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Can data save the high street?

(Image credit: Image source: Shutterstock/Maxx-Studio)

As bricks-and-mortar stores fight to get consumers back to the high street, it’s essential they begin to make better use of their data. If there’s one thing retailers have an abundance of, it’s data - ranging from transactional data to the items they have in stock and the number of people who pass through their door. However, despite this wealth of data, the majority of retailers aren’t currently getting any value from it, primarily for two reasons. Firstly, retailers may be unable to access the data they collect if they don’t own it. Secondly, and more commonly, the data that is collected is put straight into a data lake leaving retailers unable to use it effectively. At this stage, a further problem they encounter is that the data they have access to can be of dubious quality and, therefore, unreliable.

All in all, this approach is leaving retailers not only unable to trust their data but to join it up and make meaningful and intelligent business decisions from it. So, how can retailers begin to use data more effectively and will this ‘save’ the high street?

How retailers can get the most from their data

As retailers look to derive more value from their data, they must ensure that the data they have access to is consistent and accurate. This means looking for gaps and doing data integrity tests. For some forms of data, such as financials, integrity testing is standard practice, and retailers must begin to apply the same discipline and processes to their other data sets. This can then still be placed in a data lake, as long as the right solutions are in place to join up and analyse it. Once these processes are in place, the data will be more reliable and, therefore, so will any analysis.

When it comes to putting this data into use, bricks-and-mortar retailers should look to their online counterparts for inspiration. For example, online retailers capture information such as dwell time on pages to determine which products their customers are most interested in and they can tailor their homepage, to showcase items that are popular with the individual. Further to this, online retailers can see which items are often bought together and in turn lay out their warehouse in a way that will optimise the picking and sending of items. This helps them to reduce the amount of time spent doing these tasks and to get items dispatched faster. Although the use of data will differ within bricks-and-mortar stores, the aim should remain the same – enhancing the consumer experience and optimising internal processes.

Using data for the physical shop floor

With increasing competition both online and on the high street, bricks-and-mortar retailers need to change the in-store shopping experience and give consumers something they can’t get from online shopping at home. Data will be vital in achieving this and feeding this data into the right solutions will allow high street shops to evolve their offering in order to keep up with the change in consumer buying habits. According to research from REPL, 40 per cent of CIOs and CTOs in the retail sector think retailers should be investing in artificial intelligence (AI), followed by IoT networks (26 per cent) and robotic process automation (17 per cent). By adopting these technologies, retailers will be able 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.

This data can also be used to make better predictions so retailers can plan staffing more effectively. This will help to ensure they have the relevant members of staff scheduled to be in place to serve customers more quickly, with the most up-to-date information, in order to create the best in-store experience for customers.

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.

Where does this leave the high street?

While data can’t fix all of the high street’s problems, it can certainly help enhance the experience of shopping there. Data can also be used to personalise the shopping experience and move bricks-and-mortar stores towards being a ‘cluster of one’ in the same way that online is. Thanks to new technologies, such as AI and ML, retailers can make more sense and derive more value from their data to ensure the right stock is available and potentially reduce costs in order to attract people back to the high street. However, retailers must first put in the work to ensure their data is clean and consider how they can put these solutions in place. After all, these technologies will only allow stores to improve the customer experience if the data being fed into them is accurate and consistent.

Mike Callender, executive chairman, REPL Group