Knowledge Management in the Support Center

By Brian Flagg, Cincom Systems

The management of knowledge as an asset in the support center is important for operating an efficient and effective organization. The development, use and improvement, and management of knowledge need to be key areas of focus for leadership.

Customer Satisfaction

Knowledge is important for a number of reasons. Customer satisfaction for the support center is driven in a large part by the percentage of contacts resolved by the center. For a support center that has the phone call as its primary channel, this means the percentage of calls closed by the support center, on the call. Knowledge that is well-structured plays a key role in providing the call specialist with the answer, whether guiding the specialist through problem-determination steps or to information to answer a query, or through the steps in a fulfillment process.

Reducing Costs

Knowledge Management in the Support Center

Source: Thinkstock

Knowledge is important for another reason, reducing cost through handle-time reduction. Knowledge that is properly structured guides the support specialist efficiently through their interaction with the customer, leading to a happier customer, a sense of fulfillment for the specialist and a reduction in the cost of the interaction. Multiplied hundreds or thousands of times a week, efficient knowledge can drive significant cost savings for the enterprise.

Effective knowledge is complete knowledge. It means not only guiding the specialist to an answer, but to the right answer. A significant benefit of having effective knowledge is a reduction in costs as problems and questions are resolved with one call or contact to the support center. This “done in one” also leads to a more satisfied customer.

It is clear therefore, that knowledge that is properly structured or formatted leads to greater efficiencies, lower costs, higher customer satisfaction and greater morale.

This article will explore how knowledge is gathered and structured to ensure that it is efficient, consider how data about knowledge use in the support center is captured and used and examine how knowledge should be properly managed to ensure that it is effective.

Knowledge Development

The development of knowledge involves the planning, gathering and structuring activities needed to create an initial knowledge base or database of knowledge. The development of the knowledge base should be managed by the support organization as a project with a clear set of objectives and a realistic plan. Knowledge development can represent a fairly significant investment of people resources, and, if a commercially available knowledge management tool is used, capital investment as well. Project management will ensure that upfront costs and resources needed, as well as expectations and deliverables, are well understood. The development of knowledge is a process, but many times iterative, and therefore if not properly managed, can appear to last a very long time. Indeed, continuous improvement or refinement of the knowledge base is required and it is important to understand when the initial development is complete and when the effort moves to improvement. A well-managed project plan is needed to understand when this point is reached. Knowledge-centered Support, or KCS (Kay & Tourniaire, 2006), is an excellent approach to the development of not only a database of knowledge, but a knowledge-centered organization. KCS leads the support center organization through various stages of maturity, developing an understanding of what focus and measurements are needed at each stage of maturity. This section of the article focuses on the key points of knowledge development and is not intended in any way to paraphrase the entire subject of the knowledge-center organization and KCS.

The knowledge development project plan should include a set of tasks to gather information and a set of tasks to structure the information into knowledge. Gathering information from existing sources involves identifying the sources of information, whether within the organization, within other support or business organizations within the company or external to the company. Prior to beginning the gathering phase of the project, the plan should drive the prioritization of the gathering activities. Review data from the ticketing system to determine the top reasons people contact the support center. Use this to scope and prioritize your activities. Try to keep the initial set of knowledge development to a manageable level, such as the top three to five contact reasons.

Gathering information begins within the organization by identifying existing documentation, and then reviewing the documentation. Support organizations typically have some rudimentary base of information regarding troubleshooting, known errors and workarounds, basic query information and fulfillment process flows. Since this documentation is already present, this is the easiest to obtain, and it typically results in quite a volume of information. Successive steps widen the scope of the information dragnet to other support organizations—business organizations that own key processes driving the prioritized contacts into the support center, and finally, to sources outside of the company.

The next step is to identify subject-matter experts or SMEs. These SMEs will need to be interviewed according to the plan and the information from the interviews documented; then they need to be added to the information already gathered. The interview of a technical-support SME may well be focused on problems or error conditions that may occur and workarounds available. The interview of the business-process SME will likely focus on how the process is used, on workflows and on exception handling. Ensure that a list of subjects and subject-matter experts is saved during the gathering process as these will be key resources in the continuous improvement of the knowledge.

Information gathered must be structured into knowledge. The structure is focused on efficiency—can the knowledge be easily located—and effectiveness—is the knowledge complete and accurate. A well-defined structuring, and a management system that enforces it, is crucial to achieving return-on-investment objectives. The knowledge structure must support the problem-diagnosis process or classification to drive information search to answer how-to queries and business-process questions. Further, the knowledge structure should support workflow, and as such, work in conjunction with the workflow ticketing application. To achieve all of these aims, more than one knowledge structure is typically required.

Knowledge is structured for problem determination to match symptoms with one or a set of known errors or causes. Further Q&A scenarios drive the solution set narrower and narrower, until a solution is found or a new problem is uncovered. If a new problem is uncovered, the specialist uses the art of solution search to try to find a solution that will solve the problem. If they do, the solution is documented for later addition to the structured knowledge.

The knowledge structure for queries typically involves a classification of information, starting at a high-level keyword and proceeding in a hierarchical manner to further refinement of the keyword topic with additional detail, based on a breakdown of keywords or phrases at each level of the hierarchy. For example, the keyword “printer” may be classified into personal printer and network printer, and personal printer may be classified into brand or type, such as dot-matrix, ink jet and laser. This type of classification hierarchy has been augmented or replaced altogether in recent years with the advent of powerful search technology. The search engine builds the structure, saving knowledge professionals the task. Unless the information to be structured has unique characteristics that make a general search engine suboptimal, search engines are a very good approach to providing a low-cost query solution.

To fulfill service requests, especially those requiring multiple process steps, the knowledge structure should work in conjunction with a workflow engine to deliver the right information at the right step in the fulfillment process. In general, the more integrated the knowledge application with the ticketing or even the telephony system, the better. This integration is discussed in the next section.

Knowledge Use and Integration
As structured knowledge is stored in a knowledge base and the knowledge base is accessed by a knowledge application, integration with the support center’s ticketing application is important. As knowledge is accessed and used to solve problems, satisfy queries or fulfill requests, it is important to capture this activity. This capture is typically done in the support center’s ticketing application. Later reporting from the ticketing application can drive a variety of actions for further research in the support center. For example, it is important to know if the knowledge for a particular problem or query was found by the specialist. If not, it could be that a new problem or query situation has been discovered, and this can drive the development of new knowledge using an approach discussed previously in this article. It could also mean the knowledge is actually in the database, but could not be located. This is also an important outcome of the reporting as it informs the knowledge management process (to be discussed later in this article) to perform causal analysis to determine if there is a fault in the knowledge development process or in the training process. It could be that specialists are not familiar enough with the knowledge application, or perhaps the structuring was not adequate to enable specialists to find the answer. A single specialist having difficulty could lead to more training for that specialist. Difficulty by a number of specialists could indicate a general training issue, or again, a problem with how the particular knowledge is stored or structured. Symptoms may be missing or confusing, or search keywords may be missing.

A knowledge application that is well integrated with the ticketing application would enforce data gathering for reporting and analysis. For example, when a contact is being processed, whether proceeding through symptoms or problem-determination steps, query searches, or workflow steps, all of this activity could be captured within the ticketing system and assigned to the open ticket. Thus, the specialist is freed from spending precious time during or after call work to log knowledge activity.

Another novel approach to integration is to tie the IVR or ACD application to the ticketing and knowledge applications. Therefore, an incoming call, already given some level of classification by the caller (or chatter), can bring the specialist to the knowledge record or set of knowledge records that will need to be used to process the contact. This can save crucial time early in the contact handling process. For example, if the user has already preceded three levels into an IVR tree, why not use that classification to pre-populate the knowledge to be used by the specialists responding to the contact?

Integration is an important approach to delivering efficiency in the support center by automatically performing time-consuming steps the specialist would otherwise need to perform. Through data gathering of knowledge activity and subsequent reporting and analysis, integration leads to more effective knowledge management and a more effective support center.

Knowledge Management

Knowledge is a very important asset for the support center, and as such, should be appropriately managed. Knowledge management should get its data from the analysis of reports from the knowledge application and the ticketing application. The knowledge application should record every time a knowledge article is accessed and every time the accessed knowledge record was the appropriate record. In other words, every time a problem is resolved or a query is answered and every time the record was accessed and did not satisfy the problem or query. The collection of data will satisfy the goals of knowledge management: to ensure knowledge is timely and accurate—in short, effective. Knowledge that has not been accessed in a predetermined amount of time should be removed from the knowledge database. Knowledge that is used most often should be examined closely to ensure it is streamlined and efficient. Sophisticated knowledge applications record a confidence factor for each knowledge record. The more often the record successfully leads to the resolution of a problem or answer to a query, the higher its confidence rating. This will focus knowledge management on improving the knowledge with low ratings and protecting the knowledge with high ratings. A knowledge-management function should allow for much tighter control on the changing or altering knowledge with a high confidence rating than knowledge with a low rating. Therefore, knowledge management intersects with the change management function to ensure knowledge is managed as an important organizational asset.

Conclusion

Knowledge is an important asset for the support contact center. Efficient knowledge means that specialists can get to an answer quickly; effective knowledge means that the specialist gets to the correct answer most of the time. Efficient and effective knowledge and the proper management of the knowledge drives lower costs, higher morale and higher customer satisfaction. Integration of the knowledge application with the ticketing application brings additional efficiencies and enforces additional effectiveness of knowledge.

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