There’s no doubt that the internet has changed many things in our lives and one of the top ones is how we shop. Although people often foretell the death of the physical store in favour of the online one, stats show we still prefer to do a lot of shopping on the high street, even if we do the research first on the internet. An extension of this is that customers come to expect retailers to offer online-like deals and offers to them and therefore, that their every move will be tracked. Very soon, tracking customers in-store will be the norm and if stores don’t do it, they could lose business.
What is in-store analytics?
In-store analytics is also known as customer or retail analytics and even as big data. It is a term used for a series of ideas that offer physical stores the kind of data about their customers that mimics the analytics found from online shops. Customer numbers, what customers buy, where they go in the shop, all of this information can be collected and used to make a better customer experience as well as to improve profits.
The process starts with monitoring how many customers look at the store and enter or walk away. Do they study the window displays and walk away or do they come inside? And when inside, do they still look at the things they studied in the window or do they go elsewhere too?
Tracking customers is something done through the combination of CCTV cameras and the use of Wi-Fi hotspots. When a customer has the Wi-Fi setting activated on their phone, these devices automatically grab the nearest hotspot as the person moves around a location. By using a series of beacons within the shop, the store can create a track of where the person goes in the store without infringing their personal data in any way. This is because each phone has a unique identity that is registered with every hotspot it touches.
In fact, a study in US shopping malls shows that 62% of shoppers leave Wi-Fi active on their phones while shopping and this allows them to be constantly connecting with hotspots. This digital footprint can then be combined with information from CCTV cameras which offer comprehensive coverage of almost all stores.
This data includes information such as gender, age group, if the customer has children with them and even their emotional reaction to things. So, if someone picks up an item, studies it and then puts it back frowning, this might indicate a negative reaction to the product or something about it. Likewise, happy reactions to offers show that customers react positively to them and that they are well placed.
Well placed tech
In the near future, this network of information will expand and include a number of connective devices and systems to add further to the customer picture. The use of counting devices in sections of stores that use Bluetooth data in similar ways to the Wi-Fi connection hotspots is something some large stores are already testing.
Again, this collects information that builds up a picture of the customer without revealing any specific information about them and risking interfering with data protection regulations. But the combination of Wi-Fi, Bluetooth and visual data fed into the right kind of software can create an amazingly clear picture of customer behaviour in store.
Using the dataUsing specialist in-store analytics software, retailers are already using this kind of technology to build up a clearer picture of their customers – and customers are coming to expect this level of personalisation.
Promotions, for example, would once have been placed along general lines, with every store in a chain looking much the same. But with data from the analytics, it is possible to individualise each store back on its customers and ensure that promotions are both relevant and correctly placed. The use of hot and cold zone information helps with this, highlighting the areas that shoppers visit the most versus those they use the least.
Shift patterns for staff is another massive area that data is changing forever. Where once store managers completed staff shift patterns based on their past experience or even their intuition about who would be needed when the data now allows for a fact-based approach. Using the data, patterns clearly emerge that show when customers are in the store and when they aren’t – time of day, days of the week and even weeks of the month. This allows stores to create shift pattern that mimics these patterns and offer the best customer service by having the right amount of staff available.
Becoming the norm
Perhaps the most important part of all of this is that customers are coming to expect this level of personalisation and customer service from physical stores. Gone are the days when there was a separate of service standards between online and offline – while buying from a person is still very important, customers expect that to be an informed person.
People are also more willing to give access to some of their personal data in return for an enhanced service. Loyalty schemes are a prime example, offering personalised vouchers or discount codes based on past shopping history. This is likely to continue to grow with the Internet of Things coming to store – smart shelves are one example. These can register an item picked up and returned, which might then mean that the retailer would know you were interested but didn’t buy. A voucher could then be sent to tempt you and convert you to regularly buying this product.
In-store analytics offers businesses the chance to get a little glimpse inside the mind of their customers through their behaviour. And customers have already come to expect this level of surveillance. Measures don’t infringe data protection laws but do allow retailers to offer a personalised experience to shoppers that is already quickly approaching the norm for most people, both in the online and offline worlds.