CRM (customer relationship management) analytics comprises all programming that analyzes data about an enterprise's customers and presents it so that better and quicker business decisions can be made. CRM analytics can be considered a form of online analytical processing (OLAP) and may employ data mining. As Web sites have added a new and often faster way to interact with customers, the opportunity and the need to turn data collected about customers into useful information has become generally apparent. As a result, a number of software companies have developed products that do customer data analysis.
According to an article in InfoWorld, CRM analytics can provide customer segmentation groupings (for example, at its simplest, dividing customers into those most and least likely to repurchase a product); profitability analysis (which customers lead to the most profit over time); personalization (the ability to market to individual customers based on the data collected about them); event monitoring (for example, when a customer reaches a certain dollar volume of purchases); what-if scenarios (how likely is a customer or customer category that bought one product to buy a similar one); and predictive modeling (for example, comparing various product development plans in terms of likely future success given the customer knowledge base). Data collection and analysis are viewed as a continuing and iterative process and ideally over time business decisions are refined based on feedback from earlier analysis and consequent decisions.
Benefits of CRM analytics are said to lead not only to better and more productive customer relations in terms of sales and service but also to improvement in supply chain management (lower inventory and speedier delivery) and thus lower costs and more competitive pricing.
One of the major challenges implicit in CRM analytics is how to integrate the analytical software with existing legacy systems as well as with other new systems.
A new area of application and data collection has to do with Web site customer usage.