Database Marketing

What is Database Marketing?
•Database marketing is a systematic approach to the gathering, consolidation, and processing of consumer data (both for customers and potential customers) that is maintained in a company's databases.
•Database marketing is the analysis and use of customer databases to aid in the direct marketing of products.
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.

Applications of Database Marketing
In an effort to more effectively target potential customers, many enterprises use database marketing to build models of their target demographic group, track down these groups and focus their advertising budgets on them in the hope that it will result in an improved return on investment (ROI) from their advertising spend.

Sources of Data
At its most basic level, database marketing is the analysis of databases holding information about previous or potential customers. These databases usually consist of basic personal details of customers along with details of their past transactions. The information is either gathered from internal sales data or bought in from other organisations.

Business to Consumer (B2C)
Consumer information is gathered by enterprises in a number of ways, many of which consumers remain oblivious to. These methods can range from requesting that the consumer fill in and return a warranty card to running promotional contests and sweepstakes.

Ideally, enterprises prefer to gather as much information as possible about potential customers, so they will employ any available methods to milk consumers for personal data. The more information contained within a database, the more accurate the results of its analysis.

Business to Business (B2B)
B2B data is usually much more limited than consumer information, but it can also be easier to procure. Enterprises hoping to target businesses can simply get in touch personally or gather publicly available information about them. However, since B2B databases will usually only contain a few hundred or thousand pieces of information at most (compared to potentially millions of pieces in a consumer database) it is more difficult to build a targeted marketing plan.

Data Analysis
Once a consumer or business database has been compiled it can then be broken down and analysed to produce valuable marketing information. If the database is extremely limited this analysis can be performed manually, but most consumer databases will contain so much data that specialised software tools are necessary to generate useful results.

Predictive analytics software allows data analysers to construct high quality predictive models of customer behaviour. By studying the past purchases of consumers it can be possible to predict broad trends in their purchasing habits, resulting in a somewhat accurate prediction of their future purchasing (though, of course, it is impossible to make 100% accurate predictions in this area).

Using these trends it is possible to further refine the information by grouping individuals according to any other personal data held on file about them (such as income, age, gender, etc.). This grouping results in a targeted mailing list of potential customers, each of whom share a set of desired characteristics.

Marketing
Once the raw data has been analysed and a mailing list produced there is simply the matter of contacting the potential customers with targeted advertising.

Traditionally, database marketing results in the mailing of advertisements (what many people would call ‘junk mail’). The development of technology, however, has enabled enterprises to contact potential customers much more quickly than through the mail.

While a great many enterprises still use the postal service to generate leads, modern marketing methods also involve the use of e-mail and SMS messages to potential customers. As well as being less expensive than traditional mail shots, electronic messages come with the additional benefit that recipients can respond instantly, either by following a link in an email or opting-in through an SMS or asking for a callback.

Future of Database Marketing
The development of the Internet has offered enterprises a highly effective way to gather customer information. Internet users are now perfectly comfortable with completing electronic forms for everything from online purchasing to setting up e-mail accounts, so the amount of consumer information available has increased greatly.

At present, we are seeing the development of a new form of database advertising. Online advertisers now use surfing habits as a method of directing advertising towards Internet users. Search engines such as Google serve ads according to users' keyword searches, while vendors such as Amazon use details of previous transactions to build a list of user-targeted recommendations. We can expect this trend to continue until all online activities are tracked for marketing purposes.