Kevin Sung Edited Audio
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[00:00:00] Hey, Kevin. What's up, man? Good to have you on the show. Thanks for coming. It's great to be here. Thanks for having me, Jeff. You've had a really good career at a lot of companies people are gonna have heard of. So can you maybe just give us the TLDR, how did you end up here? What do you do now, and who is Kevin Sung?

Right now I'm the VP of product at Life360. I run a group that encompasses Life360's ecosystem strategy, as well as the foundational group. So infra and developer experience, and I'm working with our AI platforms team as well. So a very much backend, but still very customer centric. Nice. I'll be honest, this is one of the things that comes up, you know, with the rise of AI and kind of the push to use AI to be faster and better and stronger and, like, is AI going to take product jobs?

Are there going to be product managers in the future? I think yes, by the way, to be clear, but the thing I always hear is because good product managers have product [00:01:00] sense. But it's rare when you ask someone, like, what is that? What does that mean? It's easy to talk about, hard to kind of put your finger on.

But I think when we talked before, you had a great take on this, and I'd love to dig in a bit and understand how you saw it develop in yourself and kind of your thesis on it and where it fits. What is product sense, and how has your view on it, but also your skill with it, kind of developed over the kind of years that you just described?

Um, lots there to unpack, for sure. That's a l- complex question, I know. I, I'm trying to decide whether to answer the AI part first or the product... Maybe I'll start, give a quick aside on my perspective on AI and product management. There certainly is a lot of doomer talk about how product management roles are going away.

I'm in your camp. I don't believe that that's true. It comes down to, with the new AI tools that are out there, we have shortened our ability to learn and deliver prototypes and validate with customers. We have removed, in a lot of ways, the bottleneck that was engineering headcount, engineering [00:02:00] resources, to be able to learn a thing.

Mm-hmm. And so in a world where you can now ship lots and lots of ideas quickly, it becomes even more important now to have really strong product sense and customer centricity in order to decide that you are shipping the right things. Now, I still read a lot of perspectives out there about how, well, if you can ship anything, we should be 10X-ing our output in terms of new experiments and really just trying to push the envelope forward.

I respect that kind of thinking, but I think from a customer-centric perspective, if you are the user and you're engaging with an app or a product, that product is just every day constantly changing on you, you're a test case. And I think it actually can lead to a lot of bad experiences unintentionally.

So even in that kind of a scenario, I say yes, we can ship more, but you still need to have extremely good judgment on what you are shipping. Yeah. And I'll even go a step further and say that I have strong opinions on what's lazy product management versus what is, like, really, really rigorous and, [00:03:00] like, good product craft.

And one of the big differences between the two is the strength of your opinion, and that opinion is based on your understanding of what a customer's needs are. So if you come in un-opinionated- AI now gives you the tool to say, "Well, I really don't have a perspective, but why don't I maximize and exhaust every possibility and throw stuff against the wall and see what sticks?"

And certainly some people think that way. Even back then before AI, like when people were doing AB tests, people were like, "You should be doing like 100 tests a day and just, like, throw stuff against the wall." Remember Google famously was, like, testing different shades of blue. It's like, okay, you have that infrastructure to do the testing, but you're just switching things up on the user.

Like, are they actually getting value from this? So I think that's one way of doing it, but I personally believe you need to go in and say, "Based on my understanding of the customer and their life and their needs, this is a very painful problem we can solve. My hypothesis is we solve it this way. If we do it this way, the customer behavior should change in this other way.

It'll lead to this kind of an impact for our company." Ultimately, you have to go in with a [00:04:00] perspective, and then you can validate whether you were right or wrong, and then from there you learn a thing, and then you become that much more skilled. So to go back to the product sense question, what is product sense?

It is customer centricity It is understanding a customer's painful needs and being able to translate that into business value. Mm-hmm. Another way to think about this with the AI layer is if all you're doing is facilitating maximalist tests, an AI will always outperform you because they will be able to come up with every permutation and actually ship it.

So then you are actually obsoleting your own job as a product manager. Like, you are hired as a product manager, one, for your judgment, two, your pattern recognition, three, your experience, your talking to customers, and we are still building software for human beings who are consumers of the software, so you have to understand their needs at a human level.

Like, AI's not quite there yet. I also think an important part here is at some level if we think we can just kind of automate a lot of these things away with AI, sure, it sounds really enticing on face, right? AI cannot just tell us, you know, what's important to work on, but also what do we need to build [00:05:00] new?

What else do we need to develop? We can do all the testing that way. We only have so many models, right? It's the same reason why if you look on LinkedIn, all the writing kinda sounds the same now or there's all these kind of like the same little things you notice or everyone kind of converges on a couple of viewpoints.

And the same thing will happen if we just rely on the models to kind of derive where we're going. You're not gonna be able to differentiate yourself as a product or a solution to anyone because everyone's gonna have the same kinda couple model sets driving it. So, like, maybe there's a little differentiation, but really we're gonna see a world where everything kinda comes together.

And I think that is the value so far what we see. I mean, maybe AGI one day will be different, but so far what we've seen is AI does really well at pattern recognition and kind of the things it knows. But if you give it novel, come up with something that's never been but that will still work and still has a reason why it should work, it's not that great there, and that's kinda where humans are great at building the next thing.

How do you make that faster? Well, I think the human should definitely be in the loop. Actually, that is a place where AI adds a lot of value. Now, you can't go and prompt an AI to say, "Come up with something new." They never will. So here's [00:06:00] the challenge prior to a lot of these AI advancements is if you were the type of zero to 1 PM and you were in an organization where that organization had a very rigorous financial model and expectation that this is how their system works, it's all mapped out in Excel, you get into this very incremental thinking.

Around, oh, we gotta tweak these knobs, we gotta move these numbers to goal seek, especially for public companies. And so in those types of situations, listeners who are zero to one PMs will feel this pain. You're often saying thinking outside the box, but then you're being told you have to go and justify how much this could grow to really impact the overall business, and it's all a guessing game.

What AI has done is given you the tools to say, well, you know, in the whatever two weeks it would've taken me to build this model that I have very low confidence in and is really a hand wavy exercise to make executives feel good, I am now going to go and deliver a prototype that I can talk to 10 users about, and I can get real data that then gives me that much more confidence to build either a better model or to at least put something out in the wild to learn from.[00:07:00] 

That is actually a big game changer. But no, AI wouldn't be able to do that itself. It needs to be wielded by a very thoughtful product manager who is understanding what the customer's needs are and is directing it to think outside the box. Exactly. I totally agree. And the converse too, 'cause we saw, I think through years of especially Kind of the late teens, it seemed like product had a very easy job of you don't do much, just kind of like project manage and that kind of thing.

That's where I think, yes, some product is going to go away, some product functions are. If you were not adding much value, not great product sense, kind of operating from the either just throw stuff against the wall and hope or just kind of project manage, product's gonna be less valuable. If you are someone who really understands the customer and what they need and can come up with new solutions that are gonna enhance their lives, that's gonna keep being incredibly powerful I think.

Yeah. Product management is a very fluid type of job description. If you look at 10 companies, the product managers are probably doing different versions of the job. Some might be much more focused on data analysis, some are more focused on project management, others are just talking to [00:08:00] customers and are much more blended with design.

But I think the thing that's very consistent is the product manager has to be very flexible, good with ambiguity, and able to fill in the gaps, 'cause the structure of your team that's executing that will continue to evolve with AI, and you as a product manager need to see where that's going and create value for yourself.

I'll give you an example here. So before data science was a function, a lot of times product managers had to take that function. Mm-hmm. Then it kind of grew into a function and then product managers sort of backed out there and would just work with data scientists. In the same way, like right now if you're a product manager on a team like maybe your superpower was that you were the world's best coordinator across many teams that have to work together to go do something, but now maybe you have a really nice tool chain, you're able to actually automate a lot of that.

Well as a product manager, how do you find new value for yourself? There's always gonna be something new. Mm-hmm. If your engineering team is now very heavily dependent on cloud coding, you're having like 10 times faster the amount of things that are being delivered and PRs and things like that, a lot of your focus now shifts toward QA and management and [00:09:00] evals and making sure that the thing you're delivering is actually still high quality.

So there's always gonna be new areas and you have to be extremely flexible in figuring out where that goes. In fact, as more code is delivered, it is very common for then the quality to drop because everyone's kind of moving fast, you're trusting that the systems that have been built are just kind of doing their job, but you must always iterate, you must always test that assumption.

And I find that a lot of the product management work is actually to go into the builds and actually go and make sure that the thing that you thought you were building is correct. So yeah, there's plenty of work. There's no shortage of problems to solve, and I think people with that kind of a mindset will always find a job in product management.

People who are much more rigid and say, you know, "Hey, this is how my day is, and I spend this amount of time in one-on-ones, this amount of time writing specs," et cetera, and they can't change, they'll have a hard time. I think you nailed it, where product is even more than almost any other function I can think of, one of the roles that looks different in every company you're gonna go to.

And this came up a lot through your [00:10:00] career, and when we kind of have talked in the past about product sense- Yeah ... I appreciate this is something you saw as kinda contributing to you on how you grew an advanced taste, almost, if you will, for this kinda thing. Because, like, Smule, I think you mentioned, was really, really metrics and growth driven, right?

Yes. What did the role entail there? What did that look like, and kinda how does that compare to other places you've been? I wore many hats at Smule. It was a small company, I think, um, under 200 people. But one of the biggest areas that I led was around growth. I think growth product management has very specific playbook.

Now, it's certainly being disrupted nowadays, uh, PLG plus AI, but there was a very specific playbook, and I would say that the playbook was very focused around, I don't know, it will come across derogatory, but, like, growth hacking and, like, using ways to sort of draw attention to, to sort of move numbers.

But I felt that that practice in general was very goal-seeking. It was like, "Oh, here's a number. We gotta move it. What are the 20 things you can do to move this number?" A lot of times it was not approached from a customer-centric [00:11:00] angle. So it was a company where every single day you were looking at charts and seeing if the charts would go up, and you were constantly evaluating if the thing you did moved the chart.

The reason why eventually at Dropbox I shifted over more to the core side to kind of build engagement products is because at the end of the day, my belief is that you have to create a product that solves a real problem that is very painful or create, delivers enormous value, and that is what actually draws people in to go and buy the product.

If the product is already good and intrinsically has value, growth is this gasoline that you're pouring on the fire to make it accelerate. Mm-hmm. If your product market fit isn't there or you're a company that's on the decline and you're looking for a turnaround, or the value just is kind of a nice-to-have, I mean, the growth will kind of juice some numbers for a while, but you'll see a pop and a drop.

It's just the natural friction is there to prevent growth from being as good as it can be. So at Smule, we would see drops in key metrics, probably scramble to, like, form a tiger team to go investigate it. We would, like, come up with a list of hypotheses and, you know, the [00:12:00] funny thing is seven times out of 10 the culprit would be found by ultimately one of us running through the app itself, going through a flow, and being like, "Oh, this is clearly the thing that was broken."

I think what I learned from those years there was you could look at data all day, and data is an extremely powerful tool for in the macro understanding if you're making the app better or worse. But if all you do is look at data and you use data to then double-check other data and form hypotheses on that, and you're not talking to the customer, you're not experiencing the product yourself, you can very easily just turn this into a spreadsheet maximization game.

But really the value is understanding what is the problem? Is it being solved? I think kinda what you get at there, 'cause you mentioned kind of the, the idea that growth hacking might be seen as a negative term, it might not. I think it does depend on kinda who you're talking to, maybe what their experience with it is.

I feel like that's a big overlap for marketing and PMs, is once you get into kind of the growth distribution, how do you actually get this thing in front of people and make it bigger? You can look at numbers [00:13:00] based in growth hacking, and it can be bad or good, like almost anything. If you look at it from we know that this outcome is really, really valuable to the customer, and so we wanna back into what are all the ways we can drive the number, but because we know that that number intrinsically is good, then you can use it for good.

If you're just kind of micro, you know, we want to get this little metric up because it's part of the funnel and da, da, da, da, da, that can end up being really not good, let's say. But numbers alone can be very, very dangerous, and I think that's what you were starting to talk about at, like Dropbox, where you kind of went the other way a little bit.

At Dropbox it was very different. Now, in every year I was there, the data got better, and by the time I left it was very rigorous around data. We had a driver model. We understood how the funnel worked, and we had one funnel for everybody. But when I started there, it was much more of a, you know, we had some data, but it was very intuition-based.

We had a very large user research team. We'd go out and have real world Wednesdays and talk to customers. You had to talk to two to six customers a week, and you were just [00:14:00] constantly trying to understand how to improve the product through those mechanisms. And so it was, for me, coming from a company where all you did was look at data and obsess over numbers, a big eye-opening experience, 'cause it kind of reminded me of whenever we would do an investigation.

In the end, you end up talking to a customer or you talk to CX or you run through the flow yourself, and then the aha moment comes from experiencing it. And I was like, "Oh, I see how that, that can scale out for a very large organization." There was one year at Dropbox where I was part of the core organization and we really wanted to double down on retention.

That was actually a very cool experience because I led a group called Usability, and what that group entailed was we had to focus on the key journeys within the product that we believed our most dedicated users go through. We had to focus on performance and reliability of the services itself, which typically a core, uh, team doesn't actually own.

But I made the argument that it's actually a really important part of the user [00:15:00] experience 'cause you do feel it when something is slow. And then there was a huge portion of work around CX experience, and our community, and our forums, and our help center because the argument was, well, as a customer you have an experience within the product, but what about all the times when it's outside of the product and you're trying to figure out how to get support?

That is your holistic relationship with a product. So we created this team that spanned across all of that, and for that year we really focused on improving the quality of the product. And it's probably like one of the best years because we ended up really improving the retention of our paid users. I think at the end of the day the amount was something like 24 million in ARR just from churn reduction because of the work that we had done.

But it was a pretty interesting experience because it was one of the first times where we were able to come at the strategy from a focus on making lots and lots of little things better and then be able to actually tie it to an outcome at the end of the day. And I would [00:16:00] say one of the most interesting things about that experience is that the hypothesis is actually wrong.

Like, that we ... Like having, um, big impact on the revenue but, but we had thought when we pitched this investment we had said, "Oh well, first impressions matter a lot, and so we make all these improvements, we should start seeing a huge improvement in your first month and second month retention. That's where we're gonna see a revenue growth."

And we were completely wrong and none of those things actually moved. The thing that actually moved was, um, month 13 plus. So like very tenured users that had been with Dropbox for a very long time. Even getting them to improve by half a percentage point because there were so many of them in our system was able to lead to 24 million in terms of improved ARR.

Interesting. And I guess if I take a non-numbers approach and I kind of explain this phenomenon from like a user perspective, the type of user who would stick around for a year clearly finds value in the product and they probably have been frustrated by slow uploads, by slow loading times, by bad CX or, you know, just like things that are broken.

And when you fix it [00:17:00] they are the ones that notice. And if you can just get some percentage of them who would have quit to not quit because the experience is actually getting better, it pays off. And so it was like a huge learning experience for us. At some level they have to stick around for some amount before they pay back even just the cost of getting them there.

But the longer someone stays, they've paid back a lot more too. I mean, getting someone to extend from month 12 to 13, you're basically playing with pure, you know, profit from an acquisition standpoint at that point. Yeah, I misspoke. So it wasn't month 13, it was month 13 plus. Mm. So the way that we were looking at the data was like month one, month two, all the way to month 12.

We had both monthly subscription as well as like annual subscription. So then month 13 would've been the first Cohort in which both types of subscribers- Mm ... had actually, like, chosen to renew. And we did month 13 plus because at that point we had, what, 15 years worth of customers that have been with us.

So all of that legacy customer base, are they retaining month over month? And they retained a little bit better. And it just took a little bit of [00:18:00] better retention to say it's worth continuing to explore ways to make performance better. We had a metric called TDVC, like time to visual complete. Yeah. Which was like, hey, you're loading a page on the website, and how quickly does it take for all the elements to load in?

And I think when we looked at averages globally, it was something like 15 seconds. Like it took a long time to load. Jesus. You gotta get it down to, like, 2.5 seconds. That in itself, right? If you're, every day you're going through the web experience and you're taking forever to load and you're looking at Drive and wham, that seems to load really fast.

These things create moments of what I call, you can call them frustration moments, but it's like moments of resentment. Mm-hmm. Like, man, all my files are stuck in here. I can't remove them, but I'm frustrated, but I can't leave you. And then it just builds up and builds up and builds up. My hypothesis then was like, we have been building up resentment amongst the legacy customer base for such a long time.

Then an inciting incident happens, and whenever the inciting incident happens, all the people who have reached a certain level of being upset, they all leave. Right. That pushes them out the door. So what are some [00:19:00] inciting incidents? You have a major SEV or you have a major data breach, some kind of big trust loss moment.

Another one is you hear Netflix is always raising their price, right? So, like, we were raising our prices. Well, like every time you do a big price increase, you have a cohort of people who have been very, very frustrated but couldn't leave, and that becomes the straw that broke the camel's back that then gets those people to leave.

Right. So if you can reduce the amount of that level of frustration that a portion of your legacy user base have, you can actually deliver a lot of impact because then they become a lot more hardened to these inciting incidents, and you're keeping them much happier and delivering great value so- Right

that became how we thought about quality, and that's a principle I've kind of carried over into my current role, like at Life360, where we care a lot about quality and trust, and it's not just about shiny new things, but it's making sure that our existing current customer base that have adopted the product for very specific purposes are getting what they are paying for and are having an excellent experience.

It's funny because I think [00:20:00] maybe now even people realize this a lot more, especially post kinda 2022 and all the change to SaaS and how the select few AI ones are growing. But if you look at the result of that and the denominator for existing customers month 13 plus at Dropbox, I'm gonna go out on a limb here and say it was probably a lot larger than whatever the monthly additional was.

So, like To make up for the regular churn rate of all those customers, you had to have a just continuous giant number of monthly new onboarding. But if you could even slightly reduce that long tail of churn, you're gonna grow faster 'cause it makes your existing growth engine faster. You're probably gonna have better experiences, so they're gonna churn slightly less just from that other work you're doing.

But if you're only focusing on how do we improve month one to two and month two to three retention, there's only so much impact you can possibly have on the business, 'cause, okay, you can improve that, but it's gonna take 12 months for that to really cascade through enough people or something to have impacted a huge cohort.

That's where product sense comes in. If you have very strong product sense [00:21:00] and you can tell a story about how improving the product helps that longer cohort of users, then you can get buy-in. Mm-hmm. Otherwise, if you are a slave to the driver model you have, the model basically is based on past experiences, and the past experience was we were never able to move retention except in improving onboarding.

And then you improve onboarding and that affects month two, and then you wait 12 months for that to trickle down to month 13. There wasn't this concept of if you make an improvement that affects everyone, it lifts the entire retention curve up. That just no one had done it before, so it felt like it was impossible.

But it requires you to say, "I understand that the users are frustrated with this. I understand this is their experience. This is what we have to improve," to then sort of like break what the model says and say, the model is just one person's opinion of how this thing could play out. The model is not a predictive thing for what will absolutely happen in the future.

You can use the model to forecast some predictability. So I'm gonna go and do this thing so that we have this ace in the hole so that if it works out, good, like we were able [00:22:00] to beat the model by a lot. Yeah. I get why that was a hard thing to sell, though, because if you think about what is, what's success- Yeah

if you have a very short-term funnel of like, they need to do this, and then this, and then this, and that's gonna mean that that's gonna improve our two-month retention rate, which we know is good. Yeah. Sure, that's great, it's sexy, 'cause you can very discrete units of measurement and time. It's an easy thing to do.

Yeah. It's anyone can data-wise look at it, but this was like, we're gonna improve performance, and overall what you're gonna see is all these different cohorts are gonna have a small lift in retention. But it's really hard to say, like exactly when that happened, and and the exact mechanism. It's just they're gonna like it better.

Now, not everyone was on board when we pitched this. Yeah. So how did you sell that, I guess? Well, there were some people who were very much like, "Hey, Kevin, like if you can't justify to me that this will actually lift retention a certain way through a model, I will not give you the resources to go do this."

Mm. And how did we get this through? I credit very much the leadership of the core team that I was under, right? I was working with the GM at the time, Kirk, and he really believed in this [00:23:00] also, and so he created cover. And he said, "You know, I'm gonna give you six months to go like figure this stuff out and then we'll evaluate."

And so being able to have the backing of leadership so you don't have to constantly look behind your shoulder, you can just plunge forward and trust that, hey, at the end of the day you're gonna learn something. You're not operating in fear. It gave the team a lot of the ability to just kind of go all in.

And then we were able to get some early wins around our mobile product. Basically, churn got much worse year over year, and like we could not figure out what it was, and we ended up having to tear apart that entire mobile app, and we discovered there were all these ANRs. Basically the app was freezing for people, but when we looked at crash reports it didn't show up, and for all these other reasons.

And we went and fixed all of them, and then boom, like immediately one month later we were able to see a visible improvement in retention. I had never seen that amount of- Lift before because usually you fix retention and it takes six months for it to start showing up at the top line. But- Right ... yeah, our CFO at the time was like, "Whoa, what did you guys do?"

I was like, "Well, it wasn't one thing. We actually had to fix like 20 different things." But it all hit at [00:24:00] once and it added up. And I think that then gave us the credibility to go do a second year of this type of retention work. So how do you do it? You have to have strong leadership that backs you, that believes in it.

Part of it is you convincing them, but some people can't be convinced. So it's about aligning in what you believe is right. And thankfully, our leadership really strongly believed in our legacy user base and the importance of serving them. Mm-hmm. Then we were very lucky to have these early wins that then sort of silenced the naysayers, and then we had sustained wins throughout the year that then created momentum.

So by the end, everyone was like, "Oh yeah, this is what we've always done." It's like everyone's always for it from the beginning. Right. And, um, and I think a lot of times maybe some companies, you know, they wanna fix all the little things, but it always is a P2 task. Mm-hmm. It always gets pushed behind and it just builds up.

And what is Corey Doctorow uses the term enshittification of products. You create this baggage that, that actually worsens your product, and then no one has enough political will in the company to go and actually go fix them all. So you find these different like little tactics like let's [00:25:00] do a one-week hack week and try to fix it there, or let's do a burn down.

But I think the spirit of what actually works is you have to be aligned as a leadership team to say, "These little paper cuts add up. They hurt our customers. We care about our customers. We believe our bottom line is affected by it, and we are gonna give a team the ability to go and chase down all these different problems."

Mm-hmm. There's not one silver bullet. You have to go address them all. This hits really close to how we as a solution that is LogRocket look at like all this kind of stuff. You know, I talked about this earlier, I don't think the world is going to fully autonomous kind of this function because it removes the thing you just talked about.

Like, you can't just do the same old thing over and over and just do it rote. S- at times you have to look at it differently and go, "This is why I think this is going to help." And it takes a little bit of outside thinking, but part of the reason why is product people, engineers, every function gets stuck with this just like base level, foundation level of just crap that you have to do, and it's all ticky tack.

No one likes doing it. No one's fulfilled [00:26:00] by like, oh, there's this little error pop-up that happens a lot and it's a simple fix, but like what engineer are you really gonna put on it? Or like, oh, product is gonna prioritize that, but like who wants to actually work on it? But can you build a system that, that watches and monitors for those things and understands what actually impacts users, and then start to automate a lot of that level of fixing?

And that's where I think a great place AI comes in, is like automatically kind of do the first pass at a PR on that. Pass it, you know, create the PR, have engineering review it at this point maybe, 'cause who wants to YOLO it right now? I don't think the models are there yet. But that leaves a lot more to...

When you remove that stuff, the And on the product side, digs into when you have to go dig through dashboards or session replays or analytics. These are all the things that you can't now spend time talking to customers, understanding what they really need. How do we do long-term retention increases because we know what they actually care about and where people are really getting burned, not month one onboarding, but month 13 of Staying around what really gets annoying as it [00:27:00] accumulates.

And that's what we've kinda set out to do here, and I think it's a really interesting point you bring up, is, like, that part is really important. If you can erase that section of work or really, really minimize it, people can spend time on this much higher order stuff and come up with new, novel ways, they're gonna do things you never thought before.

I think you raise a good point. I forgot to mention earlier that a big part of fixing quality was improving our observability into the problem space itself. Yeah. Um, the first step to fixing any problem is to shine a light on it so that you can actually action around it. And yeah, there was maybe a month or so of, like, re-instrumenting metrics and just even making sure we were looking at the right things, that they were measuring the right things.

So that is an area I think AI has helped as well, is, like, you're right, like, if we're able to create good dashboards and ways to get the answers you need, that's time freed up on more important things, just, like, understanding what your customers need and ideating and things. And so now you find yourself at Life360, but I think this is a, a viewpoint you've taken.

And I, I think it sounds very balanced. Don't be too numbers focused of the, the [00:28:00] growth hacky, you know, just kinda, "Oh, move this little, you know, this little pinpoint number," but understand what people need, how do you deliver it to them. And what has, like, what does the last year look like there, and how have you kind of come in and looked to move the needle given this kind of product sense, learning, and background you've developed over so many years?

So Life360, for those who don't know, we are the leading family safety connection app. Mm-hmm. We have about 100 million monthly active users, and the main thing that our members use that makes Life360 special is our location sharing services. We make everyday family life better by helping people stay connected, we help people coordinate their busy lives, and we help keep loved ones safe.

So with that premise, you can imagine you have a lot of worried parents, you have people who love, you know, are trying to make sure that they're more connected. So this is people's intimate lives, and so trust becomes even more important. So we can even less afford to just look at numbers and goal-seek.

Yeah. We have to understand what their needs are, we have to give them real value, we have to do it in a way that doesn't [00:29:00] break the trust with our users. So stepping in, I think, being in a company where that is valued was a breath of fresh air. It was like, okay, great, like, everyone kinda thinks like this, and then it's about how do we operationalize in a way that we can live up to that.

Because it's, it, it's location-based, it's live, people's loved ones are, like, within a circle, the onus to deliver quality and precision becomes that much higher. Can you talk about maybe what are enhancements that maybe were important to hear that you've been able to look at from this sense, where at other companies where maybe they were more focused on onboarding or the very specific, what is this doing to increase growth in this cohort?

kind of thing. How do you look at those problems separately or, or differently now? I can say that from a practices perspective, right? I come from, uh, many companies where you had a very high review culture. You were always, like, writing specs. You had all these people reviewing. But the discussion was always around, oh, how is this going to, like, improve our revenue, or how is this going to, you know, boost engagement?

It was always about what is the number going to be. I think the types of [00:30:00] conversations I've witnessed and have been part of in Life360, you'd be surprised by how many of them are around, oh, for our members, is this going to create a moment of, of worry, or by implementing it this way, are we appropriately giving people a feeling of opt-in or a feeling of trust that their data isn't going to leak, or that this thing that, you know, I s- I see where my child is, that some stranger isn't gonna see that.

We're constantly thinking through what are the different negative edge cases we need to avoid? What are the ways that we can make sure when people use our product and add people to a circle, they trust that it is secure, private, and they've given, um, consent. Mm-hmm. So it's just very different. And, like, in past experiences I've had, not necessarily Dropbox, but just, like, in general, other companies as well as Smoo and others, it always felt like the consent piece or the privacy piece, it was like, okay, you have this great idea.

You talked to customers. Oh, now I gotta go talk to the privacy team. Now I gotta go talk to legal and make sure that, oh, I'm gonna have to, like, change my product. But I think at Life360, like, that's just built into our DNA of, [00:31:00] like, no, we need to have eyes on this to make sure that we're building something that we call our Peggy Johnson would be happy to use in order to, like, bring her kids to soccer practice.

Well, it's, it's like a bank at some level, where I found banks that we work with and just talking to digital leaders, they're kind of ... They're always really worried about not just the onboarding, but trust in general, and what are they doing that makes people trust the platform. 'Cause if you log into your bank app and it takes forever to load and it looks janky and maybe, you know, something's a little off, right, and it just feels wrong or sloppily put together, you're not really want to trust them with your money.

Absolutely. Um, and same way, if you're coming into something like Life360 and you're seeing it, it's slow, or it doesn't really work, or once in a while it glitches, I don't know, I'm not gonna trust, you know, any of my loved ones on that one. And I think this is something that we're going to see more and more across industries, because as the teens were one thing, and the whole period of Zerp and everyone buying everything and just money was free-flowing.

But in times when maybe growth is a little slowed, people re-realize how [00:32:00] important retention is and, and what can you do to make the product better to use and make people happy to use your thing and make it work better. And all that goes a long way to people staying with it through those little micro events that happen or the bigger spikes where it might have in the past caused churn.

Now you can weather that. So I think it's a great experience that you kind of developed and a great focus of why product sense is so important, 'cause it can open you up to so much more than just hacking certain numbers right there. But To that point, and speaking of Life 360, I do think we have to give you back at some point.

So Kevin, thank you so much for coming on. This was a blast. I appreciate you coming on. Great experiences and really a fantastic way of helping to look at PM in a way that is measurable, but maybe not completely numbers folks and how do you kind of develop that sense of, of what do you do next when you don't have the little tiny dials to turn.

So I love this. This is fantastic. Yeah. Thank you for having me. Yeah. If I can leave you with one, uh, secret of how to develop product sense, uh, it's not rocket science. It is, uh, you gotta go talk to your customers. Yeah. Talk to them, see what they're doing, learn from them. That is the secret. Kevin, thank you so [00:33:00] much.

It was a blast having you on. Hopefully stay in touch and, and talk to you again soon. Thank you, Jeff. Thank you everyone.