It should come as no surprise that a superior customer experience (CX) drives growth in customer acquisition as well as in customer revenue, and a recent Forrester report confirms this. What makes a superior customer experience possible, of course, is data.
According to a recent Accenture survey, nearly all (98%) of companies considered high performers in CX say they are data-driven around customer experience versus just 55% of all other companies surveyed. And 91% of high performers in CX say that data and analytics are critical to driving customer experience improvement versus 66% of all other companies surveyed.
We know that organizations interact with customers in many ways and the data surrounding these interactions can be captured from many sources such as IoT devices, point-of-sale systems, customer relationship management (CRM) systems, websites, and social media.
By applying data analytics, predictive analytics, and machine learning practices, businesses can analyze customer data in near-real-time for every customer interaction. This gives companies the ability to get a complete, single view of how customers behave, what they buy or need, and how they will likely interact in the future. Using this information, companies can drive decisions about business functions that have a direct impact on the customer experience; for example, inventory management.
However, companies still relying on legacy data systems often struggle to be effectively data-driven. Typically, the volume of customer data generated is so huge that these IT systems simply can’t scale quickly or cost-effectively enough to adequately support big data analysis. Also, as organizations grow—organically or through acquisitions—they often add infrastructure that scatters data across many databases and storage solutions. These siloed legacy systems are not well suited for analyzing large volumes of data coming from multiple sources. It can take weeks to pull together and process data from disparate sources into dashboards or reports that provide the insights needed to make key decisions.
To be competitive, businesses need to be able to make decisions in real or near-real time. Oracle Big Data Appliance provides that capability by processing large data workloads at speed and scale. This out-of-the-box solution is optimized for the entire portfolio of Engineered Systems products—delivering a completely streamlined infrastructure that becomes the backbone for real-time, granular analytics. In fact, companies can track and predict customer behavior by running their workloads on this integrated infrastructure where the hardware and software have been coengineered to work optimally with each other and provide the highest performance and faster analytics.
How Citi Bike Could Help New York City Deliver a Superior Two-Wheeled Experience
New York City–based Citi Bike is the largest bike-share program in the U.S. with more than 10,000 bikes and 600 stations in Manhattan, Brooklyn, Queens, and Jersey City. Members use an app to find a bike at a nearby Citi Bike station and can return the bike to any station.
In a hypothetical situation, Citi Bike faced an inventory challenge when frustrated users complained on social media that they couldn’t get bikes and docking spaces—not the customer experience Citi Bike wants to deliver, but exactly the kind of challenge that big data is well suited to help solve.
How might big data and analytics solve this problem? Using Oracle Big Data Appliance, for instance, Citi Bike could aggregate and store large amounts of streaming data from multiple sources, including social media, sensors, and machines with ease in an on-premises data lake. Or it could use Big Data Cloud at Customer to run and process heavy data workloads on a pay-as-you-go basis in its own data center (behind its firewall) without having to worry about securing, patching, and upgrading the hardware since Oracle would do the IT maintenance.
To leverage the power of big data, Oracle’s integrated Big Data analytics solution delivers advanced analytics, dashboards, and business intelligence tools that give organizations a powerful new level of visibility and insight into their aggregated data. So a bike-sharing program like Citi Bike could study historical bike usage patterns and analyze social media posts about any problems with bike and docking station availability, and then use these insights to predict demand more accurately and redistribute bikes to ensure adequate inventory at peak times. As mentioned above, powering these analytics is the infrastructure layer composed of Oracle Engineered Systems such as Oracle Big Data Appliance which has been purpose-built from the ground up to deliver performance that can’t be easily matched by a DIY (do-it-yourself) solution.
Big Data and Analytics = Business Solutions
Oracle’s Big Data Appliance delivers on the promise of big data by capturing, storing, and organizing massive amounts data from diverse sources into Hadoop. Oracle’s Big Data analytics solutions layer on the appliance to provide immediate analysis of the data through dashboards and business intelligence tools so that organizations can study customers’ behaviors in detail and gain insights that they can use to improve their businesses. Big data analytics combined with Big Data Appliance makes it possible to identify and address inventory management problems—which, in turn, can improve the customer experience, a key driver of business success.