The a16z Show - Metrics and Mindsets for Retention & Engagement

Episode Date: February 20, 2020

It's "Marketplaces Week" for us at a16z, thanks to our consumer team releasing a new index of the next industry-defining marketplaces, the Marketplace 100.  But what happens as such marketplaces and other platforms evolve over time, as do their users? This episode is a rerun of a popular conversation from a couple years ago -- featuring general partners Andrew Chen and Jeff Jordan (in conversation with Sonal Chokshi) -- on what comes after user acquisition: retention. It's all about engagement. So what are the key metrics? And if different kinds of users join a  platform over time -- what does that mean for engagement, and where do cohort analyses come in? Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The content here is for informational purposes only, should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund. For more details, please see A16Z.com slash disclosures. Hi, everyone. It's Marketplaces Week over here, thanks to our consumer team, releasing a new index of the next industry defining marketplaces. You can check that out at A6.Z.com slash Marketplace 100. But what happens as such marketplaces and other platforms and products evolve over time, as do their users? The conversation that follows is one of our more popular episodes from a couple years ago,

featuring general partners Andrew Chen and Jeff Jordan in conversation with me on how after you acquire users, which we covered in another episode, then how do you keep them engaged, retain them, and even resurrect or re-engage them, and what are the key metrics? But first we began with what happens after the initial acquisition as different kinds of users join a product or platform over time. What does that mean for engagement? And where do cohort analyses come in? One of the things that you see is that people end up using these products very differently because the kinds of users that you're getting are changing over time. You know, when you look at something like Rideshare, you know, all the early cohorts are basically people in urban areas.

And then these days, all of Rideshare is more like suburb. or rural folks because, you know, you've saturated all of the center. And so what you tend to see is as you acquire your, you know, folks, you know, your core demographic out, that actually ends up showing up in the engagement. And so, you know, going back to the natural, like, kind of gravity to the whole thing, this gravity also hits the engagement side of things as well. And then ultimately, the LTV, because your users are typically getting, you know, kind of less valuable. It may take years to see this kind of play out, but that's kind of the natural, you know, law of things. And there is a progression in these, and particularly the ones that are really successful.

Early on, it's all about getting users. Right. Just like users, users, users, users. If you're wildly successful at doing that, you run out of users. Or you start running low on users and you have to go to engagement. So Pinterest has a very high quality problem right now. Most women in America have downloaded the Pinterest app. Oh, yeah, I've got it for years.

So some growth can come through, okay, there are some number of women who've never heard of Pinterest somewhere in the country. But much more so, they need to engage and re-engage the existing audience. I mean, we love engagement from an investor standpoint because it's just, you know, that- It shows stickiness. You can often hack your way into new users. It's really hard to hack your way into true engagement. If someone's spending 20 minutes, you know, a day on your site, Offer Up, you know, Pinterest and Offer Up, the major investment thesis was,

oh, my God, look at that engagement, you know, kind of thing. And, you know, if they can scale the user base, it's a beautiful thing. What we mean by engagement is actually interacting with them and seeing their activity. Because to Andrews three points of acquisition engagement retention, the third piece is keeping them. The way that we'll often analyze this is looking at cohort analyses, where we'll look at each batch of users that's joining in each week and really start to dissect, like, well, how active are they really? And to compare all these cohorts over time, you're basically putting the users that come in from a particular time frame, let's say it's a week, right? and you're putting them into a bucket, right? And what you're doing is you want to compare all of these different buckets against each other.

And so what you typically do is you look at a bucket of a cohort of users and you say, okay, well, you know, once they've signed up, the week after, how active are they? And what about the week after that and the week after that? And you kind of like can build out a curve. And it just turns out that these curves, once you've looked at enough of them, surprisingly, you know, human nature, they all look kind of the same. I bet. They kind of all kind of curve down.

And for the good ones, they start to. flatten out and they plateau and then for the really good ones, they'll actually swing back up and, you know, people will come back to the service. What you want to do is you want to compare the various cohorts against each other in time to see if you can spot any trends on how the usage patterns are increasing or decreasing. When you add a new layer to layer cake, you know, you might unlock a bunch of new behavior. You might unlock a bunch of new frequency that didn't exist before. Or alternatively, over long thresholds of time, people tend to become less active as you move out of your core segment.

The cohort graduates. Whether or not a specific cohort of users flattens out is really important, right? Because if you're in a world where they kind of slowly degrades and then all of a sudden it'll actually go to zero, that means that you're naturally always kind of filling up the bucket. You know, you kind of have a leaky bucket. You're constantly filling it out. And what happens is that gets progressively harder as you, because, you know, if you want to keep your overall growth rate, because that means you have to double, triple, quadruple,

your acquisition in order to counteract for that. One growth accounting equation that's often thrown around is that your net MAU, so your net monthly active users, equals all the new people that you're acquiring, minus all the people that are churned, right? And then plus all the people that you're resurrecting, you know, reengaging. Reengaging, exactly, that are coming back after they've churned. And so what happens is for a new startup, you are completely focused on new users because you don't really have that many users to churn. And over time, as you get bigger and bigger and bigger, what you find is that your turn rate starts to,

you know, it's a percentage of your actives. And so, you know, the evolution of most of these companies, as they're getting bigger, tends to start with acquisition, then focus much more on, you know, churn and retention, and then ultimately also to layer in resurrection as well. And the co-workers have a couple other features that I love,

but usually in marketplace businesses, the best models are built off of the cohort curves. Oh, interesting. Because you have to understand that degradation and where it goes. Using cohorts really give you a sense of are there network effects. And network effect is the business gets more valuable, the more users that use it. If it gets more valuable, your newer cohorts should behave better than your early cohorts. Why is that?

Because the service is more valuable given how they're there. So an open table, if there's 10 times more restaurants, you're going to get a whole lot more reservations per diner because you were serving more of the need. So the open table cohorts would climb up and get more attractive over time versus, you know, we talked about typically they tend to grade over time. If you've reversed the polarity and they're growing over time, it means you've made the business more valuable and then you start projecting forward. Okay, what a better way to know the business is actually more valuable than thinking is valuable and leaving your own mate. In a network affects business, we always ask, show us the

cohorts. Everyone asserts I'm a network effect. I'm a network effect. But when you say, show me the data cohort curves. It's like show me the money is now show us the cohort. They don't lie. The other really interesting part is segmenting it. I was about to actually ask you what are the buckets of cohorts? Are they all demographic data? For a bunch of hyperlocal type businesses, the reason why segmenting it based on market geography, why that's so valuable is because then you can compare markets against each other. You can say, well, you know, this market, which has much more density in terms of the number of scooters behaves like this. And you can start to draw conclusions, you know, sort of a natural AB test in order to do that. And I think the similar kind of analysis

you can do for B2B companies is for products that have different size teams using it. If you have a really large team that are all using a product, well, are they all using the product more as a result? And let's compare that to something that maybe only has a couple, right? And so this way you can start to kind of disassemble, you know, the structure of these networks and do they actually lead to higher engagement. Slack would be a perfect example of that. You know, just if you have five people in the organization using Slack, you get one use curve. If you have, you know, if the organization, it's the operating system for the organization, you have a very different curve. Though it's not just an accident. You have to sort of architect it, not just

expect like serendipity to fall into place. So after you get the new users, the way that you have to think about it is around quality, right? You have to make sure that the new users turn into engaged users. One of the things people often talk about is just sort of this idea of like an aha moment or a magic moment where the user really understands the true value of the product, but often that involves a bunch of setup. So for example, for all the different social products, whether that's Twitter or Facebook or Pinterest, etc., you have to make sure that when you first bring a new user in, they have

to follow all the right people. They have to get, we have to figure out. It's like the onboarding experience. Which, by the way, isn't just signing up, but it's actually, you know, doing all the things to get to this like, aha, where you're like, I. I get this product. I get this product. It's for me.

And once you get that, then you have the opportunity to keep them in this engaged state over time. Is that really such a thing that there is like an aha moment? Or is it sort of like a cumulative? A lot of the later users on Facebook came because everyone else was already there. Is this only tied to new users? In the case of Facebook, actually, the fact that everyone was already there makes the aha moment that much more powerful, right? Because all your friends and family that are already there, your feed's already full of content.

and the first time that you see photos that may be of someone that you went to high school with, right? That's like what happened to me. I was so excited when I saw an old friend, right? Right, yeah, exactly. And so what that means is like you get the product. And then afterwards, you know, when you actually are getting these push notifications

or emails that are like, hey, it's someone's birthday or whatever, you've internalized what that product is. And, you know, this moment is different for all sorts of different companies. I've always heard this referred to as the magic number. When you show up and it's a blank slate, it's like, what is this about? But they would drive you maniacally to follow people because when you got to their magic number where they had statistically correlated the number of followers with long-term engagement and retention, they would kill you to get you there doing what felt like a natural act of like,

you log on, follow, and you say no and say yes. But when they got you there, it kicked in and the service then, quote unquote work for you. A lot of the entrepreneurs I work where they're trying to figure out what is my magic moment that then creates the awareness of the value problem. So take the example of Pinterest. Pinterest, when it goes to a new market first of all, they figure out they need a lot of local

content to make it compelling to local users. The U.S. corpus of images doesn't necessarily, is helpful in international markets but isn't sufficient. You're right. If I'm Indian, I want like saris, I don't only want like skirts. Yeah, yeah. Exactly. I haven't worn a sorry in North America at a long time.

But then once you have the content set, then you have to get compelling information to that individual in front of them, which you know, you don't know the individual when they walk in the door, the faster they do that, the more quickly, the better the business performs, engagement goes up, retention goes up, and it works. So different entrepreneurs have to figure out what is that? What experience do they want to deliver where people get it? And then how do you engineer your way into delivering it? Okay. So the way it kind of come up through acquisition and you've gotten new users. They get the product. You even have hopefully a way to measure that and see and track it over time.

Do you want to then go into trying to get different users? Do you take your existing users? One of the things that we covered very early on is that with SaaS, you always want to try to take existing users and upsell them because it's way more expensive to acquire a new customer in that context. Of course you want to grow your customers. How does this play out in this context? What happens next?

A lot of companies is a progression. So almost all the early activity in a company is, okay, how do I get the users? As you get users, you get more and more leverage from efforts at activation and retention and engagement. So users, Pinterest is an example. Again, a very high percentage of women in America have downloaded Pinterest. Then the leverage quickly goes into, okay, how do I keep them engaged, reactivates the one who disappears? And, you know, their acquisition efforts in the U.S. get deemphasized in all the leverage is there, except as they're going international, they're still in that acquisition part of the curve. And so,

So I think the leverage changes over time based on the situation of the company. Facebook hasn't had any users in U.S. forever because they have them all. This kind of goes back to this portfolio approach to thinking about your users. Once you have an active base of users and customers, what starts to get really interesting is to really analyze what are the things that actually set that group up to be successful, really long-term, sticky users versus what are the behaviors and profiles where users aren't successful, right? you actually throw your data science team on it to figure out all the different weights for both behavioral as well as the demographic and sort of profile-based stuff on there.

And so one of the first things that you figure out is that each one of these products actually has this like ladder of engagement where oftentimes new users show up to do something that's valuable but potentially infrequent. And you need to actually level them up to something that happens all the time. For example, when you first in sell Dropbox, the easiest thing that you can do is you can use it to just sync your home and your work computers, right? And that's great. But really the way to get those users to become really valuable is for them to start sharing folders at work with their colleagues. Because once they have that and people are dragging files in, you know, they're really starting to collaborate on things. That's like the next level of value that you can actually have on a daily basis versus this thing that kind of is in the background that's just syncing your storage. So what are some of the things that people can then do to move those users up that ladder of engagement?

Step one is really segmenting your users into this kind of engagement map. Oftentimes you'll see this visualized as a kind of state machine where you have folks that are new, you have folks that are casual, you want to track how much they're moving up or down in each one of these steps. And then once you have that, then the question is, okay, well, great, how do you actually get them to move from one place or the other? First, there's like content and education. They need to know kind of like in context that they can actually. do something. So for example, if you can get your users to set their home and work for transportation product, then you can maybe like figure out, you know, okay, should I prompt them in

the morning to try their ride based on what the ETAs are, right? Like in the app, there would be like some kind of notification. Like life cycle messaging factor in there. The second is, of course, if your product has some kind of monetary component, then you can use incentives. 10 bucks off, your next subscription. If you do this behavior that we know for sure gets you kind of to the next step. And then the third thing is really just like refining the product. for that particular use case. Maybe there are certain kinds of products that are transacted all the time. And so you maybe want to like waive fees or give some credits or you do something in order

to get people to kind of get addicted to that as a thing. And it really interesting thing is the frequency with which something's consumed. I mean, eBay had enormous levels of engagement early on for a commerce app in particular. People would spend hours just browsing because early on it was about collectibles and it was about people's passion. So if someone's passionate about depression near a glass, they will spend hours if you give them that depth of content to say, oh, my God, I just found the perfect item. Open table and Airbnb are both typically much more episodic. Most people don't dine at fine dining restaurants with high frequency.

Our median user dine twice a year on open table. And so that has completely different marketing implications and user implications. Measurement's probably even more important in the engagement retention thing because we got our data scientists to understand. the different consumer journeys through our product. And then we tried to develop tactics to accelerate the journeys we wanted and limit the journeys we don't want. But in order to develop your strategy, you really need to understand how your users are behaving at a really refined level. So what are some of the engagement metrics? One really important area is frequency. Just how often are using the product, regardless of the intensity and the length of the sessions and all that

other stuff, you know, just literally just frequency of sessions. We might often ask for a daily active user divided by monthly active user ratio. And that gives you a sense for like how many days is a user active. Doubt a male. You recently put a post out on the DAU-MAU metric. Right. And when it works and when it doesn't, there's a lot of nuances around when to apply it and when not to. DAUMAU was very much popularized by the fact that Facebook used it, including in their public financial statements. And it really makes sense for them because they're an advertising business, and it matters a lot that people use them a lot all the time, right? Right. It's like counting impressions and being able to sell that to advertisers, right?

Exactly. Their products have historically been 60% plus daily actives over monthly actives, and that's very high. You know, you're using it more than half the days in a month. On the flip side, what I was talking about in my essay about this is that Dow Mal can tell you if something's really high frequency and if it's working, but a lot of times products are actually lower Dow Mao for a very good reason, because there's sort of of just a natural cadence, you know, to the product. Like, you're not going to get somebody who is using a travel product to use it more than a couple times per year. And yet there are many valuable travel companies, obviously. So you're saying don't live and die by that alone.

Exactly. Right. Right. Product you have, the type of nature of use it has, et cetera. You just want to make sure that the metric reflects whatever strategy that you're putting in place. If you think that your product is a daily use product and you're going to monetize using a little bit of money that you're making over a long period of time, but your Dow Mao is low, is like sub 15%. Then it's probably not going to work. And then the metric called L28, which is something else that was pioneered, certainly early at Facebook.

And it's a histogram. And what you want to do is... A histogram is a frequency diagram. Right. Frequency diagram that basically says, okay, show a bar showing how many users have visited once in that month, then twice in the month, and then three times in the month, and then four times in the month. and you kind of build that all the way out to 28 days.

Because there's 28 days in the month on average, right? And the 28 days is to remove seasonality. And then related one obviously is like L7, right? So just like last seven days. And so what you want to see... So it would just be wows weekly active users? Is that really a thing, by the way? Am I just making that up?

You just coined it. I know, great. I'm a coining retainment. Why not? Right. And so the idea with, you know, an L28 or an L7 is the idea that you should actually start to see when you graph this out that there's a group of people

who just use it 28 days out of 28 days, right? And that there's a big group of people who use it 27 days out of 28 days, right? And that there's a big cluster. And so that's how you know that you actually have a hardcore segment. And that's really important because in all these products,

you typically have a core part of the network that's driving the rest of it, whether that's power sellers or power buyers or, you know, in a social network, the creators versus the consumers. I actually've heard this referred to as the smile because the one use is always pretty big. A lot of people show up one,

I don't understand what this is and disappear. So that's the one. And then they typically slide down more people use it. Fewer people use it two days than one, three days than two. Done right, it starts to increase at the end. So you basically have a smile. You just go down. And I mean, that's really powerful.

Facebook has a smile. WhatsApp had a smile. Instagram had a smile. If you take a step back, it's a precondition for investing in a venture business, essentially that there's growth. If it's in market and saying you want to, see growth, but growth by itself is not sufficient. Investors love engagement. So Pinterest, the key driver of Pinterest, it was growing, but the users were using it maniacally.

Oh my God, I think I spent an entire Thanksgiving using Pinterest. It was the engagement that blew my mind much more than the growth. Offer Up has engagement that's similar to social sites like Instagram and Snap. I mean, a commerce site, mobile classifies people just sit there and troll looking for bargaining. It's a little addictive to see. with nearby that you can buy. Why not? Yeah. So Datamau, smile, all these metrics are so core to us because a big engaged audience is so rare. And as a result, it's almost always incredibly valuable. And the engagement ends up being very related to acquisition. Because when you look at all the different acquisition loops, whether it's paid marketing or a viral loop or whatever, all of those

things are actually powered by engagement ultimately. Like, you need people to get excited about a product in order to share content off of that platform to other platforms in order to get a viral loop going. And so one of the things I was going to also add on Dow Mal and L28 is that they're actually really hard to game, right, which is fascinating. Whereas growth can be very easy to gain. Right, exactly. Yeah, why is that? What's a difference?

The typical approach is to say, well, you know, I'm going to add an email notifications. I'm going to do more push notifications. I'm going to do more of this and that. And then automatically, you know, these metrics ought to go up, right? The challenging thing is actually usually sending out more notifications will actually cause more of your casual users to show up because your hardcore users were already kind of showing up, you know, already. And what that does is that will increase your monthly active's number, but actually not increase your daily actives as much. So I've actually seen cases where sending out more email decreases your down-mow as opposed to increasing it. That's really interesting.

When you think about this portfolio of metrics, it really tells you a story about people are kind of coming but not really state. If you get an email or push notification every day, eventually you turn them off and then you just stop. So then you get counted as a Mao for that period of time and then you lose them as a doubt. Acquisition is super easy to game because you can just spend money. Or you got a distribution hack that works. Early on in the Facebook platform, companies literally got to a million users and it felt like minutes. Just because there were so many people on Facebook and the ones who were early just got exploding user bases. there were a number of concepts whose mean number of visits was one.

They never came back. So you get to see these incredibly seductive growth curves, but our job is essentially to be cynical and just say, okay, we need to go below that because there are a lot of talented growth hackers who can drive growth. I looked at a number of businesses, had tens of millions of users, and no one ever came back. This is why engagement is so, so key.

So we've talked especially about the fact that growth and network effects are not the exact same thing. Because network effects by definition are that a network becomes more valuable, the more users that use it. What happens on the engagement side with network effects? What are the things we should be thinking about in that context? Typically, network effects, if they're real, manifest in data, things like the cohort curves improve over time. Usually there's a decay. With network effects, there often is a reversal where they're growing because it's more valuable.

Another smile, essentially. My diligence at OpenTable was I looked at San Francisco, which was their first market. and sales rep productivity grew over time, restaurant churned decreased over time, the number of diners per restaurant increased over time, the percentage that booked through Open Table versus the restaurant's own website moved towards Open Table dramatically. Every metric improved. And so, you know, that's where it both drives good engagement,

but also it just improves the investment basis. The value overall, right. One of the interesting points about network effects is that we often talk about it as if it's a binary thing. Right, or homogenous, like all network effects are equal when they're not. Exactly, right. When you look at the data, what you really figure out is network effect is actually like a curve, and it's not like a binary yes-no kind of thing. So, you know, for example, I would guess that if you plotted the number, if you took a bunch of cities, every city was a data point. And you graphed on one side the number of restaurants in the city versus the conversion rate for that city.

You would quickly find that when cities have more restaurants, the conversion rate is higher, right? Actually, I'm just guessing. I'm just guessing. I'm just guessing. Almost perfect with one refinement, the number of restaurants you have is a percent of that market's restaurant universe. Okay, right. Because having 100 restaurants in Des Moines is different than having 100 restaurants in Manhattan. Makes total sense.

So not only that, what you then quickly figure out is that there's some kind of a diminishing effect to these things often in many cases. So, for example, in Rideshare, if you are going to get a car called 15 minutes versus 10 minutes, that's very meaningful. But if it's, you know, five minutes versus two minutes, your conversion rate doesn't actually go out. If you can express your network effect in this kind of a manner, then what you can start to show is, okay, yeah, we have a couple new investment markets that maybe don't have as many restaurants or don't have as many cars. But if we put money into them and invest in them and build the right products, et cetera, then you can grow.

You can do this kind of same analysis, whether you're talking about, you know, YouTube channels and the number of subscribers, you might have, having more videos is better. I'm sure you can show that. if you go into the workplace and you start thinking about collaboration tools, then what you ought to see is that as more people use your chat platform or your collaborative document editing platform, then the engagement on that is going to be higher. And you should be able to show that in the data by comparing a whole bunch of different teams.

Okay. So we've talked about engagement and also how it applies the network effects. Are engagement and retention the same thing? I mean, in all honesty, they sound like they would be the same thing. There's overlap. Yeah, there's overlap. just to give a couple examples. So weather is low frequency, but high retention.

Because you're actually going to need to know what the weather is. Only once a day. You know, unless you live in San Francisco, you got to check it like 20 times a day. Right, right, exactly. Or if you live down here, you have to check it twice a year. That's true. It's actually the same year round.

That's actually what it showed was actually more that generally people didn't really check it that often. However, you are highly likely to have it installed and running after 90 days because it's a reference thing. It's so important. Yeah. Versus if you look at something like, games or e-books or, you know, those kinds of products, like really high engagement because

you're like, all right, I'm going to get to, I'm going to finish this like trashy science fiction novel that I've been reading. I'm just going to like get to it. But then as soon as you're done, you're like, okay, there's no reason why I would go back and read it again. So the real difference is that engagement obviously varies depending on the product, the type of thing it is, whether it's a weather or e-book. And retention is, are you still using it after X amount of time? And different companies have different cadences. If the average users, twice a year, retention is, did they book annually? Other businesses or did they come daily? But the model behind retention is completely different and the model behind engagement is completely

different. The chart that I'd love to really see is one that was a bunch of different categories that's retention versus frequency versus monetization. And I think you've got to be like really good at least on one of those axes. So we've done sort of this taxonomy of metrics. We've talked about the acquisition metrics. We've talked about some engagement metrics, primarily frequency. And engagement is also time, not just how frequent someone is, but just how much time do they spend. Time spent on site, on the piece, writing comments, not just pictures. I mean, the number of businesses have great engagement is not high because there are only so many minutes in the day. And so you're just looking for where, okay, they're just passing time and enjoying it.

And they both have obvious monetization associated with that behavior. This is why Netflix is so freaking genius, because when they literally invented the format of binge watching, which you cannot do, which is, I love it because it's a very internet-native concept. I mean, they've literally fucked up everyone else's engagement numbers. I think that's one of the narratives on why building consumer products is much harder these days, right? And do you think it's true or not? Well, because it used to be that you were, you know, what kind of time were you competing for in the first couple years of the smartphone? You were competing against literally, I'm going to stare at the back of this person's head,

or I can, like, use some cool app that I've downloaded, right? Versus these days, you actually have to take minutes away from other products. And it's typically other BMS because there's, The top apps are almost all done by Facebook, Amazon, Google, and, you know, breaking through just, Mark calls it the first page. The people who are on the first screen are just such the incumbents. And sure, most people have Facebook on the screen and YouTube on the screen and Amazon on the screen. So it's like hard to take that down, right? It is a big overhang right now in consumer investing because you have to displace someone's minutes.

Yeah, I would add one more layer to that, at least on the content side, which is, I think, I think a lot of people make a lot of category errors because they think they're competing with like like-minded players. And in fact, when it comes to things like content and attention, you're competing with just about anything that grabs your attention. It's not just other media outlets. It's Tinder. It's a dating up. It's something else. I'm riding in the train for an hour.

I could see you want my friends around or Facebook, watch videos on YouTube. It actually changes at time blocks. Xerox Park did a really interesting study on micro-waiting moments. And they're literally the little snatches of time, like two seconds here and there, that you might be waiting in line or doing something so you can do a lot of snack-sized things in that period, which is also another interesting thing to think about for how people engage with. So it's actually funny because there's some businesses that have good engagement where it's one session that goes on for a while, YouTube or Netflix or that.

There are others that are multiple small sessions. Like a podcast, which you might not finish in one setting. Because it's the micro opportunities to... And Google is the best example of this, right? In fact, if you spend a lot of time on Google.com, you know, refining your searches and clicking around, that means actually the service is doing poorly. They failed. Their goal is to get you to their advertisers as fast as they can.

That's a frequency play and a monetization play ultimately as opposed to an engagement one. And then some products are more on the engagement side. So sometimes you have to optimize it based on how you're monetizing. What are some of the metrics for retention? I mean, is it just should I stay or should I go? Is that the retention metric? And I think the big thing is the concept of churn is a tricky one. In some cases like subscription, Hulu, Netflix, and then also in the SaaS world,

whether or not you're still continuing to pay or not, right? And that's really obvious. The thing that's tricky for a lot of these consumer products, especially episodic ones, and it's actually less whether they've quote unquote churned or not. It's actually just whether or not they're active or inactive. And whether or not that's happening at a rate that you in your business strategy have decided is acceptable or not. If every Halloween, you know how there's those costume stores that like open all over the place? If every Halloween you go back and you buy a costume, but you're inactive the rest of the time, have you churned or not? Like, it's not clear. And I would argue you've not churned because you're doing exactly what they want,

which is to buy a costume every, you know, Halloween. It seems like it makes assessing the retention of a consumer business very difficult. You adjust the time period that you're relevant on. If the average diner dines twice a year. So you can apply that metric, travels, a similar thing. Airbnb is, you know, for the average user relatively infrequent. You have to tailor your look to what are they trying to do. So if you're trying to stake up with your friends and you're doing it twice a year,

yeah, that doesn't work. So Facebook has got a whole different set of. One of the things that companies can often do is to measure upstream signal. So, for example, Zillow, you're probably not going to buy a house very often, right? Maybe a couple times in your life. However, what's really interesting is they can say, well, you know, maybe folks aren't buying houses, but at least are we top of mind. Are they checking the houses that are going on sale in their neighborhood? Are they opening up the emails?

Are they, you know, doing searches, right? Why do you call that upstream? In the funnel. You're kind of going up in the funnel and you're tracking those metrics as opposed to, you know, purchases. So even for OpenTable, it's like, okay, great, well, maybe if you're not actually completing the reservations, are you at least checking the app for availability? What's new restaurants where I want to dine? There's some level of content consumption. So throughout this entire episode, there seems to be this interesting dance between architecting and discovering. Like, you might know some things up front because you're trying to be intentional and build these things.

And then there are things that you discover along the way as your product and your views and your data evolves. how do you advise people to sort of navigate that dance? You iterate. You develop hypotheses. You put it out there and you test the hypothesis. I think my product's going to behave this way and then did it. Probably the most important thing is for me, marketing can be art, marketing can be science. In the consumer internet, it's more science. Some companies can effectively do TV campaigns, large media budgets, things like that. For me, the better companies typically just rip apart their metrics, understand the dynamics of their business,

and then figure out ways to improve the business through that knowledge. And that knowledge could feed back into new product executions or new marketing strategies or new something. It's consideration, but it's informed by the data at a level that, you know, on the best companies, is really, really deep. Ultimately, you have a set of strategies that you're trying to pursue. And you pick the metrics to validate that you're on the right track, right? And a lot of what we've talked about today has really been the idea that actually there's a lot of nature versus nurture kind of parts about this. Your product could just be low cadence but high monetization. And so you shouldn't look at, you know, D-A-U-M-A-U.

And so you have to find really the right set of metrics that show that you're providing value to your customers first and foremost. And then really, you know, build your team and your product roadmap and everything in order to reinforce that. Find the loops and the networks that exist within your product because those are the things that are going to keep your engagement improving over time even in the face of competition. Growth is good. Growth and engagement is really, really, really good. Okay, so that's fabulous. Well, thank you guys for joining the A6NC podcast.