The following scenario is all too common for retailers and restaurants: management enacts a new initiative at its stores, the performance of each store is compared to last year’s performance, and management discerns that the initiative was successful. A subset of stores show a strong increase in revenue, and those locations are determined to be the most responsive to the initiative.
On the surface, this seems like a reasonable approach. Without a deeper understanding of local economic trends and external factors, though, it is incomplete. What if some of those better performing stores were simply located in areas of strong economic growth, and they were actually underperforming compared to local peers?
It is critical for companies to have detailed benchmark comparisons to have a full picture of each of their locations. By integrating internal performance metrics with external data related to local economic performance, management can drill down to ‘true like-for-likes’. Leveraging this big data not only facilitates truly accurate benchmarks, but also allows companies to understand the effects of major, disruptive events.
In January 2014, for example, large swaths of the US endured a three-day ‘Polar Vortex’ that caused temperatures to plummet. The US APT Index, a data feed that integrates sales and transactions from thousands of chain stores and restaurants, showed that areas with a greater than 10°F drop in temperature experienced a 15.5% decline in sales. Further, areas with a median age greater than 35 years were impacted as well, registering a 12.2% drop in sales as customers stayed bundled up indoors.
As executives know well, substantial sales disruptions are not limited to weather events. Recently there was a massive London tube strike that disrupted transportation patterns across the region.
The UK APT Index showed that sales throughout Central London took a hit, but stores and restaurants in surrounding suburbs experienced a strong bump in business as commuters worked from home. When benchmarking performance, it is critical to appropriately factor these events into analysis.
Even anticipated events can have unexpected and important impacts. Black Friday, for example, is beginning to emerge as a major force in the UK as retailers look to jump start holiday sales, but it’s difficult for businesses to understand the true success of promotions on the holiday.
In 2014, for instance, the US APT Index showed that Black Friday Weekend retail sales in Seattle, WA were down 7.8%. Thus, if a national retailer saw its Seattle location’s Black Friday Weekend sales decline by 5%, then its performance should actually be viewed favorably.
By benchmarking against the performance of nearby stores, retailers will be able to meaningfully assess what goes well and where they lag behind on the fast-growing discount holiday.
And by integrating data about weather, demographics and other characteristics of stores into analysis, companies can see a more granular picture of performance.
Retailers and restaurants can use this data to find trends where performance is best and worst compared to local benchmarks and to understand which locations could benefit from interventions to improve performance. This analysis can be used to develop new ideas for specific areas to improve performance during the next big weather event, during Black Friday this year, and more.
With comprehensive data sets and new ideas in hand, companies can conduct robust business experiments to understand the true incremental impact of new initiatives within different regional contexts. Then through in-market testing, business leaders can assess which ideas work and which ideas don’t on a small scale before investing large amounts of capital.
>See also: Beneath big data: building the unbreakable
In the book Black Box Thinking, author Matthew Syed powerfully argues that learning from failures is absolutely critical to business success. “The reality is, when we’re involved in complex areas of human endeavour – and business is very complex – our ideas and actions not being perfect is an inevitability,” he says in an interview with Director magazine. “Success happens through a willingness to engage with, and change as a result of, our failings.”
Through rigorous testing, companies can identify failed ideas and learn from them to improve profitability. Retailers and restaurants that leverage big data to systematically test initiatives and conduct analysis will gain a sizable competitive edge over those that continue to focus solely on internal year-over-year comps.
Sourced from Jim Manzi, chairman of APT