Which Knowledge Authoring Model is Right For You?
by David Kay

When vendors discuss how to improve service delivery with a knowledge-management (KM) system, search functionality is typically the star of the show. In real-world implementations, it’s often the less glamorous capabilities of KM that mean the difference between success and failure.

KM, done right, is an incredibly effective tool for service and support. It provides:

• Increased first contact closure and reduced escalations
  
• Shortened time to proficiency and increased scope of competence
  
• Customer insights to drive product and service improvements
  
• More flexibility in staffing and outsourcing
  
• Call deflection and increased loyalty through effective self-service

A key success factor for KM effectiveness is picking the right model for your organization. In this article, we’ll examine the most popular models in use today and identify their strengths and weaknesses relative to your specific business goals.

The knowledge lifecycle
All KM processes have four interrelated components (see figure 1). While knowledge delivery, especially search, enjoys the spotlight, all of the pieces have to fit together to deliver the benefits. (The father in me says that service and support organizations must eat their vegetables—capturing, structuring, and improving knowledge—before they can have their ice cream.)

Fig. 1--The Knowledge Management Lifecycle

Capture: The first step in the process is to store the things people know for re-use; what KM practitioners call turning tacit knowledge into explicit knowledge. In hard dollar costs, knowledge capture is frequently the most expensive part of a KM initiative, far outweighing the technology investments.

Structure: A key characteristic of a successful KM system it that the knowledge is actionable—people can use it to make decisions and take action. KM systems include structure (including formatting, metadata, ontologies, and other devices) to turn captured content into knowledge by providing context for when and how it should be used. The specifics of structuring your knowledge are dependent on the technology you choose, this structuring is often the biggest factor that distinguishes KM technologies.

Deliver: The primary payoff for KM is when knowledge is delivered to resolve an issue, whether to agents and analysts in the service and support center or to customers through self-service. Knowledge can be delivered through search, through proactive delivery mechanisms, or through knowledge integrated in the products themselves.

Improve: Alongside the other three components, you must have processes for ensuring and improving knowledge quality. Even with the best intentions, knowledge bases crumple under their own weight unless organizations integrate quality disciplines. These practices must ensure the knowledge is comprehensive, timely, findable, usable by its intended audience, not redundant, and accurate.

Challenges in KM
Successful KM practices drive financial efficiency, customer satisfaction and loyalty, product value, and analyst job satisfaction. Note that at each step of the process, challenges loom. Focusing on the back-office processes of capture, structure, and deliver, the challenges often include:

Three models
Knowledge management methodologies largely differ in the way they address the challenges posed. Although there are nearly infinite variations, the core models break down into these three camps: a content team, product specialists, and capture in service workflow.

When a dedicated staff of content experts develops content, the good news is that they’re usually professional writers. They know how to write content that users and front-line agents can easily absorb. On the other hand, they’re often disconnected from service delivery, so they have little intuitive understanding about the content that would be most useful to capture. Because they’re rarely subject matter experts, they need to partner with escalation or development engineers both to create and review content, which can dramatically delay the publication of knowledge.

When product specialists or escalation engineers develop content, they do so with a firm understanding of the technical issues involved. They also write content in response to real-world issues that have been escalated to them, which provides increased relevance—although sometimes focused more on issues that are technically interesting than on issues that come up most frequently. There are times when their writing skills aren’t up to the task of communicating with users. And, when a critical escalation issue comes in, content timeliness can suffer.

Other organizations implement methodologies such as knowledge-centered support (KCS) that encourage service delivery professionals to become certified to capture knowledge in the process of delivering service. These processes can dramatically lower costs because there isn’t a separate team or task to capturing knowledge, it’s an intrinsic part of service delivery. This model drives content relevance, since the only knowledge captured is knowledge that has been developed in response to real customer issues.

These processes require fundamental organizational change; changes to job descriptions, problem-solving processes, and performance metrics. They also require thorough adoption, as implementing capture in the workflow without implementing the corresponding quality processes will quickly result in an unusable knowledge base. The biggest challenges to these processes are typically change management and leadership.

Which model is right for you?
Organizations with a high volume of low complexity cases—typically in the business-to-consumer space—are good candidates for the content team approach. Companies like Cingular Wireless have a volume of customer inquiries that lets them amortize the investment in a dedicated team over a large number of interactions. Because they face unforgiving consumers and are brand-obsessed, they appreciate the quality assurance options they have through explicit and formal review cycles. Because the issues are less complex, the fact that the content authors don’t have deep technical expertise isn’t much of an issue.

Technology organizations like Cadence and CheckPoint with complex issues do best with product specialists on the support team who work closely with their counterparts in engineering to resolve the thorniest customer issues and provide structured feedback for product improvements. These organizations’ customers are technically demanding, but are flexible with respect to niceties of grammar and formatting. (One representative vendor observed that its content quality feedback scores from customers went up after they eliminated an editorial review prior to publication—the customer perception of quality was driven more by timeliness than style. This organization now lets support engineers publish directly to the web.) For these organizations, it’s natural to leverage the expertise their product specialists have developed to improve service delivery.

Organizations that have optimized their current service delivery processes with CRM, telephony, and traditional KM approaches often find themselves ready to take the next step with a process to capture knowledge in the normal flow of providing service. Companies like HP, Novell, and Legato that have established reputations as service and support leaders and work with a broad range of product complexity and customer demands are increasingly moving to workflow-based knowledge capture to improve efficiency. For example, Legato reports that time to proficiency for new support analysts has gone from six months to just one or two months, and HP NonStop increased customer solve rates through self-service by a factor of seven just one year after adopting KCS. Each of these companies realized that skin-deep adoption of these methodologies is a recipe for disaster, and made the required commitment to organizational change needed to see it through.

Each model can provide value in different circumstances, and each provides a different risk-reward profile. Risk-averse companies who have the resources to invest in a dedicated team make different choices than support leaders looking for a unique edge in customer service and support.

About the author
David Kay is principal of DB Kay & Associates, a consulting firm specializing in applying knowledge, self-service, and collaborative technologies to improve customer service and support. DB Kay offers knowledge management workshops; contact David for upcoming dates.

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