4 Steps to Tackle Data Quality and Velocity Issues

Tackle Data

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Posted: February 2, 2015 | Marketing Automation

Let’s face facts—marketers have become complacent with poor-quality prospect and customer data. According to a SiriusDecisions study, 25% of the average B2B marketer’s database is inaccurate and 60% of companies have an overall data health that’s “unreliable.” Because it’s the norm, we’ve created many manual workarounds as temporary fixes for a growing problem — a problem that’s costs us precious money, time and performance.

This complacency has many negative consequences. Satisfaction with the status quo hinders progress, and with ever-growing revenue goals, every marketer is looking for ways to improve their performance and the customer experience. So why are we so complacent when it comes to the data that drives our marketing efforts? And, what can we do to improve our data quality and therefore our results?

It’s no secret that marketers are investing heavily in technology to boost the ability to engage and delight customers. But though we’re well into the marketing automation movement that depends on the smooth and consistent flow of prospect and customer data, we continue to downplay, or even ignore, the data quality issues that can plague our systems.

This is a problem we need to address.

For example, my team at Integrate recently examined its customers’ lead generation efforts and found that on average of nearly 40% of lead data was identified as poor quality (report to be published in January 2015).

When unchecked, quality issues have two major ramifications on marketing and automation effectiveness:

  1. Wasted usage and diminished systems ROI: Engagement marketing and lead nurturing tracks depend on imported lead data being accurate. If a lead contains an invalid email address, it inhibits our ability to deliver the our full value to engaged prospects. If the data contains invalid values (e.g., job title, purchasing timeline, etc.), it’s just wasting space—or worse yet, it effects the potential customer experience because they may be placed into the wrong track.
  1. Decreased lead velocity and impaired relations with sales: Most marketing organizations are more diligent than simply allowing poor data to pass through the cracks and enter their marketing automation and CRM systems (though some inevitably slips through). Instead, they have individuals comb through lead files to scrub the data before it’s injected (of course, this usually doesn’t help with email validation). What is problematic with this process is that prospects are often more than a week old before they’re re-engaged by a nurture campaign or sales rep. By that time, prospect interest has cooled and conversion rates through the customer acquisition funnel suffer. This obviously strains the sales/marketing relationship.

These are significant consequences, but because marketers are moving fast and looking to the horizon, their intentions to prevent these consequences are typically cast aside and it’s back to business as usual. Fixing data quality issues once and for all doesn’t have to be an Everest-like trek.

So, what steps can we take to address data quality and velocity issues?

1. Understand where and how data sources converge

This will help you identify the best place/time to implement data governance procedures. The best way to do this is to take inventory of all marketing-related systems/applications as well as the processes in place to leverage them. Then, pinpoint the tactical stages in the data flow where verification and cleansing will provide optimal efficiency and effectiveness.

2. Implement a mindset of standardization

Data communicated in multiple formats prevents quick and easy analysis. Think of the difficulty involved with following a recipe that uses the metric systems when you only have standard measuring cups—it requires a lot of extra calculation and slows everything down. Ensuring prospect data conforms to a standardized format also enables the next step…

3. Integrate systems

This will help you capture, refine, leverage and analyze customer/prospect data. Most marketers have integrations between their marketing automation and CRM systems, but these technologies are often still disconnected from lead sources that are distinct from a company’s website or email account (for example, third-party media partners, events, ecommerce sites, content syndication channels, call centers, etc.). By ensuring a smooth, automated flow of data all the way up the funnel to capture leads prevents bottlenecks that slow velocity and allow prospect interest to wane.

4. Automate data verification and cleansing

Of course, if you directly inject data from lead capture straight into a nurture track, you bypass the crucial step of checking the data quality. Adopting and implementing software and processes that automate lead verification and cleansing at the point of captured-data convergence (identified in step 1) is the best way to ensure clean data is imported into your marketing automation and CRM systems in a timely manner.

High-performing marketers are implementing these steps to face the data quality hurdle head on, and early on—before it affects the ever-important customer experience. And for their efforts, they’re seeing greater conversion rates, increased marketing automation ROI and greater marketing-attributed revenue.

Technology has helped marketers personalize, and track efforts like never before, leading to higher customer engagement. But if we continue to generate bad lead data or spend days refining it before feeding it into our marketing and sales systems, we’re hampering our ability to achieve our core goal — creating delighted customers.

Scott Vaughan is CMO of Integrate, a cloud-based, closed-loop marketing software provider. Scott leads the company's go-to-market and marketing strategy focused on serving its growing customer base of marketers. Scott’s experience and passion is focused on unlocking the potential of marketing, media and technology to drive business and customer value.

Read Scott's Blogs

How do you power your #Marketing engagement engine? According to @ScottAVaughan you should do it with clean data:

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