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Customer Relationship Management
April 2003


Knowledge Management: 
From Nebulous To Necessary For Customer Service

By Jessica Jordan, ServiceWare Technologies

Customer relationship management (CRM) solutions have been widely accepted by global enterprises seeking to improve customer satisfaction and retention. But it takes more than just technology to maintain customer relationships. It takes improved business processes and a method for providing customers with the information that they demand in an efficient and effective manner. This is where knowledge management (KM) comes in to play in the customer interaction center. 

KM has been characterized as a vague concept'a dirty word' and some say that it's virtually impossible to manage knowledge if knowledge is truly in one's head. These statements are understandable. Many of us have struggled with the use of the term knowledge management for years. It has been nebulous partly because as a practice, it is ambiguous and not always easy to explain in terms of true business applications. But this is changing. There are numerous viable applications for KM and one of them is customer service. Customer service organizations require easy access to accurate, consistent information in order to answer customers' questions. KM provides the processes to capture relevant information and make it readily accessible by agents and customers via self-service.

Today, industry experts recommend customer service and support knowledge bases as a critical component of successful CRM. According to Tim Hickernell, senior analyst with the META Group, 'Service strategies that include knowledge bases, accessible to both agents and customers across all deployed points of interaction, can optimize cost of service and increase customer satisfaction by providing a more consistent customer experience.' META concludes that by 2004, companies seeking customer service superiority will add cross-channel knowledge bases and escalation capabilities.

So, why are knowledge bases critical? The concept of a central knowledge repository for corporate information isn't new. The need to access and share intellectual capital isn't revolutionary. However, businesses have begun to better understand the business benefits of KM now more than ever'. but why now? The answer stems from many issues, but an underlying reason is one of economics. Today's tumultuous economy has sent budgets crashing and approval processes escalating up the corporate ladder. Return on investment (ROI) has become the catch phrase of the era, with total cost of ownership (TCO) running a close second. 

With the need to predict ROI for customer service solutions, businesses are finding that if they harness the brainpower of their customer service agents, access already existing information found in sources outside of a structured knowledge base, personalize and segment knowledge for all potential audiences and empower their customers by providing Web access to the knowledge, they can greatly decrease operating costs while improving the productivity of their service operations. These goals can be accomplished with knowledge management tools and methodologies applied to the needs of the contact center.

Although the practice of KM is not necessarily rocket science, there are several necessary factors that can determine if an initiative will be a winner for customer service. There is definitely a systematic approach to successfully implementing KM, and if you analyze your specific contact center goals, map out a strategy, garner support from the organization, set expectations, understand the importance of cultural change and establish a means to track and measure usage, then you are much more likely to be successful.

According to Hickernell, 'the successful application of KM in the customer interaction center cannot occur by applying existing KM systems within the center or extending these systems to agents. For example, extending enterprise search and retrieval systems to agents will only serve to overwhelm agents with a vast array of information that is not relevant to the requests they receive from customers.' META believes that organizations embarking on formal KM projects should assess the correct fit for applying basic KM principles and tools to service processes.

KM For Customer Service ' Where to Begin?
Knowledge management is often an enterprisewide initiative'a discipline that encompasses managing and sharing knowledge across all departments within an organization. However, quite often organizations choose to kick-off KM on a departmental basis. With customer satisfaction as a mission-critical driver for all businesses, especially today when repeat business from existing customers can make or break a company, many companies are choosing to invest in knowledge management for their customer contact centers. Other common implementations occur within IT help desks, human resources departments and sales organizations. It's important to remember that organizations must tailor KM processes and tools to the specific needs and goals of each department.

Focusing on the contact center, it's important to first consider culture when implementing a KM initiative. Will your agents use a knowledge base to search for solutions to customer inquiries? Do your agents use an existing call tracking or call management system to log and close cases? Is an agent-specific workflow included in your KM solution? What are your agent-specific goals and how will you measure their success? All of these questions must be answered before you can expect a true return on your investment in KM. 

The first question is one of culture. To overcome cultural issues, incentive programs are often created to reward agents that use the knowledge base or contribute new information into the system. Incentives can come in the form of a tangible reward, such as a monetary bonus, or an intangible reward, such as public recognition. Also, basic principles of KM, such as workflow and knowledge contributions, must become a part of an agent's daily work activity. 

Customer Empowerment 101
Today, knowledge management is not just for agents accessing a knowledge base. Allowing customer access to self-service knowledge bases is a must. The bonus of Web self-service (also referred to as e-service or online self-help) is that customers are happier with your company if they can quickly and easily find answers without having to contact the call center, and companies can reduce operating expenses by deflecting queries to the Web. 

According to John Ragsdale, director with the Giga Information Group, 'With budget cuts and downsizing hitting many customer service organizations, many of the questions I receive from clients involve how to do more with less. Investing in robust e-service products (knowledge bases for agents, multiple search technologies, customer Web self-service, e-mail response management) is the best approach to deflecting more customer questions to self-service, reducing average agent talk time and increasing first call resolution rate, as well as encouraging customers to use less expensive support channels, i.e., Web or e-mail as opposed to phone.'

It's not enough, however, to put the information on the Web and ask your customer to go find it. You need to make the information timely, accurate, easy to find and in the format that most customers want. By knowledge-enabling your online customer service, you empower customers to find answers quickly through dynamic FAQs or knowledge search engines. Both FAQs and search engines must generate dynamic responses in order to be useful, meaning that they must learn and adapt from usage. This type of technology is referred to as a self-learning search engine. To be considered a true self-learning search, the system must learn from previous experiences had by customers with similar issues. It must be self-organizing, in that it is always moving the most relevant information to the top of the search results. It also must be tied into a reporting system that monitors knowledge usage ' which items in the knowledge base are being used most frequently and which are not being accessed. 

An issue that often arises with Web self-service deals with the mix of calls coming into the contact center once a self-service initiative has been established. In many cases, effective knowledge-enabled self-service does change the mix of inbound calls. This is a desirable economic phenomenon. You want to serve your customer in the most efficient and economical way possible. This dictates that you use the appropriate resources for each issue. If a basic question can be solved through self-service, then often that is the best method to handle that particular question.

Here is an example. For a large retail chain, a typical question might be about 'regional store locations' or 'hours of operation.' These are issues that can be effectively handled online. If the average customer service call costs $8.00 (industry numbers range between $5.00 and $30.00 per call), you wouldn't want your agents fielding basic questions about store hours. Agents should be utilized for more demanding problems, issues, and even upsell sales opportunities. Not only will these complex questions be a better use of their time, agents will also feel more challenged and satisfied in their jobs.

Bottom Line On KM 
There is an analogy that is often heard used to describe KM and that is quite fitting for this discussion. If you're going to construct a structurally sound building, you have to start with a solid foundation. The same goes for KM. If you expect to improve customer service and satisfaction, you must provide your key audiences ' agents, customers, employees, field reps, partners and so on ' with easy access to accurate and consistent information. This can be achieved through a successful knowledge management initiative.

KM is no longer the redheaded stepchild of the IT world. It has recovered from an identity crisis and is making its mark as the foundation for large enterprises and small departments to bolster CRM implementations by enabling agents to share information, improve productivity and ultimately reduce operating costs. 

Jessica Jordan is a vice president with ServiceWare Technologies, Inc. ServiceWare is a provider of knowledge management solutions for customer service and support. 

[ Return To The April 2003 Table Of Contents ]


Using Analytics To Drive Knowledge Management For Better CRM

By Chris Eldredge, SAP

Many companies have already implemented CRM and are now looking for ways to get the most out of their investment. Much of the hope for realizing ongoing and future business value from current technology investments lies with analytics. Analytical CRM applications provide all the necessary functions companies require for measuring, forecasting and optimizing their customer relationships. With the flood of information that companies collect, store and have available today, the challenge is getting the right information to the right people at the right time to maximize the efficiency of customer interactions. By using analytics to boost the efficiency of knowledge management, companies have a powerful new tool that uses customer information to extend and deepen their relationships with customers. 

Turning Analytics Into Opportunities
With analytics as an engine to interpret all of the relevant customer information companies already collect from different sources, including customer reaction to marketing campaigns, customer shopping method preference and customer questions received through the interaction center, raw data can be better shared across the enterprise to improve customer satisfaction levels. Analytical models can be created to help companies evaluate their relationships with customers to answer the questions that will derive valuable insights from the extensive amount of data collected. 

As an example, an online retailer might discover that it was losing sales because of out-of-stock products. Using analytical insight gained from its CRM system, the company learned that customers would be willing to buy these products if they knew when they would be back in stock. Working with already collected customer data, the company might implement an e-mail program to let interested customers know when items matching their interests became available. Further down the road, the company might find that since the e-mail initiative began, thousands of customers have requested to receive e-mail updates, thereby increasing interactions between the online retailer and its customers. And as a result, the analytical discovery led to increased customer satisfaction, loyalty and sales.

When applied to knowledge management, analytics helps companies better understand customer needs and preferences to identify recurring patterns. Analytics helps ' allowing companies to deepen their relationships with existing customers through personalized communication; identify most profitable customers and optimize cross-selling and upselling possibilities; and maximize customer loyalty so that if a valuable customer shows signs of leaving, the situation can be recognized and easily identified and countermeasures can be implemented in time to prevent the loss. 

Analytics In Action
Personalized Marketing 

Of the analytic capabilities in use today, customer valuation is a critical component. When tied to knowledge management, customer valuation can help companies concentrate their limited resources on their best and most valuable customers. Taking into account customer lifetime value, which is a forward-looking view at customer profitability by segment, customer valuation identifies the true value of a customer and ensures proper allocation of resources per customer segment. 

Analytical CRM helps companies understand the information collected through customer interactions to profile their best customers and create campaigns specifically designed to deepen those relationships. For example, in the chemical industry, most products can be viewed as commodities and many of their customers have varying tolerances for the quality of the chemical products they receive. Chemical companies are beginning to realize that costs can be saved based on a customer's tolerance for lower grade quality products, known (know as 'off-spec' ) products, and the flexibility in the timeframe they have set for delivery. By taking these and other factors into account, a chemical company can discover which customers require the highest quality product with the tightest timeframe and thereby have the highest profit potential. By customizing service and product offerings based on customer value, these chemical companies are able to ensure that they are driving maximum profitability from their customer base while improving satisfaction of their most profitable customers. 

Cross-Selling/Upselling Customer Satisfaction 
Analytical models can also be created to forecast the type of information, based on customer behavior patterns identified from past data, that customer-facing employees will need to make the most of customer interactions. In many situations, there are several possible solutions that will meet a customer's needs and the knowledge gained through analytics can help direct the agents to the right solution for each customer interaction. For example, when a customer contacts an interaction center for the second time with a product-related question, the analytical model takes into account the customer's profile and history, prompting the agent to consider alternative information that might not have been available without the help of analytical insight. The computer software industry is notorious for having multiple and frequently ineffective solutions to address technical issues, which often leads to customer frustration. Analytics allows companies to collect data on which solutions are suggested to customers, as well as the success rates of those solutions and can then join that information with customer transaction data and known customer profile information such as skillsets, other installed software applications, etc. Therefore, when a customer contacts the call center with a problem, the model would run smoothly and quickly provide the agent with relevant recommendations for the customer. The agent would then be able to provide a personalized solution. In addition, a second analytical model could be used to determine the likelihood that the customer would buy additional products and services such as software upgrades, complementary products, maintenance agreements, or professional services. This gives the agent an opportunity to turn a service call into a profitable interaction.

Churn Management
Used to identify customers at risk of leaving, churn management is a new functionality of analytical CRM, allowing companies to leverage collected customer information to place a value on specific customers based on their likelihood to leave. Analytical CRM indicates which customers are inactive and in danger of straying and helps develop tactics to proactively combat the risks of customer loss. For example, three months before a cell phone contract is scheduled to end, the service provider would benefit to know which customers are likely to cancel their contracts. This would help the company contact only those customers at highest risk of canceling their service and avoid the risk of alerting content customers that they now have the opportunity to terminate their contracts. With this type of well-directed customer selection, the costs of the campaign are minimized and the maximum number of customers can be retained. 

As the above example shows, churn management can effectively be used in industries with high customer turnover such as telecommunications and financial services industries, but other industries are beginning to discover the benefits of this analytical CRM process as well. 

Knowledge management interpreted through analytics can dramatically improve CRM. By more precisely interpreting and sharing information, companies are better able to identify and segment specific customer groups. With the opportunity to increase sales and profitability through cross-selling opportunities, analytics can also improve customer retention and help prioritize the most profitable customers. By tying analytics closely to knowledge management, companies can equip the right people with the right information to meet individual customer needs, resulting in optimized resource allocation and ultimately, improved customer satisfaction. 

As a product director at SAP global marketing, Christopher J. Eldredge is responsible for all product marketing related activities for mySAP Customer Relationship Management (CRM) Analytics. 

[ Return To The April 2003 Table Of Contents ]

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