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 services for marketing practical purposes. The method of communication can be any addressable medium, as in direct marketing .
The distinction between direct and database marketing stems primarily from paid attention 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 result, database marketers also tend to be heavy users of data warehouses , because they have a larger amount of data.
There are two main types of marketing databases, 1) Consumer databases, and 2) business databases. Consumer databases are primarily geared towards consumers, often abbreviated as [business-to-consumer] ( B2C ) or BtoC. Business marketing databases are often much more advanced in the information they can provide. This database is based on the same privacy laws as consumer databases.
The “database” is usually a name, address , and transaction history, or a compiled “list” from another organization, which has captured that information from its customers. Typical sources of compiled lists are charity donation forms, forms for applying Any free product or contest, product warranty cards, subscription forms, and Credit Application forms.
The communications generated by database marketing can 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 to a customer, who wants to be contacted about the customer, benefits both the customer and the marketer.
Some countries and some organizations insofar as they are able to do so.
Database marketing emerged in the 1980s as a new, improved form of direct marketing. During the periodical “list broking” was under pressure to modernize, because it was offline and tape-based, and because lists are limited to hold limited data.  At the time Sami, with new technologies Enabling customer responses to be Recorded, direct response marketing Was in the ascendancy, with the aim of opening up a two-way communication or dialogue with customers.
Robert D. “Bob” and Kate Kestnbaum were trailblazing pioneers of the new direct marketing, who were credited with developing new metrics including customer lifetime value , and applying financial modeling and econometrics to marketing strategies.  They founded Kestnbaum & Co, a consulting firm in 1967, including Robert Blattberg, Rick Courtheaux and Robert Shaw . Bob Kestnbaum was inducted into the DMA Hall of Fame in October 2002.
Kestnbaum collaborated with Shaw in the 1980s on several landmarks – for BT (20 million customers), BA (10 million) and Barclays (13 million). Shaw incorporated new features into the Kestnbaum approach, Including phone and field sales channel automation and contact strategy optimization, campaign management and co-ordination, resource marketing management , accountability marketing and marketing analytics . The designs of these systems have been widely copied and incorporated into CRM and MRM packages in the 1990s. 
The earliest recorded definition of Database Marketing was in 1988 in the book of the same name (Shaw and Stone 1988 Database Marketing):
- “Marketing is an interactive approach to marketing, which uses the following addressable marketing media and channels (such as email, phone and the sales force): to them by recording and keeping an electronic database of the customer, prospects and all contacts, to help improve all contacts and to ensure more realistic of all marketing. “
Growth and evolution
The growth of database marketing is driven by a number of environmental issues. Fletcher, Wheeler and Wright (1991)  classified into contention from oven hand categories:
- Changing role of direct marketing
- The move to relationship marketing for competitive advantage.
- The decline in the effectiveness of traditional media.
- The overcrowding and myopia of existing sales channels.
- Changing cost structures
- The decline in electronic processing costs.
- The increase in marketing costs.
- Changing technology
- The advent of new methods of shopping and paying.
- The development of economic methods for differentiating customer communication.
- Changing market conditions
- The desire to measure the impact of marketing efforts.
- The fragmentation of consumer and business markets.
Shaw and Stone (1988) noted that they go through evolutionary phases in their database marketing systems. They identify the four phases of database development as:
- mystery lists;
- buyer databases;
- coordinated customer communication; and
- integrated marketing.
Sources of data
Although organizations of any size can be used, it is particularly well-suited to companies with large numbers of customers. This is because of a large population that provides greater opportunities for finding a business segment. In smaller (and more homogeneous) databases, it will be difficult to justify on economic terms the investment required to differentiate messages. As a result, the database marketing has flourished in sectors, such as financial services, telecommunications, and retail, all of which have the ability to generate significant amounts of transaction data for millions of customers.
Database marketing applications can be divided logically between those marketing programs that reach existing customers.
More sophisticated marketers often build large 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 prospective customers, database marketers, and marketers looking for more information about customers and prospects as possible.For marketing 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 a given name, address, and telephone number, along with demographics, some provided by consumers, and others inferred by the data compiler. Companies can also acquire data directly through the use of sweepstakes, contests, on-line registrations, and other lead generation activities.
For many business-to-business ( B2B ) company marketers, the number of customers and prospects will be smaller than that of comparable business-to-consumer ( B2C ) companies. Also, their salespeople, agents, and dealers, and the number of transactions may be small. As a result, business-to-business marketers can have their data as their business-to-consumer marketers.
One other complication is that B2B marketers in targeting teams or “accounts” and not individuals can produce many contacts from a single organization. Direct marketing can be difficult. On the other hand it is the database for business-to-business marketers which often includes data on the business activity about the respective customer.
These data can be identified in the context of a large number of applications. software procurement, etc. Customers in Business-to-Business often want to be loyal since they need after-sales-service. This loyalty can be tracked by a database.
Sources of customer data often come from the sales force and from the service engineers. Increasingly, online interactions with customers are providing the best value for money.
For prospect data, businesses can purchase data from compilers of business data, and more information from their direct sales efforts, on-line sites, and specialty publications.
Analytics and modeling
Companies with large databases of customer information and data richness. 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 on sub segments 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 can 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 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 can be used to determine which data are retained. In the United States , there is 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 and other programs that enable consumers to suppress their telephones numbers from telemarketing .
While the idea of storing customer data is in place for the customer, it is possible to gain a comprehensive history of customer behavior on-screen while the business is transacting each individual, thus providing real-time business intelligence for the company. This ability enables what is called one-to-one marketing or personalization .
Today’s Customer Relationship Management (CRM) systems use the Stored data not only for direct marketing Purposes aim to manage the full relationship with individual customer contacts and to Develop more customized product and services offerings. However, a combination of CRM, content management and business intelligence tools are made of personalized information.
Marketers trained in these tools are able to carry out customer nurturing, which is a tactic that attempts to communicate with each other in an organization at the right time, using the right information to meet identifying a problem, learning options available to resolve, selecting the right solution, and making the purchasing decision.
Because of the complexities of B2B marketing and the intricacies of corporate operations, the demands placed on any market organization can be brought to bear on the existence of a business. It is often for this reason that large marketing organizations engage the use of an expert in marketing process strategy and information technology (IT), or a marketing IT process strategist. Although more often than not, these marketers require a system integrator(SI) that can also be used to market IT process strategists, particularly at the time when new technology tools need to be configured and rolled out.
LinkedIn asserted in its Digital Marketing Research module that companies can now tap into external databases for competitor research. The significance of this development is that it can be used to monitor customer sentiment and activity based on a competitor’s website, thereby accelerating the rate of participation.
Challenges and limitations
While real-time business intelligenceis a reality for select companies, it is dependent on the percentage of the business that is online, and the degree of sophistication of the software. Technology companies like Google, Dell, and Apple are capitalized to capitalize on such intelligence. For other companies, more traditional methods still apply, either to maintain communication with an existing customer base (retention), or to build new acquisitions. A major challenge for databases is the reality of obsolescence – including the lag time when the database is used. This problem can be addressed by online methods and offline methods. An alternative approach is real-timeproximity marketing for acquisition purposes.
- Sample of Leads Generation
- Customer Relationship Management
- Lifetime value
- Marketing Resource Management
- Direct Marketing
- Drip Marketing
- Jump up^ Stone, B (1997) Successful Direct Marketing Methods, NTC Business Books
- Jump up^ Direct News-line Nov 18, 2002
- Jump up^ Shaw, R. and Stone, Mr. Database Marketing. New York: John Wiley & Sons, 1988.
- Jump up^ Fletcher, Keith, and Colin Wheeler, and Julia Wright. “Success in Database Marketing: Some Critical Factors.” Marketing Intelligence & Planning 10 (1992): 18-23.
- Jump up^ Van den Poel Dirk (2003), “Predicting Mail-Order Repeat Buying: Which Matter Variables? “,Tijdschrift voor Economies & Management, 48 (3), 371-403.