Like many companies, Clearbit's lead qualification system has evolved over time, so here are three snapshots of our scoring at different stages.
When we started out, we didn't use official scoring, and our sales reps spoke to a wide variety of leads. We filtered out leads that were obviously individuals (with Gmail addresses) or spam. This was our "very basic" stage.
Our reps soon developed a good sense for what makes a good lead, and we also ran regression analyses to see what qualities our closed-won customers shared. We used these shared attributes to build a point-based lead scoring system in Salesforce Process Builder. We were about three years old by then — read more about the model here. It bucketed companies into A/B/F groups, and routed higher scores to AEs while SDRs vetted the lower ones. Reps could see a lead's A/B/F score right in Salesforce.
It used several Clearbit fit data points, as well as self-reported data:
- Business model tags (SaaS, B2B, etc.)
- Technology used (Salesforce, Marketo, Drift, etc.)
- Estimated annual revenue
- Employee range
- Sales/marketing team
- Leadership level (indicator of purchasing power)
- Use case survey:
- When someone signed up for a Clearbit account, they answered a one-question onboarding survey about their use case (e.g., they could select "personalize your website" and "see which companies are visiting your website"). This answer was factored into the lead score.
Once a lead was scored, Process Builder routed leads to the right rep. We'd call that a "medium-stage" setup.
As we matured, we stepped into a more advanced system. Today, we use:
- LeanData for routing
- MadKudu machine learning to score leads on fit (low, medium, good, very good)
- A custom-built model for our intent scoring.
Our custom intent model uses Segment to scoop up a lead's behaviors across our marketing channels and Clearbit products (e.g., product usage, website visits, email opens). It feeds this information into Redshift, our data warehouse. A tool called dbt sits on Redshift and transforms the activity data into one nice master table. Census pushes the data from that table into tools like Salesforce and Customer.io, where our sales and marketing teams can see a lead's interactions and behaviors to use in personalized outreach.
We're still calibrating when to send a lead to sales based on this activity data — it's a work in progress.
Remember, these examples are just for illustrative purposes to show the range of scoring implementations across different companies. The situations we've described are ever-evolving and may have changed by the time you're reading this. Like Clearbit, everyone's figuring it out and iterating over time.