Hi-tech marketers have always been the companies to push the proverbial envelope in terms of new methods of marketing. They were among the first to integrate Internet marketing into their campaigns. They also have led the way in product demonstration sales seminars and, in the arena of direct marketing, are taking databasing to the next level of performance.
What exactly is the next level? To appreciate how effective databasing has become, it's important to understand how far this marketing tool has come.
For the past two decades, databasing has grown in use and importance. Originally, marketers' incentives for building databases was simply to eliminate the need for multiple merge-purges on their mailing lists. They wanted customer names available in a just-in-time environment and wanted to lower their overall direct mail costs. However, in the business-to-business and, in particular, the hi-tech marketing world, mailers saw more sophisticated reasons to build better databases.
Hi-tech marketers wanted to use databasing to better select and predict which names on their mailing lists are most responsive to different types of offers. They wanted to be able to track and influence purchasing habits. This would let them achieve better results with names that previously had been unresponsive. Additionally, they saw databasing as a tool that could help them define their present market and target new markets. New products or marketing concepts could be tested and launched in
different geographic or market segments quickly, easily and cost-effectively.
Hi-tech marketers saw another reason to perfect databasing — to build customer loyalty and retention, reactivate existing customers and increase their buying activity.
New techniques. To optimize the use of databases for these many purposes, more sophisticated techniques were required. Primarily, there are two techniques used by hi-tech marketers to perfect their databasing results.
One is the use of overlays, whereby compiled data is overlaid to identify various demographics or prospects and enhance existing marketing data. For example, information such as credit files is overlaid over transactional data (i.e., last order information, product type, method of payment) and other ancillary data (e.g., surveys, input from sales force). From this, a clear picture emerges as to the characteristics of any given customer name. Those characteristics then can be better addressed via the direct marketing approach used (i.e., which level and cost category of products to promote, what type of offer/incentive should be made, what time of year the mailing should be conducted).
Modeling is the other technique being applied. It works especially well for hi-tech marketers who want to focus their marketing geographically or by classifications of business or other key demographics. Historically, hi-tech product manufacturers who couldn't get large quantities of names to produce good results from direct marketing are now able to effectively mail large volumes using modeling. There are many reputable service bureaus whose levels of statistical modeling have improved dramatically over the past five years.
Building the database. As with many sophisticated tools, databasing is a function of the old adage, “Garbage in. Garbage out.” In other words, to build an effective database the sources of information must be solid and varied. Hi-tech marketers should rely on:
* Their own proprietary customer data — transactional files, product purchased, money spent, purchase histories, sales reports, operational files.
* Information they purchase — overlay data, lifestyle information, census tracking data, presence of children, head of household.
* Information they can rent — outside lists, response lists, compiled lists.
It's important to scrutinize the source of any information purchased to ascertain that it is quality, up-to-date data. Similarly, when incorporating house files, they should be checked for the overall integrity of the data.
Strategic databasing. Those hi-tech marketers who get the maximum results from their databases are diligent in following a strategic plan. They set their goals up front, whether their objectives are new customer acquisition, trends analysis, market testing for a new product or entry into a new geographic territory. They focus their plans on key strategies, including the qualifying of sources for database participation, organizing their information, standardizing it and prioritizing.
Next, they establish an effective direct mail plan that reflects list research by target market, which identifies all potential lists; the usage history of each list; an assembly of list counts, descriptions, prices and selections; the evaluation and subsequent ranking of potential lists; and preparation of a mailing schedule for all qualified lists. Based on the aforementioned criteria and goals, a preliminary direct mail plan can be developed.
The final element in the strategic databasing equation includes effective communications and education of list owners and list managers, both of whom can sometimes be threatened by this form of marketing. In parting with their data, list owners can experience a loss of control, and list managers worry about a loss of revenue. However, high quality databasing will quell both of their fears. This includes:
* Effective trafficking between list managers and the list owners to assure on-time, accurate delivery of mailings.
* Custom-tailored reports that keep the hi-tech marketer informed.
* Sound accounting methods to verify counts and prices on invoices submitted by list managers/owners, comparison to merge-purge reports and timely payments to list owners.
* Constant analysis of the database to identify its strengths and weaknesses and capitalize on potential opportunities.
* Benchmarking to facilitate the capture of critical data to enhance the database for future direct mail programs.
Claire Carpenter is director brokerage at 21st Century Marketing Inc., Farmingdale, NY. Her e-mail address is [email protected].
