In today’s fast-paced marketplace, prospect and customer data is more critical than ever before. In order to be successful, the modern marketer must deliver high-value, personalized campaigns to their target audience– which is only accomplished with access to clean data and accurate insights.

Yet, maintaining a clean marketing database is easier said than done. Consider these statistics (source):

  • 40% of email users change their email address at least once every two years.
  • 15% of email users change their email address one or more times a year.
  • 18% of all phone numbers change every year.
  • 21% of all CEOs change every year.
  • 60% of people change job titles within their organizations each year.

As you can see, customer and prospect data decays rapidly. If you don’t monitor the health of your data, it won’t be long before dirty data contaminates your marketing efforts. In today’s blog post, we’ll take a close look at data cleanliness – why it’s important, how to measure it, and what modern marketers can do to keep their data clean. Let’s get into it!

The Importance of Clean Data

Bad marketing data may only seem like an inconvenience or minor annoyance– but it’s actually a bigger problem than most marketers realize. Consider just a few examples of how bad data ultimately hurts your marketing:

  • You target the wrong audience: Marketers create buyer personas – or profiles of their ideal buyers – to understand their customer base and target the right audience with relevant messages. A buyer persona is built using a variety of customer data. So, if your database is rife with errors and false information, your personas will be inaccurate.
  • You deepen misalignment with your sales department: Lead quality is one of the biggest roadblocks to sales and marketing alignment. In fact, 61% of B2B marketers send all leads directly to sales, but only 27% are qualified (source).

Many marketers use lead scoring to assess which leads are qualified to pass on to sales. But, if the data at your disposal is inaccurate, your lead scores will be unreliable and misleading. As sales reps waste time on bad leads, this will only further the disconnect between the two departments.

  • You annoy prospects and customers: Let’s say you reach out to a prospect in your contact database. The problem is, that sales rep was promoted to a different position about a year ago.

Addressing a prospect using their former job title is a quick way to make a bad impression– as is a misspelled name. Contaminated data can lead to frequent sales and marketing outreach mistakes.

Now that you understand why clean data is important, let’s get into the specifics – how do you measure data cleanliness?

How to Measure Data Quality

A data audit is a process of analyzing and assessing your contact database or CRM for quality and effectiveness. Auditing your database requires three primary steps:

  1. Analyze your data.
  2. Append your database to correct errors.
  3. Assess and improve your data collection methods to avoid future mistakes.

Since this blog post is about measuring data cleanliness, we’ll focus on the first step – analyzing your data. Data cleanliness may seem like a simple matter of accurate vs. inaccurate. But, there are many factors that contribute to the cleanliness and quality of your data.

When you perform an analysis of your marketing database, we recommend you use the following questions as a guide:

  • Is the data current? As we previously demonstrated, data decays quickly. Therefore, your first step is to assess whether your data is current and up-to-date.
  • Is the data correct? Both existing and incoming data should be valid in terms of information and presentation. For example, you likely have a number of leads that contain typos or false information. If these inaccuracies contaminate your database, they can cause significant problems later on.
  • Is the data consistent? Inconsistent data can impact your lead scoring, reporting, and so much more. Let’s say your database contains a prospect “Mark Smith” as “mark sMITH”. An automated follow-up email will then address the prospect with this spelling. See the problem? Avoid inconsistencies in your data by using data normalization to standardize important values in your database.
  • Is the data complete? You may enter a significant amount of leads into your database– but if they contain incomplete information, they will do more harm than good to your marketing strategy. When you analyze your database, identify any gaps and missing fields that you’ll need to fill.

As you can see, data analysis is a comprehensive process.

Measuring Marketing Data Cleanliness

The modern buyer has more power than ever, and they require personalized messaging to fit their exact needs and preferences. That’s why successful marketers rely on clean data to deliver consistent, highly-targeted campaigns.

Remember, your marketing strategy is only as good as the data you pair it with. Measuring data cleanliness can seem like a cumbersome task – but modern technology has made data maintenance easier than ever.

About the Author: Sam Holzman is a Content Marketing Specialist at ZoomInfo where he writes for their sales and marketing blog. ZoomInfo is a leading people information database that helps organizations accelerate growth and profitability.

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