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 comprise 36 percent 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 harnessing customer data to strengthen operations, grow sales through more effective marketing, and increase profits is a key to differentiating you 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 percent 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.
Data Overload Leads to Poor Business Practices
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 McDonalds. Both have created data integration and management systems that have well positioned them to get deeper customer insights and distance themselves from competitors.
Starbucks’ Innovative Use of Data for Customer Service and Site Selection
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. While the company has tapped into GIS technology for many years, IT realized it was flooding the real estate team with mountains of data but failing to provide an easy way to interpret that information. So, the company added self-service data analytics to its GIS infrastructure to provide the real estate team with a data-rich, consumer-friendly application it can use to make decisions about new locations. This data has helped Starbucks increase new store sales and improve unit economics.
Big Mac’s Digital Transformation
McDonald’s is also undergoing a digital transformation that—CEO Steve Easterbrook has said—is centered on data and meant to bring “a new level of convenience to more of our customers as we continue to transform the McDonald's experience.”
The fast food giant used data analytics to augment customers’ drive through experience by redesigning its drive-thru windows, fine tuning the information provided during the experience, and using historical data such as wait times during specific periods, to predict future demand patterns and ensure they can staff appropriately.
The company also closely watches the data flow of in-store traffic, customer interactions, ordering patterns, point-of-sale data, and sensor data, among other data variables, to optimize operations. It used to share average results with individual stores but later switched to trend analytics to provide better correlations and more clear and relevant information to improve individual store performance.
In an effort to make the fast food giant ‘cool’ with younger, mobile app-using millennials, McDonalds is rolling out mobile ordering, curbside check-in and delivery service in a partnership with UberEats. It is also investing in touchscreen ordering kiosks, mobile charging docks, and digital payment options like Apple Pay at some locations.
Organize Data to Get Better Results
In today’s fast-paced, global economy, it is generally understood that companies must become data-driven in order to remain competitive. A report from McKinsey Global Institute says companies that are data-driven—those that gather, process and analyze data in real-time as it flows through the enterprise—make better decisions. According to the report, being data driven results in a 23 times greater likelihood of customer acquisition, a six times greater likelihood of customer retention, and a 19 times greater likelihood of profitability. Those are pretty compelling numbers and reasons to put data at the heart of your business.
Both Starbucks and McDonalds have built data management systems that quickly integrate all forms of data across systems and applications at scale so they can make data-driven decisions. In each instance, the organization broke down barriers to new and innovative markets by utilizing a higher level of information and leveraging that data in near-real-time. To do so meant having access to the most up-to-date information on customer preferences, products, services and locations, as well as robust data management practices that leverage information when and where it’s needed.
How to Get Started
You don’t have to be as big as Starbucks or McDonalds to become data-driven or throw out your entire legacy systems. But you do have to develop a strategy, make sure IT provides clean data and embrace employee self-service. To get started, you should:
- Develop a Data Vision and Strategy: It’s important to define a forward-looking data-driven strategy using clear and concise language around what your business wants to accomplish. Being data-driven isn’t an end goal; harnessing data to make better decisions and improve specific operations is. This vision creates a platform that helps the organization work toward a common goal and the framework it can execute against. A typical strategy would use a phased approach, with interim goals and milestones. When properly developed and presented, the vision and strategy can also help bolster the importance of enterprise information, and a new ‘data-driven culture’ starts to bud within the organization.
- Create a Single Source of the Truth: Data-driven organizations like Starbucks and McDonald’s embrace the use of unfamiliar or previously unavailable data sources. They use unstructured, multi-structured, and external data sources. Organizations need to ingest, cleanse and centralize all this information into a governed data lake that can then be monitored and maintained by IT, but also accesses via self-service tools. The net effect is to not only organize and qualify the massive volume of information, but to increase the number of touchpoints where data can be used to make a difference.
- Empower all Employees with Self-Service: To drive businesses forward, everyone needs to have access to the data they need. Because not everyone has a degree in data science, CIOs need to provide easy-to-use, self-service data manipulation and analysis applications and tools to business users. They also need to integrate intelligence into data management applications and workflows to make it easy for business users to leverage advanced technologies such as machine learning and natural language processing.
Businesses worldwide are awash with data, but few are harnessing it as a competitive edge. Many are more worried about how to store data than how to get it into the hands of business users. By integrating information in a way that makes data more accessible, and ensures its quality and protection while making it available throughout the enterprise, companies can find new routes to market, remain competitive and drive growth.