Just before Christmas a interesting survey was released by SeriousDecision, a source for business-to-business sales and marketing best practice research and data.
They really confirms some of my views on the importance of good data quality in the sales process.
The cost of poor data is not taken serious at the senior management
“Most b-to-b marketing executives lament the status of their databases, but have difficulty convincing senior management of the gravity of the problem,” notes Jonathan Block, SiriusDecisions senior director of research . Mr. Block continues, “The longer incorrect records remain in a database, the greater the financial impact. This point is illustrated by the 1-10-100 rule: It takes $1 to verify a record as it’s entered, $10 to cleanse and de-dupe it and $100 if nothing is done, as the ramifications of the mistakes are felt over and over again.”
The problem is tremendous.
“Focusing on b-to-b sales and marketing best practices, the firm has found that from 10 to 25 percent of b-to-b marketing database contacts contain critical errors — ranging from incorrect demographic data to lack of information concerning current status in the buying cycle.”
By the way, the tests we have done in the Nordics show the there is from 5% to 35% errors in the databases, the average is 16,7% errors.
Ongoing cleansing is more important than the one time approach.
“Organizations must shift their focus from one-time data cleansing to ongoing data maintenance to turn the tide,” says Mr. Block. “The good news is that we’re seeing a strategic shift in approach in strong organizations, from one of data cleansing (a project with a set completion date) to data maintenance (ongoing policies and procedures to maintain data quality). The fundamental trouble with one-time data cleansing is that the day the project ends, the data is the cleanest it will be until the next round of contacts is added to the database.”
The upside is huge
SiriusDecisions also estimates that organizations with an early-phase data strategy can expect a roughly 25 percent uplift in conversion rates between the inquiry and marketing qualified lead stages.
Using an example of a prospect database of 100,000 names at the outset and a constant campaign response rate of two percent, a strong organization will realize nearly 70 percent more revenue than an average organization purely based on data quality. For those marketing executives having problems convincing senior management that a permanent process upgrade rather than ‘quick fix’ will pay big dividends in the long run, this is the kind of eye-opening statistic that should prove invaluable.”
It is interesting to see these kind of estimates, since making easy ROI calculation for Data Quality Projects is difficult.