How scoring issues are a data quality problem

In our final installment in the series, written in alignment with The Trailblazer’s guide to marketing, sales, and RevOps excellence, we focus on the critical role of scoring. By honing in on RevOps data quality, you’ll learn how refining your data can significantly enhance scoring accuracy and open up better sales opportunities.

Previously, we discussed how improving data quality can address challenges related to the ideal customer profile (ICP), enhance segmentation, resolve data governance issues, and improve routing accuracy. We also highlighted the importance of managing duplicate accounts and records to maintain clean data, leading to accurate reporting and more informed business decisions. Additionally, we covered lead-to-account matching, a crucial process for effective outreach. By strongly emphasizing RevOps data quality, we demonstrated how precise data refinement directly impacts lead scoring and fosters greater sales success.

The foundation of accurate lead scoring in RevOps data quality

Sales and marketing teams are often at odds about what constitutes a “good” lead. While both demographics and activity are accounted for in scoring, scoring can be wildly inaccurate without the right base data to score on. So, maybe it’s not your scoring model that’s bad, maybe it’s your data quality.

The impact of incomplete or incorrect data

For example, let’s take Greg Collins. Greg’s title is Demand Gen Lead, and he works at a company with headquarters in Tokyo, but he’s based in San Francisco. Greg’s data looks like this:

So Greg gets scored like this:

So Greg has 0 points.

But here’s what Greg should actually look like:

Now Greg has 40 points for his demographic score. Not because anything changed but because the data provided was standardized, normalized, segmented, and enriched to be specific to Greg (not to the parent company).

The key to effective lead engagement is RevOps data quality

We’ll assume Greg engaged with marketing content and events. Instead of being disqualified because he’s in the wrong location and at a company that is too big to be a target, Greg is now a prime candidate for sales. This isn’t because anything changed about who he was or where he worked, but simply because the quality of the data was improved so that the scoring could be both complete and accurate. Moral of the story: if you want more pipeline, you might want to clean up your database.

The sales team can now contact Greg and work towards an opportunity just because of the improvement in data quality.

Want to learn more about improving your lead routing? Download The GTM guide to data quality to ensure your data is prepped so you don’t miss an opportunity, or watch a minute on lead scoring and grading.

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

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