Try to imagine this scenario. You're in the early stages of researching a new home stereo system. It's been almost 10 years since you last bought a stereo, and quite a bit has changed in the music industry over that time. Before speaking to any sales representatives, you want to educate yourself on what's new and collect some preliminary information. You visit a Web site for a company whose products seem to fit your needs and budget, but after browsing a while, you're confused. Digital rights management, music file formats, satellite radio, integration to your computer - there is a lot to consider. You turn to the site's search function to find more detailed information to answer your questions. The search results list hundreds of links, but after following several, none seem to provide the information you need. You consider moving on to another company's site, but after all this effort, you feel committed to getting the answers you need, so you call the company's toll-free number. After navigating several interactive voice response (IVR) prompts, you're connected with a company representative who then asks you several questions. After answering those questions, the agent says she cannot help you and transfers you to someone else. Does any of this sound familiar?
In recent years, there have been numerous advances in how we gather and share information. The era of Web 2.0 is raising the bar on our collective expectations for information access, service levels and customer experience. But why then, even today, are scenarios like the one painted above so common? What companies today need to realize is that they're serving a new customer, one with high expectations and little patience -"Customer 2.0."
Delivering online search and self-service capabilities at a level that Customer 2.0 requires is of critical importance to many companies. In industries like telecommunications and financial services, for example, the potential for customer churn is constant, and being able to provide instant gratification to these customers is a competitive necessity. They will go elsewhere if the company with whom they do business cannot deliver that level of customer self-service.
To understand how to meet the needs of Customer 2.0, it's important to first dissect the current state of customer service and why it falls short. Then we'll look at how people and systems need to evolve and become smarter. Finally, we'll look at a real-life example of where an intelligent customer service system can really make a difference - for the customer and service provider alike.
Companies who want to retain Customer 2.0 need to recognize that customer experience and product quality are of equal importance. Superior products may land your first sale with Customer 2.0, but do not underestimate the importance of the overall customer experience in delivering subsequent business. A company's respect for customer service and the customer's perception of the overall customer experience can mean the difference between customer bases that are expanding or contracting, and ultimately between success and failure.
In a recent blog entry, a Silicon Valley executive espoused the very same nonvirtues of today's typical customer-service experience in a tongue-in-cheek entry appropriately titled, "Click Here to Access Our Broken Web Site, Press One to Be Ignored." The article highlights the obvious need for companies to wake up and smell the revenue slipping away, unless something changes in the customer service realm. Of course, this is just one person's opinion, but the sheer volume of comments on this single blog post indicates a real problem - the public's apathy toward the customer-service experience. Customer 2.0 loses patience very quickly, understands the significance of his or her purchasing power, and is poised to take their dollars elsewhere.
Companies must adjust to user feedback and behavior and harness the appropriate tools to create a responsive, intelligent support organization. Not surprisingly, this requires a paradigm shift in how companies manage knowledge throughout the enterprise. Applying these same insights to identify and resolve knowledge gaps will improve the overall customer experience, and move customer service to where Customer 2.0 needs it to be.
When dealing with this new breed of customer, contact center agents must be armed with an individual's history of Web and phone service interactions - when they take the call. In addition, agents don't have time to search through too much information; they need the best answer in the shortest amount of time. And, while Web self-service offers a low-cost alternative, customers looking for products or support also want accurate, immediate responses, not pages and pages of largely extraneous information.
Some companies do better at anticipating and meeting the needs of Customer 2.0. One success story can be found in the complex world of security and networking solutions. Due to a series of acquisitions, this company was maintaining multiple incongruous knowledge-based systems. Different interfaces made for inconsistent user experiences, and maintenance was a challenge. The company was growing, but risked increased customer dissatisfaction if it did not consolidate its significant knowledge assets into one integrated system and user experience, with the goal of empowering customers to be more successful and self-sufficient.
The company took this as an opportunity to change its thinking on knowledge management (KM). The company's management realized that combining all knowledge assets, with their respective tags and classifications, into one system would have been a difficult, costly exercise, so they embarked on a new strategy. Ultimately, the management team selected a new system based on natural language processing, capable of both understanding customer service requests for their underlying intent and of indexing knowledge assets based on their actual meaning. This greatly reduced the need to tag each content item, as the new system did not depend upon structured field data to retrieve relevant results. Information retrieval was driven by a robust, flexible language model, rather than a manual, difficult-to-maintain classification structure.
Building the integration into the call-tracking system was relatively straightforward. With this new customer service model, call center representatives can click a knowledge-base button and immediately retrieve a set of relevant search results. Representatives can then link specific knowledge-base articles to the case. Or, if the system does not return the right information, the agent can author a new knowledge-base article and submit it to an automated workflow system for review and approval.
