The Billy Beane of Business Intelligence

BY: ZACH HEAD

SEPTEMBER 28, 2017

BEYOND BI

Business intelligence, or BI, often feels like a corporate buzzword. People tend to think of tools with capabilities such as dashboards, metrics visualization, and data integration. While these capabilities are useful, they require the user to ask the right question which can often be unclear. Even when the systems provide new information, it is difficult and time-consuming to drill deeper to understand the context and causes. BI of the past with its static dashboards, ad-hoc reports, and limited organization-wide adoption is no longer agile enough for present-day brands.

“Only a quarter of business users have access to BI tools and actually use them.”

According to a report from Logi Analytics, only a quarter of business users have access to BI tools and actually use them. These low adoption rates are due to disparate data sources, complex platforms, and poor processes that take users out of their normal workflow. Since traditional BI sometimes struggles to keep up in today’s information-loaded world, companies are searching for smarter, more adaptive tools. To best understand how companies can go beyond BI, consider the time-honored tradition of comparing things to baseball.

WHAT’S BASEBALL HAVE TO DO WITH IT?

The stats-obsessed Major League Baseball spent a century tracking outcomes such as home runs, batting average, and wins for pitchers. These stats captured player outcomes, but weren’t always the most predictive of future performance. Then, in 1980, Bill James spurred the “sabermetrics” revolution and introduced a slew of new statistics and ways of thinking about baseball. This resulted in more predictive stats, as well as an Oscar-nominated movie in Moneyball. Billy Beane—the man who popularized sabermetrics in Moneyball—used these revolutionary stats to understand which player attributes were undervalued and gained a competitive advantage in team building by targeting non-traditional players.

“Retail metrics like point-of-sale, customer traffic, and inventory are intrinsic to the business, but they offer little clue as to where to take action, simply leaving too many insights hidden and unknown.”

How does this connect to modern retail? Retailers have typically tracked metrics related to point-of-sale, customer traffic, and inventory (similar to baseball tracking hits, home runs, and strikeouts). These metrics are intrinsic to the business, but they offer little clue as to where to take action, simply leaving too many insights hidden and unknown. In the MLB, Bill James and Billy Beane isolated stats to be situation-independent and uncovered actions (such as on-base percentage) that were historically undervalued relative to their traditional counterparts. Top retailers have similarly realized that undiscovered insights exist within their BI systems, opening the door to a sabermetrics revolution of their own: data science.

SABERMETRICS FOR RETAIL

Data science applications provide context and deeper insights to poor performing metrics. Specifically, they identify outlier situations where the data deviates significantly from historical trends or similar contexts. Machine learning, another data science concept, goes a step further and allows the platform to update itself over time based on changes in the data.

“Data science applications provide context and deeper insights to poor performing metrics.”

An example: A sports retailers always sees an increase in sales for Icy Hot in the spring. However, during the most recent spring there was an atypical uptick in sales. Machine learning would surface this outlier and bring it to the retailer’s attention. The Store Manager may notice for the first time she had placed Icy Hot next to the baseball equipment. So, customers were purchasing both together, increasing Icy Hot sales. While this example is simple, it showcases the potential opportunities of machine learning in retail.

WHAT POSITION DOES SQUARE ROOT PLAY?

How does Square Root fit into this crowded field? While we aren’t related to baseball, we believe our machine learning-powered solution adds the Billy Beane approach to traditional BI. We built data science capabilities into a comprehensive platform known as CoEFFICIENT®. It takes the core benefits of BI and expands it to include curated metrics, adaptable dashboards, and actionable work plans. What’s the result? We give our “retail teams”—District and Store Managers–the right context to take action.

“CoEFFICIENT® takes the core benefits of BI and expands it to include curated metrics, adaptable dashboards, and actionable work plans.”

Business Intelligence has been and remains an important tool, but the present and the future requires more sophisticated software in order to maintain a competitive advantage. After all, it won’t be long until we’re all playing moneyball.

The power of data science is built in from top to bottom. By tapping CoEFFICENT to perform analysis, District Managers are freed up to focus on the more valuable aspect of their role: coaching store managers on performance and execution. If you’d like to know more about how CoEFFICIENT differs from Business Intelligence, then check out how we Make Business Intelligence Actionable.

ABOUT THE AUTHOR:

As a member of our marketing team, Zach helps shape up the content calendar and keeps operations running smoothly. He loves telling stories that entertain, sharing information that engages, and creating content that empowers. Ready to tackle anything that comes his way, Zach’s motto is always, “Absolutely. I’ll give it a shot.” Outside of Square Root, Zach loves to travel and has seen 43 of the 48 contiguous states. An avid basketball player, he describes his jump shot as “picture perfect.” Zach will graduate from the University of Texas at Austin with dual bachelor’s degrees in marketing and radio-television-film in December 2017.