How to Structure Your Business to Make Better Use of Data

A few years ago, Starbucks’ director of analytics and business intelligence, Joe LaCugna, said the Seattle coffee giant once struggled to make sense of the data pouring in from its loyalty card holders, which at the time was over 13 million and comprised 36% of all Starbucks’ transactions.

The same was true of the coffee conglomerate’s social media data — they have mountains of it, but still can’t quite figure out what to do with it, according to Mr. LaCugna’s comment at a big data conference.

Starbucks is hardly alone. Every CIO and CMO in the country understands that harnessing customer data to strengthen operations, grow sales through more effective marketing, and increase profits is a key to differentiating your brand and staying ahead of the competition. Many C-suite leaders have spent millions over the past few years building central data repositories, or data lakes, in an effort to eliminate data silos, integrate applications, and extend self-service capabilities to data scientists and business analysts alike to become more data-driven.

But like Starbucks, many leaders still struggle with how to make sense of and best utilize the mountains of structured and unstructured data they have about their customers. A recent Forrester survey of customer experience decision makers revealed 95% are still unable to make sense of data. According to the report they “rely on segmentation, use single data points, or provide no value when personalizing experiences, thus not doing so effectively.” So, while our appetite for data is huge, our ability to digest it is not.

Companies have wasted millions of dollars investing in the wrong systems, spent months and years trying to implement new systems or connect them to legacy applications, and then compounded the problem by handing rank and file employees mountains of data they can’t possibly analyze. Because they can’t effectively manage the data — i.e. ensuring it’s clean, accurate, governed, and accessible — they are unable to extrapolate meaningful insights. Even worse, companies can end up making inaccurate predictions and decisions based on inaccurate information, resulting in poor business performance and ineffective sales and marketing efforts.

Despite the many challenges of big data management, several companies have successfully built systems that enable more informed, real-time business decision making. As previously mentioned, Starbucks has made considerable progress in this area since its early days and so has McDonald’s. Both have created data integration and management systems that have well positioned them to get deeper customer insights and distance themselves from competitors.

Starbucks is making extraordinary use of customers’ data to enhance the overall coffee drinking and cafe experience. The company has built its mobile reward app into one of the most successful loyalty programs in the U.S.

Using its mounds of customer data, Starbucks created a 350-degree view of each customer so it can personalize special promotions via smartphones to less-frequent visitors, which helps not create a better overall customer experience but also lures the non-Starbucks groupies back to retail outlets faster than if they’d not received the promotion. The coffee company also uses geographic information systems (GIS) to alert automate alerts to customers’ phones of nearby Starbucks locations where they can redeem reward points or take part in special on-site promotions.

When it comes to opening new locations, Starbucks has a data-driven strategy.