Database marketing

From Wikipedia, the free encyclopedia

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.

Sources of data

Although organizations of any size can employ database marketing, it is particularly well-suited to companies with large numbers of customers. This is because a large population provides greater opportunity to find segments of customers or prospects that can be communicated with in a customized manner. In smaller (and more homogeneous) databases, it will be difficult to justify on economic terms the investment required to differentiate messages. As a result, database marketing has flourished in sectors, such as financial services, telecommunications, and retail, all of which have the ability to generate significant amounts transaction data for millions of customers.

Database marketing applications can be divided logically between those marketing programs that reach existing customers and those that are aimed at prospective customers.

Consumer data

In general, database marketers seek to have as much data available about customers and prospects as possible.

For marketing to existing customers, more sophisticated marketers often build elaborate databases of customer information. These may include a variety of data, including name and address, history of shopping and purchases, demographics, and the history of past communications to and from customers. For larger companies with millions of customers, such data warehouses can often be multiple terabytes in size.

Marketing to prospects relies extensively on third-party sources of data. In most developed countries, there are a number of providers of such data. Such data is usually restricted to name, address, and telephone, along with demographics, some supplied by consumers, and others inferred by the data compiler. Companies may also acquire prospect data directly through the use of sweepstakes, contests, on-line registrations, and other lead generation activities.

Business data

For many business-to-business marketers, the number of customers and prospects will be smaller than that of comparable business-to-consumer (B2C) companies. Also, their relationships with customers will often rely on intermediaries, such as salespeople, agents, and dealers, and the number of transactions per customer may be small. As a result, business-to-business marketers may not have as much data at their disposal. One other complication is that they may have many contacts for a single organization, and determining which contact to communicate with through direct marketing may be difficult. On the other hand the database of business-to-business marketers often include data on the business activity of the respective client that can be used to segment markets, e.g. special software packages for transport companies, for lawyers etc. Customers in Business-to-business environments often tend to be loyal since they need after-sales-service for their products and appreciate information on product upgrades and service offerings.

Sources of customer data often come from the sales force employed by the company and from the service engineers. Increasingly, online interactions with customers are providing b-to-b marketers with a lower cost source of customer information.

For prospect data, businesses can purchase data from compilers of business data, as well as gather information from their direct sales efforts, on-line sites, and specialty publications.

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, 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 regulations

As 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.

Evolution

While 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.

See also

• Customer Relationship Management
• Lifetime value

References

• Baesens Bart, Stijn Viaene, Dirk Van den Poel, Jan Vanthienen, and Guido Dedene (2002), “Bayesian Neural Network Learning for Repeat Purchase Modelling in Direct Marketing”, European Journal of Operational Research, 138 (1), 191-211.
• Hughes, Arthur M. (2000), Strategic Database Marketing: The Masterplan for Starting and Managing a Profitable Customer-Based Marketing Program, 2nd edition, McGraw-Hill, New York.
• David Shepard Associates (1999), The New Direct Marketing: How to Implement A Profit-Driven Database Marketing Strategy, 3rd edition, McGraw-Hill, New York.
• Hillstrom, Kevin (2006), Hillstrom's Database Marketing, Direct Academy
• Peppers, Don and Rogers, Martha (1996), The One to One Future (One to One), Current.
• Prinzie Anita, Dirk Van den Poel (2005), "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application", Expert Systems with Applications, 29 (3), 630-640.
• Tapp, Alan (1998), Principles of Direct and Database Marketing, Trans-Atlantic Publications.
• Van den Poel Dirk (2003), “Predicting Mail-Order Repeat Buying: Which Variables Matter?”, Tijdschrift voor Economie & Management, 48 (3), 371-403.

External links

• Multichannel Merchant - Database marketing articles
• DIRECT Magazine - CRM / Database
• Federal Trade Commission
• Multichannel Marketing UK - Database marketing article
• Medill IMC at Northwestern University
• Master of Marketing Analysis at Ghent University