By Larry Fleischman, Practice Director, Branding and Go-to-Market Strategies, Televerde
According to SiriusDecisions, using an example of a prospect database comprised of 100,000 names at the outset and a campaign response rate of 2%, an organization with strong practices throughout its sales pipeline will realize nearly 70% more revenue than an average organization purely based on data quality. Also from Sirius, between 10% and 25% of B2B marketing database contacts contain critical errors. This leads to seriously sub-optimized demand creation program outcomes. Those are powerful metrics!
Turning Trash (Low Quality Data) Into Treasure (Enriched Data)
Yet so many organizations still don’t pay enough attention to the quality of their marketing contact database. Garbage in, garbage out as the cliché goes. Why data quality is still somewhat ignored (or more to the point – why poor quality data is accepted) is beyond me and my data colleagues at Televerde. But turning trash (low quality data) into treasure (enriched, complete, updated data) is also an opportunity – one that Televerde leverages for many of our clients who come to us admittedly with, ahem, “data issues.”
When our conversations with clients about how to optimize their demand creation programs turn to data quality, as they often do, the volume of their voice softens and their heads tilt in the other direction in embarrassment. The behavior has become predictable. In other cases, clients proudly reference the pristine state of their data. We acknowledge their enthusiasm for their own data and then offer to do a little test to see if it’s as good as they think it is. When they agree to the data bake-off, 9 times out of 10 our data quality surpasses their own.
In fact, we’ve tracked the results of these tests and have found that organizations that conservatively estimate their data quality as no more than 25% inaccurate (and oddly some of them are okay with this) more often than not have data inaccuracy issues of at least 35%. So in other words only 65% of the data used in their demand creation and nurture programs is working for them. It’s a case of colossal lost opportunity!
Poor Data Quality – Everyone Loses
When low quality data is admitted to, the problem is solvable. But when it’s ignored, or when it’s assumed to be in good shape and not stress-tested, this is where the problems start and, sadly, continue for way too long with a compounding impact on marketing program performance. The result is sub-optimized demand creation campaign performance, wasted budget, and poor ROI. Everyone loses – sales, marketing, the organization at large, not to mention the individuals who could be genuinely interested in your products and services but aren’t aware of them because your poor data quality stands in the way getting your messages to them.
I’ll share with you what we tell our clients in these situations, which is that admitting to the presence of a data quality problem, or that you’re just not sure if your data has issues and therefore want your data diagnosed, is the first step toward data quality recovery. And then it’s followed by our suggestion for a remedy: We have a data quality improvement methodology that works. While our secret data sauce is, well, somewhat of a secret, we can tell you that the sauce is created through an expansive network of data partnerships, algorithmic methodologies, data point appends and refreshes that occur as the result of literally thousands of conversations each day with decision-makers in highly targeted markets, and undivided attention to our database of more than 60 million records.
Data is the lifeblood of our business and so we treat our Exactus™ database with extreme care. And we advise our clients to do the same with their own database. But we recognize that not all companies have the resources and know-how to do it well. That’s okay, as long as they can acknowledge those weaknesses and commit to shore them up with external help when necessary.
Is Crowd-Sourced, Aggregated Data the Solution?
Many sales and marketing execs turn to a crowd-sourced, aggregated data platform as a quick and inexpensive solution to their data problems. This blog entry is not about bashing Jigsaw or other crowd-sourced data platforms, but in all candor we’ve tested it ourselves and it’s not what it’s cracked up to be. Try a little test yourself. Check the quality of crowd-sourced contact records for the executives at your own company. Check it carefully because the outcome will affect the success of your marketing programs. How much of it is inaccurate or missing? Is your own personal contact record up-to-date? We were originally fascinated by the crowd-sourced model. But we’re also skeptics about a lot of things and are believers in the old motto: “If your mother says she loves you, check it out!” So we did. And our tests have proved out time and time again that crowd-sourced data and aggregated databases more often than not have at least 35% inaccuracy rates.
So simply replacing your own inaccurate data with more inaccurate data from another provider is not the answer. The answer comes from confronting your data issues and dealing with them by looking for the right ways to resolve them (not the quick way, not the cheap way, but rather the right way despite how painful the process might be).
Your Next Steps for Contact Data Solutions
If you want to see the impact that good data (or conversely bad data) can have on the outcomes of your demand creation programs, we encourage you to use our Data Quality Impact Calculator and read the SiriusDecisions Research Brief about data on our web site.
How have you addressed your data quality issues? Have you experienced the benefits of data quality improvement initiatives?
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July 04, 2011 5:17 am
Its really a informative stuff. You wrote about the Impact of Bad Data. Which is so much valuable information. In this blog The Impact of Bad Data on Demand Creation Program Performance and Sales Revenue you wrote very clearly elaborately about this matter. and I hope we get the same things from you near future. I got many information from your blog post. Thanks a lot. Its great.
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