Retail data scienceTechnology such as advanced analytics is now used by organisations to do pretty much everything, from understanding their customers better, to predicting more reliable, faster results. While nearly every industry stands to reap the benefits of data science, retail is in a position to be the one that will benefit the most.

With store closures becoming a regular segment in the news, data science may be the key to preventing the complete collapse of brick and mortar.


Retail organisations consist of thousands of employees, spread across multi-layered teams in hundreds of different locations. Competition grows quickly in this industry, with new challenges for brands popping up regularly.

The integration of omnichannel – creating a seamless integration of channels – from in-store, to online and mobile is one of the biggest challenges brands are having to face.

Another comes from human observation and opinion. While this isn’t necessarily a bad thing, it still leaves room for mistakes to be made and time to be wasted. Using data for these decisions cuts this out and allows time to be better spent acting on insights.

The tools and technology in retail need to allow the swift transfer of critical information. Unfortunately, the industry has fallen behind in this area and retail teams are often stuck using tools such as manual spreadsheets and legacy technology to analyse data across the business. This could be improved on a massive scale with data science, allowing for an overall positive impact on store performance.

Data Science into Action

Data science can help retailers with many things, from improving performance, unlocking insights, discovering trends, winning and retaining customers, and even driving business efficiencies. Most importantly though, it can be used to identify KPI’s to make smarter, faster decisions.

For example, imagine that sales for a specific brand of shirt are up at one store, but at another store, sales of the same shirt are down. A store manager may assume that this is down to consumer preference, but by using advanced analytics it’s revealed that the increase in sales is related to an in-store cross-promotion with a trainer brand. This information can then be shared with other stores, thus improving sales everywhere.

Another area data science can help with is customer experience. Today, decisions are usually heavily influenced by what retail leaders believe, but this doesn’t necessarily mean they are right. Arming managers with data insights allow tailored improvements to be made to the customer experience.

Although data science has already been embraced within certain areas of retail, there are still a few areas such as store operations where its potential has not yet been captured. Those that manage to make the most of data science will be ahead of the pack, having the insights needed to drive business efficiencies, improve performance and win customer loyalty.

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