How can data analysis transform store performance?
May 23, 2018
For retailers of old, data or information was limited to counting till receipts and keeping a manual log of inventory. They didn’t necessarily know who their customers were, they simply stocked products they thought people might like. But today there is data for everything: what we’ve bought, at what time of day, what we’ve browsed online, which is the most visited part of a store, what we liked and disliked about a product, what our friends bought and the list goes on. In simple terms, data can be used to assess past performance, shopping habits and more importantly help shape and predict the future of a store.
Companies are still trying to get their heads around how to use this data most effectively to make decisions and ultimately deliver results.
One fascinating area is store performance. Why is it that one retailer can be selling the same product at the same price, with the same in store promotions in a store of similar size, look and feel – and yet one is generating sales of £100K a week and another at £350K per week? The answer could be due to a number of internal or external factors, but there are hundreds of variables to consider.. This is where the power of analytics comes in.
Store analytics methodology is based on the premise that not all stores within the same chain are identical and therefore one can’t expect the same results delivered across the network. Internal factors are those that can be addressed as they are under the direct influence of the management team and staff. External factors are related to store location, local demographics, proximity of competition, availability of parking for shoppers, the list goes on – factors that are hard or impossible to influence in the short term.
Analytics tools can analyse billions of data points from 20+ sources to create peer groups of comparable stores that are influenced by similar external factors. The stores within each group are then ranked using the usual metrics such as sales or margin per sq.ft. When underperforming stores within each group are identified, further analysis can help pinpoint the cause-effect relationships much more accurately. Once the problem is identified you can find a solution that applies the tactics of the best performing stores into the lower performing stores or look at introducing further innovations that transform performance.
Decisions and beliefs made relying on “gut feeling” don’t have the hard evidence to support them. Whereas analytics enables you to ratify those decisions. For example:
- For one client, increasing rail density (i.e. more stock on the shop floor) in some of their departments did not drive sales. The analysis undertaken pointed to fewer displays and wider isles to facilitate easy access to products and deliver an all-round more enjoyable shopping experience.
- Analysis confirmed that manager tenure, a part-time staffing model, and staff churn rates did not influence sales performance. However, availability of trained staff on the shop floor had a very strong correlation with growth in sales.
- The number of available fitting rooms directly influenced the number of returns in women and men’s fashion – and so more staff and store space were dedicated to fitting rooms.
The best thing about store analytics is that it works. In a recent analytics-enabled project a retailer saw a sales uplift of 8.5%.
In an environment where the role and format of brick-and-mortar stores is changing, retailers should use the power of data analytics to innovate, design and test new ways of delivering an exciting shopping experience whilst maintaining a healthy margin and driving sales growth.