Customer Success Playbook Podcast S3 E63 - AI Revolution in Customer Success: From Chorus to Custom GPTs

AI Friday delivers cutting-edge insights as John Huber reveals how artificial intelligence is transforming customer success operations. The conversation explores practical AI applications that are already delivering results, from conversation intelligence tools like Chorus for team coaching and customer context gathering, to emerging platforms like Sturdy that analyze unstructured data across email, Slack, and support tickets to identify churn risks and expansion opportunities. John challenges the overhyped notion that AI will replace CSMs, emphasizing instead how it amplifies human capabilities and enables more strategic engagement. The discussion culminates with an intriguing experiment: using custom GPTs for renewal pricing strategy that combines deal structure recommendations with benefit articulation. This customer success playbook episode demonstrates how forward-thinking CS leaders are leveraging AI to scale their impact while maintaining the human connections that drive customer loyalty.

Detailed Analysis

The episode showcases a mature understanding of AI implementation in customer success, moving beyond theoretical possibilities to practical applications with measurable business impact. John's progression from early adoption of Chorus to exploration of comprehensive platforms like Sturdy illustrates the rapid evolution of AI tools specifically designed for CS operations.

The discussion of unstructured data analysis represents a significant leap forward in customer intelligence capabilities. Traditional CS platforms focus primarily on structured data points, but John's experience with AI-powered analysis of emails, support tickets, and communication channels opens new possibilities for early risk detection and opportunity identification. This capability addresses a long-standing challenge in customer success: the inability to systematically analyze the vast amount of unstructured communication that contains critical insights about customer health and growth potential.

John's perspective on AI replacing CSMs demonstrates thoughtful leadership in an era of technological disruption. His emphasis on AI as an enabler rather than a replacement aligns with successful digital transformation strategies across industries. The human element remains crucial for building trust, navigating complex customer relationships, and making nuanced decisions that require emotional intelligence.

The custom GPT experiment for renewal pricing represents the frontier of AI applications in CS operations. This use case demonstrates how AI can be trained on specific business contexts to provide both analytical recommendations and strategic guidance, potentially transforming how CS teams approach contract negotiations and renewal conversations.

For CS leaders, this episode provides a roadmap for AI adoption that balances innovation with practical implementation, emphasizing tools that enhance rather than replace human capabilities.

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Transcript

Kevin Metzger: 0:11

Happy Friday everyone. I'm Kevin Metzger without my co-host Roman, our guest, John Humor. We've already talked about John's number one tip for building a CS function. And debated who should own expansion. Today we're zoning in on ai, specifically how John has started using AI as a customer success leader. John, we're super excited. AI Friday, we always love talking about ai. It's, it's, it's where we're at these days. Everybody's starting to look at how to use it best, best use cases. And so I think I wanna start there. Like, what's, what's your best use case for AI and customer success right now?

John Huber: 0:56

Well, happy Friday. And, uh, excited to, uh, to jump into this topic. I, I think there's a couple, I'll go back probably a few years and, and we implemented a tool called Chorus ai, which, you know, is a, is a AI note taker. I. And I quickly learned how valuable of a tool it was, and really from a coaching perspective at that point, I had, you know, roughly 35 people on my team across, uh, CSMs and account executives. And I, I couldn't physically join. I. All of the customer calls and internal calls. So to be able to go and not only listen to the recordings, but also, you know, have it as a tool to be able to coach, uh, the team, uh, you know, on a periodic basis. But where I also found it really valuable was I. I could jump into a recording ahead of meeting with a customer and really understand where are we at in the customer journey with this, uh, with this customer? What's the context of the conversation? Are they a happy customer? Are they a not so happy customer? And be able to get that before you walk into a meeting. For an onsite with a customer was hugely valuable. I think that's one example. You know, another example, uh, more recently as I launched Customer Success Architects earlier this year, I spent a lot of time really, I. Trying to reeducate and reorient myself with the technology, right? Your traditional customer success platforms, a lot of those organizations are, are looking at different ways to incorporate AI into their platform from, you know, health scoring from, you know, engagement opportunities with customers. But what I've found is. There's a whole host of new companies that are starting to emerge, that are built on AI and focused on that, and I think one that has really caught my eyes, a company called Sturdy, and what they do is their purpose built for customer success. They take all of the unstructured data that you communicate with your customers, such as email. Um, slack channels support tickets, CRM data, and they take and aggregate that data and run it through their large language model to identify risk and risk. Early on, and I'll be honest, Kevin, this is a tool I wish I had when I was, you know, an operator last year getting ready for customer health and churn meetings. So it's pretty cool to see how quickly the technology evolves.

Kevin Metzger: 3:31

That's pretty cool. I actually haven't heard of Sturdy yet, so that's the first time I'm gonna have to check that one out. That's definitely one of the areas where I'm starting to hear more and more as a primary use case is looking at real churn reasons and uh, I. Sat in the local, in Atlanta, there's Atlanta Customer Success, and we had our monthly meeting, I think it was last week. Yeah, it was last week actually. Former guest was on or was speaking, uh, at that meeting, but talking about the exact same I. Use case, they developed their own process, but mm-hmm. For doing it. They weren't using a sturdy, but, um, went back through all of the, they had gong in place and they went through, back, through all of the calls, gong calls, and started looking for, basically did a reclassification of churn risks and reasons for churn. And we're able to really kind of do a, a much more detailed analysis of why customers were churning and it really actually was able to see some significant changes from what they thought it was versus the updated analysis. When you're really able to look at that unstructured data and capture the actual reasons when you go back looking through it.

John Huber: 4:50

Absolutely. What's. Really interesting about those types of platforms, you know, not only to be able to identify risk early on and, and those, you know, hearing kind of conversationally from your customer, but also starting to identify opportunities, right? On the other side of it, boosting net retention and seeing, you know, opportunities for cross-sell and expansion, you know, within your customer base as well. So it, it's fascinating how quickly this technology is moving.

Kevin Metzger: 5:21

And it's really interesting because every use case you kind of get and refine. Companies are trying to figure out, okay, where do I apply? Where do I spend money? What the general guidance from an AI perspective is, start small, find a use case that provides some kind of return that makes sense to do. Mm-hmm. Try that. And then make a decision about what you do next. Coming up with each of these individual types of use cases, whether, whether if you're not already using it to do transcription, recording and, and all that, you're definitely, as a company, you're definitely behind, right? I mean, those are, that, that's kind of a fundamental thing, but once you start capturing that data. And really having all of that data now you can really, it, it, the tools are there and the implementation process is available. It takes some time, it takes some effort. You gotta figure out what you want to achieve with it. But you can reclassify all your turn cases and actually figure out where, what, what, where your indicators were, what your early indicators were. You can really figure out what. Hey, this customer, these customers are talking about this need they have, and our CSMs haven't identified it, but we realize it's a need because look, you can have the AI go back and look across all the conversations, and now you've got product updates. You've got expansion opportunities. You, I mean, all of the information that has been tapped is, or, or as you. If you capture it, you can then now tap it and you can tap it with intelligence that's able to look across all of the, all of the data in a way that you just never could before. It's amazing.

John Huber: 7:13

It really is. And it's, I think it's, you know, it's gonna break down all of those silos of data, you know, that we've been talking about for years and, you know, be able to tie all that data together, especially the unstructured data. So it's exciting

Kevin Metzger: 7:27

as you walk through this and, and see this, and as we identify these obviously. Have you had any pushback from CSMs or, or seen anything where you've got people who are concerned about what it's doing?

John Huber: 7:40

Not really. When we did implement chorus, there was a little hesitation, but I think I. You know, when CSMs quickly realized how much more efficient they could be, you know, where they could automate the task or capture recordings, you know, versus trying to take notes and have a conversation with the customer at the same time. You know, everybody was quickly on board and I think those that. Aren't willing to adopt or embrace AI and AI platforms. You know, they're gonna quickly get left behind, unfortunately. So, but I think, I think most are quickly starting to realize the benefits

Kevin Metzger: 8:16

I had put in a little, kind of lightning round this week. I'm not sure if you maybe answered one of the questions already, but we'll go ahead and, and, and give it a shot anyway. Uh, so what's your favorite AI tool right now? I

John Huber: 8:27

would say sturdy from an enterprise perspective. And then I'm a chat GPT guy, so I use that, uh, daily, personally and for work. So do you use the subscription or do you use the free

Kevin Metzger: 8:39

model?

John Huber: 8:39

Uh, I use the free model right now, so I've been kind of pushing that and, uh, but I'm, I'm close to, to pulling the trigger on the subscription. Subscription.

Kevin Metzger: 8:49

Yeah. Cool. All right. Most overhyped AI customer success.

John Huber: 8:55

I think this is easy. I think, uh, this, this myth that, you know, AI is going to replace, you know, the CSM is, is silly. There's still that human to human connection that I think is required. I think AI is gonna allow us to do more, but, uh, I don't think you're ever gonna replace the human element.

Kevin Metzger: 9:15

And what's the, uh, next AI experiment on your roadmap? So

John Huber: 9:20

this is an interesting one. I've heard this on a, um. On a webinar about two months ago, and it really p piqued my curiosity. So, um, the, the premise is somebody built a custom GPT to support all of their renewal pricing, so to align with kind of their pricing strategy and their pricing policy. And so, A CSM could go in and say, customer A, B, C. Is interested in a three year and a five year renewal term, um, help me derive pricing for it. And, you know, you can enter in user data and product data and just as you know, our deal desk or a pricing, you know, um, calculator would, you know, provide those answers back. It would provide it back, but also provide it back with, you know, benefits on why. Um. You know, that go along with that renewal. So I just, I found it fascinating that people are starting to build these and train these models for, you know, things like renewal pricing. I think that would be a really interesting, uh, you know, perspective to, to explore

Kevin Metzger: 10:29

that. That actually sounds like a pretty cool, um. Use case. I, I, I like the idea, and especially with the idea that it can kind of help return, not just the model, but hey, here's the, here's the benefits, here's the upsell. Package, basically give delivering broader than just the pricing, but the, the full package benefits, the how you deliver it, all, that probably can really get, uh, wrapped in That's a, that's a good use case. Jake. Thank you for your time and really appreciate all the, your insights on how you're using ai. That wraps up our three part series with, uh, John John Huber. Connect with him on LinkedIn at. www.linkedin.com/in/ John M. Huber, H-U-B-E-R. If today's episode sparks Ideas, hit subscribe. Leave a five star review and share the Customer Success Playbook podcast with a colleague. We'll be back next week with fresh tactics to elevate your CS game. Until then, keep on playing.