In the world of online shopping, it’s relatively easy to track and analyse customer behaviour, as every action is digitally logged. Retailers will know who has shopped and where, how long they browsed the site, what they purchased, and whether they redeemed a marketing offer or collected loyalty points.
Back in the physical store, however, the same shopper will browse and buy with relative anonymity. Much less is known about in-store shoppers by store associates – and this makes it much more difficult to personalise purchasing journeys.
Retailers urgently need ways to gather the same in-depth insights online and offline, if they want to optimise the customer experience. Now is the time to stop guessing what in-store behaviour patterns look like, and start knowing how consumers shop.
In order to understand bricks-and-mortar shoppers in greater depth, many retailers are implementing location-based analytics across their store estate. This technology is more sophisticated than people counting alone, as it allows decision makers to understand shopper behaviour in greater depth.
Here are some examples of the ways that location-based analytics can help retailers to track in-store shoppers as closely as they track online shoppers:
With traffic insights in place, combined with sales data, retailers can ascertain the ‘total sales opportunity’, which reveals how many shoppers in the store can actually be converted to buyers. This can then be mapped against other metrics, such as in-store journeys, dwell times, queue times, abandonment rates, and speed of service.
By evaluating behavioural trends through location-based analytics, patterns will emerge in the context of each individual store, region or network. From here it becomes clear which operational strategies – from queue management to merchandising – drive higher conversion rates.
With a framework for understanding the total sales opportunity in place, retailers can then use real-time shopper data to monitor how strategic decisions impact store performance. By using KPIs, it is possible to measure results in order to optimise in-store operations, refine marketing strategies and inform store managers. More importantly, it ensures that decision making is based on solid data, as opposed to gut feelings and guesswork.
Not only that, but retailers benchmark results across the store network, to assess which stores are performing best and which are in need of improvement. By comparing what is being done differently in the better performing stores, best practices can be shared across the business to drive customer experience standards.
For retailers who are considering location-based analytics, another key benefit is the ability to empower store employees. If staff are supplied with reliable, accurate information – for instance, how to be prepared for ‘power hours’ when traffic and conversions peak – their work as fully supported brand ambassadors becomes more engaging and rewarding.
In addition, retailers can reallocate labour across the store in line with geographical shopper insights, to ensure that front-line staff are where they are needed most.
The store no longer acts in isolation; it is part of a multichannel, multiple touchpoint journey for most customers, and therefore physical retail experiences need to be contextualised alongside consumers’ digital activities.
Location-based analytics delivers the shopper insight needed to develop best practices for serving shoppers across all channels. By understanding shoppers’ bricks-and-mortar behaviour in greater depth, as well as how they shop online, retailers can optimise all their channels to ensure outstanding customer experiences.
To find out more about leveraging location-based analytics to understand in-store shoppers, download our location-based analytics eBook.
To find out more about how in-store analytics can benefit your business, visit our insights page.
Sensormatic News Desk