In the quest to generate new leads, marketers proactively scour the landscape for the latest and greatest lists to support their direct marketing campaigns. Whether marketing directly to the consumer or to a B2B, the effectiveness of every campaign starts with the quality of the list.

In mining for prospects, it is easy to miss opportunities within house lists, which contain customers ripe for upselling or cross-selling, as well as models for prospects derived from the profile of current customers. 

Most organizations have already invested heavily in generating inbound inquiries and data capture through 1-800 numbers, advertising, direct mail, and the Web, and may have more information on their customers' profiles than they realize. It's information that could help identify fresh prospects.

If your company is like most these days, in-house resources may be stretched and it may not be feasible to have a full-time data specialist on staff to manage accumulated and incoming data feeds. Keeping house lists up to date, as well as analyzing and creating segments for targeted campaigns, becomes a challenge. This scenario often leads marketing departments to make significant investments in new lists. Sometimes this is the best answer, but not always. The time taken to carefully analyze current data can often lead to more intelligent purchases if there is indeed a need to purchase additional data.

The value of working with good, credible, outside data experts can not be overstated. Whether you chose an independent consultant or a data vendor, a careful analysis from a qualified outsider can bring a fresh perspective on the state of a company's data—before any additional lists are purchased.

First things first
By investing in services such as National Change of Address, Delivery Point Validation, deceased suppression processing, and basic merge and purge practices to clean up dusty house lists, a company can increase the accuracy of a mailing list and save significantly in postage, cut down on print overruns, and reduce sorting time, while taking advantage of the best postal rates available.

For example, a major national financial company wanted to reduce postal waste and the insensitivity of mailing to deceased individuals, so it ran deceased suppression processing against its existing lists. The company discovered that for every one million records processed, 22,700 names were deceased individuals (almost 2.3 percent). This represented a net postage and materials savings of $8,850 for just one mailing.

Data profiling and modeling
Data profiling integrated with data modeling can help a company discover challenges by highlighting inconsistencies in data, while providing a map to discover the key elements of a company's best customers. By carefully calculating statistics and metrics, a data quality expert can analyze a company's house list to create valuable customer profiles based on various segmentation criteria. These profiles can be used to generate a model by which highly targeted lists can be created, appended, and purchased. This streamlining of data decreases lead generation costs while helping to drive revenue.

Once profiles and models are revealed, companies can begin to append in-house lists, adding valuable demographic and contact information—including email addresses—in order to implement targeted marketing programs. Adding data points to existing contacts in the database also allows for fully integrated multichannel campaigns, which can help reach prospects on their terms, reduce waste, and increase ROI.

Another valuable benefit of having more detailed data appended to the in-house list is the ability to create highly personalized campaigns based on variables, such as purchasing habits, discretionary spending, wealth, gender, household income, presence of children, hobbies, donations, etc.  The emergence of cost-effective variable data printing has opened up the creative field beyond the old personalized mail merge. In print and digital mediums, variable imaging is as sophisticated and diverse as the data used to drive the campaign.

There are many reasons to consider mining your own in-house data for leads including getting a fresh look at demographic purchasing patterns, cost-effective maintenance, and preparation for intelligent data enhancement.

About the author: Marc Sabatini is senior vice president of RediData