Does Data Analytics In Retail Need To Be Top-Down?

With several successful examples, it may need to be


The benefits of adopting data analytics in retail are many and persuasive, for both online and bricks-and-mortar operations. Customers now interact with retailers at a number of stages in the journey to purchase, across a multitude of channels. This provides retailers with a wealth of data that, when used in conjunction with analytics tools, helps them to discover actionable insights they can use to improve customer service and the entire shopping experience, and enables them to provide the goods that people are looking for, when and where they are looking them.

The key to leveraging this data to its full potential is creating an analytics culture that aligns the entire staff behind its data goals. By empowering everyone at every layer of the company with the right information at the right time, employees can make better decisions. They can also identify patterns of customer behavior and potential procedural improvements that only they may be in the position to see. To do this quickly and successfully, it needs to start at the top, at executive level.

A data-driven CEO, reinforced by a data-centric C-suite, will best put a company in a position to use the advanced data analytics software and methodologies that generate data insights necessary for maintaining a competitive edge. Obviously, they are best positioned to do this in terms of making investments in the appropriate tools and staff, but they also need to emphasize the importance of using data to everyone below them, and ensure that they are demanding data as evidence for every decision. Decision makers need to be investigating every event, what does the data say?

Bill Robinette, manager of business intelligence (BI) systems at Advance Auto Parts, a Roanoke, Va.-based retailer with about $5 billion in annual revenue, described what happened when a former Best Buy executive took over as Advance‘s CEO in 2008, noting that he came in and ‘put a priority on improving the mix of parts in different stores based on local demand. Instead of the previous one-size-fits-all approach to merchandise planning, the company now uses data mining and predictive analytics tools to help automatically set plans for populating individual stores with parts.’

The rewards, he says, were tremendous. Robinson continues, ‘My big cultural challenge now is that I have people who want [analytics] and I can‘t deliver it fast enough. Operational improvements enabled by the analytics tools have helped to solidify the software‘s place in the company. For example, in the past, about 20% of the parts stocked in stores didn‘t sell within a year. Advance has used analytics to lower that figure to 4% — a reduction that is ― worth millions of dollars to our bottom line.’

Admittedly, this makes it sound easy, whereas in reality changing the culture of an entire company is incredibly difficult. Driving home the expected change must be constantly reinforced though various mechanisms until it is completely ingrained in the fabric of the company. It needs to be incorporated into every aspect of business strategy, based on business goals, company culture and business intelligence landscape within an organization.

McKinsey analysis of more than 250 engagements over a five year period revealed that companies that put data at the center of the sales and marketing decisions improved their marketing ROI by 15%-20%. For retailers to create the culture necessary to deliver this sort of ROI, it needs to start at the top.

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