit and in which order, and how much they spend time at each location, and so on. Naturally, all this is fully anonymous unless the customer deliberately opts in for location-based services and thus lets her be identified.

5. From patterns into retail analytics that tells how to improve the shopping experience

This data forms the basis for dozens of metrics and retail KPIs that reflect actual and real-time customer behaviour. This enables retailers to be in better control how their stores perform at any given time. Also, this makes it possible to continually test new things such as layouts, merchandising and shopper marketing, and then select the things that work best to please customers.