Answering Customers’ Questions the Intelligent Way

Enterprises face a difficult challenge when it comes to simultaneously improving the quality of customer service and reducing service costs. More products, growing product complexity and rapid change substantially increase the amounts of information required to answer customer questions and troubleshoot problems. Paradoxically, this growth of information availability increases the difficulty of finding relevant solutions.

For enterprises to improve self-service adoption rates, increase call center efficiency and improve response accuracy, they need solutions that help agents, customers, partners and suppliers find answers more efficiently. The traditional methods of search and retrieval use keyword, simple text and Natural Language Query (NLQ). In many cases, search-based knowledge management solutions emphasize their ability to sift through multiple enterprise systems to present results. This typically generates long hit lists with many irrelevant entries. Another failing of this approach is that results are presented indiscriminately. The user does not know if the information is accurate or current, increasing the possibility of an incorrect or out-of-date answer. Unfortunately, these methods are best suited to expert users who are familiar with the content and terminology and know which words will most quickly yield a correct answer. Novice users without domain expertise cannot easily apply the terminology precision these techniques require and, frequently, people need guidance to find the answers to a question.

Call center agents, customers and partners can all benefit from knowledge management solutions that organize and structure access to information, with intelligent guidance that matches each user's level of sophistication and skill. In addition to keyword, text and NLQ search, a knowledgebase should deliver a set of sophisticated search and retrieval methodologies that guide users through the issue resolution process so first-time self-service users and highly experienced agents can quickly find the right information including: 

• Case-based reasoning combines NLQ with clarifying questions. The user enters a text string that yields a solution set and a series of targeted questions to further narrow the problem definition. Based on the user's answers, case-based reasoning narrows and reorders the solutions in order of relevance.
• Decision trees guide users through structured diagnostic scripts. Each time the user answers a question, the decision tree dynamically presents new questions and narrows down the number of possible solutions until the most appropriate solution is identified.
• Expert modeling ranks potential solutions in order of relevance to the problem as determined by subject matter experts. Expert models define precise relationships between problems, causes and solutions.

Using this wide range of search and retrieval capabilities, enterprises can offer multiple levels of guidance and allow users to select those techniques that best match their skills and preferences. However, the search methodologies should be blended seamlessly so that users do not have to make a conscious decision about which method to use. For example, users can begin a session with a text string search, and then be presented with a series of questions that combine aspects of case-based reasoning, decision trees and expert modeling to quickly refine the general description into a specific problem description. KANA customer John Harrigan of Siemens states, "What attracted us was that these are the kind of tools that you can get the average user up to speed on without requiring much background knowledge of the actual system."

Reporting and tracking capabilities should complement these methodologies to dynamically score and rank potential solutions by popularity based on users' experiences. Users provide feedback with each search on the helpfulness and accuracy of the solution, which is incorporated into future search results so that solutions are scored higher or lower on subsequent similar queries. Paul Kinsella, VP worldwide customer response at Creative agrees, stating, "with the knowledgebase feedback mechanism, we can update and revise content to reflect customer choices and preferences."

As the management of and access to knowledge grows in importance, so does the ability to deploy a comprehensive, yet maintainable knowledge management solution that increases customer acceptance of self-service, enables call center agents to answer inquiries more quickly, accurately and consistently and enhances the value and use of information stored in enterprise systems. By providing multiple search methodologies, enterprises can empower users of all levels to diagnose and resolve problems with ease.

Chairman and CEO, Michael Fields

Michael Fields has spearheaded successful sales organizations at a number of large corporations, including Oracle U.S.A, where he served as president, and Applied Data Research and Burroughs Corporation. In addition, he was a founder, chairman of the board of directors and chief executive officer of OpenVision Technologies, Inc., which was acquired by Veritas in 1997. Currently, Fields serves on the board of directors of Imation Corporation and ViaNovus. He has also served on the advisory board of the Ford Motor Company Customer Service Division from 1999 through 2001.

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