Acting on Data Analytics – More than Food for Thought

Aligning the business and IT stakeholders in the use of data analytics applications in decision-making is a key business challenge we often discuss on the Trends and Outliers blog. According to web analyst Antoaneta Nikolaeva, there are four distinct factors for analytics success in an organization. In her white paper, “Acting on Analytics,” she discusses these factors (originally studied by Tom Davenport and Jeanette Harris in 2007 in Competing on Analytics: The Science of Winning). 

We took these four competitive factors and applied them to a recent LA Times interview featuring Kroger CEO Dave Dillon. In this article, Dillon discussed how his company uses data analytics to foster real-time feedback and change in their grocery stores. Read on to see how one company developed a competitive edge by making data real.

Competitive Factor #1: Strategy

As Nikolaeva writes, what makes the company competitive is what drives their success. And, “analytics should support the organizational distinctive capability” to “assure an alignment between the corporate business strategy.”

Food for Thought – Kroger has a sophisticated system for deriving data on their customers’ purchases through their loyalty program (outsourced to London-based Dunnhumby). Dillon uses the data to go find out first-hand why customers buy what they do by shopping alongside customers anonymously, looking in customers’ cupboards and dropping into stores unexpectedly to check on staff.

The data strategy works for Kroger. The grocery chain’s most recent earnings report showed a 6 percent rise in revenue, while competitors Safeway and Supervalu Inc. fell 1 and 9 percent, respectively.

Competitive Factor #2: Enterprise-Wide Adoption

This one is obvious, but often the factor that causes companies to have limited success in achieving data analytics goals and maximum ROI. Nikolaeva writes, “Companies that compete on analytics do not assign analytical activities to only one department or group of individuals.”

Food for Thought – The Kroger example is proof of how a good data strategy can work across a large organization with distributed locations. Tying it back to a central source such as a shopping card is what makes the strategy work.

In addition, using the data to make changes company-wide is a key success factor for Kroger. Dillon says they use the data from the loyalty cards and 50,000 monthly surveys to make adjustments in marketing, sales and inventory at specific locations.

Competitive Factor #3 – Executive Commitment

According to Nikolaeva, the third key to analytical success is a true belief in and support of an analytics strategy. She writes, “The adoption of an analytical culture will have a strong impact on the organization, the employees and processes.”

Food for Thought – Having such strong support from the top down makes the use of data to drive decisions that much more impactful and helps drive not only adoption but buy-in across the organization.

In the interview, Dillon explained that the data they gather from their loyalty program helps him determine what to look for when he heads to the stores for “shop-alongs.”

For instance, during the recession, Kroger discovered that the number of shoppers using food stamps doubled. Dillon took this knowledge and asked staff and shoppers how Kroger was doing in helping first-time food stamp users determine what their benefits would buy.  After many first-hand accounts of confusion, he took steps to add more informational signage and training for employees.

Competitive Strategy #4 – Large-Scale Ambition Nikolaeva’s fourth and final measure for competitive analytics players is using the data to better their competitive advantage.

Food for Thought: Kroger certainly puts most of their eggs in the analytics basket, but they follow-up it up with real-time feedback, which may be their secret to success.

Dillon tells Wall Street analysts that their organization “has sophisticated consumer data none of its competitors can match.” Without a forensic investigation of the data they collect, their financial results speak to their success. And, their strategy for combining the data with feedback has led to positive changes in the customer experience – better staffing, training and technology to create faster checkout lines, more helpful employees and better store presentations.

Dillon really sums it up the best: “We can use our own intuition, our own eyeballs, our own sense of how the store should work, but that can be hugely enhanced by applying real data.”

Amanda Brandon
Spotfire Blogging Team

Image Credit: Microsoft Office Clip Art