The array of technology and software for the data mining/data warehousing functionality has grown enormously over the last several years. There remains, however, a real need for data mining and warehousing in the after-sales service portion of the CRM infrastructure. Once products are sold and installed, there is an extremely interesting and highly valuable data set that begins to emerge. Even basic products garner information about the sale and initial warranty if the customer fills out and returns a warranty card. Additional information regarding the purchase of a maintenance and service contract is also very useful. High-tech products might include information dealing with installation, service requests for preventive maintenance, predictive maintenance or emergency failures, and requests for moves, additions and changes in configuration or location.
The service engineer tends to be the most knowledgeable about present and emerging customer requirements and needs. While the sales and marketing staff is most involved with the customer before the sale, a relationship with ser-vice personnel continues well beyond the sale. The ideal marketing and sales team, particularly in high-tech areas such as information technology, office automation and copiers, telecommunications and medical technology, all recognize that its experienced service engineers are the best sources of information about a customer's operating structure, current experience and future product needs and requirements. However, this information, which has great strategic value, is often overlooked while developing CRM specifications for data warehousing and mining.
To illustrate the critical value of data warehousing and mining in after-sales service, consider this simple process. For each service request:
• Record the problem and/or complaint when the call is received centrally.
• Augment this data with additional information on problems and symptoms gathered by the handling center from the customer.
• Once the service call is assigned to a field service engineer, track the call, ultimately reporting the identification of the problem and the corrective action initiated. This could include information on specific failures, need for software or hardware updates, use of service parts, as well as information on costs or the mean time between failure and repair.
If this information is carefully collected and recorded on an online real-time basis using a direct connection between the field service engineer and the central call handling facility, it is possible to build a diagnostic decision tree linking product problem and symptom to cause and corrective action.
Once this data is collected, its availability and access on a real-time basis can literally replace and significantly improve the standard manually generated diagnostics and repair manual. Having an online real-time accessible database that includes the standard data warehousing and data mining technology used in the front-end sales-based CRM practice can offer extremely valuable benefits on the field service side, including more effective control of reliability and maintainability, the ability to identify the need for predictive maintenance actions to avoid failure, reduction in the mean time to repair and a significant reduction in calls broken off in the field because of a lack of parts or skill sets.
The real bottom-line value of data warehousing and data mining on a day-to-day short run, as well as long run, lies in their applicability to the field service portion of the CRM equation.