Much has already been written about the role of Artificial Intelligence (AI) in financial services. […]
In this first deep-dive article of the Connected Stores Point-of-View (PoV) we go, together with data analytic experts, in-depth into how data analytics will revolutionise offline stores. There are many types of high-tech analytic technologies that will allow retailers to retrieve a vaster and more specific amount of valuable customer data. Consequently, this will bring a high amount of value to their business.
Personalise offline stores with data analytics
When customers shop online, they expect a certain level of personalisation. That’s because most retailers use digital analytics to follow and monitor every single move and click their customer makes. Retailers measure customer behaviour on their digital channels meticulously, including visits from customers that are just browsing the site. They track what products were added to the shopping cart, how much time was spent on a page, and mouse movements down to the millisecond. The result is an optimised experience that’s tailored to the customer’s needs. But what if you could bring this same level of insight and personalisation into your brick and mortar stores? Today’s analytical technology lets you do just that.
The power of analytics: it’s not just for digital anymore
Traditionally, collecting data from brick and mortar stores has been limited. It’s confined to transactions, store traffic, loyalty cards, and of course, good old-fashioned face-to-face interactions. But stores of the future are using advanced technology like RFID tagging, facial recognition, and computer vision to create more in-store measurements on customer behaviour. By embedding these technologies into physical stores, retailers can now retrieve and measure a vast amount of customer data and take full advantage of the same analytics used online.
Let’s take RFID tagging in fashion retailing as an example. RFID tagging lets retailers know what products have been picked up in their stores, how those products move through the store, if the product is frequently paired with another product, if the product makes it to the dressing room, and if the product is eventually purchased. From an operational level, RFID lets associates know if a product is in-stock and, if so, exactly where in the store it is. If a product is commonly taken to another location in the store and abandoned, this information can help optimise the store layout. It also allows for data-fuelled decisions about store assortments.
Computer vision can track customer movement through the store, giving retailers a real-time understanding of key walking routes. Retailers can see if customers never make it past the first two aisles or rarely go upstairs. If this is the case, retailers might decide to change the store layout to increase traffic flow to other parts of the store. They might experiment with new signage or design.
Computer vision also monitors how customers interact with products on the shelf and in displays. It captures what products are frequently picked up, touched or inspected by customers, and analyses how that relates to sales. With these insights, retailers can make informed decisions regarding product location: should it stay where it is, be removed from the display or be repositioned?
Through computer vision, retailers can employ facial recognition to get a more nuanced idea of who their customer is and better understand the store’s demographics on a granular level. If you aggregate this data, you can use it to adjust your store to the needs of the community you’re serving (think changing the storefront, the look and feel of the store, the store assortment, or design).
Facial recognition also lets retailers recognise returning customers and build up a profile about them–what does this customer pick off the shelf? What have they bought in the past? What’s their size? Retailers can recognise this person each time they come in and greet them accordingly, much like how it is already done online with returning customers. By getting a fuller picture of someone’s preferences and tastes and creating a single customer ID, retailers can start to personalise offline experiences for returning customers and meet their needs more comprehensively.
Make the most of your data
There are endless ways to aggregate the information you collect and use it to benefit your store. But using it on a customer level, that’s the key to the castle. The level of personalisation that you can implement by using these technologies goes far beyond anything we’ve ever seen in brick and mortar stores. Online is all about personalisation–being relevant for a specific person at a specific moment. But being able to recognise a customer offline, observe every product they touch or consider, and see where they linger in the store, would bring that high level of personalisation to your physical stores, too.
Success in small steps
If you want to set your store up for analytic success, you must start small. Don’t apply each new technology to all of your stores right away. That’s far too expensive. Testing things out is crucial. And analytics can help you test accurately and make sure you’re prepared to roll out to your entire store fleet.
For efficient testing, you need to determine which selection of test stores will be a good representation of your entire store fleet. The first step is to segment the stores in your portfolio and cluster them by like-type (customer demographics, location, or competitor environment). Test stores need to be distributed across the store clusters to get insights into which technologies will work in which type of store. Then you can roll your selected technology out across segments where you expect the same returns and efficiency gains. And remember: there will never be one technology that works for all of your stores.
Attract talent to drive analytic success
So, how do you attract the expertise you need to drive analytic success? From our experience, data scientists are not your standard employees. You want to look for people who are genuinely curious, who are passionate about learning, and driven by curiosity. The worst thing you can do to a data-scientist is force them into a rigid framework for how to do their job. For the data-science mind, freedom to innovate and experiment, and even fail now and then, is the key. If you create the right environment, they will come.
If you want to read more about how to take the power of digital analytics offline and deepen your understanding of what customers want in your brick and mortar stores, then download our recent Connected Store PoV.