Analytical CRM

Analytical CRM can apply business intelligence and reporting methodologies like data mining and OLAP (Online Analytical Processing) to CRM applications. These applications are customized to analyze report on and foresee customer behavior. Marketers have developed marketing functions and have dramatically improved their bottom line by collecting, mining and analyzing customer data. However, many companies have missed the opportunity to optimally target the right customers with the right products and prices because they have settled for one-off applications.

Many marketing managers do not even know they are sitting on a virtual goldmine of product, price, untapped customers, and channel information. Marketers must understand a consumer’s segment behavior in order to differentiate their products and appeal consumers because of competition and the exposed thousands of messages. The only way to analyze behaviors, desires and wants is through integrated data asset which stores proper data and enables complicated examination. Many companies which invest in leveraged data and analytics to optimize marketing functions see extraordinary results when using an analytic CRM system.

An example of one whom uses analytic CRM would be Wal-Mart, in which the company collects hefty amounts of data about its products and shopper’s habits. The goal is to increase operational efficiency and maximize product sales. Wal-Mart has even used predictive modeling analytics on its data to determine top-selling items before a crisis, like a hurricane. In the case of the Florida hurricane, analysis revealed that that the most bought items stretched beyond expected items like flashlights, water. Beer was their top selling item while strawberry Pop-Tarts increased sevenfold during the crisis. Because of the analysis, Wal-Mart knew they needed to preorder and stock the optimal quantities of the above items during hurricane seasons. As a result of the broad and efficient use of data analytics, Wal-Mart has become the worlds leading retailer. Another example is when Best But mined data to determine that as many as twenty percent of its five hundred million annual customers were not profitable. Customers bought products, applied for rebates, returned merchandise, and then bought them back at returned merchandise discounts. These actions affected the company’s bottom line. Analytics gave Best Buy insight to remove these customers from mailing lists and scale back promotions which attracted them to stores. To make robust annalistic work for your company, like companies across all industries, you must start with a plan.