What’s So Bad About Bad Contact Data?

The tremendous volume of prospect and buyer contact data now available to B2B brands has opened the doors to new levels of audience segmentation, targeting and content personalization. It’s also released floodgates of frustration, poor results and missing metrics. Along with all that data comes increased risk of poor quality. Poor data quality only compounds any other issues B2B marketers must deal with in data-driven campaigns. 

Even in an era of sophisticated behavioral targeting, much of the conversation surrounding bad marketing data focuses on the most basic unit of customer data — contact information. Any number of things contribute to poor quality contact data. 

By its nature, contact information ages rapidly. Over time, a percentage of contacts in the average CRM become unreachable due to things like job changes or relocation. There’s also the degradation of interest over time, (they make a purchase or move on.)

Why Marketers Hate Incorrectly Tagged Contact Data

Tagging issues can seriously injure low-funnel campaigns down the road. A key issue is whether data is collected via IP or other location-based technologies, or if it’s gathered via device-based cookies. Once bad data comes into your systems, you’re already starting any campaigns from a poor position. 

Mobile users, particularly those who travel for their jobs (or work from anywhere these days) create confusing matching patterns. Is this person just working out of a coffee shop on the West Coast, or are they not actually an employee of the account you have them tagged for? When data is assigned to the wrong account or geography, you get a wildly inaccurate picture of the actual level of topic interest exhibited by accounts in your ABM universe. 

How to Overcome Bad Targeting Data

The sheer volume and complexity of data sources demand quality checks as you onboard data into your systems, prior to running any large-scale campaigns against it. Obvious anomalies in reported behavior patterns are the main red flags when onboarding behavioral data. 

For example, a company’s interest in a topic, as expressed in intent signal data, isn’t likely to explode from zero searches to 100,000 searches in just a month. Every email you send to an account is not going to be viewed. Look for the patterns that do exist and go from there. 

Run Periodic Tests Against Your Database

Obviously, monitoring and scrubbing contact data is essential for B2B marketing success. Aside from the basics of purging non-responsive emails and duplicate records, run periodic tests against your database for readily identifiable errors, such as location or company information that contradicts IP and other location tracking data. 

This maintenance is especially critical in account-based marketing (ABM), since the marketing team is building interest-level profiles for overall accounts, not individuals. ABM campaigns require third-party data such as intent signals, for a complete picture of prospects’ purchasing profiles. 

Be a Better Data-Driven Marketer

Of course, the obvious way to avoid bad data is to source your data from the most reputable suppliers. We know a thing or two about premium quality intent data, and we’re happy to share. Instead of trying to guess what’s on the minds of our prospects, or looking at the past to try and predict the future, we can tap into real data, based on real, current online activity. At our Spring Summit, we look at how AI helps drive more relevant, accurate buyer engagement. Maybe you should register?

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