Database Prospecting Solutions: Performance at Any Price

InfoManagement Direct, February 2001

Steve Scruton

Relational database prospecting solutions now come in three basic sizes: small, medium and large – one of which almost certainly fits any direct marketer's size, budget and ambitions. The upshot is that marketers no longer have to wait until they become a top 10 credit card marketer or a leading direct telecommunications provider before they start using some of the same tools and enjoying some of the same advantages that benefit the bigger players. In fact, a good argument can be made that smaller marketers should not wait. Industrial-strength relational prospecting tools let organizations get to market faster with more finely targeted offers that garner much higher positive response rates than the alternative – which is to rely on data providers or mainframe (i.e., nonrelational) service bureaus. Getting these benefits early means marketers waste less money on inefficient approaches and start gaining knowledge sooner – knowledge not only in how relational tools work, but knowledge about which of their offers and demographic strategies work best. That is experience they can leverage later as they move up the ladder to more robust solutions. Furthermore, that knowledge can work both ways. The marketer gains knowledge about the solution, and the solution provider gains knowledge about the marketer – assuming, of course, a single provider can offer the right size solution throughout the marketer's growth cycle. That reduces solution ramp-up time and increases the probability that the solutions offered actually fit the organization's needs.

Three sizes is actually an oversimplification. There are actually three categories of solutions: one for smaller, one for medium and one for more sophisticated, larger marketers. Within each category, however, there is a continuum of solutions which differ depending, again, on the specific needs of each marketer. They will differ according to basically three criteria: scope of functionality, robustness of data and flexibility (i.e., the more high-end a solution category, the more the solutions within that category can be tailored to fit specific situations).

The three categories can be defined as shared, hosted and integrated. Their most obvious difference is mechanical – representing three different ways to access applications running on a computer, for example. But those differences have implications beyond mere mechanics and include the organizational, operational and financial conduct of the marketer's business. Which solution is chosen also depends on the terms of the marketer's day-to-day relationship with the solution provider; for example, with respect to the type of support the marketer receives.

The Three Approaches Defined

Shared Model

In this approach, the marketer operates through a service bureau and needs not shoulder the entire expense for the associated software licenses – e.g., database management, campaign management and market analytics. These annual costs can range from $250,000 to $2 million. But, while shared models always employ service bureaus, not all service bureaus implement shared models. To qualify, a service bureau can't just be a pipeline to data providers. It must also offer marketers the following:

• Indirect access to genuine relational database prospecting applications.
• Scheduled data mart builds using direct feeds from data sources.
• Builds keyed on criteria relevant to identifying good prospects.
• On-demand record extracts from the data mart using marketer-supplied keys.
• Counts of records that would be extracted using marketer-supplied criteria.
• The ability to import, at least, some marketer-supplied data into the mart.

Typically, the service bureau does a "build" on a predefined basis – i.e., an extract, transformation and load (ETL) process – in order to pull data from the feeds and populate its own data mart according to criteria that it expects will satisfy its customer base of marketers. Examples of keys might be homeowners in the United States with a credit score above a specified number. Service bureau customers (i.e., marketers) can then contract to have the bureau's account service team (but not its own employees) do "counts" based on the marketer's more selective criteria such as households within a 12-state region. Once the marketer is satisfied with the count (i.e., there are enough qualifying households to launch a campaign), the marketer asks the service bureau to provide the corresponding records. As results of the campaign come in, the marketer may want to collect information that will help it more effectively target future campaigns – information, for example, about which creative concepts worked best or how many times a prospect was contacted in the last 30, 60 or 90 days. That information can be generated in the database at the time of extraction and defined to the service bureau as "creative" codes or "times mailed" codes which become additional keys to be used for generating counts and extracting records for future campaigns.

Hosted Model

In what might also be called the application service provider (ASP) approach, the marketer has desktop access to the applications as if they were running on the organization's own equipment. The marketer's personnel connect to these applications over the Internet — which means the marketer has to buy the software licenses. The marketer shares the cost of buying, integrating, maintaining and upgrading the equipment on which the software runs. Because the marketer does not rely on the service bureau's personnel as intermediaries to generate counts or extract records, the marketer can produce these counts and extracts faster, meaning they are also more likely to recognize market opportunities sooner. However, such as with the shared model, when it comes to responding to those opportunities, marketers must still defer to the solution provider's ETL cycle. They can't, for example, change build times in response to some market event that might happen to occur mid-cycle.

Integrated Model

This is the most functional, robust and flexible approach because the marketer takes responsibility for the application while the provider acts as a partner. It is also the one that takes longest to implement and is the most costly – both in terms of up-front investment and ongoing support. Once again, the role of the solution provider shifts – this time from ASP to systems integrator/solution provider. In the hands of a great integrator, everything becomes a potential ingredient for the mix – everything is customized and optimized to plan and execute effective campaigns.

System integration generally occurs in four phases over a four- to six-month period: definition, design, development and deployment. It is not necessary for the order of execution to be linear – e.g., you don't have to finish definition before starting design. The hallmark of a great integrator, in fact, is an ability to define work so that parts of each phase may occur in parallel, thereby speeding implementation.

Which to Choose

Which one of these models the marketer should choose will be discussed in Part 2 of this article (February 19, 2001 issue of DM Direct). It is important to recognize two things at the start: first, no one model will be right for every marketer; second, almost every marketer will be right for one of these models. Even in the shared approach, a marketer gains significant advantage over competitors that can't analyze the content of their data mart before putting that content to work – because there is no data mart. Moreover, the models themselves are not rigid – there are more or less robust examples of each. In the decision to adopt any of these models, the first step is the same: finding out what the needs are.

Steve Scruton has been senior vice president of sales and marketing since 1996 for MITI Prospecting Solutions, a company that is focused on providing the best and most effective multichannel prospecting and acquisition solutions in the industry. Scruton has worked in the data/database marketing industry for 11 years, concentrating on consumer data. He has more than eight years of credit-based experience, and has developed a strong background in credit card, retail banking, finance and insurance database marketing applications. He has developed large scale prospecting solutions for companies such as First Union, CENTEX, The Money Store, Wells Fargo and Nationwide Insurance.

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