Companies are turning to data and their insights to drive revenue and surface new opportunities. But that process requires heavy lifting and having data that you can trust. Poor data quality is one of the biggest problems in the B2B tech industry today—and often, by the time that issues come to fruition, revenue will already be lost. But just how pervasive is the problem?
Integrate, an outbound demand generation marketing software company, recently put this question to the test by analyzing the quality of the data provided in 778,585 lead forms. They found that on average, 40 percent of generated leads were poor quality in terms of incomplete and inaccurate information. And these findings are likely only scratching the surface of the problems that marketers are likely experiencing: remember that conversion funnels are long, winding, and complex. Low-quality leads at the top of your marketing funnel are like parasites: if you don’t give careful attention to your processes, you’ll risk wasting your organization’s valuable assets—time, brain-power, and financial resources.
So what steps can B2B marketers take to improve data quality? The key is to build a feedback loop back to analytics teams. With an ear to the ground, marketers are in the optimal position to deliver real-world perspectives, cross-referencing theory with reality. Three marketing analytics leaders weigh in with tips on ensuring data quality:
1. Narrow your data collection processes
If you’re exploring causes behind poor data quality, it’s often easy to blame the statistical processes behind the scenes: when modeling real world scenarios, analytics must handpick the techniques they’re using. Every nuance matters, and one wrong assumption may derail an entire measurement framework.
As Cheryl Max, Dun & Bradstreet’s market segment leader for chief data and analytics officers points out, however, is that the culprit is often something simpler—data collection practices at the ground level.
“I think a lot of what’s happened in the market and with marketers, and to no fault of ours, is this whole discussion of big data, and the fact that every company you work with has an API,” says Max. “There’s been a push from companies to collect absolutely everything that’s happening with every customer or prospect, at every touch point.”
If you want to improve precision of your analysis, you’ll need to determine what data is going to be relevant to help you answer your questions. Not all data is meaningful or relevant in solving a business problem.
“The key is to focus on insights and analysis that can help you make an informed decision,” says Max. “The key is to know the relevant pieces of information for making a decision. Know what to choose and what to disregard from the noise.”
2. Prioritize relationships in data
Customer behavior doesn’t exist in a vacuum. But often, data does. Companies will often look at information about their customers and prospects from a few limited perspectives—perhaps through their own first-party data or through the lens of a few third-party data licensing partners.
But in the real world, decision cycles are much more complex. And relationships—between people, ideas, and things—are the heart of a successful sales process.
“Having an in-depth understanding of many-to-many relationships across people, accounts, products, and places, plus keeping information about those relations current is now an important aspect of data quality,” says Ajay Khanna, VP of product marketing at Reltio, a company that creates data-driven applications.
The key is to look for relationships within accounts. Graph technology, a database structure that connects many different types of nodes, is presenting itself as an alternative to record-based customer relationship management (CRM). For instance, if you’re applying graph technology to an account-based marketing model, you can examine whether companies have previously done business with your company, were referred by existing customers, or work with common integration partners.
“Graph technology is, at its heart, a discovery tool,” explains Khanna. “It’s a surgical approach to marketing.”
This level of precision will allow you to prioritize your highest value customer and prospect relationships.
“We’re able to get a lot of signals from different data sources,” says Max. “And when we bring them together, we’re able to build out a clear trajectory for the company we’re analyzing.”
3. Shift the conversation to real-time
Over time, data ages: unless companies invest in updating their records regularly, information can become obsolete. But maintaining records is inefficient and costly—which is why many marketers are moving to a model of real-time targeting. And by building relationships with your prospects sooner rather than later, the more likely your company is to succeed in this effort.
“Companies often rely on longer-term targeting frameworks,” says Nicole Borneman, sr. director of advanced analytic services at D&B. “The term that most marketers are familiar with is predictive analytics, which describes the process of decision-making using historical data. Example use cases include propensity to buy models, which allow sales and marketing teams to prioritize their operations but can also take weeks or months to run. Moreover, these models are not always precise, as they do not emulate real-world market dynamics. These models are most effective for customers that make purchases at a steady, predictable pace. But what about prospects and customers that are outside of this group?”
That’s where the concept of anticipatory analytics enters the picture. It leapfrogs predictive analytics in that it enables companies to forecast future behaviors quicker than traditional predictive analytics by identifying change, acceleration and deceleration of market dynamics. Anticipatory analytics addresses businesses more challenging issues and places the onus on the business to take an action to reach a defined outcome.
“Anticipatory analytics allow for measurement beyond the traditional,” says Borneman. “Companies that find the most value in this process may be newcomers to the market, changing business direction, or growing at an accelerated pace. For instance, take a look at Uber. On the surface, they may look like everyone else. But all of a sudden, they’ll take off, and the need for technology surfaces as a result.”
Anticipatory analytics enable the responsiveness that sales and marketing teams need.
Final thoughts on data quality
When you’re looking to make improvements to your data quality, think in terms of trade-offs. Do you want to spend more time scrubbing leads, or do you want to close more deals, faster? Ensure that the data you are collecting is providing you with a trusted view of your customers and prospects. Think carefully about what data you have and what data you need to do the right type of analysis to answer your toughest business problems, and help your company achieve its growth plan.