Case studies

Double your MQL to OPP rate in three months

See how Thinkific doubled their MQL to OPP rate in just 3 months after implementing their first lead scoring model.

Case Study - Thinkific

What you’ll learn

  • How to identify lead quality and positively impact your MQL to OPP rate
  • How to get marketing and sales to speak the same language

What you’ll need

  • A lead scoring tool that clearly identifies the best MQLs to reach out to first
  • A marketing and sales team that’s done with finger-pointing and ready to align on common language and metrics

The Problem

When discussing lead quality with your marketing and sales teams, it’s important to focus on the value of a lead and the impact on the MQL to OPP rate. Without knowing what good lead quality looks like, it’s hard to make sure your MQL to OPP rate is as high as possible.

Christie Horsman, CMO at Thinkific, knew it all too well.

Leading those lead-quality conversations was particularly challenging as there was no data available to back any claims about what made an MQL good (or bad.)

And it wasn’t a data problem. Thinkific collects numerous data points, including firmographics, demographics, engagement, and in-product data. But the data was scattered across diverse systems and was incomplete and inconclusive.

Thinkific had a problem: they didn’t know who their best leads were. Lead quality conversations were going nowhere. The data didn’t make any sense. As a result, their MQL to OPP rate was abysmal when compared to industry benchmarks. 

The Hypothesis

Christie’s first step was making sense of the data. She believed that by better understanding all the data they had scattered across different systems, the team would have a clear, actionable view of what lead quality meant for them.

If they could combine different data sources, such as HubSpot, Salesforce, and their website, they’d be able to get a comprehensive view of their leads and score them to surface which ones had the highest potential for conversion.

The Solution

1. Run a Reveal analysis to make sense of the data

Christie’s team ran a Reveal analysis of the Thinkific HubSpot account. They got a detailed view of what their data looked like, both in terms of how valuable the data was and where it could be enriched, and if there were gaps in fill rates. 

Case Study: Double Your Mql-To-Opp Rate In Three Months, Thinkific

Reveal also showed her what their engagement data was like. She was able to explore events that happen most frequently and surface actions with a positive impact on revenue.

2. Create a lead scoring model

With the insights that Reveal gave them, the team was able to quickly create the first iteration of their acquisition scoring model. When getting the results from Reveal, they simply chose the categories that had the highest revenue impact.

That’s when Thinkific launched its lead scoring system using Breadcrumbs. It scored each lead based both on fit categories, such as Job Title and Industry, and engagement categories, such as website visits, page views, and time spent on pages. 

Case Study: Double Your Mql-To-Opp Rate In Three Months, Thinkific

The Impact 

The combination of the insights coming from Reveal and the actual scores for that first scoring model informed the team’s acquisition strategy. It enabled them to focus on leads who were likely to convert and had a positive impact on revenue, as they already knew which lead categories had the biggest impact.

Leads that scored higher were more likely to convert and create opportunities for the sales team. The result was a dramatic improvement in the MQL to OPP rate, doubling the number in just 3 months after implementing their first scoring model.

And those difficult conversations around lead quality? They became way easier now that the team had a unified understanding of who was worth pursuing and why.

The score also helped the team make better decisions about the types of leads they wanted to pursue more aggressively so that their time and resources weren’t wasted on low-quality leads.

In Christie’s words:

“With Breadcrumbs, we can have data-driven conversations and say, ‘Hey, this lead, or this MQL, it was scored because of these three attributes on their fit side, and then these three or four actions on their engagement side,’ and we can talk about, why that is, and who they are.

So, we’re using data in terms of fit and engagement to really understand how to talk to these people and how to prioritize talking to these people, which has had a tremendous impact.”