Checkr Uses Conversation Data to Drive Product Strategy
The biggest change is how quickly we can move. With Rippit, we’ve compressed the cycle time from insight to action from weeks to hours. Now the business can respond faster and even predict issues before they escalate.
Eric Berdulis
SVP of Operations, Checkr
About Checkr
Checkr is a data platform that powers smarter hiring and trust decisions — from background checks for enterprise employers to safety screening for gig platforms and consumer apps.
B2B SaaS
~1,000 Employees
Checkr serves more than 100,000 customers — but product conversations were often driven by the loudest enterprise voices.
Meanwhile, the broader SMB customer base generated enormous amounts of conversation data through support tickets, chats, and other customer interactions — but the company lacked a fast way to turn those conversations into product insight.
Surveys captured less than 10% of interactions, leaving most customer signals buried in everyday conversations.
Checkr needed a faster, more complete way to understand what customers were experiencing — and turn those signals into decisions the business could act on.
With Rippit, the team began using conversation data to answer product and executive questions with far greater speed and clarity.
Listening to every customer: Predictive CSAT across 100% of conversations
The first step was replacing an incomplete signal.
Traditional CSAT surveys captured less than 10% of customer interactions and tended to reflect customers at the extremes — those who were very happy or very frustrated.
Checkr built a custom predictive CSAT (pCSAT) program in Rippit to analyze sentiment across 100% of conversations, including the silent majority who never fill out surveys.
The team also learned that sentiment needed to be measured differently depending on the interaction. Customers communicate differently when speaking with a human agent versus a chatbot, so Checkr built separate models for each source and combined them into a unified signal.
Today, predictive CSAT provides a far more representative view of customer experience and is reported directly to leadership, including the COO and CEO.
Instead of relying on a small set of survey responses, Checkr now has a real-time signal across every customer interaction.
“We still survey our customers, but we don’t actually look at that. We look at this one hundred percent. And we report it all the way up to the C-suite.”
Kristen Johnson
Director of Shared Services, Checkr
Atomic Problems: Turning conversation data into product insight
For Checkr, the most important application of conversation intelligence was improving how product decisions were made.
The company serves more than 100,000 customers, but identifying the most important problems affecting those customers was historically difficult. Product teams often relied on fragmented signals or anecdotal feedback.
To solve this, Checkr built an Atomic Problems framework in Rippit.
Instead of relying on slow manual categorization or broad support categories like “billing,” AI classifiers analyze every conversation and identify the specific issues driving poor sentiment or unresolved outcomes.
This allowed the team to move beyond high-level categories and pinpoint the problems that actually matter.
“We produce a report that goes to the C-suite and product teams. It’s how we talk, measure, and explain what’s actually going on.”
Kristen Johnson
Director of Shared Services, Checkr
For the first time, product teams could see patterns across thousands of conversations and understand which problems were truly affecting customers.
Examples of Atomic Problems in action
Fixing billing friction
Atomic Problems surfaced a cluster of billing issues producing a 47% predictive CSAT score and a 58% unresolved rate. Within days, the team pulled customer quotes and thematic data to present a report to the Chief Product Officer outlining which product improvements would have the greatest impact.
Understanding why SMB customers churn
Structured data alone wasn’t revealing clear patterns. By combining conversation insights from Rippit with operational data from Snowflake, the team uncovered signals that had previously gone undetected — helping push new churn-prevention improvements onto the product roadmap.
Accelerating product roadmap discussions
During internal roadmap reviews, Checkr teams use Atomic Problems reports to identify the specific issues driving customer dissatisfaction and prioritize product improvements with real evidence instead of assumptions.
“Before, you’d have to get the narrative right, look at all the tickets side by side. Now we can pull the insights quickly and spend our time understanding the problem.”
Kristen Johnson
Director of Shared Services, Checkr
Answering executive questions in hours instead of weeks
Just as important as the insight itself was the speed.
Because AI now handles large-scale conversation analysis, the time required to extract insights and turn them into decisions has been compressed dramatically. Work that once required weeks or months of manual analysis can now happen in hours.
“The biggest change is how quickly we can move. With Rippit, we’ve compressed the cycle time from insight to action from weeks to hours.”
Eric Berdulis
SVP of Operations, Checkr
In one example, Checkr’s CEO raised a late-night question about a potential issue affecting background checks in a specific state. By the next morning, the team had a detailed report confirming the issue and identifying the signals behind it.
That kind of turnaround changes how a company can use conversation data.
Instead of helping teams explain the past, it helps them answer business questions in real time — and increasingly anticipate issues before they escalate.
From Support Data to Company-Wide Customer Intelligence
For Checkr, support conversations are just the starting point.
As additional sources are added — including sales and customer success conversations — the intelligence becomes even more powerful.
What started as a way to understand support interactions is evolving into something broader: a system that helps Checkr understand customers, prioritize product decisions, and move faster across the business.
Conversation data is no longer just a support artifact. It has become a strategic signal for how the company learns from its customers.
Where conversations become
insights
actionable data
business intelligence
enterprise visibility
insights
