Database Marketing

The single most important benefit of database marketing is the ability to target your marketing efforts, which means specific groups in your marketing database get specific messages that are important to them. You will focus your marketing pounds on customers that are most likely to buy and spend less on customers that are less likely to buy. The result is an increased return on your marketing investment.

1. Your Sports shop is going to have a sale on shoes. Rather than mail a postcard to the whole customer list, you mail the postcard only to customers who have purchased shoes in the past. The result of that targeting effort will reduce marketing costs and increase return on your investment.
2. You are about to introduce a new product line of very attractive blue widgets. In your database, you select two lists: people who have purchased your widgets in the past, and people who haven’t. You design a special introductory offer for past widget buyers and send it to all of them. Then you send a special ‘first time buyer’ offer to just part of that list of non-widget-buyers to see if your offer works. If it produces good results, you send the offer to the rest of the list. If not, you either save your money or design a new offer and test again.
3. A Telecoms company has a web site on which people can sign up to receive special offers. At the same time, people can indicate their areas of interest – telephones, broadband, etc. They can also indicate a preference for receiving offers by mail or email. At the beginning of a sales quarter, the telecoms company selects four different lists:
1. customers that have purchased telephone equipment or indicated an interest in telephone and prefer email offers
2. customers that have purchased Voice over IP equipment and prefer offers by mail
3. prospects that have indicated an interest in broadband and a preference for email
4. prospects that have indicated an interest in telephone line rental and a preference for mail

Four different marketing campaigns are designed, implemented, and the results are measured for effectiveness. Everyone has received offers that interest them (camping) using the delivery method (mail or email, see email marketing) they prefer. There is no “junk mail” in this effort, and marketing pounds have been utilised very efficiently. The chances for a profitable marketing campaign have been maximised.

By analysing your marketing database, you determine that your best customers meet certain identifiable criteria, say married couples with children. On the mailing list sign up page on your web site, you ask people to indicate if they are married or have children, thereby identifying them as likely prospects to become good customers. Further, you rent a prospect mailing list and limit the selection to married couples with children as a way to target your prospecting efforts more accurately.

Although organisations 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 within a customised manner. In smaller (and more homogeneous) databases, it will be difficult to justify in 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.

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.

For many business-to-business marketers, the number of customers and prospects will be smaller than that of comparable business-to-consumer 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 organisation and determining which contact to communicate with through direct marketing may be difficult.

Sources of customer data often come from a sales force employed by the company. 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.

Direct marketing describes a collection of tactics and communication channels (direct mail, email, telemarketing, etc) that share a common attribute – the results can be measured. (“I mailed 1000 letters, 10 people responded, and I made 2 sales”). A common, if simplistic, definition of direct marketing is “marketing with measurable results.”

Database marketing organises a company’s customer and prospect data so that it can be used more effectively in a direct marketing effort. It is a way of organising the whole marketing process. Database marketing allows you to choose what to market to whom - and when, based on the sum total of your knowledge and experience with a customer or prospect. Then, assuming the use of good direct marketing techniques, you can measure the effectiveness of the effort.

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.

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. See also Key Performance Indicators.

The most successful marketers on the Internet today are database marketers. Dell is a great example.

• Send targeted promotions to any segment of their customer and prospect lists
• Measure the value of their individual customers
• Track promotional efforts, measure responses, purchases, and the return on investment for every dollar spent on their promotional efforts.

Database marketing has long been in the domain of the FTSE100 –companies that spend millions of pounds on their marketing efforts. It is now in the domain of smaller organisations with smaller budgets!

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.