At the time, Gremlin had raised a great Series B and was growing significantly. They had product-market fit, and were looking to scale. Their goal was to make the sales and marketing funnel more efficient, so they decided to test an account-based (ABM) approach.

Where many companies go wrong here, is only focusing on target account engagement: blasting targeted accounts with ads, and seeing “impressions” going up. But Peter wanted a metric that would be a leading indicator that what they were doing would pay off. So he and the team designed an engagement score metric that combined quantity and quality.

The metric produced a cumulative total “engagement score” based on all contacts’ lead score, impressions, and engagement. For example, they could have 1 contact at Nike who loved them (browsed the site, downloaded content, etc.) w/ a lead score of 100. So their avg. lead score per contact and total “engagement score” would be 100. But if they attracted 9 other contacts from Nike who didn’t engage with their content, and had a lower lead score, it would bring the overall engagement score down.

This provided a number of benefits. If they had content that played well among other highly engaged accounts, and it wasn’t playing well for a new target, they’d know that account might be less likely to close. Or if they found an account that had lower cumulative lead scores, they might pump the brakes and serve up more top-of-funnel content and slow down their approach.

And it didn’t merely serve as an early performance indicator. They could also use their cumulative engagement score to test different channels or content that might drive all target accounts higher across the board.

By using a more holistic, custom metric to measure their ABM strategy, they saw a 50% increase in engagement among targeted accounts in just 1 quarter. They also added a few points to the marketing qualified lead (MQL) to qualified opportunity rate.