Email Data Quality: Compliant, Correct and Complete

Data is a vital part of our industry and it is incredibly important that marketers have at their disposal the accurate data they need and can use.

In this report, written in collaboration with Validity, we will dive into how organisations measure the quality of their data as well as current data collection and management practices.

Data reveals that most organisations (62%) know how important data quality is for them and 67% believe they are above average when it comes to best practice. But how do they get to such a conclusion?

We asked businesses how they measure the quality of their data and we found that just 61% have clear KPIs and even then, the most popular metrics show a tendency towards choosing ‘good news’ measures engagement rates (40%) and sales/revenue (37%).

So how can organisations improve something they aren’t even aware of in the first place?

As Guy Hanson, Vice President of Customer Engagement at Validity, said “all-too-often, data quality represents a classic case of not knowing what you don’t know, which is why the findings in this report are so important. The results surface what’s important when it comes to the “3Cs” of data quality: compliance, correctness, and completeness. Providing a benchmark of where you and your organisation are on your data quality journey, as well as flagging up opportunities for improvement”.

There is also a clear opportunity for many businesses to do more to ensure the correctness of customer data at the moment of its collection – 5% of the marketers’ organisation have no verification process in place at the moment. For instance, having a customer data platform (CDP) in place to store customer data, emerged as a key indicator of organisations having a more developed approach.

Download our report and learn more about the key practices, pitfalls and opportunities organisations have in terms of data quality, as well as unique insight into how and where they could improve in both the short-and long-term.

Email Data Quality: Compliant, Correct and Complete