The Digital CX Podcast: Driving digital customer success and outcomes in the age of A.I.

AI-Powered Customer Success & Predictive Analytics with Arun Balakrishnan and Preetam Jinka of FunnelStory | Episode 057

June 18, 2024 Alex Turkovic, Arun Balakrishnan, Preetam Jinka Episode 57
AI-Powered Customer Success & Predictive Analytics with Arun Balakrishnan and Preetam Jinka of FunnelStory | Episode 057
The Digital CX Podcast: Driving digital customer success and outcomes in the age of A.I.
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The Digital CX Podcast: Driving digital customer success and outcomes in the age of A.I.
AI-Powered Customer Success & Predictive Analytics with Arun Balakrishnan and Preetam Jinka of FunnelStory | Episode 057
Jun 18, 2024 Episode 57
Alex Turkovic, Arun Balakrishnan, Preetam Jinka

Send us a Text Message.

Preetam Jinka and Arun Balakrishnan, co-founders of FunnelStory, join the podcast to discuss how we’re just scratching the surface with AI and machine learning.  They chat with Alex about how FunnelStory is revolutionizing customer success strategies, enabling teams to predict churn, optimize user engagement, and drive revenue growth through data-driven insights.

Additionally, Arun & Preetam share their own insights on A.I. and how Product teams best work with Customer Success.

Chapters:
04:35 - Arun's background in cybersecurity and product managemen
05:25 - How Preetam got started in AI and data engineering
14:00 - The difference between AI and machine learning
18:10 - Some practical applications of AI in customer success
21:14 - An overview of the FunnelStory platform
24:26 - Predictive and prescriptive elements
27:46 - Product telemetry and customer insights
30:26 - Collaboration between product and customer success teams
33:11 - Innovative uses of predictive analytics in customer engagement
38:19 - Discovering hidden metrics through data analysis
39:16 - Leveraging AI and ML for pattern recognition in CS

Enjoy! I know I sure did...

Preetam's LinkedIn: https://www.linkedin.com/in/preetamjinka/
Arun's LinkedIn: https://www.linkedin.com/in/balakrishnanarun/
FunnelStory: https://funnelstory.ai/

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This episode was edited by Lifetime Value Media, a media production company founded by my good friend and fellow CS veteran Dillon Young.  Lifetime Value aims to serve the audio/video content production and editing needs of CS and Post-Sales professionals.  Lifetime Value is offering select services at a deeply discounted rate for a limited time.  Navigate to lifetimevaluemedia.com to learn more.

+++++++++++++++++

Lifetime Value Media
Lifetime Value aims to serve the audio/video content production and editing needs.

Support the Show.

+++++++++++++++++

Like/Subscribe/Review:
If you are getting value from the show, please follow/subscribe so that you don't miss an episode and consider leaving us a review.

Website:
For more information about the show or to get in touch, visit DigitalCustomerSuccess.com.

Buy Alex a Cup of Coffee:
This show runs exclusively on caffeine - and lots of it. If you like what we're, consider supporting our habit by buying us a cup of coffee: https://bmc.link/dcsp

Thank you for all of your support!

The Digital Customer Success Podcast is hosted by Alex Turkovic

Show Notes Transcript Chapter Markers

Send us a Text Message.

Preetam Jinka and Arun Balakrishnan, co-founders of FunnelStory, join the podcast to discuss how we’re just scratching the surface with AI and machine learning.  They chat with Alex about how FunnelStory is revolutionizing customer success strategies, enabling teams to predict churn, optimize user engagement, and drive revenue growth through data-driven insights.

Additionally, Arun & Preetam share their own insights on A.I. and how Product teams best work with Customer Success.

Chapters:
04:35 - Arun's background in cybersecurity and product managemen
05:25 - How Preetam got started in AI and data engineering
14:00 - The difference between AI and machine learning
18:10 - Some practical applications of AI in customer success
21:14 - An overview of the FunnelStory platform
24:26 - Predictive and prescriptive elements
27:46 - Product telemetry and customer insights
30:26 - Collaboration between product and customer success teams
33:11 - Innovative uses of predictive analytics in customer engagement
38:19 - Discovering hidden metrics through data analysis
39:16 - Leveraging AI and ML for pattern recognition in CS

Enjoy! I know I sure did...

Preetam's LinkedIn: https://www.linkedin.com/in/preetamjinka/
Arun's LinkedIn: https://www.linkedin.com/in/balakrishnanarun/
FunnelStory: https://funnelstory.ai/

+++++++++++++++++

This episode was edited by Lifetime Value Media, a media production company founded by my good friend and fellow CS veteran Dillon Young.  Lifetime Value aims to serve the audio/video content production and editing needs of CS and Post-Sales professionals.  Lifetime Value is offering select services at a deeply discounted rate for a limited time.  Navigate to lifetimevaluemedia.com to learn more.

+++++++++++++++++

Lifetime Value Media
Lifetime Value aims to serve the audio/video content production and editing needs.

Support the Show.

+++++++++++++++++

Like/Subscribe/Review:
If you are getting value from the show, please follow/subscribe so that you don't miss an episode and consider leaving us a review.

Website:
For more information about the show or to get in touch, visit DigitalCustomerSuccess.com.

Buy Alex a Cup of Coffee:
This show runs exclusively on caffeine - and lots of it. If you like what we're, consider supporting our habit by buying us a cup of coffee: https://bmc.link/dcsp

Thank you for all of your support!

The Digital Customer Success Podcast is hosted by Alex Turkovic

Speaker 1:

We've dealt with folks in the organization who say we need to handhold them. But that might be like a 45-minute call and like what do you tell them? Don't touch the product once the call is done till the next call. That doesn't work. Yes, you have to expect the user to be led to use the product, because everything cannot be controlled. And if it's controlled to that extent, what happens when they purchase and then somebody else is caught?

Speaker 2:

Once again, welcome to the Digital Customer Experience Podcast with me, Alex Turkovich. So glad you could join us here today and every week as we explore how digital can help enhance the customer and employee experience. My goal is to share what my guests and I have learned over the years so that you can get the insights that you need to evolve your own digital programs. If you'd like more info, need to get in touch or sign up for the weekly companion newsletter that has additional articles and resources in it. Go to digitalcustomersuccesscom. For now, let's get started. Greetings and welcome to episode 57 of the Digital CX Podcast. So great to have you back.

Speaker 2:

My name is Alex Turkovich and I've been having a lot of cool conversations with founders recently Because there's a lot of cool apps out there that are doing some great things for CS. You know, for a long time CS was kind of like a desert for technology. We leveraged sales tools, we leveraged support tools, we leveraged all kinds of different tools to kind of duct tape together various solutions that we needed. But now we're seeing a proliferation of great apps pop into the marketplace and great apps that are centered around artificial intelligence. I've talked at length about AI and one of the things that I find fascinating about the use of artificial intelligence is not just generative AI right, there's so much more to it, and we've been using in technology, we've been using machine learning and those kinds of algorithms for quite a long time. It's not a new thing.

Speaker 2:

And my guests for today are two-thirds of the founding team of Funnel Story, which is a fantastic platform that helps predict churn and helps predict risk in accounts and really helps to kind of track the entire customer lifecycle, but using machine learning and using data analytics, which is super cool. And so today we have Pritam Jinka and Arun Balakrishnan on, again two-thirds of the founding team, who are, you know, preetam is head of AI, so he has a lot of just very intrinsic knowledge about artificial intelligence. So you know, we don't just spend the time talking about Funnel Story. We talk about artificial intelligence, the state of AI and CS and kind of what's happening in the AI industry today. Arun is a longtime product leader, has a rich history as a product leader, and so with Arun, we talk a lot about that interaction between post-sale teams and product teams and how those two organizations, can you know, better and more efficiently work together.

Speaker 2:

So I really hope you enjoyed this conversation with Arun Balakrishnan and Pritam Jinka of Funnel Story because I sure did. We are going and recording and I want to welcome both of you to the podcast, excited that you guys are on, because I'm a huge fan of Funnel Story and what you guys do, so I figured it'd be good to A get you guys on, talk about some of the cool things you're doing and then just wrap CS and wrap about cool insights and predictive analytics and all that fun stuff. So welcome to the show. It's good to have you both on. Yeah, thanks, alex.

Speaker 3:

It's a pleasure to be on and we're great fans of the podcast and the community.

Speaker 2:

So you're the one fan or the two fans. It's great. It's the stupidest joke ever. So I've spent some time with Alok We've talked briefly kind of in preparation for this show. So I feel like I've gotten the lay of the land in terms of who's building Funnel Story, which is cool. But just by way of introduction, maybe starting with you, Arun, do you want to give a quick intro into who you are and really some of your background, what led you to where you are today?

Speaker 1:

Sure, yeah Again. Thanks, Alex, for having us on the show. I'm Arun Balakrishnan and one of the co-founders of Funnel Story, and at Funnel Story, I primarily focus on the products and roadmap. My history has primarily been in cybersecurity for over 16 years, with companies the likes of Trend Micro, Symantec, Imperva and, most recently, ShiftLeft. That is where me, Preetam and Alok, three co-founders, met, and in those companies, the rolesok three co-founders met, and in those companies.

Speaker 2:

The roles I primarily played started off with engineering, transitioned into product marketing and then, most recently, for the last say about seven years in product management.

Speaker 3:

Great, that's awesome. We're definitely going to talk about that, but before we do, pritamam, you want to give your intro? Sure, I'm Preetam. I'm the technical co-founder at Funnel Story. And, yeah, arun mentioned the three of us worked together at our previous startup, shiftleft, and then, prior to that, I actually started my career at a database monitoring startup. So I'm not from CS, but my background is mostly in backend data and I did my undergrad at the University of Virginia studying probability statistics. So that's kind of where my like AI background comes from.

Speaker 2:

Yeah.

Speaker 3:

Yeah, at Tunnel Story, I lead the AI efforts and I work closely with the engineering team based out of India.

Speaker 2:

Awesome, that's great. I love people that aren't in CS that find their way into CS because well, okay, granted, everybody kind of who's in CS has found their way into CS because it's a relatively newish thing, especially compared to sales and marketing and those kinds of things. But I like that outsider's perspective on CS because there's usually something fresh to bring to the table, so to speak, and with all the transplants that I've talked to, but yeah, that's awesome. I'm glad you guys found each other and are working through the challenges that you are at Funnel Story and we'll get into that. But first it is the Digital CS podcast and I'd be remiss to not ask you about your kind of elevator pitches of what digital CS is, and I don't know if you both want to like collab on that or each kind of give your own version or spin on it, or if you prepped something. I don't know, but I'm curious to see what you guys want to say to that.

Speaker 3:

Yeah, maybe, arun, I'll let you take the lead on this one.

Speaker 1:

Yeah, sure I can go first. Yeah, so my perspective on digital CS is a lot of folks who use enterprise products today as we shift generations. They grew up using software mostly on their phones, grew up. There were no computers to begin with, and then we entered the world of computers and internet and tinkering with hardware and going through the pains of installing software. My first PC did not even have a hard drive on it, like everything was running off a disk, a floppy disk in fact.

Speaker 1:

So it's a huge generation change when, if the first software you experience is something you just get off a Play Store, hit a button and you expect everything just to work and it knows what you need to be doing and exposes and takes you through the features and benefits. So I think that is the expectation of enterprise software as well. So for me, digital CS in the B2B enterprise world is how can applications and platforms help users benefit without having to involve multiple meetings and people and conversations for every single thing? People is needed, conversations are needed, but it should not be for? Okay, let me guide you how to sign up.

Speaker 2:

That is, that is not the expectation yeah, what you said earlier bring brought back a lot of nostalgia. I recently showed my son what a command prompt was with a like an ms-dos emulator or something like that. He's like that is stupid. I was like that's what it was. But yeah, I really like what you said about the fact that these, the digital experience, should really be kind of that table stakes of. Well, you mentioned onboarding, right. I mean, that's something that a human doesn't necessarily have to do, so that your humans can focus on driving value and those kinds of things, and that's very much been a theme of the show, for sure. So did you have anything to add to that?

Speaker 3:

Yeah, I think, especially being younger and also I've only worked at startups and so the whole enterprise, how things work in enterprises, was very new to me, and when I started becoming a developer I thought there's like a engineering solution to everything, right. So like, if you need to help onboard users, it should be done digitally and through the product, right? Just having a meeting with someone I mean engineers typically dislike meetings and talking to customers also is kind of a scary thing and not something that engineers typically do. Yeah, that's kind of my lens on how I view digital CS.

Speaker 2:

Yeah, I think, especially in the startup world, it is the thing to do to lean in heavy on those digital experiences, because otherwise you don't have the budget to go higher. You can't burn cash like that to hire onboarding people and stuff like that. So, focusing digitally and yeah, look, I mean I think there's a lot of customers who actually prefer a digital motion over talking to a human, especially in certain certain industries. Those industries, like I don't know that, cater towards highly technical people or whatever. They just they want a chatbot or they want an in-product experience versus having to sit through training courses and things like that. Yeah, have you ever been involved in a product that was so complex that you couldn't do the onboarding purely digitally?

Speaker 3:

Yeah, I was just thinking about our last company. It was the security product that did code analysis, and it's very tricky to set up one of those tools, especially in enterprises, because enterprises they're running all kinds of software on all kinds of platforms and coding languages and build systems, and so I was one of the engineers that Arun and Nolo could have on those onboarding calls just to make sure there's any weird environments that we haven't seen before that I can help with the debugging and stuff like that. So I mean, for the most part we try to automate as much as we can, but when you're running in the field, especially dealing with code, other people write. You never know what you're going to expect.

Speaker 2:

Yeah, you automate for the 80% and then the 20% is where your humans come in.

Speaker 3:

Yeah, exactly.

Speaker 1:

And also, with these, any product right. When, like we, like Pritam and I, we've dealt with folks in the organization who say, okay, hey, yeah, we need to handhold them, guide them through. But that might be like a 45 minute call, an hour call, like what do you tell them? Don't touch the product once the call is done till the next call. I mean, that doesn't work. So like, yes, you have to expect the user to be led to use the product, because everything cannot be controlled. And if it's controlled to that extent, what happens when they purchase? And then it's somebody else's fault. So, even if onboarding is controlled, what about the next steps? They might be inviting other users to come in. They haven't spoken to you. You can't expect to train every single user in the target. So, yeah, even in complex products where handholding is required, I believe, yeah, you just cannot handhold every single thing.

Speaker 2:

No, yeah, and I think there's a balance right between crafting those experiences and just letting your customer run wild with stuff.

Speaker 2:

Like there's a balance there, because in some platforms, if you let them run wild without really knowing the implications of doing that, they could break stuff.

Speaker 2:

That's where the variables come into play, because every platform is different and it requires different things that way. So I'm curious you know, preetam, you are head of AI at Funnel Story and given that it's an AI native platform, it probably carries some weight to it, because you're kind of having to, I'm guessing, not only just really be the heart of the solution, so to speak, but then also you're I'm guessing you're very plugged into like what's happening in artificial intelligence and machine learning and kind of what the what the state of things are. Because from my perspective and I've had the pleasure of speaking with quite a diff, quite a few who are directly involved in AI, in CS specifically and from my perspective it's still very fragmented right. There's use cases all over the place and various platforms doing various things, but there's no unifying thing necessarily that's started to come out. So I would love your take on the state of AI and CS and where you feel like it's going over the next year or two.

Speaker 3:

Sure, I think the first thing I want to start with is defining what AI is and what ML is, because I think people have different definitions and it's not always consistent. From my perspective, ai is more of a category that broadly encompasses data, data tied to capabilities like doing automation or getting insights. It's this broad category, machine learning, specifically coming from my background, it's all about the tools, right? So you have things like regression, decision trees, random force, all these algorithms that are the tools, and then putting those together and tying together the data and the decision-making, the automation inside. That's sort of what I view AI as. So, to use an analogy, it's in computer science.

Speaker 3:

You have data structures and algorithms, but there's more to it and that's what makes CS. It's kind of the application of those things to solve problems. So for me, ai is using machine learning as one of the tools, but there's more to it to actually solve problems, whether that's giving information back to people, being able to answer questions or doing automation. And the big thing that's come out in AI recently is LLMs, and I like to tell people, when people say AI, it's not just LLMs. There's more to it than just LLMs. And even with LLMs, ai is more than drafting email templates or reading transcripts and generating summaries. Right, that's just the tip of the iceberg, and that's a common use case and it's valid, but there's way more to it.

Speaker 2:

Spot on and you hit on a couple of things that I want to go a little bit deeper on, because you're right, when a lot of people I would say the majority of people think about AI, immediately they're like, oh, open AI, okay, chat, gpt, like yeah, that's where I asked it to write this article and it gave me a shitty response or whatever it is Like, and a lot of people kind of stop there. But then you think about what all has led to where we are with AI Gen AI isn't necessarily new and machine learning definitely isn't new. It's been around for a while. We're just seeing it all culminate right now into a really exciting time where we have all these interfaces and things like that that are being used and and whatnot. But the back end of this isn't new technologies, it's been around for a while, it's just all.

Speaker 3:

It seems to be all culminating at the moment, right, yeah, and I think we're talking about machine learning and, uh, especially in cs, a lot of it has been applied to things like health scores and churn prediction, right. So what's the trend now and where is that trend coming from? And I think it's kind of two things. I think one is it's all about data, right? I mean, yes, all of the models that were used for churn prediction or whatever, like insurance companies, have been doing this for a long time. Right, when they're calculating risk, all of this science has already been done, but for them, they have a lot of data. Right, when they're calculating risk, all of this science has already been done, but for them, they have a lot of data. Right. When you look at insurance companies, they have all of these stats on every single individual and expected life expectancy and all these things.

Speaker 3:

What about in CS? So all of this, like rich data, wasn't really available or accessible until most recently. With the trend of cloud data warehouses, it's getting easier for CS teams to get access to this data and also seeing historical data, and that sets a great foundation for a lot of machine learning models to be trained on. So that's where I think the data is really helping, and the other trend is with LLMs, where there's a lot of automation that can now be leveraged using LLMs, like reading transcripts right, I mean getting sentiment. This used to be something that people would kind of come up with on their own, just based on their meetings. But when you have objective analysis of transcripts and sentiment, that just feeds into all the other.

Speaker 2:

Yeah, it's huge. I mean it's like pulling us out of the dark ages a little bit.

Speaker 1:

And Alex, I also think there's multiple levels to this right. Where LLM says you have experience yourself, I'm sure you ask it something. Yes, it will have a very confident answer. So if I just ask, saying hey, acme is my customer, they're churning, what should I do? Obviously it'll give you a full paragraph of things that you should be doing, which is very generic. It might say try to have a meeting, try to convince them of value, whatever. And then, as Preetam was saying, yeah, hey, acme is churning, craft me an email that I can send them Again. There would be a response for it.

Speaker 1:

But a system that is really I mean these gimmicks which people might, oh, wow, but would you use it every single day? I doubt anybody would. But when you get a system where it knows exactly what Acme has done, has said, has not done, how have they realized value? And then, if you have an AI ML model on top of it, as Pritham was saying, trained to understand what those things mean, and then if it can say that, yes, acme is churning because, compared to your other customers, they have used it only once a week and only three people have logged in.

Speaker 1:

None of them are core users, they're not decision makers. They've not used these features. They've experienced bad sentiment about this other feature. They're not decision makers. They've not used these features. They've experienced bad sentiment about this other feature. They've asked for this feature request and we've not delivered it. Now it is like talking the same language and then, if that is then fed into, okay, send me, create an email or give me a transcript which I can use for a call. Create an email or give me a transcript which I can use for a call there.

Speaker 2:

Then the real value comes in Totally, and I feel like the one thing that you didn't say that I have experienced a lot is that generative AI tends to lie when it doesn't know and come up with stuff. So if you ask it like, hey, acme is churning, what should I do? It may as well tell you to like bribe the CEO or something like that, which is totally wrong thing to do. That's where the prompting comes in, that's where all the data analytics come in to, where you tell it specifically to really adhere to these certain things. And I think the more you feed into it, obviously, the better the results are going to be. That's put simply if you prompt it to write you an article about XYZ and don't give it any details on it, it's going to give you a crappy result.

Speaker 2:

But if you feed it some results and tell it your expectations and tell it what you want it not to do, which is to lie to you, then your results are going to be a lot better. And so I think that's like half the battle that a lot of folks kind of don't get into, and I also think, as a way of transitioning, that's kind of where Funnel Story fits in as well, because you guys are essentially creating the operating system that essentially does that for you, right? Do you want to expand on that a little bit and tell us what Funnel Story is all about and what you guys are building?

Speaker 1:

Sure, yeah, so Fun and tell us what Funnel Story is all about and what you guys are building? Sure, yeah, so Funnel Story essentially, at the end of the day, is a revenue platform, right? So what we do is help organizations and teams and organizations use customer data to improve their revenue. And our core IP the most value that we provide is the ability to connect to data that is behind the product, so this includes product usage data, conversations they are having. Who is having the conversations? Who is using what feature at a customer level? But now think of this as across all customers.

Speaker 1:

The analogy that we often use is, if you are going, say, on a metro system like think of a complex metro system, like, say, the London tube, if you're new to London, you are at a loss where you're like, okay, how do I go from point A to point B? There might be three, four ways of getting there. Versus, I had a friend who, of course, is from London. He had the knowledge of getting on this door if you're going to be getting out at this station, because that's the closest to the exit, where you don't even need to go through the crowds. So a system which knows what you are doing, what you're about to do, and guide you. And then if you're doing well, great, you don't need to be even bothered, right. But the ability to know when to get in and intervene and offer you help with context is like a good metro system, a good app for that, and that is what we do with Funnel Story. For customer success as well Know what your customers are doing and help your customer success teams do proper digital CS.

Speaker 2:

Yeah, that's really cool. Hey, I want to have a brief chat with you about this show. Did you know that roughly 60% of listeners aren't actually subscribed to the show, on whatever platform they're listening to it on? As you know, algorithms love, likes, follows, subscribes, comments, all of that kind of stuff. So if you get value out of the content, you listen regularly and you want to help others to discover the content as well, please go ahead and follow the show, leave a comment, leave a review. Anything that you want to do there really helps us to grow organically as a show. And while you're at it, go sign up for the companion newsletter that goes out every week at digitalcustomersuccesscom.

Speaker 2:

Now back to the show, and it sounds to me like admittedly, I haven't seen the product, but it sounds to me like there's a predictive element that probably looks at some regression testing on your data to apply towards current accounts, to figure out, like, who might be in trouble, and then some prescriptive elements to suggest next steps. And so that's where the kind of the human comes in, cause I think a lot of people think that digital is just like hey, let's get these emails out, let's do some in-app messaging, let's do all that kind of stuff. But it's like no, it's like more than that, it's the assistant to the human, and so you're giving these, the humans in the program, essentially the insights that they need to react quickly and engage appropriately, and those kinds of things. Right, Exactly.

Speaker 1:

I mean like when I was running a CS and UTS company, like if you knew that, okay, hey, after onboarding, the best success is if you're inviting your colleagues. Now, if that is a known fact in your platform, you shouldn't have your CS. People have to email folks, get them on a call continuously, remind them to invite colleagues. That should be automated, right. And if the magic number of invitations is five, your historical data should tell you that five. So, till that point, your platform should be nudging and getting that done. But if there is realizing value, which means maybe sitting with a team understanding what they've done, explaining it, this is where a CS person can truly shine, where they have that, as you said, the human intelligence that comes in. But they can take it's like the blood tests and the doctor on top of it, right, blood test gives you a bunch of numbers. The doctor is the one who's able to interpret what that means and get you the diagnosis, and that is what a CSM should be doing.

Speaker 3:

I think the other thing that our platform helps with is a much broader trend, and there's a great term that I love. It's called the democratization of data, the idea that data should be accessible to anyone that needs it. And especially it's important in CS, because you need to know what your customers are doing and how they're using the product. And coming from smaller startups, that's never really been a problem, especially from the engineering perspective, because we're the ones that are building the tools. We have all these observability systems.

Speaker 3:

But then when I talk to CS teams, some of them don't even know when their customers are logging in. Teams. Some of them don't even know when their customers are logging in. So the idea that you might have a customer meeting in 15 minutes and you don't know when the last time they logged in is that's kind of scary. And if you had to, if there is bottlenecks or silos for this data and you have to ask somebody to give you a dashboard or ask somebody to answer that question, you might not get the answer by the time you meet that customer, right?

Speaker 2:

So you're not geared with the right information you need to make your customer successful and how important is that stuff cross-functionally too? Because as you get into the renewal cycle, your sales team or your renewals team is going to need to know those things as well and get in there and really understand what the like, what the opportunities are that they should, they should double down on and those kinds of things really understand what the like, what the opportunities are that they should, they should double down on and those kinds of things. So I think I think what's interesting about the name funnel story is that the name points to the fact that this is a customer like life cycle tool as much as it is a CS tool, right yeah?

Speaker 3:

Yeah, that's right and I think, yeah, we're also very like. We like to think from first principles and the best way to really understand how customers are doing is based on what your successful customers have done in the past and really understanding the full story and also looking at things happening outside the product. Right, it's not just what they're doing inside your UI. There might be some analytics tools that developers use and product teams use, but those aren't capturing. Like, what are they talking about in the meetings? Are they filing support tickets? So all those things as well.

Speaker 2:

Yeah, Well, we were talking about that. I think it was a LinkedIn conversation, honestly, on the back of a post that I did. But it was about hey look, if you don't have product telemetry, if you don't have product data, like where else are you going to get the pulse of your customer? And it's like, well, are they posting in the community, Are they active in community, Are they taking education courses? Like what are they doing in your ecosystem of like support infrastructure to like get some signs of life? Because the product definitely tells you a story, but it may not tell you the full story.

Speaker 1:

And one thing to add to what Pritam said was that complete story. Like Alex you mentioned. Yeah, the product is the one that can tell you, but a lot of times it's also it changes with whoever is in the leadership right. So, like some person might say, okay, let's put in this tool and let me track how many people are logging in, and somebody else might say, okay, let me see how many times somebody has done something else.

Speaker 1:

Now, if you have the approach where you put in something and you can say from today what is happening, that takes, depending on what you're collecting, weeks or months to even give you an insight. Yeah, so one difference that our platform does is it's able to go back in time for however long you have data for and tell you from day one what has happened, even if you're connecting, say, three years after you've started the solution, have started the solution, and that is something which I think I mean personally. I wish that was possible in my previous companies, because then I could say, hey, this customer is successful. I know, let's see what they have done, versus waiting six months to figure out who the next successful customer is.

Speaker 2:

Yeah, Telling the story, taking a complete left turn. Arun, I wanted to spend a little bit of time chatting with you just about the interplay between product and CS a little bit Because I've had a few product folks on specifically. But I think it's always great to delve into the world of product Because ultimately, between product and post-sale we're interested in the same kinds of things just on a different level. Whereas you're really focused on feature adoption, on the aggregate and those kinds of things, in post-sale we're still focused on that, but maybe not in the aggregate necessarily, but on an account level. So I was curious to kind of get your sense of what your experience has been with product versus working with post-sale and maybe some best practices that you've seen about how the two teams can work better together.

Speaker 1:

I think the answer also depends a lot on the size of the company. I've worked in companies where a single product has 10, 15 product managers and at that level each PM, as you rightly said, is very focused on one feature. So they are focused on adoption for that feature among customers and that's very important for them because they've spent so much effort in building it and getting it out there. Effort in building it and getting it out there. But from a CS perspective, what matters is, hey, how does the customer become successful and how do they expand and renew For all customers who expand and renew without even touching that one feature? Because, yes, it's great, but maybe that is not what drives the revenue behavior. So, from a product manager's perspective so from a product manager's perspective, it's, as you rightly said, metrics, sometimes the feature levels across customers, versus from a CS, what I've seen is what is that drives value? It might not even be features, right, it might be the good quality documentation, the easy to use, the ability to get help when needed, and all of those are outside of features which are not normally measured by product teams. So, yeah, and then you asked about good practices in working together.

Speaker 1:

I had the benefit and luxury in my previous company and this company, where product team and CS team kind of part of the same team previous company and this company where product team and CS team kind of part of the same team. So there's a lot of good overlap where when we're building something in terms of analytics and metrics, we are looking at both the granular and the big picture so that there's something in it for both teams. And then the CS2, the stronger the bond is, I think, the better, because the CS team is the one who, day in, day out, is talking to the customer. The PM might do like QBR, or they talk to the customer whenever there's one new feature to talk about, and there's a lot more wealth of information that CS teams often have, sometimes not something PM wants to hear, but that relationship has to be strong, right, because you don't want to only hear the good feedback. You definitely want to hear the daily users' pains and struggles as well.

Speaker 2:

Yeah, the bad stuff makes you better, absolutely. Are there interesting things that you've seen either your customers do or you've seen kind of out in the wild, where a company or a digital CS person is using these predictive analytics in a really unique way to engage their customers, like any digital emotions that you've seen that are just kind of neat and you've been like, oh, that's really innovative.

Speaker 3:

I think the general trend has been B2C companies are really good at digital CS. I mean, they really don't really have CS right. It's the product doing most of the things, and I think what we're seeing is more enterprise products learning and borrowing those kinds of capabilities, like sending nudge emails, and especially these days with a lot of integrations happening with different products. Like, a lot of times I get emails and stuff from heads of CS or whatever, but they're all automated, but they're all like timed at precisely the right moments. Yeah, it feels like I'm getting handheld by somebody who wants me to learn more about features or get onboarded with a product, but in reality, it's just all the automation that's happening behind the scenes.

Speaker 2:

Yeah and Dunn. Some of that stuff is very obvious and not done well and some of it is like you don't notice that it's an automation. Like not long ago I was chatting with Josh Schachter of Updateai and I was telling him that, like the emails that you get from Update AI, when you've like stopped doing stuff, it feels like it's just Josh reaching out when in reality it's not. But it's like those kinds of things where it feels like personable and I think to do that well, you got to have your data on lock, because sending emails like that is if you're not confident with your data. Sending emails like that is like a no-go.

Speaker 3:

Right, and that's kind of where our platform also does really well Because, again, like Arun said, we look at journeys. We understand where people need to be reached out to or where they're going off track. People need to be reached out to or where they're going off track, so it's not like everyone is getting the same message every time they reach certain activities or something. It's like just the ones that have gone down the bad path, those are the ones that are getting those targeted messaging, so it really does feel like it's made for them, but in reality it's like the data kind of targeting them at precisely the right moments.

Speaker 2:

I also love when companies spend time celebrating their customers. I don't think we do that enough, like we like to get in the weeds when something goes awry or logins dip or whatever it is, but I think there are a few companies out there that really celebrate their customers when they do something that you want them to do right.

Speaker 3:

Yeah, I think a great example for me for that is we use Cloudflare and hosting the website and stuff and it's kind of behind the scenes. I'm not using it every day or logging in, but every once in a while they send me this summary email saying hey, this is how many visitors you've had, this is how many attacks we've prevented and this is your distribution of users across continents and stuff like that and this is your distribution of users across continents and stuff like that.

Speaker 2:

Yeah, like those kinds of things are so valuable, I think, on multiple levels. Right, and that's where you also get into like personalization, because if I'm an executive or an exec decision maker for a certain account, I'm probably going to want to know some different things than, let's say, an admin would or an end user like you, where it is helpful to look at some of the detailed data that's behind that. But as an executive, you may not want that. You may want just like indicators of return on investment and those kinds of things, and I think companies that are focused on those kinds of things are making. When you do have a QBR or when you do have a meeting with a customer, you're not spending time showing a boring slide on where stuff is, because you're distributing that stuff all the time You're actually focused on. Okay, this is where we are, but let's focus on next steps.

Speaker 3:

Right, and I think the best kind of product-led companies are really great at this because it's naturally part of everyone's workflow. You're not really consciously thinking about how you're getting value out of products, but every once in a while when you get that summary, it's like oh, you've saved this much time because you're recording these Loom demos and sharing it with people not having meetings and stuff like that.

Speaker 1:

Yeah, and Alex, one of our customers. They do something very interesting with data, right? So what they figured out using data is they have customers who log in every day, they access their portal, do a certain activity and from the outside you might think, oh, this is great, you have daily active users, you're using that feature, your portal, great. But they figured out there is this, another metric. You had to do that a certain count every day, or at least once a week. Only those actually realize value and would then buy more from you in six months or renew from you in a year. So that kind of a hidden metric which they could discover because of data, which then they now start tracking towards. And that is one of their North Star metrics with every customer. And that was interesting because it kind of goes against the traditional B2C of like, yeah, they've logged in, they've experienced the tool, that's good. No, there is that one hidden thing which only data could tell you.

Speaker 2:

Yeah, that's so cool. And I love that next level of data because, like logins let's just take logins for a second Guess what that spike of logins is them getting all their data out because they're moving to another platform. It's like it's not really that indicative. But if you can really hone in on this section of the platform or the user, even on a persona level, the user level data that you want to drive with all your customers, and so if you know that which sounds like this customer got a good handle on, then you can build around that and build your digital programs to drive that behavior that leads to stickiness and value. That's so cool, I love that.

Speaker 1:

Yeah, and I think Pritam may be able to expand a bit on that, because this is a place where things like AI and ML and these algorithms are really good at and humans really struggle with in terms of finding those patterns. Preetam, maybe you can expand on what that actually means.

Speaker 3:

Sure, I think if you had to intuitively come up with the right signs when somebody is ready to expand their license count or is ready to renew. You're kind of very subjective and you don't know the right metrics to track or what metrics are even useful. And this is where, like, machine learning, algorithms have been useful for like decades. Right, they are the one where you just like feed models, lots of data, and they figure out hey, these are the four metrics that matter the most, based on your previous customers, and from there you can use those as the benchmark to predict future churn and revenue risk or expansion opportunities. Right. And these days I think it's gotten a lot easier because there's just a wealth of data that we can take advantage of. And I think this is where the trend has changed. Before, it was mostly people coming up with health scores based on their own intuition, because they didn't have the data, and now, because there is data, you have something to compare against and you can keep improving on those things.

Speaker 2:

Yeah, I mean the analogy I would use for that is like there's a reason why command F exists for you to like look through a long document for certain words, because the human is not.

Speaker 2:

We're not programmed to scan through data and look for specific things, and that is, I think that's especially true for pattern recognition, especially true for me. I can't solve a Rubik's cube to save my life there are some people who are better at that than others. But like that sense of like you could, I mean I feel like maybe we've done this in the past, but what used to take a room full of data analysts pouring over printouts is now in the box, so to speak, and it's super exciting and underutilized. Right, I think there's a ton of folks out there who are still doing it the old school way because hey, guess what the new school is coming like really quickly and it's exciting. But, like I think we're going to start seeing, like, this massive shift. The more platforms implement this kind of thing and the more vendors like you guys exist, the easier it'll become for organizations to be really customer-centric, because they know what the frick is going on.

Speaker 3:

Yeah, it's funny when, just casually, when we say like hey, we have this AI product, people like to joke okay, is AI going to take my job? But the reality is it's not replacing humans. It's just reducing the busy work. Right, because otherwise, like, how much time do people spend going through their call notes or deciding whether they remembered an action item or not? Right, these things take up time and take up energy, and it reduces effectiveness. Right, if you have to spend all this energy making sure you're not forgetting things. But that's where AI can help improve your efficiency and accuracy. And it's not going to replace you as the ultimate decision maker, but it helps you make those decisions a lot easier.

Speaker 2:

Yeah, it makes us all better, makes us perform better and look smarter than we actually are.

Speaker 1:

And I also at the same time, I fear organizations and vendors should use this in a meaningful way too, Because I was joking with Pritam, where I did something. Some vendors AI, figured out what I did and sends me a summary email, and maybe I have an email client which uses AI to summarize what that AI sent me and give me another summary, and it just might lose all sense and meaning with all those layers in the middle. So it shouldn't be like just AI talking to AI.

Speaker 2:

I mean it should be used as a means to an end, not the end in itself. I mean, look, here's my take on it, right? When it comes to analytics and pattern recognition and those kinds of things. I think AI is the most fantastic thing ever when it comes to sending emails and even call transcripts and things like that. Sure, it's great, really useful and great to remind you of things and whatnot, but I don't trust it at all to send like I don't really want you know, an AI generated email to be sent without my eyes looking at it first or without me like going in there and wordsmithing things and making it a little bit more me and those kinds of things and so. But that's again, it's a tool, right, it's here to help and it's here to reduce, like, your level of effort on these things. But, my God, if you're just relying on a bot to send out emails, then good luck. I don't think we're there yet.

Speaker 3:

Yeah, I don't think we're there yet. I mean, I'm bullish on AI, and probably more than many people, but yeah, even I am skeptical, but we'll see. I mean, one thing that's really surprising me, like kind of every day, is the capabilities that these systems are getting. Openai just released a new model that's twice as fast, half as expensive, and the trend keeps continuing, right. Yeah.

Speaker 2:

I mean, look, we're going to get sentient behavior relatively quickly, I think right, and that's either a scary thing or a good thing, depending on how you look at it, or whatever. So it'll be interesting to see at what point we reach that level where these bots will basically act like you, because they've analyzed your voice, going back forever, they analyze your email account and they know exactly what kind of language you use, and then act as you. It's kind of scary, but whatever.

Speaker 1:

It's not here yet.

Speaker 2:

Look, I mean, this has been a phenomenal discussion and, as we kind of round things out a little bit, I always like to ask what are my guests paying attention to in terms of content books, podcasts, videos, youtube channels, those kinds of things like what, what are you two paying attention to?

Speaker 1:

to stay on top of things, one of the podcasts I listen to, which I think a lot of folks in the valley also listen to, is acquired. They release once a month. They're long series but it really helps, especially I mean this is my first role as a co-founder same as Pritam ability to get deep into how an organization functions. What they went through versus just reading a thousand word article on this company got acquired or they did this. This podcast goes a lot more into detail, so that's one of my favorites.

Speaker 2:

I love that, so that's one of my favorites. I love that, and I think that's really important, not just for founders, but I think it's important for folks that work especially for startups, because understanding what those key drivers are in like getting investment or moving towards an exit or all those kinds of things help the individual or the management team to drive those same things and understand the context of like why certain decisions are being made. So that's cool. I love that.

Speaker 3:

Yeah, for me, I think. Yeah. I get most of my news from Hacker News, especially being so involved in AI stuff. There's always something relevant for me on Hacker News. I also like to read the comments, especially to see what the general sentiment about like news is what people are doing with AI and some other new things coming out these days. Yeah, I love following Jason Lemkin on Twitter and reading his blog posts Kind of a great source of information about SaaS and startups and finally just following people on LinkedIn seeing what posts are kind of relevant to us stuff like that he was saying something interesting about his news consumption, which is to say he doesn't consume news.

Speaker 2:

All he does is pay attention to the comments in his videos. And, yeah, he has millions of followers and all that kind of stuff, but that's ultimately where the conversation is happening, and so he finds out about world events just by reading his comments section and I feel like people sleep on comments sections, especially with very visible people. A, you usually find some funny stuff, you find some divisive things in there and you got to kind of take things with a grain of salt, but it's always interesting to see what is the conversation behind a certain topic versus just what the author has said. Cool. Are there any people that you two want to give a shout out to that are doing like really cool things in digital or in AI? Maybe Arun, do you want to start?

Speaker 1:

Sure, I think you mentioned their name already Update AI. Good friends with them, interesting to see what they're doing. The platform is pretty cool. It goes beyond the things that other vendors are trying to do, which is just slapping on some kind of LLM. They're doing pretty cool things, so, yeah, that's the name I would give a shout out to.

Speaker 2:

Yeah, they do so much cool stuff and people don't realize that update AI is, it's not just a note taker for CSMs and stuff like that. There's a whole backend data engine that correlates all this data and gives leadership like massive insights about customers is like so, so cool. So, yeah, definitely.

Speaker 3:

Yeah, not specifically to CS, but yeah, there's companies like Limitless AI that have like hardware devices. I think that's really cool to see, like beyond just text and stuff, how it connects to the real world and in-person interaction. I think it's really cool to see.

Speaker 2:

That's cool. All right, awesome, lovely. I've enjoyed having both of you on. We'll have to give Alok some crap for not joining us, but it was awesome speaking with both of you, love what you guys are doing. Obviously, joining us, but it was awesome speaking with both of you, love what you guys are doing. Obviously, people can find you on LinkedIn, anywhere else you want to point people to.

Speaker 3:

Yeah, twitter. My handle is my full name, pritam Jinka.

Speaker 2:

Thanks for calling it Twitter.

Speaker 3:

Yeah, I'm old school.

Speaker 1:

Yeah, just LinkedIn for me. Yeah, last name, first name.

Speaker 2:

Sounds good, all right, thanks, both of you. Have a good rest of your day and enjoy Pulse, because we're recording right before Pulse. Yeah, it'll be our first time and really excited for it. Thank you for joining me for this episode of the Digital CX Podcast. If you like what we're doing, consider leaving us a review on your podcast platform of choice. If you're watching on YouTube, leave a comment down below. It really helps us to grow and provide value to a broader audience. You can view the Digital Customer Success Definition Wordmap and get more information about the show and some of the other things that we're doing at digitalcustomersuccesscom. This episode was edited by Lifetime Value Media, a media production company founded by our good mutual friend, dylan Young. Lifetime Value aims to serve the content, video, audio production needs of the CS and post-sale community. They're offering services at a steep discount for a limited time. So navigate to lifetimevaluemediacom, go have a chat with Dylan and make sure you mention the Digital CX podcast sent to you. I'm Alex Trigovich. Thanks so much for listening. We'll talk to you next week.

Digital Customer Experience and AI
AI and Data in Customer Experience
Data-Driven Customer Success Approach
Leveraging Data for Customer Success
Advancements in AI and Digital Engagement