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Database marketing | ||
Database marketing is a form of direct marketing using databases of customers or potential customers to generate personalized communications in order to promote a product or service for marketing purposes. The method of communication can be any addressable medium, as in direct marketing. The distinction between direct and database marketing stems primarily from the attention paid to the analysis of data. Database marketing emphasizes the use of statistical techniques to develop models of customer behavior, which are then used to select customers for communications. As a consequence, database marketers also tend to be heavy users of data warehouses, because having a greater amount of data about customers increases the likelihood that a more accurate model can be built. The "database" is usually name, address, and transaction history details from internal sales or delivery systems, or a bought-in compiled "list" from another organization, which has captured that information from its customers. Typical sources of compiled lists are charity donation forms, application forms for any free product or contest, product warranty cards, subscription forms, and credit application forms. The communications generated by database marketing may be described as junk mail or spam, if it is unwanted by the addressee. Direct and database marketing organizations, on the other hand, argue that a targeted letter or e-mail to a customer, who wants to be contacted about offerings that may interest the customer, benefits both the customer and the marketer. Some countries and some organizations insist that individuals are able to prevent entry to or delete their name and address details from database marketing lists. Analytics and modelingCompanies 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, in which customers are placed into subsegments based on the recency, frequency, and monetary value of past purchases. Van den Poel (2003) 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 rank orders customers on their likelihood to respond to a promotion. Commonly employed statistical techniques for such models include logistic regression and neural networks. Laws and regulationsAs database marketing has grown, it has come under increased scrutiny from privacy advocates and government regulators. For instance, the European Commission has established a set of data protection rules that determine what uses can be made of customer data and how consumers can influence what data are retained. In the United States, there are a variety of state and federal laws, including the Fair Credit Reporting Act, or FCRA, (which regulates the gathering and use of credit data), the Health Insurance Portability and Accountability Act (HIPAA) (which regulates the gathering and use of consumer health data), and various programs that enable consumers to suppress their telephones numbers from telemarketing. EvolutionWhile the idea of storing customer data in electronic formats in order to use them for database-marketing purposes has been around for decades the computer systems available today make it possible to have the complete history of a client on-screen the moment he or she calls. Today´s Customer Relationship Management systems use the stored data not only for direct marketing purposes but to manage the complete relationship with this particular customer and to further develop the range of products and services offered. Copyright 2008 - France BtoB from Wikipédia
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• Attribution theory
• Advantages and Disadvantages of Direct M&hellip • Generic brand • Consumer behaviour • Marketing research and analysis • Positioning • Penetration pricing | |
