|Sidebar: Optimizing Knowledge Resources To Drive Customer
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
- Integrating auxiliary resources into the support experience when
- Jumpstarting knowledge creation for new issues, products, areas of
- Leverage incoming information to drive knowledge development:
- Developing knowledge workers and knowledge assets,
- Getting clear visibility into trends, opportunities, issues in
- 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
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
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.
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August 2004 Table Of Contents ]
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.