G2

Eric H.

Lead Analyst, Customer Insight Analytics

Enterprise (> 1000 emp.)

7/17/2026

"Game-Changing Rippit+Claude Integration for Deep, Scalable Conversation Analysis"

5/5

What do you like best about Rippit?

The Rippit+Claude integration has been a complete game changer for me. Rippit was already way ahead of the curve on AI enriched conversation data, but now I ask Claude a question in plain English and it drives Rippit to pull the data, build a worksheet, tag the conversations, and write up the analysis. That conversational layer over real customer conversations is what sets it apart.

It lets me analyze customer conversations at a depth and scale I couldn't reach before. I recently ran a deep dive into why our Casino ticket rate was climbing and it surfaced the real driver, as well as fixes, in an afternoon. The old way, that's a team of three spending a week, and even then you're only reading a small relative sample. With Rippit and Claude I'm enriching thousands of full transcripts to tag root cause and theme, not guessing from a handful.

Performance holds up at that scale, which matters. And onboarding was a real differentiator: the Rippit team (shoutout to Robby) has been hands-on and actually shipped on our feedback. The honest ROI is that it's replaced work I'd otherwise need a team of analysts and with sql skills for. Review collected by and hosted on G2.com.

What do you dislike about Rippit?

The main one is a learning curve. The ceiling is really high, but getting the most out of the Claude-driven analysis means learning how to ask the right questions, and my early sessions were much weaker than what I get now. To their credit, they're actively closing that gap: they're building more out-of-the-box skills, and they've been great about documenting our resources so the outputs come back more predictable. Robby also personally shared a few Claude prompts that helped me a ton. That last part is the caveat, I don't expect them to hand-hold every customer that way, so the more of that guidance that gets baked into the product, the better. It's a young product moving fast and I expect this to keep improving. Review collected by and hosted on G2.com.

What problems is Rippit solving and how is that benefiting you?

A few big ones.

First, it's cut our reliance on manually QA'ing individual agent cases to understand agent-driven quality. Instead of reading case by case, I can analyze quality across every conversation at once, and that lets me find the large-scale, systemic things worth coaching agents on rather than one-off nitpicks. The benefit is twofold: I get time back for actual insights, and the coaching we do is aimed at the themes that move agent-touched quality at scale.

Second, and this is the biggest, it lets me do root-cause analysis on our actual conversation data instead of agent-applied tags or anecdotes. That distinction matters constantly. For example, senior leadership was convinced soccer extra-time confusion was driving a large volume of tickets. When I dug into the real conversations in Rippit, it turned out to be 3.3% of tagged conversations, real, but nowhere near the driver we assumed it was. Being able to settle those questions with what customers actually said, instead of going with the loudest anecdote in the room, has turned my team into the one-stop shop for conversation analytics, a role we couldn't credibly play before.

Third, it's changed how we think about our chatbot. Reporting showed most bot conversations were self-contained and resolved fine. Rippit is showing us that customers actually leave those conversations frustrated far more often than we believed, and it shows us why. That's forced us to rethink and tighten the whole experience. Personally, it's made me the leader in how we QA all of our AI, which is a great place to be right now. Review collected by and hosted on G2.com.