Retention Starts from Day One: Strategies for Success through Personalization and Experimentation
CEO & Co-Founder
Today on the show we have Krish Ramineni, Co-Founder and CEO of Fireflies.ai.
Krish shared some amazing insights on topics like transitioning from product management at Microsoft to CEO of a startup, the challenges and advantages of working in a small organization, and the importance of synthesizing qualitative and quantitative insights.
We also delved into the impact of ChatGPT and generative AI on the market, the strategies Fireflies has used to stay ahead of the competition, and the future of AI in meeting assistance.
We can't wait for you to tune in and, as always, your feedback is invaluable to us. Let us know your thoughts on this episode!
[00:00:00] Krish Ramineni: I honestly think churn and retention starts from the day a person signs up. Think of it as a ticking time bomb and what you can do to foster that relationship.
[00:00:10] Andrew Michael: This is Churn.FM, the podcast for subscription economy pros. Each week, we hear how the world's fastest growing companies are tackling churn and using retention to fuel their growth.
[00:00:30] VO: How do you build a habit forming product? We crossed over that magic threshold to negative churn. Unique to invest and customer success. It always comes down to retention and engagement. Completely boosts that profitable and growing.
[00:00:21] Andrew Michael: Strategies, tactics and ideas brought together to help your business thrive in the subscription economy. I'm your host, Andrew Michael, and here's today's episode. Hey, Krish Ramineni, welcome to the show.
[00:00:55] Krish Ramineni: Hi, Andrew. I'm excited to be here.
[00:00:58] Andrew Michael: It's great to have you. For the listeners, Krish Ramineni is the co-founder and CEO of Fireflies.Ai, a voice assistant that records, transcribes and analyzes all your meetings. Fireflies.Ai is on a mission to automate work for meetings and is used across tens of thousands of organizations. Before Fireflies, Krish Ramineni was a product manager at Microsoft. He was part of the first growth team for Microsoft Office, building the A/B test experimentation and customer voice analytics platforms. So my first question for you, Krish Ramineni, is what has been the most challenging aspect of transitioning from product management in an organization like Microsoft to CEO of Fireflies?
[00:01:37] Krish Ramineni: People say that product management is wearing multiple different hats, working with many different stakeholders. But you don't really see that until you go join a startup and you then realize you truly have to work in many different departments. Because in larger organizations, product is sometimes siloed away from the customers, even though one of the first rules of product is to go talk to customers and understand what they need. Usually a lot of the product decisions are made in the rooms, conference rooms. It's based on experiments and hypotheses versus actually observing what people are doing. When you were working on a startup or founding it, I was the first support person. I was the first salesperson. So you have to channel a lot of the things from design to engineering to what support requests come in. I think it gives you a full 360 degree experience and makes you more appreciative versus larger org, PM, just writing specs. It doesn't quite do you justice.
[00:02:36] Andrew Michael: What would you say though is actually easier to make better decisions? Because now that you mentioned, I was just connecting all the dots in my mind. When you're at an early stage startup, there literally is just so many different data points that you're picking up on. Whereas when you get to a large organization, things are a lot more structured, a lot more organized. They're coming to a lot more distilled formats. You typically might have, even a research team providing insights and data analytics, providing research for you to make decisions on. Whereas at this early stage now, literally you are picking those insights up from the support tickets and from the engineering team and the feedback that you're getting. How do you juggle this difference between synthesizing the information that's coming at you?
[00:03:14] Krish Ramineni: At a larger organization, you can have small 1%, 0.1% wins. And that's still considered success because you're operating at such a large magnitude. When you're at a startup, you need to hit home runs. You need to essentially get 10% plus delta. Otherwise, it's not significant enough to move the needle for the business. So you do have to take bigger swings. You have to be more aggressive, but you also have more leeway to make mistakes. I think that's the advantage. I think that for someone that wants to be told what to do and given a nice platform and wants to operate in a stable environment, large companies are great. If you enjoy chaos and if you're a little bit strange in that way where you enjoy the pain of figuring out the unknown and constantly working in a place where there's no process, then there's certain people that thrive really well at startups. So I think that it really depends on the personality type because in school, you're told that there is a right answer. You are told that there is a particular way to solve something or get a particular grade in a class and then go on and work a particular job. I think the path to entrepreneurship is, there are no right answers. Sometimes there are no wrong answers either. So you'll have to identify and take a chance and go with your gut instincts. And it's only after you've climbed halfway up the mountain, you realize, oh, I'm going the wrong direction. I need to go back down and go through another route or do I give up?
[00:04:49] Andrew Michael: Definitely I think one of the quotes that I've loved, like was given to us, I went to previously through a startup accelerator program. I think it was like on day one, the program lead at the time sort of said, if you're building a startup, you need to learn to embrace uncertainty. And like that's the only way to survive in this space, I think, is if you're able to be agile and be able to sort of understand that things are going to change all the time. Like if you're not a person that enjoys change, like you're not going to enjoy building a startup. And I've kept that very close. So I think it's interesting as well, looking at your background, heading up, being one of the first growth teams for Microsoft Office, going through experimentation framework and then also being the champion on the customer voice analytics platform. You definitely have from the outlook, like a background, combining qualitative and quantitative insights to form your hypothesis then and drive decisions. What does it look like today where you are at Fireflies building product? How does your team operate and how are you making decisions? What gets shipped to customers?
[00:05:45] Krish Ramineni: I will say that working at Microsoft was very instrumental in order for me to learn both sides of the table. So what I mean by that is you have to be very data oriented, understand metrics, understand why an experiment failed or succeeded and how you pull data and make sense of data. When I worked on customer voice analytics, I took another route where I said, hey, I'm not just going to look at the data like what did a user click inside an app. I'm going to actually see what are they saying in terms of qualitative feedback and then use that feedback to see if that pairs up with what's happening inside of our system. And I took that very seriously to heart because you can sometimes see that certain experiments while on paper might look good. It could be that they're adding more friction and that's why there's more engagement. They're spending more time than they should inside a platform. So I think the feedback piece, the qualitative piece is just as important. And someone for me, if you have that product engineering mindset, you sometimes fail to empathize with the people that are at the end of the screen that are using your product. So I do believe that it has to be a healthy balance.
[00:06:59] Krish Ramineni: And when it comes to Fireflies, we take a very balanced approach when it comes to understanding what customers are saying, why are they churning, what feedback do they leave, what are they rating us inside of our own little scores, what are we doing for customer satisfaction, CSAT, understanding everything around the voice of the customer. And then we're also running experiments and then pairing it with the data. But we always like to start with what customers are saying because that tells you what you need to build. That tells you where the gaps are in the product. And so we're set up where we have a growth team. We also have the growth team very much tuned not just into metrics, but how users are interacting with the product. What is the UX like? What is the feedback like? And sometimes the feedback may be completely irrelevant and you can choose to ignore it. But at the end of the day, I know that when I sit through and every day I read about 100 to 200 pieces of feedback, every single day for the last four years, it helps you build a really strong internal compass of where you need to go with the business.
[00:08:04] Andrew Michael: Yeah, absolutely. I think being so close to the feedback like that and having that constant stream is really powerful. It's interesting you mentioned as well that the team runs experiments and I'm keen to understand, like at which point did the team start to run experiments? Obviously, like at early stage startups, typically we don't have like the significant data to get any statistically significant results and, but I know now that you serve tens of thousands of organizations, at what point did you sort of decide, okay, now is the right time in the company's growth for us to actually build this team and to start with an experimentation program?
[00:08:38] Krish Ramineni: There's a lot of infrastructure work that has to go in place. First, you have to build out the platform. You have to architect it right, you have to instrument it. So that is definitely something that wasn't easy and we built a lot of that stack by ourselves in-house so that we had full control. You can definitely use other tools to help you and some of the data analytics platforms are great at helping you get off the ground, but you may not be able to measure everything and understand what's going on. So there was significant work almost a year's worth of work that went in before we could fully be aggressive. I still think that there's more things we could do to run proper stats type experiments, but like rolling out new features and functionality to a small cohort of customers, seeing how they react to it and then increasing that rollout over time. Those are simple things in theory. But like when you actually have to implement it and you accidentally roll something out that needs to be reversed so there's a lot that goes into that. Once you have that infrastructure in place, I think that you have to look at what sort of things are going to be worth experimenting on and what are the things that we can try to help optimize. So one thing we've heavily tested and improved on is our AI meeting summaries and notes. And we also collected qualitative feedback on what notes people like. So after hundreds of thousands of pieces of feedback and lots of testing, we're able to determine, okay, what is the best sort of notes that a lot of people would actually find value from? And how do we structure that?
[00:10:14] Krish Ramineni: So there's a lot of learnings and insights there because the way you take notes is fundamentally different from the way I might take notes and what I think is important is different from what you think is important from the same meeting, so we said we have to provide an interface where you can see action items, you can see an outline of the meeting, you can see the summaries, you can see shorthand notes and that was a culmination of a lot of that feedback. And even there, I don't think we stopped with that because we realized that at the end of the day, that sort of data will still leave a certain amount of the audience where they're craving for more and with that, we decided to say, look, let's start guiding customers and really personalizing the experience. So if you will have a particular style of taking notes, we'll help you tune Fireflies to take notes the way you do. And so that's like the next holy grail of what we're doing is personalization. And this happens in the consumer space as well, right? Classic example is, the Facebook newsfeed or Google search. You start seeing things that are more relevant to your interests and needs and what you care about.
[00:11:18] Krish Ramineni: So in the same way, I think experimentation at the baseline is understanding what works or what doesn't. But at a more advanced level, it's about tailoring to your particular audience, your particular customer base, that's really when you have things in action because you realize no one solution is a perfect fit for everyone.
[00:11:38] Andrew Michael: Yeah, I can definitely see as well, like the need for personalization in the space. I think the one question that came to mind is when your team reached out as well, is that I think you're in a hyper competitive space that wasn't hyper competitive maybe two, three years ago. When you got started, you were probably one of the first, like two or three companies in the space and probably over the last year, there's maybe been like, I don't know, like 50, 100 new entrants like just popped up out of nowhere and obviously with the rise of ChatGPT, I think that just like popularized things. How have you noticed this shift internally at Fireflies? What is, some of the movements that you're seeing in the markets and has it impacted the business in any way?
[00:12:22] Krish Ramineni: ChatGPT and generative AI are extremely popular today and they created this whole buzz and hype. We were fortunate to be able to start working with that technology a lot sooner, almost a year or two years ahead of the general rollout, partly because of our work with OpenAI, us having the same investors as OpenAI. So that was always helpful but we were always thinking about this problem, LLMs, large language learning models and how to address that. So for us, when we started working on it, improving it, it now became table stakes that we do a really good job with it and we have this opportunity and privilege to being early in the market and being early doesn't necessarily mean you're the winner, but it does give you the ability to learn a lot. And there's a lot of fast followers who can then come and try to copy you, which we've seen but I think that when you think about where we are as a business today, we look at a couple key areas and why someone would choose to come in and enter the market. So one is the cost of transcription is much more affordable than in the past. The accuracy has definitely improved and we've had to deal with a lot of those challenges in the beginning. Now, no one's really talking about that. Everyone assumes transcription is great and the cost is sustainable. So it creates an opportunity for more people to enter but we entered a market when we knew that that's where things were trending so we were able to start ahead early.
[00:13:51] Krish Ramineni: The other thing is AI and LLMs are actually making it much better to understand the intent and meaning of conversations. So that was the problem back in, even 2016. People couldn't solve that, there was like this huge chat bot, Slack bot wave, but a lot of them couldn't fulfill on the promises of what you want, like just an agent or assistant that you can talk to and interact with. So again, we were there at the right place at the right time, but that was a function of us also thinking about the long term direction where the market and technology was heading. And I think when a company comes in today, whether they are a small startup or a larger company that's trying to emulate, they have to think about a few areas.
[00:14:32] Krish Ramineni: One is Fireflies has some great network effects, it's being used across over 150,000 organizations. When people think about AI notetaker, usually the first thing people refer to is, oh, you're using Fireflies. People see it in meetings, we've spent zero dollars close to zero on marketing and most of that growth and all the notes that we've taken for millions of people has been a function of people seeing it, talking about it. Meetings are just naturally very, very viral. So all of those factors, you have that, you have the technology advantage, you have the economies of scale so where, you can provide Fireflies at a much more affordable price point than some of the other folks out there. So today, Fireflies is, you can get started for like $10 a month. There's also a free tier and a free trial and there are other products that offer, same as what we do, maybe even less features that are charging $100 to $150 per seat oriented towards the enterprise.
[00:15:30] Krish Ramineni: The other thing is there are some niche products that are coming out and saying, okay, we can't do what Fireflies does, but we'll just do that AI notetaking just for recruiting or just for sales or just for customer support. And I think that's fine, like if that's the direction people go but because the AI notetaker is such a perfect business use case that fits for everyone inside an organization, we're willingly able to go after that market. I think that's the market dominant strategy that we think about because if there's another company out there that's trying to compete with Zoom, for example, on the video conferencing side and said, okay, we can't compete with Zoom. It's everywhere, everyone's using it, the small 10 person businesses all the way up to the large enterprises so we're going to go make Zoom just for inside sales. And I'm sure there's some companies that can do that. But our approach is that in order to win this market, in order to do well in this market, we're willing to compete. We're willing to price aggressively so that people can get access to this and the mission has always been, how do we democratize AI and meeting assistance for everyone in the workplace? And I think that's been the fundamental driving goal for us and the reason why we're able to do what we're doing today.
[00:16:46] Andrew Michael: Yeah, it's interesting. You sort of highlight as well, like going down a specific niche and focus, I think, because that's like typically the first piece of startup advice that people are given is like, pick a really small niche, focus on that and then build out from there. Whereas in your approach, like you're basically saying, like, this is one problem that fits across the board for everybody. Like, why should we narrow down on this when we can take the whole sort of space? And I think if you think about it from a company's perspective, especially in today's times, they're a lot more likely to adopt a tool that everybody can use that they don't need specialized services for and then have to manage multiple different accounts with different providers and different fees and stuff than where you are today. So I think that makes a lot of sense.
[00:17:30] Andrew Michael: I think it's just like it was a very fast and rapid change and I think it's interesting, like to hear, like, what sort of reactions did you have internally at the company? Was there any, like, very quick product decisions that were made? Did you make any quick shifts in strategy from your side, because it literally was, I think, like, from one day to the next, like everybody was talking about the service and then everyone's like how am I going to build something on top of the service? And then all of a sudden, it just like things started popping up but I do like, I hear all the points you ticked off, you see that in the markets and Fireflies is definitely prevalent as a meeting note taken, all of meetings I attend and so forth but I'm just interested and keen, like, was there any sort of big shifts in the company that you made during this period?
[00:18:15] Krish Ramineni: I believe last year, end of last year, right around when ChatGPT was there and we were rolling out some features related to LLMs and generative AI but I made an executive decision at that point in time saying, we are going all in on this. This is the flag that we need to put down and say that every part of Fireflies, every feature, every function, even every internal tool that we build will have something to do with generative AI and will implement and leverage that technology. And so I think that was a shift in the minds of everyone in the org in terms of, how can we make a feature better? How can we solve a problem? And then can generative AI help us make that even easier? Right. I rather think about it that way than you see a lot of these companies coming out of accelerators today where they're building wrappers on top of the generative AI interface. I think we were fortunate that we've already built a proper SaaS business, set up the foundation, all the core parts of the business and then generative AI was that accelerator that can take you from 90% to 110%. Whereas if your hook and your entire product is based on just saying, I'm going to take this generative AI tech and I'm just going to try to come up with some use case or cool use case and build a wrapper around it or UI around it, I don't think that's sustainable.
[00:19:38] Krish Ramineni: And you will see with the AI hype cycle, the faster you go up, the faster things come down. And so when the dust settles, what is going to actually happen to a lot of the companies? So there's a lot of consumer use cases, which are cool in theory, but maybe aren't actually practical and not something you can build a business around. And I think it's the same case for companies where they're trying to come in with a unique hook. So we consciously thought about, okay, we know this generative AI stuff is relevant. How can we use it to improve our business? And that was like a really immediate pull, it's no longer, this is a nice to have, this is something that you have to put in place. And I feel like a lot of large enterprises that may not have been able to move as quickly will soon realize that if they don't adopt generative AI and all of this AI transformation, they'll be left behind by competitors or their entire business will operate slowly.
[00:20:32] Krish Ramineni: So in the last couple of months, we've seen incredible demand at the enterprise level for Fireflies, which historically we thought it would be like all the dabblers, the early adopters that would want to try this stuff out because enterprises know today how important this stuff is, how much it can save them in terms of time, money, costs, automation. So all of those things coupled, it's like the perfect storm and I feel like we were positioned well to take advantage of that storm versus if we realize the wave is starting and then you catch it late and then you build something quick because this is like four years in the works, it's not something where you have that, where you build it over like overnight and you automatically have an enterprise grade product.
[00:21:13] Andrew Michael: Yeah, I know for sure. I think that's definitely like a strong, strong point because it does take a really long time to build a great product. It's not something you can just spin up and like, have an amazing experience, user experience and all around. Like you can build a good product for early adopters, but you can't build a good product for the mass market very quick, I think, and it takes its time. I think the other aspect that you made points of as well is that like there's a lot of services that have just popped up, which are essentially just a wrapper around GPT itself and some of them even like reaching billion dollar valuations and I think quite soon like we'll see a lot of those crashing down. I mean, ultimately like ChatGPT, now you can go in and generate the copy you need every day. They're adding new features and ways in order for you to do it. So services like Jasper and others, I think it's going to be interesting to see how this all plays out over the next year to two. And yes, you can get fine grained and improve things, but it feels as well the rate of improvement on large language models all around is getting to a level where you can basically get what you want when you want.
[00:22:19] Andrew Michael: I think the quality side of things as well, I've seen, I've been exploring, like podcasting tools now. I considered looking into the space, but obviously it's very, very small as an industry but just how many, like tools popped up to help create clips and transcribe and do all of this sort of stuff and like 99% were just crap. Like you go in like half of them, on working, they're uploading, they managed to break like number two on product tons of the day and people get excited by them but then they just don't work. So I'm keen to see, like how many of these products and services are using in the markets? Like what do you think are some of the most interesting use cases and what are you seeing are some of, like the things that you believe are just going to go nowhere?
[00:23:01] Krish Ramineni: That's a very interesting question because it's hard to tell who is solving what that will be fundamentally valuable in the long run. So for example, you have, copilot for GitHub that will help you write code. We've implemented some elements of that into our own business and that helps us move things quickly. We've been doing work with, using LLMs for our support tickets and I think that's a common thing, right? How do you answer support tickets? Search, enterprise search is something that I think will be a really valuable use case for folks out there to help you better query and index information. So I do believe that when you look at creative fields, right, where you can use some of this technology, maybe you use AI to remove background noise or you generate videos or clips or images, that's a little unclear to me how that will play out because I still feel like it's not going to replace a creator. You're still going to need someone that's going to be able to do good work. Or the way my co-founder says it is, everyone is using generative AI to write up like a blog post then the bar for blog post being really excellent to stand out above the noise is going to be way higher. So it might do well getting the low performers up to a certain level into the top 75 percentile quartile but I still think great content, great videos, great visuals will require human touch. Maybe generative AI will help people move things a little bit faster but I think that if you level up and you just are pumping out more noisy content, I don't think that's going to make a big impact.
[00:24:39] Krish Ramineni: So it's just going to be more noise for you to filter through and I think that's why also Google and other platforms might put, like filters saying, hey, we're not going to allow generated content because it's not like high quality. So I think there's different avenues to it but at least from what I can speak of in the enterprise, those are the big areas that I think about is how do I query data faster? Search elements, writing code, answering support tickets, the universal use case, like I say, is the AI notetaker. It's things that can actually help solve a particular problem versus just recommending or creating clips in podcasts and stuff that may not necessarily suit to the liking of the customers. Right. The other thing I think about deeply is in the AI world, you need to first have a data strategy before you have an AI strategy. If everyone is building their products as a wrappers or be on the same data source like public data, I don't think there's a unique value that you are creating because what is the defensible note? If you are using the same public data to create a solution, is UI the defensible mode at that point in time? Is your community the defensible mode? So there's different elements to that that are really important to think about because with Fireflies, all of the data is unique to your organization, your meetings, the value of the LLMs goes up exponentially and how you can leverage that. So that's why I'm more interested in the enterprise workspace and what you can do for people on top of their meeting data or all of their unstructured data and I think that gives you unique insights that you can deliver to customers versus building a wrapper on the same data set that everyone else uses.
[00:26:27] Andrew Michael: Yeah, for sure. Data like having unique data sources is the key and I think moving forward, more and more people and companies will realize that and try to find ways to unlock the data that they hold internally to leverage them through LLMs and other AI models. The thing I wanted to ask as well, like from a perspective, obviously you introduced a growth team. You've been experimenting quite a bit, what would you say is, being one of the biggest breakthroughs or changes or projects, if you even want to call it that, that really helped you boost retention? Because obviously I think your product can be widely used, it's easy to adopt, but also potentially to forget. What was one of your biggest breakthroughs when it came to retention and increasing it for the business?
[00:27:11] Krish Ramineni: We've seen a lot of uptick in adoption of AskFred, which is our ChatGPT-like assistant where you can query based on the meetings and it's allowed people to be so creative, come up with things and find answers to questions that the summary itself wouldn't answer. So I can go in and AskFred, how many seats did the customer want to buy instead of having to sit through a one hour long call? I can go and have it create sound bites of, hey, I want you to go to all the parts of the call where they're talking about feature requests and then go create some sound bites or clips related to those feature requests that I can go look at. And then it'll suggest to me, I can see which ones are good, which ones are not bad, and then I can save them. So in a way, the sound bites feature and the AskFred feature puts the power back in the user's hands to decide what information they want to pull or extract. And then the next evolution of this, as I alluded to earlier, is these general super summaries are great, but what if I can say, hey, I like to write my notes in a particular way. These are the five pieces of information I care about from a sales call or support call or a hiring interview. And I want you to be able to write those notes that way.
[00:28:23] Krish Ramineni: Okay, let's create a way for you to teach the AI without writing any code, no code or low code, guidance, and then have that guidance to generate really amazing notes. So if we can allow our customers to better interact and work with the technology, that's the underlying technology, I think that also directly addresses churn. Because now you're giving people what they want versus thinking, what they need and you providing that to them. We're like, basically giving them an open buffet and saying, you pick what you want to eat and how you want to use the technology.
[00:28:56] Andrew Michael: Nice. And really like taking that personalized approach. Personalization, I think, essentially is like the strategy alluding to here. I see we're running up on time, so I want to make sure I ask you a couple of questions I ask every guest. Let's imagine a hypothetical scenario. You join a new company, churn and retention is not doing great at this company. The CEO comes in and says, hey, Krish, like you're in charge. We need you to turn things around. You have 90 days. What do you do? The catch is you're not going to tell me I'm going to read customer feedback or look at the data. You're just going to take a tactic and run with it blindly, hoping it works at this company. What would you do?
[00:29:33] Krish Ramineni: A specific tactic that I would look for is understanding exit interview surveys. I know that they're overrated, but those are some things that you definitely want to do. I love a resource, price intelligently, which tells you how to price strategically, understand the value customers want from it. So, doing exit interviews or collecting feedback through that and then using that to make product informed decisions, I think is really, really valuable. I think that's a missed opportunity if you're a PLG company specifically and you're not doing that. If you are an enterprise company, I would focus on orienting. You're not getting the sales or like after POCs, people are not adopting your product, then I would orient around creating what exactly is the time, money, value that you're saving for that organization. So understand the value you are creating for folks. And then if you want to get really good in terms of product tactics, go look at the funnel and see where the drop off is that activation versus usage and determine what does success look like for a user and how many steps are they having to take to get to that level of success?
[00:30:50] Krish Ramineni: The simple formula, but then you'll see there's drop off at every step and you'll understand, oh, maybe my onboarding is not clear. Oh, maybe the time it takes for someone to get value is not fast enough and maybe I need to get them to do two or three actions so that it's not a cold start. So it's, usually there's no one silver bullet, but I always like to follow the customer journey starting from the bottom. Exit interviews when person is churning. There's very little you can do when someone's about to quit on you, right? But it gives you an opportunity to not make that same mistake for future customers.
[00:31:27] Andrew Michael: Very cool. What's one thing that you know today about churn and retention that you wish you knew when you got started with your career?
[00:31:33] Krish Ramineni: Churn and retention starts from day one, not at the end of the cycle, because just like in any relationship, it's not the breakup that you can save. It's like what you could have done right in the beginning to make sure you didn't get to that place where you're breaking up, so I think it's the same way. Churn is a painful experience, it's where the customer wants to break up with you. You guys made a promise to each other and you to the customer saying, we're going to deliver X and the customer did not receive that or did not appreciate the value that was generated. So, I honestly think churn and retention starts from the day a person signs up, think of it as a ticking time bomb and what you can do to foster that relationship. So, I always say this when it comes to customer success because you only really hear from customer success at the QBR or when renewal is coming up. And it makes you wonder, is your job only to inform me that a renewal is coming up or are you there to actually help me get value out of the product or understand what I'm not doing and proactively suggest to me what I should be doing inside of your service.
[00:32:42] Krish Ramineni: So if you tell me that at the time of renewal when I've already made up my mind to move to another service, I don't know if I still want to even give you the time of day, even if you can provide me all that value. So relationships should not be transactional in the business sense. So that also directly has an impact on churn but notice how I try to distinguish between enterprise level churn as well as self-service and PLG churn because you can still do customer success at scale, multi-touch without having to have humans in the loop. But is there someone on the other end actually looking at the data and making sure that we're preventing users from falling into the at-risk cohort?
[00:33:21] Andrew Michael: Yeah, absolutely. I like the analogy of the relationship and definitely agree. It starts on day one and it starts from the moment that promise is made and it's up to you to make sure you keep it and you keep nurturing and building that relationship throughout because it's not the day that you get dumped that the decision was made. It's all the little tiny experiences on the way that led to it. Very nice. Krish, it's been a pleasure having you on the show today. Is there any final thoughts you want to leave the listeners with before we drop off?
[00:32:42] Krish Ramineni: I thought this was a fantastic conversation for those folks that are thinking about churn and retention and generative AI and how to build SaaS. This is definitely my bread and butter. So absolutely follow me on LinkedIn if you're interested in this topic and more so than anything else, if you aren't using Fireflies today, definitely bring it in. Have it take notes on your customer interviews, on your sales calls, on your recruiting calls. It will definitely save you hundreds of hours every month.
[00:33:21] Andrew Michael: Very nice. For those listeners, we'll make sure to leave any of the references today in the show notes so you can find them there. And yeah, thanks, Krish. It's been a pleasure having you today and wish you best of luck going forward.
[00:32:42] Krish Ramineni: Thank you, Andrew.
[00:33:21] Andrew Michael: And that's a wrap for the show today with me, Andrew Michael. I really hope you enjoyed it and you were able to pull out something valuable for your business. To keep up to date with CHURN.FM and be notified about new episodes, blog posts and more, subscribe to our mailing list by visiting CHURN.FM Also, don't forget to subscribe to our show on iTunes, Google Play or wherever you listen to your podcasts. If you have any feedback, good or bad, I would love to hear from you. And you can provide your blunt, direct feedback by sending it to Andrew at CHURN.FM . Lastly, but most importantly, if you enjoyed this episode, please share it and leave a review as it really helps get the word out and grow the community. Thanks again for listening. See you again next week.
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My name is Andrew Michael and I started CHURN.FM, as I was tired of hearing stories about some magical silver bullet that solved churn for company X.
In this podcast, you will hear from founders and subscription economy pros working in product, marketing, customer success, support, and operations roles across different stages of company growth, who are taking a systematic approach to increase retention and engagement within their organizations.