3 Reasons Why You Need to do a Data Health Check


Clean, accurate and up-to-date contact data is the lifeblood of every high-performing sales and marketing program. Quality data means better alignment to the right audience, higher quality leads, greater conversions and an overall positive impact in your ROI.

Gary Katz, Chief Strategy & Marketing Officer of Marketing Operations Partners, agrees. “High integrity data provides an essential foundation to successfully execute marketing strategy and reach the right audience with the right message at the right time,” he says.

But what about bad data?

“60% of companies have an overall data health scale of ‘unreliable’ and more than half of all leads in the average B2B contact database are obsolete.” Gary Katz, Chief Strategy & Marketing Officer of Marketing Operations Partners

And the economic impact is nothing to sneeze at.

While there are a lot of reasons why it pays to keep your data clean and accurate, here are three biggies.

#1 Improve Lead Scoring and Predictive Analytics

Everyone today is doing some form of lead scoring within their marketing automation platform. At the rudimentary level, lead scoring helps prioritize leads from companies that already exist in your CRM. It essentially helps you sort through what you know, and score them appropriately so they can be put in the right nurture track.

When it comes to the efficacy of the scoring, the more data points you can look at and score—from additional attributes ranging from professional social networks to buyer intent—the more accurate the score indicator is going to be.

The predictive analytics piece of this equation is more nuanced. Predictive analytics help companies identify the right accounts and contacts that they should be targeting. Predictive analytics takes first party data and combs through the web searching for b-to-b behavioral data, with the ultimate goal of unearthing organizations that are exhibiting behavioral signals related to your product or service.

If you’re looking at predictive analytics, the more data points the algorithm considers and correlates with a performance metric, the better.

But what happens if the lead scoring or predictive analytics data is bad? Ultimately, if you’re using outdated data or you’re missing a bunch of fields, the end result isn’t going to be nearly as effective.

Here’s an example: Let’s say a contact record showed that a prospect was director of marketing, but in actuality the prospect was really a marketing coordinator. That’s certainly going to mislead the model. Or what if you thought the company had 500 to 990 employees, but it was really 50 to 99 employees?

You can see how the impact of inaccurate or incomplete data can give you a false sense of security. A lead might get scored higher, when in fact it’s not a good lead. And because they may not dig down into the details, salespeople often take a score at face value without thinking about the underlying conditions that might affect the accuracy of the scores.

#2 Boost Dashboard Performance Metrics 

Removing bad or inaccurate data, making incomplete records whole, and cutting out misaligned contacts ensures that communications reach the ideal audience, increasing engagement metrics.

Many companies are including bad or inaccurate data within their marketing programs, and for the most part no harm is done. If bad data is used and the recipient isn’t aligned to the audience definition or the content isn’t applicable to them, the recipient most likely won’t engage. No harm, right?

A contributing factor to this involves inbound marketing. By its very nature, inbound is going to bring in people that may not align to your little square box that you’ve identified as your target audience, It’s never going to be 100% in sync.

But issues arise when marketers think that 90% of their database is aligned to their target audience, and they’re deploying campaigns and doing marketing against that—when in fact, our experiences reveal that, on average, only 40-55% of client’s contact data aligns to the audience definition.

Ultimately, when looking at response rates to campaigns and top of funnel activity, a lot of statistics within the funnel are being depressed because there’s a percentage of the audience that is thought to be appropriate, but isn’t.

But if you were to use a data health check and siphon out all the bad and inaccurate data and only focus your communications on people that met the criteria of your target audience, you’d see a significant lift in performance. 

#3 Enhance Persona-Driven Marketing

Marketers love putting their contacts into buckets. To effectively categorize contacts you need the right job title associated with the person along with additional social attributes that provide added insight into the person’s roles and responsibilities.

When the data is accurate and robust, you can better segment contacts into the right persona so you can map appropriate content and messaging to each step in the buying process. According to an Experian study, personalized emails generate over 6 times the revenue than non-personalized.

But it’s not limited to just higher engagement—it’s also about opt-outs. Think about it: if you’re a marketer and all you keep getting are emails on sales training but you’re marketing operations, it’s inevitable that you’ll opt out.

By sending irrelevant emails and pushing someone to opt-out, you’ve effectively cut the email channel to this person forever. This comes up a lot within enterprise-level companies that have multiple business units or solutions and the same contacts are being reached.

Let’s say one of the organization’s solutions may not be applicable to the prospect, but something else that the company sells is. If you’ve sent them the wrong message and they opt out, now they’re on the master opt out file unless your process empowers the user to remove themselves from specific products or services. What’s the cost to the business to not being able to communicate to that person via email again?

Ultimately, when your data is accurate and enriched with intelligent attributes such as social and buyer intent, you can better segment, personalize and target. The end result is higher engagement, lower opt-outs, and a more personalized and relevant experience.

Maximizing Growth

So how can you gauge the quality and completeness of your contact data? A data health check is a highly effective way to assess the state of your data.

And to truly optimize the cleansing process, think about using a consultant to set up message tracks and execute campaigns. Using inside knowledge of your data combined with the Data Health Check diagnostic can ensure your hygiene and enhancement investments are allocated for the highest return.

Overall, the quality of your data can either drive growth, or hinder it. As Katz sums up, “Effective marketing is a direct result of relevance, and from the perspective of the receiver, all starts with relevant data.” And one of the easiest ways to assess the current state of your contact data is by running a data health check.

If you’d like to learn more about performing a data health check on your data, feel free to reach out to me @brianhession. Or check out our no-cost health check.

The post 3 Reasons Why You Need to do a Data Health Check appeared first on RevEngine Marketing.

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