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Using a broad definition of CRM analytics, this technology can range from being as simple as query and reporting capabilities to sophisticated data mining and predictive modeling over large quantities of data. Data mining is a component of CRM analytics. Here are what I've found to be key success factors to data mining specifically for CRM:
- Mine data that's been pulled from ALL customer touch points to create one view of a customer. Customer intelligence based only on activity from one channel or product area will be limited in its use in CRM initiatives.
- Provide strong data warehousing capabilities that include consolidation, cleansing and formatting to specifically support analytics. This is a critical foundation to data mining.
- Quickly deliver knowledge derived from data mining results to front line employees. Those who take advantage of this knowledge can better serve customers.
While data mining has been around for years, following CRM guidelines such as those listed above make this technology the basis of creating profitable relationships within CRM initiatives. It's a necessary component of strategies aimed at acquiring and retaining customers as well as maximizing the value that companies provide to and derive from customers.
For more information, check out searchCRM's Best Web Links on CRM Analytics.
This was first published in December 2001
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