As we continue to experience widespread store closures and social distancing rules, bricks-and-mortar retailers need to be considering new ways to attract consumers back to the high street post-pandemic. To get ahead of the game, they will need to make best use of the data available to them.
Despite having a wealth of data available to them, including transactional data, stock availability and footfall, currently, it is often the case that this is put straight into a data lake, leaving retailers unable to use it effectively. Many also find that the data they have access to can be of dubious quality and, is therefore, unreliable. Consequently, this approach is leaving retailers unable to not only derive value from their data to make meaningful and intelligent business decisions, but also unable to trust it. Therefore, it’s essential that retailers overcome these obstacles now in order to gain the insights needed to attract shoppers back to the high street once restrictions have been lifted.
Firstly, to derive value from their data, bricks-and-mortar retailers must ensure that they are able to trust it. For this to be the case, the data they have access to must be consistent and accurate which they can ensure by identifying gaps and performing data integrity tests. While this is standard practice for some forms of data, such as financials, retailers must begin to apply the same discipline and processes to all types to guarantee its quality. Then, providing they have the right solutions in place to join up and analyse it, retailers can still place it in a data lake. The combination of these processes and the right tools will ensure that their data is more reliable and, therefore, so is any analysis.
Taking inspiration from online
While bricks-and-mortar stores are likely to see their online counterparts as competitors, there is a great deal they can learn from them when it comes to putting data into use. Online retailers capture an even greater amount of information on their customers, ranging from the most popular types of products among individuals to the dwell time on pages. This data can help online retailers to determine which products their customers are most interested which the retailer can then use to target the customer with the most relevant items and deals via email or on their home page, for example. Additionally, the data they collect can inform online retailers about which items are frequently bought together which they can then feed into the layout of their warehouse to optimise the picking and sending of items. Particularly in large warehouses, this approach helps to reduce the amount of time spent on these tasks and allows for items to be dispatched quicker. Ultimately, while the types of data available in stores and processes they can put in place will differ, the goal should remain the same – to enhance the customer experience and optimise internal processes.
Enhancing the high street experience
In light of ongoing events, when possible, bricks-and-mortar stores must find ways to draw people back to the high street and offer them an experience they can’t get from online shopping. This is where feeding data into the right solutions comes in. Research from REPL has found that 40% of retail CIOs and CTOs think they should be investing in artificial intelligence (AI), IoT networks (26%) and robotic process automation (17%). Implementing the right technology will help retailers make more sense of their data and feed the insights gained back into their operations. In turn, this will enable high street shops to elevate the consumer experience and enable them to evolve their offering in line with changing buying habits. These technologies will also allow retailers to use data for forecasting to improve supply chains and category management to ensure they are able to meet consumer demand and provide an improved shopping experience.
Unlike online retailers, bricks-and-mortar stores don’t have the luxury of large warehouses containing all the stock they need, therefore, it’s essential they have the right number of products in store. One supermarket to have adopted this mentality is Sainsbury’s which has introduced machine learning to discover what convenience store customers want from its Local stores. For example, it has found that Hula Hoops and milkshakes are popular products in the City but maybe not so much in more rural convenience stores. This approach signals a shift away from a one-size-fits-all approach to that of a ‘cluster of one’ in which each store is adapted based on data on its local market. In the face of falling footfall as consumers increasingly head online, this will enable retailers to stock each store effectively in line with its findings. Using data to ensure the right products and the right quantities are stocked dependent on the store will not only ensure consumers are able to buy the items they require, but it could also inspire impulse purchases.
This ‘cluster of one’ approach can also be adopted in relation to personalising the shopping experience for customers. If retailers are able to link their data to customers, they can target specific customers with deals based on their profile, for example. This will help to inspire brand loyalty and repeat custom as consumers find that their specific needs are being catered to. With the ability to make more insightful decisions to better target customers, this will ultimately result in increased revenue. Additionally, using data for better supply chain management would allow supermarkets to get products instore more cheaply and efficiently. This will help to drive down costs by boosting the volume of orders from suppliers, in turn giving the supermarkets more margin to cut prices in the supply chain and lower prices to customers.
Can data save the high street?
Although it’s unlikely that data can solve all of the high street’s problems, its use with the right tools can help bricks-and-mortar retailers to vastly improve the shopping experience and move physical stores towards being a ‘cluster of one’ in the same way as online. However, it’s vital that retailers first recognise the importance of ensuring that their data is clean and reliable, as only then will they be able to feed into latest technologies, such as AI and ML. By feeding their tools accurate and consistent data, retailers will be able to make better business decisions to ensure the right stock is available when shoppers visit stores and offer consumers an experience that can’t be matched by online retailers. This approach will help prepare retailers for when people return to the high street, particularly as many are likely to desire a physical shopping experience after weeks of not having that option available.