How deduplication issues are a data quality problem

In the second post of this blog series, designed to complement The Trailblazer’s guide to marketing, sales, and RevOps excellence, we discussed how improving your account data quality can resolve many analysis issues and significantly enhance your ability to identify and engage with your ideal customer profile (ICP). In this blog, we dive into enhancing your data quality by managing duplicate accounts and records. This blog series and ebook provide a comprehensive roadmap to optimizing your RevOps data quality strategy. By addressing common pitfalls and implementing robust deduplication processes, you can ensure your data is accurate, actionable, and primed to drive your sales and marketing efforts to new heights. 

Enhancing and managing duplicate accounts in CRMs and marketing automation platforms with data quality

So, maybe it’s not your processes that are a problem; it’s your data quality.

Duplicates are a frequent problem in CRMs and marketing automation platforms.

Duplicate accounts can cause multiple sales reps to go after the same company, sometimes conflicting with each other. They can also cause a lack of insight into activity at that account, because different leads belong to different versions of what should be the same account.

Addressing duplicate person records in RevOps

Duplicate person records can cause multiple sales reps to reach out to the same lead, causing confusion to the person and, honestly, making the company reaching out look like it doesn’t know what it’s doing. It can also mean that activity isn’t appropriately attributed, because some activities go to one record and some to another, and, possibly, neither of those records has enough activity to get flagged for a follow-up. 

Overcoming inadequate deduplication processes

While most companies with large databases have some kind of deduplication process, many of them need to be more comprehensive. For example, if deduplication is based on email addresses only for personal records, there could be an issue like this:

To an individual, these might obviously be duplicates, but with a simplistic deduplication process, the typo in record 2 and the abbreviated name in record 3 go undetected. A better system is needed.

Detailed record analysis for improved data quality

If we look at more of the detail on those records, we can see the following:

Cleaning and standardization: a crucial step in RevOps data quality

If we start with basic cleaning and standardization before deduplication, we have three records that look like this, with cleaned, standardized, or inferred information is bolded:

Now an email address-based deduplication will match records 1 and 2. A match on first name, last name, company, state, and country will deduplicate record 3.

The necessity of multi-level deduplication processes

There are, of course, much more complex examples we could use to detect less-common but still prevalent duplication issues and edge cases, but even from this simple demonstration, it’s easy to see how a multi-level deduplication process that is preceded by cleaning and standardization is much more comprehensive.

RevOps success through enhanced data quality

All these deduplication issues can be solved, just with the improvement in data quality.

Ready to learn more about improving your data quality? Download The GTM guide to data quality to ensure your data is prepped for your RevTech systems, or watch the Openprise Master Class on Deduplication: the unwanted data doppelgänger.

If you’re ready to take the next step, schedule a demo today!

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