Data Quality Best Practices Boost Revenue by 66 Percent

If you've ever been challenged to prove the return on investment of a data quality initiative, research firm SiriusDecisions may have a resource for you. In the company's recent research brief, "The Impact of Bad Data on Demand Creation," Sirius shows that following best practices in data quality led directly to a 66 percent increase in revenue. In today's economic climate, that's nothing to sneeze at.

The level of dirty data is what separates typical B2B organizations from those that follow best practices. Even at process-optimized companies, 10 percent of customer and prospect records contain critical data errors, according to Sirius. These errors can include incorrect demographic data, out-of-date disposition, and other basic flaws. At the average company, however -- one that fails to follow best practices for data management -- the data-error rate can balloon to as high as 25 percent. In either case, the amount of data doubles every 12 months to 18 months, resulting in a comparable rise in overall data-cleansing costs -- while potential revenue from those flawed records is never realized. This is summarized by what the Sirius report calls the "1-10-100" rule: "It takes $1 to verify a record as it is 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."

"Despite its criticality to the business, the databases of B2B organizations are akin to an attic, filled with contents that have not been properly labeled, managed, and maintained," writes Jonathan Block, senior director of research at SiriusDecisions and author of the report. "Most B2B marketing executives lament the status of their databases, but have had a difficult time convincing senior management of the need not just to temporarily clean things up but to permanently change the manner in which data is treated."

There are several key areas where superior data management can have discrete benefits, according to the report. These follow the SiriusDecisions Demand Creation Waterfall methodology:

• From inquiry to marketing-qualified lead: It's most cost-effective to manage data at this early stage, rather than let flawed information seep through the organization. A data strategy that solves conflicts at the source can lead to a 25 percent increase in converting inquiries to marketing-qualified leads.
• From marketing-qualified lead to sales-accepted lead: Bad source data is compounded by the use of multiple databases and formats, leading to distrust of marketing's work by sales. Unifying the data, whether into one database or by using technology for virtual integration, can lead to a 12.5 percent uplift in conversion rates to the next stage.
• From sales-accepted lead to sales-qualified lead: Scoring becomes important at this stage, as the sales team goes to work on the leads it can use -- and returns others to the marketing team for further nurturing. Clean data can reduce by 5 percent the time spent conducting the kind of additional research that precedes initial contact with a prospect.
• From sales-qualified lead to close: The benefits seen between sales qualification and close magnify those accumulated during the previous stages, as salespeople continually update the status and disposition of the potential customers. "Given that the average field-marketing function spends no more than 10 percent of its budget in support of this final conversion, accurate data is a must for applying the right tools and resources to the right audience at the right stage of the buying cycle," Block writes. A single system of record to keep marketing and sales on the same page -- cultivated by timely updates by all involved parties -- is critical.

The impact of these abstract concepts -- the true value of data management -- becomes quite clear as soon as real numbers are applied: From a prospect database of 100,000 names, an organization utilizing best practices will have 90,000 usable records versus a typical company's 75,000; at every stage thereafter, the strong company has a larger pool of prospects with a higher probability of closing. In the end, SiriusDecisions can show 66 percent more revenue for the company with high-quality data management.

"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," Block writes.

So forget the old argument of whether it's the sales department or the marketing department that's responsible for lead quality. "A best-in-class data strategy is shared by marketing and sales, and is focused on quality from [inquiry] to close," Block writes. "Although it is a job that consumes both money and time, paying more attention to data quality is not only worth it, it is something that your organization simply can't afford not to do."

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