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Customer Relationship Management
August 2004

Sidebar: Optimizing Knowledge Resources To Drive Customer Value

By John Chmaj, Kanisa Inc.

Knowledge management is an umbrella term comprising all the tasks associated with gathering and disseminating information to meet specific needs for learning and action in an organization. Because the very nature of support and service business is knowledge transfer to customers, knowledge management is an area of core competency required to drive customer value and internal efficiency. However, there are some unique aspects to the support task that place specific requirements on how support-related knowledge management operates. Some aspects include:

Findability. The consumer and the provider of knowledge do not usually exist inside the same organization ' they use different vocabulary, and they have varying levels of expertise and understanding. Customers have little or no awareness of the formats and assumptions through which organizations deliver information.

Usability. Customer expectations of the presentation and consumption of knowledge comprise a range of tasks associated with deriving value from the products they've purchased. These tasks span everything from quick how-to questions to detailed diagnosis and research of complex problems. It is not known up front what expectation and outcome is expected from the knowledge as it is delivered. Posting knowledge without addressing customer purpose leads inevitably to frustration and negative perceived customer value.

Scale. Knowledge sources inside the organization often derive from areas of the company with a related but separate business purpose: product documentation, marketing literature, bug reports, spec sheets, etc. This additional dimension of scale places a further burden on the support organization's ability to generate meaningful views into data that may be relevant, but that can quickly degrade and wash out the most relevant information in a search or information request.
The amount of data potentially relevant to the support purpose can run into the hundreds of thousands, even millions, of data objects, most of which are developed and maintained in different places and for diverse goals. The unique KM challenge for the support organization in this regard is to find ways to place this valuable but diverse information in proper relation to the range of support tasks suggested above. Merely dumping all information resources into a searchable index, or providing a browsable interface across stores of product data, does not meet these criteria. The definition of a discrete, support-specific knowledge base has provided a baseline format for addressing the customer-specific scope and content required for support, but even these information sources usually suffer from the same issues of findability, usability and scale.

Many, if not most, support sites today give the impression of an expert library: the user must know what to look for and must be able to navigate the company's terminology, retrieval process and data formats to piece it together. Customers find this approach onerous, time-consuming and ultimately frustrating: if they knew what they needed they wouldn't be looking! Even support agents inside an organization find this approach difficult to use, as the information resources provided often bear little match to the specific interactions necessary to identify and resolve customer issues. The resulting loss of productivity has a direct bearing on the bottom-line value of quick, accurate, first-time resolution that is the basis of support efficiency and customer satisfaction.

Attempts have been made to automate support knowledge management processes for the past 20 years. While the industry has improved from a baseline of desktop binders and yellow sticky-notes, analysts and researchers report that most knowledge management activities have failed to deliver expected results. To illustrate this, fully 84 percent of the cost of the support center is consumed actually solving customer problems ' even more if the incident lasts more than one day (Source: Service and Support Professionals Association research). Yet, knowledge management is the primary tool that can reduce the cost of problem resolution. A new approach is needed to address these shortcomings and to deliver the value inherent in the great store of useful information organizations often carry about their products. A new approach must bypass the pitfalls of the past and optimize the creation and consumption of support-specific information. It must also focus on the support tasks, interactions and expectations of support users and content creators.

Clearly, there's more work to be done and an optimized knowledge delivery process will leverage the natural inputs and interactions involved in identifying and resolving a question, quickly and effectively bridging the gap between what customers or support agents know and the best information available at any point to satisfy the evolving context of the question. An optimized knowledge development process also will provide inherent input to expand and extend the system to meet evolving business needs, both through incoming content and by providing visibility into customer and product trends.

The key tasks in support knowledge management relate to the gathering, structuring and delivery of content to meet specific support needs. A robust system must be capable of defining a valid support context for knowledge, pulling in all relevant sources from across the organization, and driving improvements through views into customer and product trends. To do it right, you must be able to:

  • Create a knowledge base: a structured support-specific view into high-value content:
  • Integrating auxiliary resources into the support experience when relevant,
  • Jumpstarting knowledge creation for new issues, products, areas of focus.
  • Leverage incoming information to drive knowledge development:
  • Developing knowledge workers and knowledge assets,
  • Getting clear visibility into trends, opportunities, issues in knowledge development/delivery.
  • Develop relevant self-help knowledge interactions:
  • Relating product knowledge to the customer context,
  • Creating effective interfaces to elicit and scope customer needs.

To achieve the joint goals of scalability, maintainability and user relevance simultaneously, it is necessary to create a complete environment for content capture, completion and improvement. As we have seen, an effective support knowledge management system must address all of the following requirements and areas of functionality:

Support-specific context and interactivity. It's important to create a system with support-specific mapping of the key issues and activities in the domain of products and services customers use.

Content integration and scalability. For the best resources to be provided in relation to the wide range of possible questions, users and topics, it is necessary to be able to link and scale all relevant resources into a common view.

Efficient content capture and creation workflow. A key factor in achieving the full leverage and efficiency available by disseminating support knowledge is the ability to rapidly create and deliver new solutions.

Analytics to drive continuous improvement. A critical component of any knowledge management system is its ability to drive improvement through visibility into key trends, needs and content holes. It's important to be able to define and spot key support concepts and determine the highest-value content and subjects to focus on. This means that the self-help query stream can be distilled into relevant trends and that the full range of support resources used can be evaluated against a common set of topics.

Effective maintenance tools and processes. Finally, an effective knowledge management system must be able to grow and evolve as business needs change. The process of creating and updating knowledge must directly support the top-line objectives of the support organization.

The ability to fully optimize knowledge resources to drive customer value addresses several of the points of failure of previous approaches, which have not considered knowledge management explicitly in the context of support users and tasks. This approach provides new leverage within the support process for knowledge creation, drives efficiency in making smart knowledge creation decisions and assures that new knowledge is rapidly and meaningfully integrated into a relevant support-focused view to drive both assisted service and self-help.

John Chmaj has worked for 15 years in high-tech customer service and support at Microsoft, Lotus, Primus and the Consortium for Service Innovation (CSI). As a member of CSI, Chmaj co-chaired committees that published The Solution Centered Support Strategy, The Multi-Vendor Support Model and Incident/ Solution Exchange Standards. In addition, Chmaj is a frequent speaker and panelist at industry conferences. Currently, he serves as director of Industry Marketing at Kanisa Inc., which provides knowledge-empowered customer service applications.

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