How to Improve Your Database Marketing and Mine New Sales from Old Data

Companies doing database marketing often struggle with disparate customer, prospect and partner databases as they try to lever¬age existing lists to drive cross-selling, up-selling and new sales. Often, traditional database marketing cleanup programs only replace outdated data with fresh data, but do not use market segmentation to target "best names" that ensure profitable campaigns.

Is there a better, more effective alternative to either contacting all names in the databases or throwing out every name and starting over? Yes! By applying market segmentation techniques and testing segments or "cubes" as you undertake the database marketing cleanup effort, sales and marketing executives can determine which segments perform best. They can stop wasting time and resources cleaning up data that otherwise would perform poorly.

Using market segmentation techniques with our B2B clients, we have improved some individual database marketing campaign results by as much as 50 percent while decreasing costs by as much as 35 percent.

Traditional Database Marketing Cleanups Miss the Mark – and the Potential

But that's easier said than done, however, as these databases are often characterized by outdated, inaccurate or sparse basic "firmographic" data needed to support market segmentation testing. In addition, they usually lack sales opportunity information, including the following:

• Current "pain" or challenges at the company
• Current product environment (as it relates to the potential solution)
• Correct decision making team and buying process
• Plans for short- or mid-term purchases

Still, if you’re a seasoned marketing or sales professional, your gut tells you there is opportunity hidden in these databases. The question is, “How can I best clean them and mine them for value?" Conventional wisdom calls for running a Phase I database cleanup initiative, followed by a Phase II database marketing-driven lead generation program, setting in motion a "one-two punch" with high expectations for success.

However, all too often with this approach to database marketing return falls far short of potential. Campaigns that should do well don’t. Time, resources and dollars are wasted. Why? Traditional cleanup programs only focus on replacing dirty or absent data with fresh, correct data. They do not add the market segmentation or prioritization value needed to predict success. As a result, database marketing campaigns can only target all cleaned names, because best names have not been identified.

Let's assume that ABC Software has three older in-house databases that need to be scrubbed and then utilized in a new database marketing campaign. These databases are:

• A licensed customer database
• A maintenance customer database
• A prospect list purchased from a technology list vendor

ABC Software knows there are many opportunities for new sales, up-sells, cross sells and point sales in the three databases. Its plan of attack is to:

• Call and update "firmographic data" for companies in the databases
• Verify decision makers and contact information
• Identify current addressable problems and potential projects for ABC Software
• Segment the results by opportunity, timeframe and budget
• Distribute the hottest opportunities to field sales

Using a traditional database cleanup and database marketing approach, ABC Software cleans up one list at a time and runs a lead generation program into it. Predictably, less than ideal outcomes occur. While the cleaned database now has updated contact information, the lack of a strategic, targeted approach means the cleanup has added no market segmentation or prioritization value to the records.

ABC Software’s large investment of dollars and resources in the cleanup has failed to provide high-return direction or projected potential for database marketing. This assumes, of course, the cleanup initiative hasn’t broken the budget and left nothing for the marketing initiatives that follow.

Market Segmentation Can Unlock Secret Information in Older Databases

Without the market segmentation intelligence needed to hierarchically rank the valuable records that deserve to be contacted, a traditional database marketing program targets all cleaned names in the database, when only a fraction of the names warrant investment.

Let's fast forward and assume that after all the traditional cleanup and lead generation programs are completed, ABC has achieved the following lead rates from its three databases:

Software licenses -- 5% lead rate
Maintenance file -- 7% lead rate
Prospect list -- 3% lead rate

Hindsight is 20/20, but ABC generated less than optimal database marketing results because it failed to deploy dollars and resources against better performing segments. Without benchmarks, there are no market segmentation performance metrics and no wisdom to measure success and apply to future programs.

While traditional cleanup and database marketing initiatives take a freestanding "flat file" approach, there is a more efficient, cost-effective way to generate higher return. At PointClear, we use a market segmentation approach that links multiple customer and prospect databases in a relational manner. We call test segments "cubes."

The underlying assumption is that cubes can be tested with differentiating characteristics to determine the most valuable segments, and this market segmentation knowledge can be predicatively applied to generate higher return on future database marketing programs across larger files.

The steps to achieving higher return through market segmentation include the following:

• Identify discriminating characteristics among the databases and lists
• Segment the lists into small homogeneous cubes or layers of similar companies
• Conduct tests to profile and uncover opportunity in the cubes
• Analyze cubes to find high return segments and rank them as separate mini markets
• Use this intelligence to fully fund the right model for future programs

The company should group its 1,000 prospects from the three databases into five distinct relational cubes consisting of smaller samples (200 names) that test for whether or not "maintenance" is a predictive variable of database marketing success. Of course, ABC could use the same technique to see whether company size, SIC category, geography or other factors were important predictors of marketing success.

The two cubes of 200 names using maintenance as a predictive variable would be:

• Maintenance customers and prospects
• Maintenance customers only

• Software license customers and prospects
• License only customers
• Prospects only

Simple Market Segmentation Testing Can Lead To Dramatically Better Database Marketing Performance

The results are clear: ABC Software's cube of companies that are both maintenance customers and prospects is clearly an indicator of sales success. In fact, the first two samples resulted in 32 of 50 leads or 64% of all leads, and the first three samples resulted in 42 of 50 or 84% of all leads. More importantly, by focusing on the first three cubes, ABC Software could have generated 84% of all its leads with only 60% of the marketing spend. Now, that's greatly improved marketing ROI! This test now provides the market segmentation intelligence needed to fully fund and roll out database marketing programs specifically targeting high-return segments.

Furthermore, while comparative results from equally sized samples offer important decision making information for database marketing, this market segmentation cube model can be even more useful by predicatively weighting test segments. For example, any company name that appears in the maintenance customer and prospect cube appears in two databases. A company of this nature is likely to be a more highly qualified prospect, and as such, we should both increase the sample size of this cube in tests, and increase marketing focus on these "two-hit" companies in corresponding marketing activities. Conversely, we expect names appearing on a one-match list to generate a lower return than multiple-match names.

Conclusion

About the Author