Using big data analytics to gain insights into customer buying habits and improve overall performance is one of the most common goals for retailers when they are adopting the technology. But in order to get the best results, there are a wide range of factors that need to be considered when analysing data.
For example, one common approach is to use analytics to study the effects when corporate management enacts new initiatives across all its stores. By comparing the data from each location against each other, as well as previous years' performance, the board will expect to be able to identify which stores have been more responsive to changes.
But simply looking at performance indicators such as sales numbers does not tell the whole story – and this is where an effective big data strategy will separate itself from more traditional analytics operations. It was noted by Information Age that unless businesses are able to take into account external factors, this data will not be of much use.
For instance, if companies are only looking at sales data, how can they be sure that a seemingly top-performing store is not simply located in an area of strong economic growth and is actually not meeting its full potential?
"It is critical for companies to have detailed benchmark comparisons to have a full picture of each of their locations," Information Age contributor Ben Rossi stated. "By integrating internal performance metrics with external data related to local economic performance, management can drill down to 'true like-for-likes'."
Leveraging big data to add these external factors to an analysis can not only ensure their benchmarking is as accurate as possible, but can also help retailers plan for major disruptive events in the future.
For example, the 'polar vortex' weather event that hit much of the US last year had a major impact on retail sales, with locations that experienced a drop in temperature of greater than ten degrees F seeing a 15.5 per cent drop in sales. However, this was not the only factor impacting performance.
Further analysis of the data also revealed that locations where the mean age of the population is over 35 were also affected, even if the temperature drop was not as severe, as older consumers also stayed at home.
"By integrating data about weather, demographics and other characteristics of stores into analysis, companies can see a more granular picture of performance," Mr Rossi said.
Retailers and other businesses such as restaurants can use this information to gain a much more accurate picture of which locations are performing well and where more attention is needed.
This fuller picture can also help enterprises be more innovative and give them the confidence needed to try new things, safe in the knowledge that they will be able to spot quickly what is working and what isn't.
Mr Rossi said: "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 [comparisons]."