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

Speaker A: They got the game Customer successes that claim to fame. Happy Friday everyone. I'm Kevin Metzger without my co host Roman. Our guest John Humer. 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, uh, 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 where we're at these days. Everybody's starting to look at how to use it best use cases and so I think I want to start there. What's your best use case for AI and customer success right now?

Speaker B: Well, happy Friday and uh, excited to uh, jump into this topic. I think there's a couple, I'll go back probably a few years and we implemented a tool called Chorus AI, which is an AI notetaker. And I quickly learned how valuable of a tool it was. And really from a coaching perspective, at that point I had roughly 35 people on my team across, uh, CSMs and account executives. And I couldn't physically join 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 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 or an on site with a customer was hugely valuable. I think that's one example. You know, another example more recently, as I launched Customer Success Architects earlier this year, I spent a lot of time really trying to re educate and reorient myself with the technology. Your traditional customer success platforms, a lot of those organizations are looking at different ways to incorporate AI into their platform from health scoring, from engagement opportunities with customers. But what I 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 eye is 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 an operator last year getting ready for customer health and CHURN meetings. So it's pretty cool to see how quickly the technology evolves.

Speaker A: That's pretty cool. I actually haven't heard of Sturdy yet so that's the first time I'm going to have to check that one out. That's definitely one of the areas where I'm starting to hear more and more as the primary use case is looking at real churn reasons and uh, uh, 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 use case. They developed their own process but for doing it they weren't using ah, Sturdy but um, went back through all of the, they had GONG in place and they went through, back through all of the calls for GONG calls and started looking for basically did a reclassification of Churn risks and reasons for Churn and were able to really kind of do a uh, much more detailed analysis of why customers were churning and really actually was able to see some significant changes from what they thought it was uh, versus the updated analysis. When you're really able to look at that unstructured data um, and capture the actual reasons when you go back looking through it.

Speaker B: Absolutely. And I think too what's also really interesting about those types of platforms, not only to be able to identify risk early on in those hearing kind of conversationally from your customer but also starting to identify opportunities on the other side of it, boosting net retention and seeing opportunities for cross sell and expansion, you know, within your customer base as well. So it's fascinating how quickly this technology

Speaker A: is moving 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? 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. Try that and then make a decision about what you do next. Coming up with each of these individual types of use cases, whether if you're not already using it, uh, to do transcription, recording and all that, you're definitely as a company you're definitely behind.

Speaker B: Right?

Speaker A: I mean those are, 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, the tools are there and the implementation process is available. It takes some time, it takes some effort. You got to figure out what you want to achieve with it. But you can reclassify all your churn cases and actually figure out where what, what, where your indicators are, what your early indicators are. You can really figure out what, hey, this customer, these customers are talking about this need they have, and, and our CSMs haven't identified it, but we realize it's a need because, look, you can have the AI, uh, go back and look across all the conversations, and now you've got, uh, product updates, you've got expansion opportunities. I mean, all of the information that has been tapped is, or as you, if you capture it, you can then now tap it. And, um, you can tap it with intelligence that's able to look across all of the data in a way that you just never could before. It's amazing.

Speaker B: Yeah, it really is. And I think it's, you know, it's going to break down all of those silos of data, uh, you know, that we've been talking about for years, and you'll be able to tie all that data together, especially the unstructured data.

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

Speaker B: Not really. When we did implement chorus, there was a little hesitation, but I think when CSMs quickly realized how much more efficient they could be, where they could automate the task or capture recordings, 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, they're going to quickly get left behind, unfortunately. But I think most are quickly starting to realize the benefits.

Speaker A: 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 give it a shot anyway. Uh, so what's your favorite AI tool round right now?

Speaker B: I would say sturdy from an enterprise perspective. And then I'm a chatgpt guy, so I use that, uh, daily, personally and for work.

Speaker A: So do you use the subscription or do you use free model?

Speaker B: I, uh, use the free model right now. So I've been kind of pushing that, uh, but I'm close to pulling the trigger on the subscription.

Speaker A: Subscription, yeah.

Speaker B: Cool. All right.

Speaker A: Most Overhyped AI myth in customer success.

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

Speaker A: And what's the next AI experiment on your roadmap?

Speaker B: So this is an interesting one. I heard this on a, um, on a webinar about two months ago and it really piqued my curiosity. So um, 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 ABC 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.

Speaker A: That actually sounds like a pretty cool, um, pretty cool use case. I, I like the idea and especially with the idea that you can kind of help return not just the model but hey, here's the benefits, here's the upsell package, basically delivering broader than just the pricing but the full package benefits, how you deliver it all that probably can really get uh, wrapped in. It's a good use case. Jake, thank you for your time and really appreciate all your insights on how you're using AI. That wraps up our three part series with uh, John Huber. Connect with him on LinkedIn at www.LinkedIn.com in johnmhuber. M h u B E R if today's episode sparks ideas hit subscribe, leave a 5 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 um, playing.