Bad data have massive impact on demand creation, conversion

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In another time and context, a popular cautionary phrase was, “Lose lips sink ships.” In a marketing context, one could say, with equal confidence, “Damaged data dumps demand.”

Bad data can infiltrate databases in a variety of ways. Sales teams can update customer relationship management files without marketing taking notice; rental lists can be inaccurate or out of date; and fraudulent or duplicate survey respondents can make for badly skewed results and bad marketing decisions.

“Way too many times an organization will continue to replicate bad data,” said Jonathan Block, senior director-research at marketing advisory company SiriusDecisions. Block said that customer relationship management software can catch duplicate names and simple mistakes, but that “it’s all the other data around that contact that often continue to be dirty, which can mean that marketing doesn’t have a correct view of the prospect.”

According to Block, the bad data syndrome is endemic. He said between 10% and 25% of customer and prospect records include critical data errors, from incorrect demographic information to a lack of current buying disposition.

How exactly do bad data impact on-demand creation and, ultimately, conversion?

According to benchmarking statistics by SiriusDecisions, the effect is greater at the beginning of the marketing pipeline. Validating and managing data in the earliest stages of collection can lead to better lead scoring and lift conversion rates by about 25% between the customer inquiry stage and the point where marketing qualifies the leads.

Inappropriate marketing offers
A common problem with bad prospect information, Block said, is that marketing may continue to market to potential customers by sending offers intended for leads elsewhere in the pipeline.

The impact of bad data also affects the ability of sales to accept a lead as qualified, a process worsened by multiple databases that are not coordinated, Block said. Combining or integrated various databases well can contribute to a conversion increase of 12.5%.

In its analysis, SiriusDecisions examined the data cleansing practices of some 400 b-to-b organizations, ranging from $20 million in revenue to more than $1 billion.

By considering a sample prospect database of 100,000 names and a campaign response rate of 2%, the company estimated that an organization with a strong commitment to data quality can produce nearly 70% more revenue than a company with only average data-quality procedures.

“What we’ve seen over the past 18 months is that marketers increasingly are focusing on the quality of their data,” Block said. “As crazy as it sounds, most marketing organizations we work with feel they have enough leads and don’t feel it’s essential to stuff the top of the funnel. Instead, they’re focusing on quality now, and the data that leads to that.”

You said what?
Inaccurate survey results also contribute to bad data and can negatively impact product and marketing decisions. According to marketing research company MarketTools, companies that don’t use technology to validate survey respondents can as much as triple their risk of making incorrect business decisions.

“Some people want to fill out surveys for other reasons than offering their opinions and helping companies develop their products,” said Michael Conklin, chief methodologist at MarketTools. “Sometimes people might discover that if they say something really nice about a product every time they’re asked, they stand a better chance of having someone send it to them.”

Conklin said the online survey market is growing fast; more than 1 billion online business surveys were completed in 2007, with a global investment of more than $4.3 billion in 2008. But the dangers of bad data from such instruments can have far-reaching consequences.

A MarketTools’ study, conducted online last summer with 622 resondents, “What Impact Do ‘Bad Respondents’ Have on Business Decisions?” reveals that for a survey containing 30% bad respondents—those who are not real (who they purport to be), engaged (interested in really sharing an opinion), and unique (not signed up multiple times to take the same survey)—the risk of making a bad decision doubles. With 40% bad respondents, the risk can triple.

Moreover, the larger the survey sample, the higher the risk. The study revealed that when the number of survey takers is increased significantly—from 600 to 6,000, for example—it takes only 10% bad respondents to double the risk of making an inaccurate product or marketing decision.

MarketTools offers a survey cleansing tool called TrueSample; the company says to date the tool has been used to remove an average of 29% of survey participants among its clients.

Getting it right
Poor survey construction also can lead to bad data, and poor decision-making, according to Arthur Middleton Hughes, VP of the Database Marketing Institute.

“Amateur in-house survey makers might ask the same question in several different ways in different surveys,” said Hughes, who also is a senior strategist with e-Dialog. “So when you come back to find out the results, each survey produces different answers, and you can’t combine the data over time.”

Hughes said that companies can fix the most glaring database errors easily, sometimes by just examining name fields for such errors as numbers or punctuation marks, or U.S. ZIP code fields for anything other than numbers.

“There are plenty of things you can clean up, but the one thing to remember about databases is that they always contain junk,” said Hughes. “You’ll never get it completely clean—ever, ever, ever. That’s something to remember when you do your next direct-marketing campaign.”

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