Much of the promise of big data lies not in the ever-increasing volume of information available to organizations, but in the insight – notably with regard to customers – that lies buried within these large data sets.
Customer intelligence – including the sentiment behind consumers’ buying choices and the types of product offers that may appeal to them – can only be gleaned by moving beyond the traditional consumer “transaction” to using business analytics to delve into consumer-generated content across the social Web and boost the bottom line by focusing on the overall customer experience across all channels.
“Unfortunately, today’s traditional intelligence tools were designed for two-dimensional transactional systems,” notes R “Ray” Wang, a Forbes contributor. “The shift from transaction to engagement to experience depends on better business analytics. Success requires that new business analytical tools support the information supply chain as data moves from a cacophony of upstream data sources to new and innovative downstream modes of consumption.”
The revenue benefits of a better customer experience are well documented. A recent Forrester Research report finds that the revenue benefits of a better customer experience range from $31 million for retailers to around $1.3 billion for hotels and wireless service providers.
“When your customers like the experience you deliver, they’re more likely to consider you for another purchase and recommend you to others,” Forrester notes. “They’re also less likely to switch their business away to a competitor. These improved loyalty scores translate into more actual repeat purchases, more prospects influenced to buy through positive word of mouth, and less revenue lost to churn.”
But this customer experience that’s so vital to growing revenue has been turned on its head as consumers rely on suggestions from their online friends via social networks and other unstructured data as primary sources of interacting with a company or a brand.
At one time consumers relied on salespeople to give them advice that they would use to fuel their transactions. But this scenario can be replaced by using business analytics and customer relationship management analytics to bring together formerly disparate data sources to more effectively engage customers and steer them to experiences that will make them more likely to accept a firm’s offer.
“Companies are beginning to craft offers based on where a customer is at any given moment, what his social media posts say about his interests, and even what his friends are buying or discussing online,” notes Harvard Business Review.
Wal-Mart, for example, is finding ways to predict shoppers’ purchases on Walmart.com based on their social media interests. Wal-Mart is also looking into location-based technologies that will help customers find products in its stores.
In another example of focusing on the customer experience using the analysis of big data, Microsoft has found success with e-mail offers for its search engine Bing.
“The e‑mails are tailored to the recipient at the moment they’re opened. In 200 milliseconds – a lag imperceptible to the recipient – advanced analytics software assembles an offer based on real-time information about him or her: data including location, age, gender, and online activity both historical and immediately preceding, along with the most recent responses of other customers,” the HBR article notes.
These ads have lifted conversion rates by as much as 70% – dramatically more than similar but uncustomized offers.
According to McKinsey & Co., a European telecommunications company turned to big data analysis to boost its market growth. To find out what prompted customers to choose one brand or product over another, the telco analyzed online search data and real-time information – shared by consumers across social networks and other Web-based channels – about the company’s products and services.
The company created a collective insights team that built targeted data “mash ups”of customer data that it could analyze quickly to gain actionable insights. The telco learned what types of programs and offers would lure customers into buying specific service offerings.
The telco’s analytics team aligned itself with company strategy by asking two key questions:
- How competitive are our brands in the minds of users when they make purchase decisions?
- What key buying factors matter for users, and how well positioned are we to communicate with customers about these factors?
Is your company positioned to move beyond the transaction to engaging the customer through analyzing experiences that promote buying?
- To learn more about how user-driven “analytic” or “data discovery” technologies help businesses uncover insights, speed action and maximize the returns on analytics investments, check out our complimentary ”5-Minute Guide to Business Analytics.”
- See how Spotfire version 4.5 empowers users to discover actionable insights hidden in big data and unstructured information in our on-demand webcast, “What’s New with Spotfire 4.5.”