Editing Database marketing (section)

== Analytics and modeling ==
Companies with large databases of customer information risk being "data rich and information poor." As a result, a considerable amount of attention is paid to the analysis of data. For instance, companies often segment their customers based on the analysis of differences in behavior, needs, or attitudes of their customers. A common method of behavioral segmentation is [[RFM (customer value)]], in which customers are placed into sub segments based on the recency, frequency, and monetary value of past purchases. Van den Poel (2003)<ref name="Poel">Van den Poel Dirk (2003), “[http://econpapers.repec.org/paper/rugrugwps/03_2F191.htm Predicting Mail-Order Repeat Buying: Which Variables Matter?]”, ''Tijdschrift voor Economies & Management'', 48 (3), 371-403.</ref> gives an overview of the predictive performance of a large class of variables typically used in database-marketing modeling.

They may also develop predictive models, which forecast the propensity of customers to behave in certain ways. For instance, marketers may build a model that ranks customers on their likelihood to respond to a promotion. Commonly employed statistical techniques for such models include [[logistic regression]] and [[Artificial neural network|neural networks]].

Other types of analysis include:
* '''Impact assessment''' helps a business to understand how actions taken by the business affected their [[customer behavior]], and also allow for some predictions of customer reaction to proposed changes.

* '''Customers as assets''' measures the [[lifetime value]] of the [[customer base]] and allows businesses to measure several factors such as the cost of acquisition and the [[Churn rate|rate of churn]].

* '''Cross-sell analysis''' identifies [[Product (business)|product]] and [[Service (economics)|service]] relationships to better understand which are the most popular product combinations. Any identified relationships can then be used to cross-sell and up-sell in the future.

* '''Critical lag''' allows a business to deliver specific customer communications based on an individual's purchase patterns, helping to increase loyalty and improve [[customer retention]].