Best Practices in Knowledge Management for Customer Service

Optimizing Knowledge Resources to Drive Customer Value

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Introduction.......................................................................................................................................... 3

Support Knowledge Management Goals .......................................................................................... 3

Adaptive Knowledge Management: The Knova Approach ........................................................ 4

Traditional Approaches to KM: The Legacy Knowledge Base ......................................................... 5

Past Approaches to Support Knowledge Management................................................................... 6

Metadata-Driven Text Search........................................................................................................ 6

The Associative Model ................................................................................................................... 6

Case Based Reasoning and Decision Trees ................................................................................... 7

What’s Missing from All These Approaches ................................................................................. 8

The Knova Approach: Service Resolution Management (SRM) ....................................................... 9

Features of the Knova Approach.................................................................................................. 9

Support-Specific Context and Interactivity................................................................................. 10

Content Integration and Scalability............................................................................................ 11

Efficient Solution Capture and Content Creation Workflow.................................................... 12

Analytics to Drive Continuous Improvement ............................................................................. 13

Knova Enables Knowledge Management Methodologies............................................................. 14

Knowledge-Centered Support .................................................................................................... 14

Product Specialist Authoring....................................................................................................... 14

Content Team Authoring............................................................................................................. 15

Knova Supports, Extends and Evolves Today’s KM Strategies .................................................. 16

Summary............................................................................................................................................. 16

Appendix: Knowledge Management System Requirements ......................................................... 17

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IntroductionIn support and customer service, Knowledge Management (KM) refers to the strategies and tasks associated with developing and delivering relevant knowledge, efficiently and quickly, to meet evolving customer and support needs. Effective support KM optimizes both knowledge creation and delivery inherently in the KM process.

Knova’s approach to support knowledge management addresses several of the points of failure of previous approaches. In addition, the Knova platform and application suite 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.

Support Knowledge Management GoalsKnowledge 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. Since 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 issues include:

• Findability. The consumer and the provider of knowledge do not usually exist inside the same organization—they use different vocabulary, and 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 for 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 organizations’ 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.

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As a result of these and other considerations, support-focused knowledge management must excel in the definition and delivery of knowledge specifically for reuse in a support context. 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 provides a baseline format for addressing the customer-specific scope and content required for support, but even knowledge bases usually suffer from the same issues of findability, usability and scale.

Many if not most support web sites today give the impression of an expert library: the user must know what to look for, and 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.

Adaptive Knowledge Management:The Knova ApproachAn optimized knowledge delivery process leverages the natural inputs and interactions involved in identifying and resolving a question, quickly and effectively bridging the gap between what a customer or support agent knows and the best information available at any point to satisfy the evolving context of the question.

An optimized knowledge development process provides inherent input to expand and extend the system to meet evolving business needs, both by stimulating content creation 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. Tasks include:

• Creating 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

• Leveraging incoming information to drive knowledge development

- Developing knowledge workers and knowledge assets

- Getting clear visibility into trends, opportunities and issues in knowledge

• Developing relevant self-help knowledge interactions

- Relating product knowledge to the customer context

- Creating effective interfaces to elicit and scope customer needs

An effective support-focused knowledge management approach must drive the efficient development and delivery of the highest value content, in all relevant contexts where it is requested, in direct relationship to customer needs and expectations.

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Traditional Approaches to KM : The Legacy Knowledge BaseAttempts have been made to automate support knowledge management processes for the past twenty 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, 84% of the Contact Center costs are 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. Clearly, there’s more work to be done.

The traditional knowledge base is the tool most frequently used to try to enable knowledge management in the support organization. There are specific technical approaches employed, in some combination, by all of these tools. Before looking at the technology, though, here are areas of pain with knowledge bases identified in a recent survey of support executives:

1. The “Empty Box.” Knowledge bases are software and database repositories that must be filled with content before they’re of use—in effect, vendors ship an empty box for knowledge. This means that the knowledge base must be seeded by a costly authoring or content conversion process, or the Contact Center must resign itself to not getting value until later, often substantially later, in the deployment cycle.

2. Complex authoring and structuring. Knowledge bases require content to be written in their own proprietary formats. Then, authors must also add metadata or context to the content to enable it to be found later. Whether it is case authoring, statement structuring, decision trees, category assignment or some other manual tagging process, it adds a burden to the authoring process that requires expensive training and ongoing management, and discourages knowledgeable employees with valuable information from contributing it to the knowledge base.

3. Lack of integration with the support workflow. Knowledge bases are typically not integrated at a process level with case management or CRM systems. This means that agents must go through an awkward screen-flipping process to use both CRM and KM systems, often with additional copying and pasting. After investing often millions of dollars to automate the support workflow with CRM, Contact Centers are faced with a KM process that is clumsily tacked-on—and, as a consequence, infrequently and inconsistently used.

4. No adaptivity to optimization for important classes of support incidents. Every Contact Center has them: incidents that require special handling, need additional data collected, require access to an external tool or just happen so much that they need to be optimized. Traditional knowledge management systems don’t do anything to help with these kinds of incidents. They don’t have a business rules infrastructure, they don’t tailor themselves to the context of the interaction and they can’t optimize the resolution process for important situations.

5. Incomplete resolution functionality. While knowledge search and creation are important enablers of problem resolution, they are only part of the required functionality. A complete problem resolution application also should support managed collaboration, interview scripting, automated customer responses, and integration with external service applications. These features are not supported by traditional knowledge bases, requiring yet more screen flips, cutting and pasting and IT integration headaches.

6. Lack of integration of enterprise content. “Look within” is excellent guidance for a student of Zen, but represents a serious limitation of knowledge base technology. Much of the information support analysts need is created and managed outside of the support organization—in product documentation, expert forums, release notes, professional services records, partner product information, marketing white papers and an extensive list of other,

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unstructured content sources. While many knowledge base companies have bolted search engines onto their core offerings, none has been able to provide integrated access to unstructured information with findability comparable to structured knowledge.

At the end of the day, traditional knowledge bases have failed to deliver the functionality required to really reduce the cost of service and support delivery. The next section explains this by exploring the strengths and weaknesses of knowledge vendors’ technical approaches to knowledge management.

Past Approaches to Support Knowledge ManagementThe effort to define practices and tools to drive support knowledge to call center agents and customers has spawned a variety of approaches to both structuring and delivering content. Three fundamental approaches attempt to develop relevant support context and interactivity either in the content structure itself, through tags assigned at creation time, or through interaction tools linked to specific content sources.

Metadata-Driven Text Search“Mark What Every Piece of Content is About”Many support knowledge bases incorporate one or many pieces of fielded information to help define what specific solutions are about, and to drive the scope of search results. The fundamental advantage of this approach is that it is easy to manipulate and index in most database and search systems, so it can be well-tailored for a specific domain of content. Some high-level scoping of content is certainly useful in segmenting content at a gross level, such as what basic products or topics are covered. However, trying to use general metadata to define specific support issues and categories has three major drawbacks:

1. It is difficult to create a comprehensive set of definitions and tags that fully express the range of support issues and topics

2. It is difficult to achieve consistent, correct tagging across the full range of authors and content sets required

3. Users have trouble effectively navigating such a system for problem resolution

Any fixed set of definitions requires a consistent interpretation to work, and different authors and users just don’t interpret high-level tags the same way. As a result, end users of such systems are often faced with long lists of categories they don’t really understand and inside documents that have not been tagged consistently by the various people authoring the content. The linkage to the vocabulary and task of the end user is tenuous at best, resulting in low success in finding and using relevant material. Finally, the maintenance required to continuously update definitions and documents results in a costly continuous manual review and update of documents to keep the performance of such a system meaningful.

Metadata-driven systems work well at the high level, and are used most often to perform general segmentation by products or other general groupings. Systems that attempt a detailed representation of support issues in this way typically die a slow death of neglect and misuse.

The Associative Model“Bake Context Into Content”

In an effort to create a document and search context that’s closer to an end-user’s experience of an issue and its resolution, another approach is to render content itself into areas of diagnostic focus (symptom, cause, fix, etc.), and optimize the authoring and searching process around these definitions. Thus both authors and searchers work to define and navigate specific content within these areas. This approach can get closer to linking the content with the support process and with the user’s starting point (often a symptom or goal that can be tightly searched against). In this way the content is associative—specific sections or statements are associated directly with diagnostic elements.

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When it works right, the associative approach can create good matches between user symptoms and content. The fundamental problems with this approach pertain to the amount of work required to properly create and maintain such content and to create a meaningful user interface to it.

1. As with the metadata-driven approach, it is difficult to get authors to consistently and properly author content within the specific definitions required.

2. The fact that the format of the content is required to conform to the diagnostic model limits the knowledge base to only those objects that can be created as such.

3. The associative approach has not been proven to scale outward well to end users for self-help or research.

4. The associative model is completely unforgiving in cases where the user’s question does not directly match one of the definitions in the diagnostic model.

Case Based Reasoning and Decision Trees“Model the Problem Resolution Process into the Content”

In an effort to link the user’s understanding of a problem with the right content and answers, many approaches have been tried to overtly model their experience: the steps they’ve taken, specific things that happen, and conditions they can validate. Both simple decision trees and their more sophisticated case-based relatives aim at the same goal: to qualify and scope the user’s situation before providing an

answer. Users answer specific questions and validate certain steps to qualify their problem, and specific answers or ranges of probability are returned to them as they define their situation.

The decision tree approach works well in cases where the user’s task is fairly simple to define, and straightforward to describe. Such systems predominate in situations where there’s a specific task or item the user needs—often in customer service environments with a high volume of repeated questions about upgrades, returns, hot issues, etc. The well-defined and bounded nature of logical trees limits the usefulness of such an approach beyond the most rudimentary question/answer types of issues. When such trees attempt to represent situations with more than one answer, or more than a couple of variables, they run into several issues:

1. The range of possible inputs, interdepend-encies and answers in even moderately complex problem solving situations makes the definition and maintenance of the logic difficult if not impossible to do easily.

The associative model seems to work best where the size of the knowledge base is relatively small (less than a few thousand objects), and where the users share a strong common vocabulary and understanding of the problem set. As the size and complexity of the knowledge base grows, associative systems quickly become unwieldy, and can yield worse results than simple text searching. Associative models are rarely used for customer self-help.

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2. Users get easily confused about what choices to make, why they may be making them, and the bounded logic of this approach virtually guarantees a poor match to their issue if they don’t make exact and correct choices.

3. It’s very difficult to scale and maintain such a system as issues and needs evolve. Trying to remember where to change even one definition or answer in all the places it may be mentioned in a multiple tree system can be time-consuming and hard to do.

Decision trees work best when the task is well understood, simple and well bounded. They don’t perform well when users are trying to define and resolve issues with any haziness or complexity. The maintenance required to continuously update decision tree logic makes them difficult, if not impossible to scale with any efficiency or user value.

What’s Missing from All These ApproachesMany combinations of these approaches have come and gone, and they all share a common set of fundamental failures:

1. Lack of ability to define a support context that’s meaningful to customers and consumers of the knowledge.

Knowledge is dynamic, multi-dimensional and demand-driven: any model of support context must respond to the user’s need, as they understand it, and bridge the gap between that understanding and the information and assumptions inherent in the knowledge objects themselves. Furthermore, an effective support context must be exposed in a way to support the iterative, give and take nature of problem solving without frustrating, misleading or losing the user in that process.

2. Difficulty linking and scaling support context across the array of content sources and structures that must participate in a complete support interaction.

It’s not enough to create a robust knowledge model–that model must be able to shift and develop as the content itself changes and grows, and as user needs, terminology and issues change. Tens, often hundreds of thousands of knowledge objects are candidates for a complete knowledge system in even moderate sized organizations, yet in a support context only a few are likely to satisfy the specific needs in any particular scenario. In metaphorical terms the support search task is often like looking for a needle in a hayfield—and the system must respond, immediately and positively.

3. Lack of meaningful visibility into how effective the content is, or how it should be improved and evolved to meet changing needs.

Any system that leverages a predefined set of interactions or dimensions for customer interactivity will be limited by the scope of that interactivity. If all documents are tagged to a specific product list, the visibility into user choices or usage will be confined to their choices in that list. Likewise, any tree or statement-driven interactivity inherently presupposes what paths users must travel–any deviations or additions to that list will never be known. This means that the content tagging and user interaction approaches used in the past can only report on the mechanisms they’ve created, and as such fail to show any new opportunities, and at best give some sense of the proportion of traffic in the most general terms.

A new approach is needed to address these shortcomings, and deliver the value inherent in the great store of useful information organizations often carry about their products. Knova has defined an approach that addresses these needs, bypasses the pitfalls of the past and optimizes the creation and consumption of service and support information.

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The Knova Approach: Service Resolution Management (SRM) The Knova approach to knowledge management for service and support does just that: focuses on the support tasks, interactions and expectations of support users and content creators. Previous attempts at creating diagnostic interactions force the user to learn and interact with a pre-set group of steps, statements or definitions. Knova’s approach to the user experience is to build a bridge between the user’s understanding and vocabulary and the best information available given how much the user can describe and absorb at the moment. Previous approaches to creating support definitions have attempted to force the content schema to conform to a specific model. Knova’s knowledge management philosophy acknowledges that knowledge is created, nurtured and stored in a variety of places, and must be modeled in a way that preserves this diversity while still providing a consistent support-related interface when used for that task.

Knova’s approach to knowledge management integrates business process with universal access to content. This combination creates an adaptive resolution process optimized for each specific customer and issue--what Knova (increasingly joined by others in the industry) calls Service Resolution Management (SRM). The result is a seamless user experience with policies and resolutions consistently applied across and between service channels.

Knova’s platform and application capabilities cluster around the ability to define user-friendly, meaningful support dimensions, apply those across all relevant content sources automatically and create highly focused, personalized interactions to drive user success.

By defining support definitions in user’s language and terminology, the guessing game and confusion of dealing with an organization’s internal lingo is removed from the user experience. By automating the process of applying these definitions, maintenance is decreased and scalability is enhanced (both in the amount and types of sources that can be included). Combined with integrated tools for knowledge capture and improvement, and powerful analytics to give insights into trends in key support areas, the Knova approach provides a comprehensive platform for knowledge development and maturation.

Features of the Knova ApproachIn order 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

• Content Integration and Scalability

• Efficient Content Capture and Creation Workflow

• Analytics to Drive Continuous Improvement

The Knova system offers the complete range of platform capabilities, applications, and analytics to drive knowledge development in each of these ways.

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Support-Specific Context and InteractivityThe Knova system includes a pre-built ontology that maps the key issues and activities in the domain of products and services customers use. This representation serves as the intermediary between customer terminology, experiences and input and the vocabulary of the support organization. The ‘support knowledge map’ provides a customer-focused view into the experience and expertise of the support organization. It also provides the

opportunity to create a meaningful self-help session by helping users define and refine their needs dynamically and iteratively, by responding to specific topics identified as relating to their query.

User queries are identified with specific issues and activities as well, to create a dynamic response capability that relates the best resources and feedback based on the level of detail and focus of each question.

Figure 1: Macromedia Guided Search, helping users refine their request

Refine the Request

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Figure 2: The Knova Unified Knowledge Management Platform integrates all relevant resources

Content Integration and ScalabilityIn order 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. Knova leverages its Unified Knowledge Management Platform to integrate resources to the customer-

focused context defined by the knowledge map. Knova’s patented indexing and classification tools make it possible to integrate any relevant content source into this common schema, achieving the goal of a unified representation, without compromising the inherent format and definitions of individual content sources.

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Figure 4: Knova Contact Center creates templates of content formats and workflow as needed

Efficient Solution Capture and Content Creation WorkflowA key factor in achieving the full leverage and efficiency available by disseminating support knowledge is the ability to rapidly create and deliver new solutions. Knova’s Contact Center application suite integrates content development into the support workflow, by integrating all the relevant data necessary to start a new solution (customer input, case information and solution steps developed during problem resolution). The Contact Center resolution flows enable support representatives to consistently gather all the

information needed to resolve specific problems, then push relevant new content into further development workflow, all as inherent features within one common resolution workspace.

Knova’s authoring workflow and templates provide a flexible environment to allow for the development of any desired format and structure of content, and associated stages of development and review. Solutions, FAQ’s, documentation, troubleshooting steps, any relevant content format can be defined, presented and associated with its appropriate workflow dynamically as needed.

Figure 3: Starting the authoring workflow from recommended content

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Analytics to Drive Continuous ImprovementA critical component of any knowledge management system is its ability to drive improvement through visibility into key trends, needs and content holes. Knova’s ability to define and spot key support concepts from system users through the knowledge map enables the roll-up of support traffic into relevant views at the topic level. 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. Some of the key reports provided by this system include:

• Site Traffic – Traffic trends by any relevant profiled dimension (users, products, groups, etc.)

• Knowledge Gaps – Identification of areas of high traffic with low document count and use

• Coverage Summary – Correlation of traffic by significant combinations of symptoms, activities, topics and products

• Search Outcomes – Profiling specific queries against specific topics

• Hot Documents – Identification of the most commonly used documents

• Best Bet Candidates – Identification of documents viewed often when presented

• Coldest Documents – Identification of documents rarely viewed when presented

In addition to the flagging mechanisms provided by assessments of traffic within specific topic combinations, Knova analytics provide linkage to the specific queries and documents viewed to provide content managers direct access into the range of inputs and outputs delivered. This capability allows further analysis and identification of weak or missing content to drive continuous content improvement.

Activity

(OS) installing & upgrading

(OS) hardware & device drivers

(OS) starting & shutdown

(OS) internet & networking

(OS) system admin & functions

(IE) installing & upgrading

(Outlook) mail

(OS) security & access

(IE) browsing

(IE) not sure

(OS) file system & storage

(OS) printing

Other

Queries

11,046

9,438

7,703

6,278

3,681

2,847

2,469

2,457

2,292

2,264

2,117

2,116

11,863

Figure 5: Knova analytics give insights into user topics and trends across all support channels

Issue

Support context for customers and user

Traditional Knowledge Base

Static models do not bridge the gap between knowledge authors and consumers

Knova

Support-focused knowledgemap connects the vocabulary in support content with how users ask for it

Integrated access to multiple sources of content

Separate platforms and experiences for searching structured and unstructured content (if there’s any support for unstructured content at all)

Integrated access to knowledge in many forms and repositories, structured and unstructured, with a common user experience

Lack of visibility into knowledge effectiveness and how to improve it

Siloed reports on queries, keywords and documents

Cross-channel analytics about topics of interest, knowledge gaps, and content effectiveness

Table 1: Overcoming the Limitations of Traditional Knowledge Bases

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Knova Enables Knowledge Management MethodologiesThe proof of support knowledge management technology is how well it enables the business processes that Contact Centers use to create and maintain knowledge. Knova’s approach is distinguished by its ability to work inside of all popular methodologies. This stands in contrast to traditional knowledge bases, which were typically designed to support one particular approach to the exclusion of others.

Industry expert David Kay of DB Kay & Associates identifies three common strategies for knowledge development in the Contact Center: Knowledge-Centered Supportsm, Product Specialist Authoring and Content Team Authoring. In this section of the paper, we briefly explore how Knova enables each of these strategies.

Knowledge-Centered Supportsm (KCS)Who Authors Content: All support analysts

Key Principles:

• Searching starts the authoring process

• Knowledge is captured in the support workflow

• Knowledge is published and improved based on use

Works Best For: Complex technical support environ-ments with highly skilled analysts.

The Knowledge-Centered Support strategy advocated by the Consortium for Service Innovation and Help Desk Institute focuses on techniques and practices to intensify and optimize knowledge capture and creation as part of the support process. Support agents capture incoming information as they interact with customers, search existing solutions and resolve new issues. The insights and resolutions gathered from these interactions are quickly converted into new solutions that are readily available for re-use in the knowledge base. Explicit workflows, quality measures and performance metrics are put in place to encourage optimal, valuable knowledge creation.

Knova’s platform and applications suite provide extremely strong support for the KCS approach. The Knova platform provides an inherently integrated knowledge capture and development environment,

to support the “Solve” dimensions of the KCS model. Knova’s authoring environment simplifies the tasks associated with gathering incoming information to frame a solution, and the amount of work required to structure content for re-use. Knova’s problem resolution environment is inherently collaborative and knowledge-driven, making it easy to include knowledge from other people as well as multiple resources during problem resolution and solution creation.

Knova Software is certified as “KCS Verified” by the Consortium for Service Innovation.

Product Specialist Authoring

Who Authors Content: Tier 2 or 3 support analysts who are product specialists

Key Principles:

• Content is authored based on repeated escalations, product experience

• Knowledge may be edited for readability, but rarely requires technical review

• Authors also perform root cause analytics; provide feedback to development

Works Best For: Contact Centers with senior analysts, organized by product, with a broader charter than pure incident handling. Often used when lower tiers are outsourced.

Product specialists are a feature of many Contact Centers. These senior support analysts work in Tier 2 and 3, or sometimes just in Tier 3, and their responsibilities go beyond just handling escalated incidents. They also work with the product group during the product release process to give support a head start on support delivery. They perform root cause analysis of incidents and web traffic to provide feedback for product improvements including reliability, usability, and serviceability. As the conduit between product development and the rest of the Contact Center, including outsourced providers, they have a primary focus on knowledge transfer, including developing support content.

Knova empowers product specialists to be more effective at knowledge transfer through a portfolio of features, including solutions authoring. First, lower-tier support analysts who are able to resolve issues themselves but are not tasked with authoring content directly may use the “Suggest Content” feature to take all the details about the

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All KM Approaches

current support interaction and start an authoring workflow in which a product specialist can generate final content. Second, the most appropriate product specialists can be automatically identified by lower tier agents and engaged in a structured collaboration, which can then form the basis of a new piece of content. Finally, the root cause analytics provided by Knova can ease some of the product specialist’s burden in developing both product feedback and support content to deal with product issues.

Content Team AuthoringWho Authors Content: A dedicated content development team

Key Principles:

• Content is authored based on experience, support and product group input

• Knowledge may have a technical and legal review, but rarely requires rewrite

• Authors are trained technical writers and knowledge managers

Works Best For: Contact Centers with less complex products, a consumer customer base, high incident volumes.

Many customer support and service organizations with a high volume of incidents and a less technical user base use a dedicated content authoring team to develop content. This aligns to the needs of customers who require less complex technical support, but do need easily readable, well-written content. Content groups employ a range of strategies for what content to develop based on how existing content is (or isn’t) being used, and the feedback it is getting. They also take advice from product groups, especially before the product is launched, and from support groups, especially for emerging support issues.

Knova provides a range of tools to make content development teams more effective. First, as was mentioned before, support analysts can suggest content right in the workflow, streamlining the process of getting input from the support organization to the content team. Also, Knova’s flexible document templates and review workflows

Table 2: Knova Satisfies Requirements for Empowering KM Methodologies

Identifying needed content during

support delivery

• Support for “capture in the workflow”

• “Suggest content” feature

• Capturing collaborations

• “Suggest content” feature

Identifying needed content through

analytics

• Knowledge gap • Root cause• Knowledge gap

• Customer issues• Content

effectiveness• Knowledge gap

Review processess • Knowledge can be reviewed based on use statistics, or in workflow

• Flexible technical and legal reviews

• Flexible technical and legal reviews

• Easy to use• No manual tagging, statement structuring, or case

authoring required

Authoring process

Authoring templates

• Integrated access to authored and other enterprise contentKnowledge access

Core Capability Knowledge-Centered Support

Product Specialist Authoring

Content Team Authoring

• Flexible templates that support multiple document or solution structures

• Different templates can support different content types

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provide content teams with a broad range of options for providing readable, accurate, approved content. But most importantly, Knova provides unique analytics identifying customer trends and issues, document effectiveness, and content gaps, taking the guesswork—and, in fact, most of the work—out of the content team’s own analysis of content usage and requirements.

Knova Supports, Extends and Evolves Today’s KM Strategies

The Knova Platform extends knowledge development and delivery practices beyond pure knowledge creation for diagnostic support. Knova Contact Center can engage the entire spectrum of service delivery content and channels. Key stakeholders outside of support, such as product engineering, can be integrated into a continuous loop of knowledge development. Knova Expert Forums extends knowledge capture, development and delivery to both internal and external communities of interest, including customers, partners and support affiliates.

The Knova system can be used to jump start knowledge development from inception, to migrate and integrate existing tools and knowledge bases, or to enhance and extend existing functionality to whole new levels. Core techniques for real-time knowledge capture and development can be modeled within Knova resolution flows and solution templates. The Knova system also addresses key requirements of support knowledge management not well met by approaches to date, including the ability to:

• integrate multiple knowledge sources,

• drive optimized diagnostic scenarios based on each specific case, and

• provide rich feedback on trends and issues to maintain the knowledge base.

Finally, Knova capabilities go beyond pure solution development and capture, to drive the design of a comprehensive knowledge-driven organization spanning all the critical players and content in the support process.

SummarySupport knowledge is created and used to drive a very specific and challenging purpose: the rapid identification, diagnosis and resolution of support issues and questions. As such the requirements for support-focused knowledge management place a heavy emphasis on capabilities that foster creation of reusable content, rapid knowledge development and maturation, and continuous visibility into trends to allow for immediate improvement. Knova’s platform and suite of applications are designed expressly for the support knowledge management environment, and enable the continuous development and delivery of knowledge that is customer-focused, relevant to current business demand, and integrated with all other necessary knowledge sources. The resulting system optimizes knowledge creation, provides insights into the value derived from knowledge delivery, and drives a superior user experience in finding and using support information.

Knova has learned from the lessons of the past and defined a knowledge management solution that addresses the deficiencies of previous approaches. The resulting solution is powerful, flexible and scalable. It represents the next generation of tools and capabilities in support knowledge management.

Knova Software is headquartered in Cupertino, California. For more information email info@knova.com or call 1-800-572-5748

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Support-specific Context and Interactivity

Technical content and input can be sorted and represented in ways that are relevant to support tasks (Q and A, diagnosis, research).

Knowledge delivery can be linked to customer context and expectations to drive meaningful self-help and online interactivity.

A consistent, clear view into the organizations’ products, features and issues can be rendered across all relevant content sources and application resources.

Efficient Content Capture and Creation Workflow

The authoring environment is inherently linked into the overall job task of the content creators: appears within workspaces, and draws from all necessary sources to populate and refine knowledge.

Well-structured, unique, usable content is easy to create, by those with the necessary subject matter expertise.

The content creation workspace helps identify the most needed and relevant content.

Content authoring can take place in a variety of locations, and formats, and still roll into an integrated, effective knowledge delivery process.

The content created by a single SME is instantly linked to concepts and terms that consumers of the content can understand and use to find it when needed.

Workflow to review and publish content is optimized to get the most useful and important content out, and to minimize redundant or unnecessary review.

Analytics to Drive Continuous Improvement

The content that’s most in demand can be identified on an ongoing basis.

Measurement mechanisms exist that continuously validate the impact of new content, which can be aggregated to provide an overall view of the progress of knowledge development in driving customer satisfaction.

Content creators and managers can assure that new topics, issues and items are properly identified and exposed as customer and business needs evolve.

Content creators and managers can see the impact of their contribution, how successful users and customers are with the knowledge delivered.

Tools and analytics allow for immediate, flexible review of trends in specific product and subject areas, around specific issues or problems, across all content being delivered.

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Appendix: Knowledge Management System Requirements

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Effective Maintenance Tools and Capabilities

The definitions used to drive context and customer interaction can be easily updated, manipulated and reviewed.

The ability to drive specific content in relation to a search or web page element (‘Best Bets’) can be clearly and easily controlled.

The ability to define authoring workflow and content formats can be flexibly controlled.

The ability to incorporate multiple content sources can be easily defined and updated.

Content Integration and Scalability

Legacy content sources can be incorporated into both the authoring structures and workflow, and the knowledge delivery structures and mechanisms.

Feedback from knowledge delivery is integrated into the authoring qualification, creation and publication process, to provide a continuous loop of validation at each step of the knowledge creation process.

Content is immediately associated properly with other relevant content, and redundancy avoided.

All requirements can be met real-time in a system supporting potentially hundreds of content creators, and potentially hundreds of thousands of knowledge objects.

The knowledge delivery experience remains effective and efficient for users, across multiple content sources, 100K’s of objects, and diverse inputs.

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