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Does your company utilize database marketing? Are you wasting dollars, resources and time trying to clean up outdated, in-house databases and, at the same time, omitting the market segmentation techniques needed to generate profitable leads? Are your databases becoming "black holes" and final resting places for hundreds, and even thousands, of records characterized by sparse, inaccurate information? 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 Often, traditional database marketing cleanup programs only replace outdated data with fresh data, but do not use market segmentation to target "best names" that ensure profitable campaigns.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 PotentialThe mandate to clean up and use in-house databases is grounded in a rock-solid objective: leverage existing customer and prospect data to drive cross-selling, up-selling and new sales. But that's easier said than done, however, as these databases are often characterized by outdated, inaccurate or sparse basic "firmographic" data needed to support market segmentation testing.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:
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:
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:
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 DatabasesWithout 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 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 steps to achieving higher return through market segmentation include the following:
Assume, for example, that prior to investing the time and effort to clean up all of the databases, ABC software decides to see if customers that have maintenance contracts are better prospects for new sales. 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:
The three cubes of 200 names using "no maintenance" as a predictive variable would be:
Simple Market Segmentation Testing Can Lead To Dramatically Better Database Marketing PerformanceComparing marketing response rates for five equally-sized test cubes offers a dramatically different picture from traditional database marketing response rates. Let's assume 50 leads were generated as follows: The first test group of 200 maintenance customers and prospects resulted in 18 leads; maintenance customers only resulted in 14 leads; software license customers and prospects resulted in 10 leads; software license-only customers resulted in 6 leads and the prospect database test resulted in only 2 leads. 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. ConclusionThe market segmentation techniques described here balance the principles
of statistics with the realities of today’s marketing budgets.
They can predict the likely success of B2B database marketing programs,
helping eliminate wasted dollars, time and resources. Ultimately,
they deliver more profitable sales in a timely manner at a lower
cost. |
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