Wi-Fi analytics in retail stores: How it works

Juha Mattsson

The way that retailers measure patterns of customer behaviour in brick-and-mortar stores is disrupting with an unprecedented pace as we speak.

The main driver of the disruption is the triumph of online stores that we’ve been witnessing for several years. They have been successful in building win-them-all shopping experiences, carving out living space from the high street.

This is thanks to a secret weapon web stores have possessed for more than a decade: obtaining detailed, real-time analytics data on each and every shopper returning to their site, and optimising and personalising the shopping experience based on such data.

Physical stores, in contrast, have been measured merely by operations-driven methods such as footfall counters at doors and transactional data from point-of-sale systems for decades. Naturally, these methods won’t even come close to the kind of in-store behaviour analytics data that online stores are currently using to fine-tune their shopping experiences for each individual shopper, real-time.

Wi-Fi is the most versatile technology for modern retail analytics

This gap between online and “offline” retail has sparked a recent boom of technological innovation in analysing our patterns as we visit high street stores. Wi-Fi has emerged as the most widespread and versatile technology for this, and is being adopted by many retailers.

In comparison to people counting and POS-data, Wi-Fi is able to analyse entire (and repeat) visits to a store, it provides real-time data from entire store spaces, and it’s also capable for providing wayfinding and proximity based services and marketing for opt-in customers. Importantly, none of the above uses require an app to be installed on customers’ devices, meaning a significantly better ability to reach people compared to app based technologies.

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.

Will Wi-Fi analytics transform how retailers manage stores?

With the above capability, physical stores quickly reach par with online stores in terms of measurability and optimising the shopping experience based on actual customer behaviour. In a freshly published free eBook (Wi-Fi Analytics for Retail Stores: Buyer’s Guide), we review in-store Wi-Fi analytics against a list of the most common web metrics. The conclusion is that practically all of the same things can readily be measured in physical stores.

This means brick-and-mortar now has the ability to start managing stores just like online does: measuring detailed conversions, continuous monitoring and optimisation, A/B testing new things, zooming into shopping paths and behavioural patterns, segmenting based on behaviour, and so on. We also see completely new kind of use-cases emerging: queue management, optimising staff allocations based on actual customer needs, tracking and managing assets like shopping carts, and triggering marketing messages based on customer location.

We believe this will have a profound impact on the way successful retailers will manage their stores. To learn more about the facts and details, download the free eBook and read about things like:

  • Comparison of sources for in-store analytics data
  • Wi-Fi analytics vs. web analytics
  • Key use-cases of in-store analytics
  • The three principal levels of in-store analysis
  • The five dimensions of accuracy in Wi-Fi analytics
  • A buyer’s checklist for choosing and deploying a Wi-Fi analytics solution

Wi-Fi Analytics Buyer's Guide - FREE DOWNLOAD


Walkbase provides a retail analytics solution for improving the impact of marketing on physical stores and personalising in-store shopping experience.