For many businesses, a successful model includes the combination of physical stores and an online presence. Online, customers are used to our every move being tracked and websites gently spying on them to learn what they like and what they don’t. Now systems inspired by these online norms are available for physical brick and mortar businesses. But what sort of systems can be used to collect this data and how can its interpretation help business success on the high street?
There are various terms used to describe the process of collecting information about a customer in store – customer analytics is one, as is retail or data analytics. Even Big Data is a term applied to the process, although this often includes a combination of online and offline information to create a holistic picture of the business’ customers.
The idea that brick and mortar stores can collect information about customers in legitimate ways and then use data analytics software to analyse this is a relatively new one but there are already a number of platforms and data analytics tools available to help make the most of the information. Some business sectors have already embraced the ideas behind customer analytics while others are a little hesitant, worried that they might alienate customers.
Understanding how data is collected
To understand how a business can benefit from data analytics, it helps to understand some of the methods used to collect this information and what kind of data it will yield. Top of the list of systems is one based around Wi-Fi beacons.
Most people don’t turn off their Wi-Fi on their smartphone when they are out of the house so those smartphones go around constantly connecting to the nearest hotspot. Therefore, by making use of Wi-Fi beacons within the shop, businesses can track where a particular smartphone goes while in the premises. Often retailers will set up a grid of Wi-Fi beacons that criss-cross the whole shop and make use of the data given freely by people simply by having their smartphone switched on for Wi-Fi. It is considered ‘clean’ data because there is no personal information attached to it.
Another concept that is relatively new is the use of smart QR codes, virtual displays and brand applications, all of which can help see where customers go and what they do while in the store. Customers agree to download apps or software for the retailer and this allows them to collect non-personal information about their movements while in the store. Information can even be relayed to staff in the shop, armed with tablets, who can then understand more about what that person is looking for.
Viewing the store
Few stores don’t have CCTV systems in place for security and to prevent theft but these systems can also be used to view a customer as they move around the store. This can generate some information about the person such as gender, age group or if they have children with them that can be paired with data from the smartphones to give a picture of what people use what areas of the store – or what areas they avoid.
Inventory local information in the form of smart price tags or even hangers will also match up with sale and foot traffic to help see what areas of a store are getting the most footfall and what areas
are quiet. This means retailers can look to see if sales points, special offers and other important stands are in the right place for the customers they are targeting or if they are simply not being seen.
Working with the data
Big data platforms are paired with all of this information to begin to paint the kind of picture of the store and its customers that is considered normal online. It can look at where people visit, what they look at and study what they buy. Paired with loyalty schemes, it can even make the person into an individual while keeping within data protection laws.
So, while collecting all this data is legitimate, how can it help the business in practical terms? Knowing lots about the store layout might seem helpful in a vague way but what are the quantitative benefits of using analytic software?
One of the big benefits from data analytics is the ability for stores to look at customer visitor numbers versus time and date. It will create a picture of when the store is busiest and when it is quieter and this is crucial when creating staff rotas. Previously, managers would act almost on gut feeling as to when they needed more or less staff available. But using data analytics software means you have hard data in front of you to form those staff planners.
Good software will even help you with this planning and allow you to keep profiles of your staff such as hours contracted and any times they cannot work. This allows managers to apply a scientific and fact-based approach to staff shift patterns that can also be changed quickly if there is a change in plans.
Store layout is another area that was often done at corporate level and would result in shops that all looked the same. But the information gathered can show that this layout may work in some shops but not in others and allows managers to make amendments at store level. This helps with placement of promotional materials, popular ranges and also helps avoid store dead zones where no-one goes.
The real-time nature of this software means that businesses can see on an hourly basis how the day is progressing and if there are any problems. The balance of staff management and promotional placement along with the flow of customers through the store and the amount of assistance available to them can paint a vivid picture of what is working within the shop and allows quick alterations to make the most of the customers the business is receiving. That way, the guess work is removed from the job of store manager and hard data can be supplied to corporate levels.