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The Agile Brand with Greg Kihlström®: Expert Mode Marketing Technology, AI, & CX
#589: Product-led Growth and AI with Sonal Mane, Databricks
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The Agile Brand Promo Voice
The agile brand.
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Greg Kilstrom
Welcome to Season six of the Agile Brand where we discuss marketing, technology and customer experience, trends, insights and ideas with enterprise and technology platform leaders. We focus on the people, processes, data and platforms that make brands successful, scalable, customer focused and sustainable. This is what makes an agile brand. I'm your host Greg Kilstrom, advising Fortune 1000 brands on martech, marketing operations and CX best selling Author and speaker. The Agile Brand Podcast is brought to you by Tech Systems, an industry leader in full stack technology services, talent services and real world application. For more information go to teksystems.com now let's get on to the show. Today we're going to talk about the
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innovative use of AI in driving product led growth with Sonal Mane, Senior Director of Experience and Growth at Databricks.
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Sanal, welcome to the show.
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Sonal Mane
Thanks Greg. How are you doing?
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Greg Kilstrom
Good, good. Let's get started with you telling us a little bit about your role at Databricks.
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Sonal Mane
Yes, absolutely. First off, thanks for having me on the podcast and in the little bit research that I've done. Congratulations on your 500 plus episodes.
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Greg Kilstrom
So thank you, thank you all in
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Sonal Mane
all to see the growth and the relevance of the topics. To start with intros, I'm Sonal Mane, as Greg was mentioning, and I'm responsible for driving growth for data practitioners. So think data engineers, machine learning engineers, data scientists across the databricks ecosystem. And this includes both our customers as well as our partner organization. And this essentially means that I get to lead out on how digital experiences get built out for these audiences. My journey in tech has been quite a ride with 17 plus years and now going and driving both Product go to market as well as growth in companies such as databricks, Qualtrics and Microsoft. One thing that I've absolutely come to love is this trifecta, if I may, of AI meets product go to market meets customer experience. And you know, it really is a place where you can sort of imagine and put in place AI powered solutions and really build go to market strategies and delight your customers ultimately to land value. Prior to Databricks, I did found the digital customer success team at Qualtrics and then at Microsoft. I was responsible for the Microsoft for Startups team and worked on the products. Org as well across Office and Windows. So that's just a little bit about my journey here and of course happy to be helping out any aspiring tech leaders in my free time. But yeah, it's been fun working with all sizes of audiences and organizations as part of this journey. Excited to talk and share more about AI, digital growth and tech. So happy to dive in.
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Greg Kilstrom
Yeah, yeah, that's great. And I love how you characterize the trifecta here. I think we're going to hit all of those points here in our conversation. So the definitely a few things to touch on. I want to start with the combination of product led growth and AI. So you know, we've had some people on the show talking about product led growth before, but I think there's a lot to explore here, particularly when we add in the layer of AI as well. But could you explain how do you define product led growth and why is it increasingly important?
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Sonal Mane
Yes, you know, just, just to go back maybe a decade or two for our listeners, when I was part of the Office and Windows teams, you know, AI was this afterthought or like a separate team in a separate building that nobody really knew much about what was happening there. It's all the cool kids and the whiz kids, you know, would be working on it. And that also meant that the way we would create product, the way engineering would operate in terms of developing both features for the product, but also developing the user experience. It was sort of in a silo. Right. So product and eng lived in the product and did some usability labs and customer validation and that was it. And go to market. Teams lived mostly outside the product, which meant they would go to market, talk to customers. Everything from sales to marketing was all about brand and perception and prospecting and landing those big deals. And that delineation was very, very evident. And as I've reflected in the past, even just the past three years with the infusion of AI now Available to everyone. Not only can you build your infrastructure, so think your backend schema to your data architecture to then building out the user experience itself, AI can now power pretty much the full stack, but it can also power out of product growth strategies. And often the common ground and why that happens is data. You need customer data when you're thinking about what is the next user experience that we want to build and evolve the product to, in the same way that you need customer data to understand, okay, how do I crack this deal or how do I get through into this particular prospect that I'm trying to reach, what are their user behaviors and patterns? What have they. So data being that common ground, more so than AI has created this sort of infusion where AI is now available as a solution and creates this common ground. Right. And so what that does is be it product led growth or sales led growth, depending on whatever strategy you're taking advantage of, you can actually build solutions for your go to market teams around account intelligence and growth scores, the same way that you can actually build features that are contextually available to your users. So it's spanning this entire life cycle of the customer. And said differently, when you think about product led growth, what it has evolved into is this AI powered, cross functional, go to market strategy. It's no longer just, hey, we're going to use product as a driver of acquisition. And yes, that is the heart of it. So. So if you ask me, conceptually product led growth pretty much is where we use the product to drive both acquisition and retention and then finally expansion of our customers. But really it's evolved past that to where it's now hitting across all the different layers of an organization. The product still being the nucleus, right? Still being that sort of primary driver of user adoption, of driving revenue, but it's sort of taking over what one might think of as traditional sales or marketing efforts. It's almost creating this sort of fluid layer across organizations.
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Greg Kilstrom
Yeah, yeah, you touched on the concept of contextual ux. I want to get to that in a second. But before I do that, just to talk about data and certainly I'm talking to someone from the right company to have this conversation. It seems like we're in a great position right now with AI because I think you're saying the same is we have this data as the foundation, right? You know, whether, whether machines are using it or humans are using it. You know, humans can use it and get insights from it just like AI can, can as well. But really it's, it's good data. Is at the foundation of all of that, is that, would you agree with that?
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Sonal Mane
100%. 100%. And you know, any AI driven initiative, be it, you know, on the product or the engineering side or the sales or the marketing side, you know, if you're thinking about modern user experiences, if you're thinking about geez, how do I optimize my trial? Like every single problem you know, really comes back to well, how integrated is your data? Are you thinking about user engagement? Are you thinking about, you know, where do your users go even before they hear about your brand? So just everything from that sort of brand awareness, top of the funnel marketing down to then customer success and adoption and really driving that sort of expanded multi product experience and then closing it out with this viral growth loop that you might be building like all of the above. Right. Whatever your strategy is, data really is at the core of it, the advantage. And I know you mentioned being at the right company. I mean data and AI is really what we live and breathe as part of our data intelligence platform. And one of the things we pride ourselves in is this ability to work on the customer data. So it's not really about let's reshape or rethink what data assets you have, but really let's bring in and start working with your existing data as a customer. And to me that's really powerful, this ability to work with structured as well as unstructured data. Because as we know it, there's lots and lots of different sources, right? Everything from a podcast like this one to user patterns and clicks on a website. Like data can be in many shapes and forms and so solutions that really thrive in this space are the ones that help you synergize it across different sources and then on top of that be able to actually understand your customer to then build, you know, that sort of product led growth solution.
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Greg Kilstrom
And so we're going to link to a blog post that you recently wrote in the, in the show notes and where you're talking a little bit more about product LED growth and AI and some other things as well. But you know, you had touched on earlier the idea of contextual ux, but I just, I'd love to get definition of it as well as you know, where, where's, where's the value in contextual ux?
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Sonal Mane
Yeah, if we think about a typical user experience. So when you look at Google Docs or if you look at Figma, or even if you're looking at, if you're using lucid to create workflows, a lot of Times these user interfaces have mastered this whole idea of contextual UX to the point where you're not really thinking about the user experience as much as you're thinking about getting your work done. So the number one thing when we think about contextual UX is this idea that you're tailoring that front end or that user experience based on the user's context, right? What is their behavior, what is their Persona? Are they a student? Are they an enterprise worker? Are they a developer? What are their preferences and needs and what do they need at what point in time? It's interesting, I was actually building out a workflow in lucid recently and it's been a while since I did that and it was super easy. There were tons of templates, so I was able to kind of draw from sample data and then be able to real time as I think about the workflow. And this is just a very tactical example, but brings to life the point that I don't know what the next block is that I'm going to need if I'm just rethinking this workflow on the fly. Is it a square? Is it a diamond? Decision box? And so as you think about the contextual UX coming to life, the really that's what it is, you know, predicting the user's needs, surfacing that relevant feature. So, you know, showing me that, hey, these are like your four options, which one do you want? Right? But limiting them in a way that they're relevant. You know, thinking about the fact that I'm real time building this workflow as I'm thinking through my diagram and then really providing me tool tips and guidance with those templates and sample data. And oh, by the way, while I'm at it, also showing me enough of contextual background UX to then maybe share my content or be able to provide another user to co author it with me. There's all of these very simple design principles that come to life when one refers to a tailored user experience, which is essentially what contextual UX is, right? In the context of where the user is at and why that's relevant. And we can talk about how AI plays in is needless to say it improves user productivity, but it also reduces your friction to product adoption. That's the key. The minute you reduce your friction to adopt the product, now you have suddenly unlocked the product value. Your time to value for the product has reduced, your feature utilization grows, you go from basic to advanced feature adoption and suddenly now this entire product has become attractive and sticky to the user. So that is really powerful in the concept and the context of the broader context of product led growth, which is where as we think about how AI can start influencing some of those design principles, I think that's where we can start seeing that future contextual UX meets AI experience coming to life.
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Greg Kilstrom
Yeah, and so speaking of reducing friction, another big area, and I've seen quite a few examples of this even in recent weeks, of reducing friction is self service. And so generative AI certainly can play a role here and I know you've written some about this as well. Can you share how do you look at generative AI and self service and really how this can change the end user experience?
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Sonal Mane
Yeah, so when you think about self service, there's multiple layers in terms of the offering. A lot of times and traditionally maybe three years or five years ago, we didn't really think about this concept of digital LED services or customer success. That's digital led customer success or self service. And a lot of times even when we did, it was tied to the long tail. Right. Today, self service is almost the de facto of what you want, you know, for your product experience to be. Because there is a lot of noise, there are a lot of products on the market, especially if you're a B2C product and even if you're a SaaS platform, you know, this whole consumerization of SaaS movement is, is deep and pretty omnipresent everywhere. And so with that in mind, you know, self service has sort of become this much like AI, almost like a basic need for how and when we think about product development. So what is self service? Right. So one is, you know, when, when a customer comes in or when a prospect comes in, it really starts with when they hear about the brand for the first time, you know, what is their first landing page experience on your website, what are they reading and seeing? Everything from that first click to then activating your trial, converting your trial and converting into, let's say a paid user or then coming in with a larger enterprise license. All of these different scenarios and purchase paths can be augmented by what one might think of as a natural, dynamic and context aware, again going back to that contextual UX experience, conversational almost interface. So we're generating everything from personalized landing pages and tutorials, guides that might be available based on who you are. I don't want to show my data engineering Personas or users the same kind of content that I want to show my data scientists. They both operate in different worlds and so yes, data is the common denominator but with my feature set, they might care about a completely different feature set where model serving and fine tuning would be way more relevant for the data scientist. And so I should surface to him what that might look like. Right. And for my analyst, I might want to show them what custom reports and visualizations might look like. And so transforming the product, but also the purchase path in a way that users can naturally go from awareness to activation to then actually adopting the product in a seamless way, like that entire art and science of self service. And what you're also trying to do as part of that is, you know, enable sophisticated, you know, troubleshooting. You're trying to help with problem analysis or said differently, you know, simple solutions where you can actually answer questions for the customers on fly. And you know, we're all talking about agents and bots lately. And so all of those sort of experiences which could be digital LED would be woven into self service. But that's, that's where like it's not just limited to, hey, I have a bot and I have an onboarding tutorial and I'm successful. That, that, that's. Yes, that's great. You know, you have a great onboarding experience. That's tailored, that's awesome. But have you thought about personalized product recommendations? Have you thought about adaptive learning paths as part of your, you know, academy or educational website where you're enabling your users? Have you thought about how you communicate with your end users, be it through your campaigns or be it through notifications, you know, what is, what are their preferences? So every single aspect again is touched when we think about self service and personalization. In fact, and I would say a key backbone of self service is this whole art of predictive and personalized messaging where you're trying to reach users at the right time and help them, you know, get to that next step in their evolution?
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Greg Kilstrom
Yeah, and it sounds like definitely super important for that personalization and customization on the end customer experience. But also you touched on some things as far as internal, because a lot of the hurdles that organizations face are those internal, whether it's process people, process platform, whatever it is, sometimes all of the above. So would you say then personalizing the experience for the internal teams is as important?
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Sonal Mane
Yeah, 100%. And that, you know, that in and of itself is a very, very deep statement when you say personalizing the experience. Because where my head goes then is, well, I'm an account executive and I have my account intelligence dashboard which only shows me my top 10 accounts. And what is my Next best action. You know that's a great example of a gen AI powered experience. But at the same time we can also do simple things like you know, especially now with all the LL automating, content creation, Q and A within the product test case quality assurance. We can do personalized trainings, one to many activities that include both live and recorded webinars and workshops, lots of segmentation analysis, cohort analysis can be done on the backs of with databricks for instance GENIE is one of our features that will literally let you talk to your data and ask questions as if you're a business user without even ever having to need an analyst to build all the fancy dashboards. So there's a ton more beyond what the experience of a day to day employee looks like and then assisting those employees with all of these technologies that come to life. So yes, absolutely.
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Greg Kilstrom
So last topic I want to talk about is just more about growth and helping to overcome some of those barriers to growth with AI. I was wondering if you could share an example or two of how have you used generative AI to contribute to growth and where do you see the opportunity there?
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Sonal Mane
Yes, absolutely. So there's just to anchor back. There are like a ton of different barriers and just because there are barriers doesn't mean you wait for everything to be connected. We live in a really fast world where if your data isn't in the best shape or if you don't have your customer360 user360 data integration there are still things you could be doing with your existing data, though they might be limited. You could still work on ensuring that in the smaller data sets that you have, you can start building out solutions and at the end of the day it's all about proving value to the rest of the organization and landing those investments in terms of both removal of that tech debt but also bringing those data silos together. So I'll just start with that. Tons of barriers including ethical legal implications, including things like integration across silos, privacy, data security and on all of the above. Assuming that that's all taken care of and we have to go through off their share of reviews with our legal team, the word personalized would always bring a ton of red flags. So the pain is real for anyone trying to implement. But if you think about really two or three simple solutions that are pretty powerful, one of them has been building out AI powered assistance in our product and really significantly reducing our response times both from a support but also from a product adoption perspective. So typically what would have been a ticket now the bot will actually serve up. That's likely the most single most common scenario across a lot of companies these days. If you think about some of the releases we've had in the past few months, building out our open source DBRX large language model was a huge release for us. As a result of that. What that also meant is we could actually have an interactive customer engagement interface that could help with smart recommendations, content generation, and really power a conversation with our users while they're in the product. Yes, for the bot, but then really bringing in DVRX as the LLM at the back end was very, very powerful from a data privacy perspective. We eat our own dog food, so we'll actually use things like Unity, Cat blog to actually ensure governance. And then, you know, if you're thinking about, you know, what are, what are some of the ways that you can actually personalize. It's not just about, you know, Persona development, brand and voice, yes, those are important. But really honing in on segments and AI is the single biggest unlock when it comes to hyper personalized comms or, you know, personalized product experiences or analytics. And so we've really, you know, gone all out when it comes to predictive analytics and building our own platform when it comes to understanding our cohorts and how they function. So, you know, we have solutions like Amplitude and Heap in the market. We build our own and that's huge investment for us. Right. To really understand that audience on the, on the other side.
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Greg Kilstrom
Yeah, yeah. And to talk a little bit more about metrics, you know, I think AI is one of those interesting things where certainly, you know, there's not a reason to throw out the, you know, the traditional KPIs and all those kinds of things for marketing. But it also, there's also efficiency plays and other kinds of things that I think it enables that maybe some other, you know, technologies or features or things have not traditionally advanced. How do you look at measurement and what kind of metrics or indicators are you using to measure success of AI driven initiatives?
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Sonal Mane
That's a great question. And done right, this is something we talk about internally. Done right, AI and self service are both invisible, meaning if you have to think about or if the customer has to sort of have this thing about, oh, like I was trying to do this and I couldn't do it, then self service didn't do its job. Right. So from a metrics perspective, I would say the same holds, right. If we can prove that the cost to serve a customer is on par or actually much, much, much more reduced than what it would be with a human. The metric in and of itself, which is the cost to serve holds. Right. But the targets that you set and the thresholds that you're trying to hit, I think those will vary significantly. So just to then zoom out, we're monitoring things like user engagement rates, we're thinking about what's the time to value for new customers. It used to be a significant amount of time to where now it's a matter of minutes. Based on a few recent releases we've had. We also look at how many issues that, you know, you can actually resolve, like I was giving that chatbot example. So how many issues or tickets could be deflected. So none of these metrics are earth shatteringly new. But what is new is we're seeing a ton more increase, you know, in things like feature adoption or churn reduction. We're seeing way more conversion from our trial into our, you know, sort of deep end of the trial where people will actually start going into our advanced features. So, so those are improvements and significant ones at that. But really from a bread and butter metrics perspective, consumption, you know, user activation rates, monthly active users, those are very much still the bread and butter.
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Greg Kilstrom
Yeah. Yeah. Well, Sonal, thanks so much for joining today. One more question here before we wrap up. We've talked about quite a few different topics here, but you know, all some common threads here. What are some of the emerging trends that you're seeing in using AI for product led growth and how should companies prepare to integrate these advancements to stay competitive?
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Sonal Mane
Yeah, like we were discussing earlier, Greg, it all starts with the data. So to me it has always been a parallel track of sanitizing and creating the infrastructure aspect as much as building out the customer facing solution, be it through comms or through the product. So the first tip I would say is try and make sure that you have your data teams or your engineering teams bought in and your support teams spot in when it comes to things like integrations across the different data silos. So that's number one. Number two is in terms of actually thinking about implementation, the core of product LED growth is all about a B testing. So we didn't really get into experimentation. But MVPs or your first few pilots will likely fail. So be comfortable with that failure. Right. Fail fast. And then the third thing is really about when you start hitting those growth loops and when you start building out end user experience. It's very underrated in terms of getting that sort of customer interview or customer validation or product feedback loop. Going. A lot of people will run those usability labs or they'll run these segmentation surveys upfront. But keeping that constant drumbeat and having an active product feedback loop is the single most effective way to have a successful PLG play. Because you're learning as you grow and as your users engage, they advance. And so it's sort of this sort of win win solution when you try to think about improving the user experience as well as the product hand in hand with your customers. And the most successful companies have cracked that in terms of emerging trends in the use of AI. Like we talked about everything from infrastructure, your customer 360, your user 360, your support, how customers engage and interact with your product, all the way down to your personalization of your segments and cohorts. Every single thing can be an AI powered solution. The one thing I do often think about is do you really need this beefed up data science team? No, you don't. You actually need a mix of personalities or mix of thought leaders in the room. You might need a few data scientists, a few data engineers, but don't undermine the skills that you already have on your team. AI is something that you could also self learn. So that's just my $0.02 on the future of AI, both from a organizational team upskilling development perspective, but then also in terms of future emerging trends. We're seeing it cut across pretty much every aspect of plg.
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Greg Kilstrom
Great, Great. Yeah, I'd love to talk about several of those topics with you, so we'll have to have you back on the
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Sonal Mane
show, but thanks for having me as a guest. Thank you Greg.
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Greg Kilstrom
Yeah, thank you so much. Well again I'd like to thank Sanal Monet, Senior Director of Experience and Growth at Databricks, for joining us today and sharing her expertise on leveraging AI to drive product LED growth. You can learn more about Sanal and her work at Databricks by following the
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links in the show notes. Thanks again for listening to the Agile Brand brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find the show more easily. You can access more episodes of the show at www.greggkillstrom.com. that's G R E G K I H L S T R O M While you're there, check out my series of best selling Agile brand guides covering a wide variety of marketing technology topics. Or you can search for Greg Kilstrom on Amazon. The Agile Brand is produced by Missing Link, a Latina owned strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, Stay Agile.
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