Database marketing

Background

Database marketing emerged in the 1980s as a new, improved form of direct marketing. During the period traditional "list broking" was under pressure to modernise, because it was offline and tape-based, and because lists tended to hold limited data. At the same time, 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 modelling and econometrics to marketing strategies. They founded Kestnbaum & Co, a consulting firm in 1967, and this was the training ground for many of database marketing's leading thinkers, 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 landmark online marketing database developments - for BT (20 million customers), BA (10 million) and Barclays (13 million). Shaw incorporated new features into the Kestnbaum approach, including telephone and field sales channel automation, contact strategy optimisation, campaign management and co-ordination, marketing resource management, marketing accountability and marketing analytics. The designs of these systems have been widely copied subsequently and incorporated into CRM and MRM packages in the 1990s and later.

The earliest recorded definition of Database Marketing was in 1988 in the book of the same name (Shaw and Stone 1988 Database Marketing):

"Database Marketing is an interactive approach to marketing, which uses the individually addressable marketing media and channels (such as mail, telephone and the sales force): to extend help to a company's target audience; to stimulate their demand; and to stay close to them by recording and keeping an electronic database memory of the customer, prospect and all commercial contacts, to help improve all future contacts and to ensure more realistic of all marketing."

Growth and evolution of database marketing

The growth of database marketing is driven by a number of environmental issues. Fletcher, Wheeler and Wright (1991) classified these issues into four main categories:

1. Changing role of direct marketing
2. The move to relationship marketing for competitive advantage.
3. The decline in the effectiveness of traditional media.
4. The overcrowding and myopia of existing sales channels.
5. Changing cost structures
6. The decline in electronic processing costs.
7. The increase in marketing costs.
8. Changing technology
9. The advent of new methods of shopping and paying.
10. The development of economical methods for differentiating customer communication.
11. Changing market conditions
12. The desire to measure the impact of marketing efforts.
13. The fragmentation of consumer and business markets.

Shaw and Stone (1988) noted that companies go through evolutionary phases in the developing their database marketing systems. They identify the four phases of database development as:

1. mystery lists;
2. buyer databases;
3. coordinated customer communication; and
4. integrated marketing.

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 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 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 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.

Advances in database marketing

While the idea of storing customer data in electronic formats to use them for database-marketing purposes has been around for decades, the computer systems available today make it possible to gain a comprehensive history of client behavior on-screen while the business is transacting with each individual, producing thus 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 but to manage the complete relationship with individual customer contacts and to develop more customized product and service offerings. However, a combination of CRM, content management and business intelligence tools are making delivery of personalized information a reality.

Marketers trained in the use of these tools are able to carry out customer nurturing, which is a tactic that attempts to communicate with each individual in an organization at the right time, using the right information to meet that client's need to progress through the process of identifying a problem, learning options available to resolve it, 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 marketing organization to formulate the business process by which such a sophisticated series of procedures may be brought into existence are significant. 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 technical in nature than often marketers require, a system integrator (SI) can also play an equivalent role to the marketing IT process strategist, particularly at the time that new technology tools need to be configured and rolled out.

Challenges and limitation of database marketing

While real-time business intelligence is a reality for select companies, it remains elusive to many as it is dependent on these premises: the percentage of the business that is online, and the degree of level of sophistication of the software. Technology companies like Google, Dell, and Apple are best positioned to capitalize on such intelligence. For other companies, more traditional methods still apply, either to maintain communication with an existing customer base (retention) or, as a more established growth driver, to build, acquire or rent new databases (acquisition). A major challenge for databases is the reality of obsolescence - including the lag time between when data was acquired and when the database is used. This problem can be addressed by online and offline means including traditional methods. An alternative approach is real-time proximity marketing for acquisition purposes.