DSC Tech Library
Customer Relationship Management
This section of our technical library presents information and documentation relating to CRM Solutions and customer relationship management software and products.
Providing timely customer service information is vital to maintaining a successful business. Accurate information provided in an organized and thoughtful manner is key to business success.
TELEMATION, our CRM and contact center software, was originally built on this foundation. The ability to modify Customer Relationship Management software is important in this ever changing business environment.
Telemation Customer Relationship Management solution and contact center software is ideally suited for call centers throughout the world.
CRM Best Practices: Working with Customer Data
The following is an extract from the article "CRM Best Practices: Working with Customer Data" by Joe Outlaw from CRM Daily:
"Garbage-in leads to garbage-out" is a famous old commentary on what happens when you don't enter appropriate or accurate data into computers.
When it comes to bad customer data and CRM applications, the ramifications go way beyond not getting the right answer -- the lack of quality customer data is one of the leading causes of CRM failures.
There are three primary customer-data challenges in CRM. First the organization must determine what customer data to collect and manage. Second, the data must be collected and prepared for the initial CRM application load. And third, the data must be maintained over time.
What data to collect and manage for your CRM initiatives depends on your objectives, which in turn are driven by your CRM strategies.
Strategies vary, but two typical customer-data views are enterprise-wide and by line-of-business. In larger companies, it is more common to see the line-of-business approach to customer data. This primarily is due to the business logic of keeping the data somewhat segregated.
But even in these LOB data-view organizations, it can be important to have at least a high-level, consolidated view of major customers, if not all customers. For example, a bank may choose to keep its trust-customer data separate from its commercial loan and home mortgage-customer data, but needs to be aware that a trust customer also has a commercial loan or a home mortgage.
Typically, the customer data for CRM comes from two sources: existing operational and customer-facing applications, and directly from customers and prospects.
Both sources of customer data can be error-filled, and data-collection processes error-prone. Errors or inaccuracies can be introduced during the collection process due to the unwillingness of customers and prospects to provide accurate data. Errors of omission and commission can occur from the loading of inaccurate, inconsistent, or incomplete customer data.
Finally, errors can arise from the failure to maintain the data as it naturally changes over time. On this last point, for example, it is estimated that each year about 15 percent of the population of the United States changes its home address.
There are two aspects of data clean-up prior to the initial CRM application load -- removing duplicate records and improving the completeness and accuracy of the data.
"It is critical you determine what attributes constitute a unique customer organization before beginning the de-duplication process," advises Deloitte Consulting senior manager Frank Yang. "It can be company name, or name and address, or something else, but you need to have clear rules for what constitutes a unique customer." Then you should completely eliminate duplicate company records before loading the customer data.
Correcting or improving the accuracy of customer data is a more difficult problem. Companies should strive to have accurate and complete customer data, but must recognize it is a never-ending effort. They must also be sure there is an ROI associated with the collection, cleaning and maintenance of the data.
Maintaining the Flow
The customer data challenges do not end after the initial system load. Customer data is continually being collected and refreshed. For data from other applications, there are two primary approaches to data cleansing. The first and most commonly used is to manage the process at the points of entry to the CRM application. With this method, the data needed from the other applications is extracted and cleaned-up using the same processes as for the initial system load. The second approach is to modify the other applications to apply keys, collect additional required data, and clean-up the data at its source.
For data collected from customer interactions , there are two best-practice approaches to creating new customers records. The first and most common is to let the system's users create new customer records.
"With this approach the users must be properly trained in the use of the system, the implications of duplicate records, and particularly how to conduct a thorough search for customer records prior to creating new ones," advices Yang.
The second approach to creating new customer records, typically only used in larger organizations, is to permit only a central organization to assign a new customer identifier. This approach is, of course, more secure, but can introduce a 2 to 3 day turnaround bottleneck, which can impact customers.
For B2B organization, corporate data maintenance can be at least partially automated through the use of a third-party service, such as Dun & Bradstreet......"
To view the entire article, visit www.crmdaily.com.