Here’s the old way of doing customer support: Set it up as a buffer zone between the company and its customers to react to problems and get trouble-tickets closed fast. Buy a bunch of technologies to cut labor costs (e.g., fewer fully-loaded support staff on the payroll) and put up support centers in the cheapest places you can find. Roll out self-support tools with the goal of keeping call volumes down. Paper over any problems that go public and offer free products and services to the really ticked-off customers.
Companies can’t afford to operate that way anymore. As the IT world goes to open-source software running on commoditized hardware, manufacturers are scrambling to differentiate themselves and keep customers happy and loyal. That means investing in tools and practices to analyze the huge pools of customer data collected on the Web, in call centers, and elsewhere, then turning the analysis into action to improve the customer experience. This is where Total Customer Experience Management Meets Big Data.
Customers are generating more data in more places about their buying behavior, preferences, and pet-peeves than ever before. In addition to data stored in traditional CRM applications, customer satisfaction data lives in tweets, blog posts and other social media/networking content. This explosion of data has spawned the term "big data".
Big data is different from traditional customer data in that it is usually loosely structured, distributed and, of course, there’s much more of it. Traditional tools and systems are inadequate for analyzing big data. New ways of processing and analyzing big data are emerging, most notably the Hadoop MapReduce framework and massively parallel processing data warehouses.
The strategy for customer support is to apply emerging big data processing and analytics methods to customer data to move beyond customer satisfaction and create loyalty by more deeply understanding the customer’s total experience, and then continuously adjust all touch-points and modify the products themselves accordingly. To take a simple example, through big data analytics a company might learn that a certain demographic of customers prefers blue widgets over red, and text messages over e-mails. The company might then target an SMS marketing campaign for blue widgets at other customers in that demographic make-up to increase sales.
But to do this, the organization has to invest differently.
The IT customer support world woke up to this shift awhile ago. Several years ago, the Support Services Professionals Association (SSPA), now part of the Technology Services Industry Association (TSIA), actually created an award for “Best Use of Metrics and Business Intelligence” by a customer support organization. Winners have included IBM, Symantec, Netezza (acquired by IBM in 2010), and for the past two years, EMC.
It’s not surprising to see two companies in this group with a focus on data analytics — Netezza and more recently EMC — making investments in customer support business intelligence.
A small firm with about $200M in revenues when IBM bought them last year, Netezza established a customer experience management (CEM) program several years ago that focused on generating analyzable data through regular customer surveys, with the goal of developing strong customer loyalty. Each month, Netezza evaluated the customer experience through three Web-based transactional surveys – one each for its Help Desk/Technical Support, Technical Account Management, and Installation/CAE units. The company also benchmarked its customer survey results against competitors’ with help from an outside firm and analyzed its data for key performance indicators (KPIs) it may have been overlooked.
In EMC’s case, the company basically took apart its customer support approach about seven years ago and re-built it starting from the customer’s point of view – its “Voice of the Customer” initiative. Part of this meant devising a new survey with help from EMC’s Lean Six Sigma team and creating a customer loyalty index that looks at three factors: a customer’s satisfaction, willingness to recommend EMC’s products and services, and intent to re-purchase from EMC. The quarterly survey goes out to 5,000 people, generating data that is reviewed with every business unit. EMC Customer Support Services (CSS) actually has its own data scientist team that reports to VP-Customer Service Tony Kolish.
Four years ago, CSS began a “management by metrics” program, using business analytics to provide decision support by giving a quantitative and qualitative view of the business. The organization employs interactive dashboards to drive conversations within EMC and with customers. The “Voice of the Customer” data collection initiative is used to pull out meaningful trends that EMC can turn into products, services or risk profiles. The data is also presented quarterly to EMC’s Executive Committee.
Ironically, EMC’s Big Data approach to customer support also helps cut down the number of support calls: If EMC uses big data analytics correctly, it means that customers have to contact customer support less often, because EMC is taking the experience that customers have and feeding it back into its products. Not needing customer service is the best customer service, Kolish says.
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Action Item: The best way to look at Netezza, IBM, Symantec, and EMC are as exceptions, because most IT firms are just starting to use data analytics and business intelligence techniques to understand their customers’ experiences. With data analytics tools becoming more affordable and available, customer support operations need to step up and invest in them. In the big data era, companies need to redefine their goals for customer support to include building customer loyalty in addition to responding to customer complaints. To do this, companies should tailor their support services to the preferences of individual customers. This requires a change in mindset to include an appreciation for the total customer experience and an investment in big data processing and analytics technologies to take advantage of the plethora of customer data being created both on the Web and inside the enterprise. (Note: Jeff Kelly contributed to this Professional Alert.)
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