Self-service CRM initiatives fail if the systems do not answer customer questions.
Posted Apr 1, 2006
High costs and scarce resources are driving companies to try a self-service approach to CRM help desk and call center operations wherever possible. However, what's often missing is the key element that can make it successful - providing customers with the quick, correct answers to their questions.
Self-service CRM initiatives fail if the systems do not actually answer customer questions, leading to frustrated customers calling into the traditional contact center/help desks. Frequently the cause of the failure is that the self-service system attempts to "educate" the customer to answer their own questions-not simply providing situation-specific answers or recommendations.
Self-service CRM education approaches have three main problems:
1. Most people do not want to be taught to be an expert; they just want a quick answer to their immediate question. If you are sick, you don't go to a doctor for a medical degree - you just want to know what's wrong and what you should do.
2. Technical, regulatory and many other areas are far too complicated to easily teach the customer enough that they can reliably make a good decision - and a little knowledge can be dangerous.
3. Many case-based or business intelligence systems that try to intelligently find relevant information are actually guessing based on pattern matching algorithms. They do not analyze the customer's situation logically and the guesses may be wrong.
The ideal solution is an automated Web-based system that provides specific advice and recommendations comparable to a human expert, and which emulates the one-on-one conversation a customer would have with an expert.
Self-Service CRM Initiatives Need An Underlying Expert System Solution
There are some questions that only require data: "What is my checking balance?" But most real-world customer questions need more than data. An example of this would be a web-based help desk that applies knowledge to analyze the customer's situation, and provides advice or a recommendation that reflects the customer's needs. Expert decision-making knowledge can be captured in a way that allows it to be automatically delivered over the web. This is the basis for knowledge automation expert systems.
Expert systems are built by using knowledge capturing software to describe the decision-making logic and processes of human experts in rule form. These rules describe all aspects of a decision, and can be applied to a wide variety of situations. In each customer session (typically via a Web browser), the expert system inference engine uses the rules to ask the customer questions that are needed to make a decision. Data can also be obtained automatically from a database, or other parts of the CRM system.
The expert system interacts back and forth with the user asking questions needed to reach a recommendation. Based on each customer's input, the system will skip irrelevant questions, and drill down where appropriate. The final results and recommendations are specific to the individual situation, and based on the same logical process that the human expert would have used.
The logic in the system may be very complex, but the end user only sees simple questions that he can answer and precise recommendations that he can use. They are not expected to learn or understand how the decision was reached (although that can also be implemented). They are simply given a valid answer that they can immediately use. Another benefit is that the advice is consistent-if two customers provide the same input, they will get exactly the same recommendation, and it will be based on the expert knowledge in the system.
Expert system technology is widely used for diagnostics, regulatory compliance, product selection, help desks and many other decision support areas. With the ubiquitous nature of the web as a company's primary means of communication with customers and employees, expert system technology is more important than ever before. With current development tools, expert systems can be created quickly, successfully, and at relatively low cost. They can be easily integrated into existing CRM systems. They don't require a costly or risky multitier development or implementation effort so common with other CRM projects. For example, DuPont, who has probably built more expert systems than any other Fortune 500 company, reports a typical 100 to 1 ROI in fielding expert systems-for every $10,000 they spend on expert systems, they save $1 million.
Expert systems can be added to a CRM system as individual modules to answer questions. They can also be integrated as a "front-end" to traditional CRM systems as a very capable first level of support; with the ability to open tickets and pass along problems beyond the scope of the system to human staff. Either way, expert systems should be a key part of any advanced web-based self-service CRM. They are the only technology that can automatically provide precise, detailed recommendations for the wide range of increasingly complex issues that CRM systems have to deal with.
About the Author
Dustin Huntington is the president of Exsys Inc., a knowledge automation company and author of the popular Exsys software tools. Please visit www.exsys.com
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