I recently did a podcast with Ryan Hoover, co-founder of Product Hunt and my sister Ada Chen Rekhi, previously SVP Marketing at Survey Monkey – here’s what we talk about:
The network effects that makes Silicon Valley what it is. The uniqueness of the Silicon Valley tech ecosystem, how network effects conspire to create a “rich get richer” situation for cities, and why new communication tools enabling distributed teams to work together across continents could mean that there will be no “next Silicon Valley.”
Big companies versus small ones. Ada shares her insights on the contrasting skill sets needed when working at a big company versus a small startup, after having herself gone from a small startup to a huge organization like LinkedIn back to a two-person startup with her husband.
Personal life OKRs. How to port the concept of OKRs — objectives and key results, a personnel management framework originated by legendary Intel CEO Andy Grove — to your personal life from your business (and why you would want to). We talk about you can use them to help manage your exercise, social life and relationship with your SO.
Of course, we also chat about some of our favorite products, including an app that lets you pop in to a luxury hotel for a few hours to shower or have a nap, a super cool way to greet visitors to your office, and a new app for emailing yourself.
“When you’re executing at a small startup, or a small team, or just by yourself, it really comes down to ideating, picking and prioritizing, and then rolling up your sleeves and just getting things done as quickly as possible. It’s a night and day difference from a big company.” — Ada
“If you graph cities, there’s a power law: the biggest cities are really big and there’s this long tail of all these little tiny cities, and the reason for that is that there’s a network effect within cities. These ecosystems emerge because the designers are here, because the engineers are here, because the capital is here, because the marketing people are here, and on and on and on.” — Andrew
“When it comes to working at a large company, it’s much more cerebral and much more about the heart. You’re thinking about how to collaborate and communicate across a cross-functional team to get the initiative done: can you communicate what it’s about; can you motivate people to get it done; can you manage all the working pieces?” — Ada
“Either these network effects will continue to hold and the Bay Area will continue to be strong, or we make big structural shifts in how we organize teams and workforces and the network effects become less strong. But that doesn’t mean some other city becomes the next Silicon Valley, there won’t actually be a “next” Silicon Valley — it either continues or will just be distributed.” — Andrew
“The irony of it is that sometimes when you are working on projects with such large scale, because the skill set is so different, it actually feels like you’re not doing anything at all — you’re merely managing the appendages of the other groups and trying to make sure everyone is staying on track and executing.” — Ada
On joining a venture capital firm: “The idea that I would do the thing I want to do for fun as my full-time job feels like I’ve won an ice cream eating competition, and the prize is more ice cream.” — Andrew
Ryan: Hey everybody, this is Ryan Hoover with Product Hunt Radio and I’m here at Andreessen Horowitz down in Menlo Park with two people I’ve known for a little while now, two brothers and sisters, Andrew Chen and Ada Chen. This is the first brother and sister duo and hopefully the first of many. Thanks for having me over here. First off, Andrew, you joined Andreessen Horowitz, is it six months ago?
Andrew: Yeah, I think I’m on month five. I’m quickly reaching my half year mark, which has gone incredibly fast.
Ryan: Are you completely swamped with meetings and pitches or how has it changed since before Andreessen Horowitz?
Andrew: Yeah, so when I was at Uber I really loved meeting with startups and hearing about new ideas and staying in touch with the tech community, but I can only do it first thing in the morning and on weekends and it quickly filled up my schedule. So I would work at Uber and then I would do that [meet with founders] basically. The idea that I would do the thing that I wanted to do for fun, like as my full time job sort of feels like I’ve won an ice cream eating competition and the prize is more ice cream. I could do as much as I want, which is super awesome.
Ryan: Yeah. And so your, your background, just maybe for those that aren’t super familiar, you were at Uber right before this and then what’s your short version of your history?
Andrew: Yeah, yeah, totally. We were just talking about. So Ada and I, who’s my little sister, by the way, I want to clarify —
Ada: [laughter, eye-rolling and protestation]
Andrew: So we grew up in Seattle, and we both made our way to the Bay Area. Actually, the funny thing is my first job ever was actually in venture capital and was something I did right after college. Then after that I ended up working at a series of startups, I moved to the Bay Area 10 years ago to start my own company. I had actually met Marc and Ben [of Andreesen Horowitz] here and they actually led the seed round for a startup I was working on during the Facebook platform days when everyone was working on crazy viral apps.
Ryan: So that’s around when we met.
Andrew: Yeah, right. Yeah, that’s exactly, that’s right around when we met and they invested out of a Horowitz Andreessen Angel Fund, which was really funny because that would have been like H16N and so different. So, I met them and I worked on that for a while and ended up basically deciding that it’d be better to go to a larger organization, ended up at Uber running various growth teams there. So I spent three years there, like a really, really fun experience —
Ryan: Probably pretty wild too, right?
Andrew: — Yeah, the first 18 months was like really, really incredible startup like hockey stick growth, then the last 18 months were very eventful and everyone’s read about it in the news. So I don’t have to summarize that.
Ryan: Yeah, and Ada, you’ve had a pretty interesting journey at Microsoft, LinkedIn, Survey Monkey, and then a two-person startup with your husband.
Ada: Yeah. Yeah. Actually multiple two person startups as well as, I spent some time in the game space as well at Mochi Media. So, after I graduated from college, I was in Seattle at Microsoft for a year and Microsoft at the time I think was around 80,000-100,000 employees? Very, very structured. Worked in the ad center space and the online advertising space when search marketing was just becoming a thing and exactly 367 days or so later, moved out to the Bay Area in 2007 and so worked at a tiny little startup that had just raised Series A called Mochi Media, which was an online games ad network, and spent multiple years there after it was ultimately acquired by Shanda Games and then actually started my first company which was a contact management app called Connected. It was all about contact management without the work. We raised some funding for that, ultimately sold it to LinkedIn and I had my experience sort of joining LinkedIn as a just as a company that was really maturing at the time. They had just had their IPO. There are about 1700 employees and experienced hyper growth for the first time, focused on things like relaunching Connected as LinkedIn Contacts, growth, learning a lot about subscriptions and consumer SaaS and was recruited out of that to work at Survey Monkey, where I was SVP of Marketing and then recently left a couple of years back to start a new company that’s actually a husband and wife team with Sachin Rekhi and we started a company called Notejoy, which is a collaborative notes app for teams and so we’re really focused on, how do we actually create a fast and focused workspace for teams that gets them out of the noise of chat and email.
Ryan: Yeah, team collaboration and productivity is so important because if you can even improve collaboration and efficiency within a team by like even just 10 percent, it can have such a huge impact on both your productivity but also just like your joy.
Ada: That was actually part of the inspiration behind the name and it’s one of those things where even when you go to a small team like small tight-fitting teams or larger organizations, you see this friction today that still exists when it comes to communication and collaboration and just think about how many decrepit out-of-date Wikis you see and Google Docs that are sort of lost in the ether and then people joining and getting forwarded random emails from way back when because that’s the only place that knowledge lives, we were really thinking about how do we create something that tackles that and productivity has always been a huge space where I’ve been passionate about.
Ryan: This is a really broad question, but what’s it like working at such a big company like LinkedIn and Microsoft and others to now just you and your husband?
Ada: Yeah, I mean it’s hugely different and I think the biggest dimension where I would say working at a large company versus a small startup is different is that effective execution looks completely different. It’s a night and day difference. So when you’re executing at a small startup or a small team or even just by yourself, it really comes down to ideating, picking and prioritizing and then rolling up your sleeves and getting things done as quickly as possible from an execution pace. When it comes to working at a large company, it’s actually much more cerebral, right? And it’s much more in the heart. You’re actually thinking about how do you communicate and collaborate across the cross functional group of teams to get the initiative done. So can you communicate what it’s about? Can you motivate people to get it done? Can you manage all of the pieces?
Ada: And the irony of it is that sometimes when you’re working on projects with large scale, because the skillset is so different, it actually feels like you’re not doing anything at all yourself. You’re actually merely managing the appendages of all the other groups and trying to make sure that everyone’s staying on track and executing. And so as organizations scale, the execution work around how much collaboration it takes gets orders of magnitude greater in terms of how hard it is to get everyone aligned and marching in the same direction versus one person. And so, I really think that that’s one of the biggest differences, like you go to a startup to learn how to do things and maybe not very well and you go to a large company to see how things are done really well, but across a broad range of disciplines and functions and really see how the whole thing comes together as an engine sort of humming smoothly and operating.
Ryan: You mentioned communication is one skill or trait of people in larger companies. And Andrew, you used to blog, I mean you still do, but you used to blog a lot. That’s largely how I think you built a pretty massive following over the past decade or so. How did you even get into writing to begin with?
Andrew: So, first I love writing. That’s kind of the very first thing, and I was always one of these, teenagers where like, I kept a journal and I would like write in it and then delete it and then start a new one and literally I was the only audience. I just like enjoyed it myself. And so before starting my current professional blog, I think I had like three other blogs that I had started over the years. Just basically, just getting going and then deleting them and not really sticking with it over time.
Ryan: Why did you delete the previous blogs?
Andrew: Because you get bored with it, and you’re just kinda like, okay, I’m done, kind of thing. And then like I think on those it was literally, it’s like who’s reading it? It’s like Ada, like my parents, like —
Ada: — Fun fact about Andrew’s early blogs: he would actually forcibly subscribe us to the emails to make sure that we wouldn’t miss anything.
Ryan: That was before some of the ICANN email laws and certainly before GDPR.
Andrew: Yeah, right. Yeah, exactly. So I think, don’t tell a 20 year old who they can subscribe to a blog or not. So I really enjoyed that. And then when I moved to the Bay Area 10 years ago, what I basically decided to do was I was like, I’m gonna write down everything that I’m learning and I’m just gonna start, like going out and so the funny thing, I was learning so much in my first year that I was just writing a lot of, like pretty random snippets, some of it would be like a paragraph or two, and I would do it like, maybe twice a week or something like that. So like pretty often and that’s actually how I met Marc Andreessen originally. It turned out that he somehow randomly had stumbled on my blog via Hacker News and then through that, had ended up seeing some of my content and then he cold emailed me and that’s how I met him in 2007. So it was like a pretty random and amazing adventure but at the time, I was an entrepreneur in residence. I was a 24 year old entrepreneur in residence actually across the street from here, which is really funny. And one of the things that my colleagues would tell me is they would say like, why are you wasting your time blogging, you’re giving away all your best ideas? Like, what are you doing? Like, these are the secrets that you’re going to use to understand the thing. And at the time I was like, well, I’m never going to be a venture capitalist so like it doesn’t matter. And so as a result, I’m just going to give away all this stuff and then, and it’s obviously so ironic now that like, so much of the job is, is obviously, sharing your ideas and giving back to the community via Twitter and Medium and writing, writing essays and all that.
Ryan: Now that’s the norm.
Andrew: Yeah, right, exactly. Yeah. And in fact it was like, it would have been considered very contrarian I think to actually share a bunch. But anyway, so I’ve kept it up and I think, I’m, I’m well into the many, many hundreds of essays, over 10 years and I think at times I’ve taken like a hiatus, I think I took a two year hiatus in the middle. But like I think my goal now is really to publish like regularly, but to do it at the kind of like a high level of quality and to go deeper into ideas and to sort of break new concepts and new kinds of data to the community versus literally the, the early days it was like, it’d be like 500 words, like what did I learn today kind of thing.
Ryan: So I’m going to take a tie into that a little bit. You mentioned a term called, correct me if I’m wrong, but something along the lines of mullet startups, is that correct? Or do you remember that there’s a tweet in a conversation with you and some others around the distributed nature of companies?
Andrew: Oh yeah, okay, mullet, yes.
Ryan: Mullet startups is a catchy term because it’s a trend that we’ve identified. Product Hunt is a mullet startup I guess, we’re headquartered in San Francisco, but we have a distributed team.
Andrew: So The Economist’s cover for this week is Peak Valley, is, is it over in Silicon Valley?
Andrew: So then I think there’s been, there’s been a lot of like really interesting dialogue around that. I think, and obviously a lot of it has to do with like housing and the Bay Area and there’s so much to unpack there, right? But I think that one of the reactions to it has been that we see many companies, with their leadership and their executives in the Bay Area, but when it comes to hiring engineers and designers and all sorts of other folks, then they’re much more likely to distribute the team, anywhere.
Andrew: And so, yeah, to your point, this is sort of the mullet, because it’s sort of business in the front and party in the back kind of thing. AndI think it’s fascinating because it is actually just the reverse of one of the models that we’ve seen over the years where, for example, you’ll have a really strong technical team out of Paris or out of Israel or out of Singapore and they’ll get started, they’ll get funded and then they’ll realize, okay, hey, all of our customers are in the US, let’s move the CEO and the sales and marketing function to the Bay Area. And so you end up with the, the mullet, but just like kind of, but now you do it in reverse. Right. So I think that’s like a pretty interesting, reverse mullet, which is kind of an interesting trend these days.
Ryan: Yeah. So it’s just you two right now Ada at Notejoy, but if you were to, let’s say you needed to hire 10 people tomorrow, how would you approach it? Would you hire in the Bay Area or would you go remote?
Ada: Yeah, I mean that’s actually a fascinating question because it’s something that we’ve debated and thought about because things have changed so much. Not only from the costs, but then also, what is the ability for you to access and interact with people at scale, if they’re located in other places. We actually talked to this close friend of mine who’s a founder who, built his company and scaled it to revenue, pretty substantial revenue in the Bay Area. And he basically said to us, if I were to do it again, I believe that Silicon Valley is the worst place to self-fund a company or to start a company or even to have funding and try to build a team. And the biggest challenge that he was having was actually access to talent. I think it would really depend. I think on one hand I think we have really strong networks within the Bay Area and so it would be possible to kind of peel people off and that’s really how many startups start with their founding team. They pull people that they respect, that they work with, that have shared belief in to kind of create that initial nucleus of a team and that gets you to your first couple of headcount. So maybe we can get to 10 that way, but I do think that now when it’s coming to scale, like yeah, we would definitely be looking very closely at could we build a remote team and create a really distributed workforce for Notejoy.
Andrew: I think one of the distinctions is do you hire a lot of folks who are doing the kind of individual contributor work versus the managers because I do think that it ends up being really hard once you want to find the engineering director that’s managed 200 engineers to find that elsewhere, versus it being, kind of a main thing. So, so there’s a really interesting thing about cities, right? Which is like if you graph the population of cities and sort of like, stack rank them, you’ll see that there’s a power law in it. And like the biggest cities are really, really, really big and then there’s this like there’s this long tail of all these like little tiny cities. And the reason for that is that there’s really like a network effect within cities, right? Like, whether it’s show business in LA or it’s, finance in New York, like these ecosystems that emerge happen because, you end up with the designers who are here because the engineers are here because the marketing people are here because the capital is here because and on and on and on and all in one place. And so one of my colleagues here at Andreessen Horowitz, Darcy, had mentioned, he tweeted the idea that, one of two things will happen, right? Either these network effects continue to hold, meaning that then, actually the Bay Area will just continue to be what it is, right? Or, we actually make really interesting structural shifts in how we organize teams and workforces and all that stuff. In which case the network effects become less strong. But what that means is not that then all of a sudden, some other city like becomes a quote unquote the next Silicon Valley. It actually just means that everyone just lives where they want to live and eat and that’s that. And so, so if you believe that thesis, then you’d actually say there is no quote unquote next Silicon Valley. It either just continues or it’ll just be distributed. Right. I think that’s like a pretty interesting —
Ada: — I think you see that already emerging even within online communities. So when you think about where the discourse actually taking place, right, it’s taking place on Medium, it’s taking place on Twitter, it’s taking place on Product Hunt. We went through the experience of launching on Product Hunt and we were really amazed by how international the community was in contrast to the..
[Dear readers, this essay is on the future of marketplaces. Is there still room for marketplace startups to innovate? We answer, emphatically, yes! Am excited to share a vision on the past and future of the service economy, in a collaboration by my a16z colleague Li Jin. From “Unbundling Craiglist” to “Uber for X” – we lay it all out in a single framework. Hope you enjoy our thinking! -A]
Above: 4 eras of marketplaces focused on the service economy – and what’s next
Goods versus Services – why a breakthrough is coming
Marketplace startups have done incredibly well over the first few decades of the internet, reinventing the way we shop for goods, but have been less successful services. In this essay, we argue that a breakthrough is on its way: While the first phase of the internet has been about creating marketplaces for goods, the next phase will be about reinventing the service economy. Startups will build on the lessons and tactics to crack the toughest service industries – including regulated markets that have withstood digital transformation for decades. In doing this, the lives of 125 million Americans who work in the services-providing industries will join the digital transformation of the economy.
In the past twenty years, we’ve transformed the way people buy goods online, and in the process created Amazon, eBay, JD.com, Alibaba, and other e-commerce giants, accounting for trillions of dollars in market capitalization. The next era will do the same to the $9.7 trillion US consumer service economy, through discontinuous innovations in AI and automation, new marketplace paradigms, and overcoming regulatory capture.
In contrast, the service economy lags behind: while services make up 69% of national consumer spending, the Bureau of Economic Analysis estimated that just 7% of services were primarily digital, meaning they utilized internet to conduct transactions.
We propose that a new age of service marketplaces will emerge, driven by unlocking more complex services, including services that are regulated. In this essay, we’ll talk about:
Why services are still primarily offline
The history of service marketplace paradigms
The Listings Era
The Unbundled Craigslist Era
The “Uber for X” Era
The Managed Marketplace Era
The future of service marketplaces
Five strategies for unlocking supply in regulated markets
Let’s start by looking at where the service economy is right now and why it’s resisted a full scale transformation by software.
Software eating the service economy, but it’s been slow
We’ve all had the experience of asking friends for recommendations for a great service provider, whether it be a great childcare provider, doctor, or hair stylist. Why is that? Why aren’t we discovering and consuming these services in the same digital way we’ve come to expect for goods?
Despite the rise of services in the overall economy, there are a few reasons why services have lagged behind goods in terms of coming online:
Services are complex and diverse, making it challenging to capture relevant information in an online marketplace
Success and quality in services is subjective
Fragmentation – small service providers lack the tools or time to come online
Real-world interaction is at the heart of services delivery, which makes it hard to disaggregate parts of a purchase that might be done online
Let’s unpack each reason below:
First, on the complexity and diversity of services, services are performed by providers who vary widely, unlike goods which are manufactured to a certain spec. Even the names of services can vary: what one home cleaning service calls a “deep clean” can be different from another provider’s definition. This lack of standardization makes it difficult for a service marketplace to capture and organize the relevant information.
Second, services are often complex interactions without a clear yardstick of success or quality. The customer experience of a service is often subjective, making traditional marketplace features like reviews, recommendations, and personalization more difficult to implement. Sometimes just getting the job completed (as in rideshare) is sufficient to earn a 5-star review, whereas other higher-stakes services, like childcare, have complex customer value functions, including safety, friendliness, communicativeness, rapport with child, and other subjective measures of success.
Third, small service providers often lack the tools or time to come online. In many service industries, providers are small business owners with low margins; contrast this with goods manufacturing where there are economies of scale in production, and thus consolidation into large consumer products companies. As a result of industry fragmentation, service providers often don’t have time or budget to devote to key business functions, such as responding to customer requests, promoting and marketing themselves, maintaining a website, and other core functions. While major e-commerce platforms have taken on the role of distribution, merchandising, and fulfilling orders for goods, there are few platforms that service providers can plug into to manage their businesses and reach customers.
Fourth, real-world interaction is central to services, which can pull other steps of the services funnel into the offline world as well. Many services are produced and consumed simultaneously in real-world interactions, whereas goods entail independent stages of production, distribution, and consumption. The various stages of the goods value chain can be easily unbundled, with e-commerce marketplaces comprising the discovery, transaction, and fulfillment steps. Conversely, since the production and consumption of services usually occur simultaneously offline, the discovery, distribution, and transaction pieces are also often integrated into the offline experience. For instance, since getting a haircut entails going to a salon and having interactions with the providers there, the stages of the value chain that precede and follow that interaction (discovery, booking, and payment) also often get incorporated into the in-person experience.
All of these factors make it very hard for services to come online as comprehensively and widely as commerce – but there’s hope. We’ve seen multiple eras of bringing the service economy online, and we’re on the verge of a breakthrough!
The 4 eras of Service Marketplaces, and what’s next
There have been 4 major generations of service marketplaces, but coverage of services and providers remains spotty, and many don’t provide end-to-end, seamless consumer experiences. Let’s zoom out and talk through each historical marketplace paradigm, and what we’ve learned so far.
Above, you can see that there have roughly been four major eras of marketplace innovation when it comes to the service economy.
1. The Listings Era (1990s) The first iteration of bringing services online involved unmanaged horizontal marketplaces, essentially listing platforms that helped demand search for supply and vice versa. These marketplaces were the digital version of the Yellow Pages, enabling visibility into which service providers existed, but placing the onus on the user to assess providers, contact them, arrange times to meet, and transact. The dynamic here is “caveat emptor”–users assume the responsibility of vetting their counterparties and establishing trust, and there’s little in the way of platform standards, protections, or guarantees.
Craigslist’s Services category is the archetypal unmanaged service marketplace. It includes a jumble of house remodeling, painting, carpet cleaners, wedding photographers, and other services. But limited tech functionality means that it feels disorganized and hard to navigate, and there’s no way to transact or contact the provider without moving off the platform.
We’ve all had the experience of a listings-oriented product, like Craigslist. You find something you want, but everything else – trust/reviews/payments/etc – that’s all up to you!
2. The Unbundled Craigslist Era (2000s)
Companies iterated on the horizontal marketplace model by focusing on a specific sub-vertical, enabling them to offer features tailored to a specific industry. We’ve all seen the diagram of various companies picking off Craigslist verticals – it looks something like this:
Angie’s List, a home services site founded in 2005, carves off Craigslist’s household services category. The platform has features including reviews, profiles, certified providers, and an online quote submission process. But the marketplace doesn’t encompass the entire end-to-end experience: users turn to Angie’s List for discovery, but still need to message or call providers and coordinate offline.
Unmanaged vertical marketplaces like Angie’s List go a step beyond Craigslist and take on some value-add services like certifying providers when they meet certain standards, but customers still need to select and contact the service provider, place their trust in the provider rather than the platform, and transact offline.
Like previous listing sites, these platforms in this era try to use the ‘wisdom of the crowds’ to promote trust. These platforms have a network effect in that more reviews means more users and more reviews. But user reviews have their limitations, as every user has a unique value function that they’re judging a service against. Without standardized moderation or curation, and without machine learning to automate this process, customers have the onus of sifting through countless reviews and selecting among thousands of providers.
3. The “Uber for X” Era (2009-)
In the early 2010s, a wave of on-demand marketplaces for simple services arose, including transportation, food delivery, and valet parking. These marketplaces were enabled by widespread mobile adoption, making it possible to book a service or accept a job with the tap of a button.
Companies like Handy, Lugg, Lyft, Rinse, Uber and many others made it efficient to connect to service providers in real-time. They created a full-stack experience around a particular service, optimizing for liquidity in one category. For these transactions, quality and success were more or less binary–either the service was fulfilled or it wasn’t–making them conducive to an on-demand model.
These platforms took on various functions to establish an end-to-end, seamless user experience: automatically matching supply and demand, setting prices, handling transactions, and establishing trust through guarantees and protections. They also often commoditized the underlying service provider (for instance, widespread variance on the driver side of rideshare marketplaces is distilled into Uber X, Uber Pool, Uber Black, Uber XL, etc.).
Unlike the previous generations of marketplaces, in which the provider ultimately owns the end customer relationship, these on-demand marketplaces became thought of as the service provider, e.g. “I ordered food from DoorDash” or “Let’s Uber there,” rather than the underlying person or business that actually rendered the service.
Over time, many startups in this category failed, and the ones that survived did so by focusing on and nailing a frequent use case, offering compelling value propositions to demand and supply (potentially removing the on-demand component, which wasn’t valuable for some services), and putting in place incentives and structures to promote liquidity, trust, safety, and reliability.
4. The Managed Marketplace Era (Mid-2010s) In the last few years, we’ve seen a rise in the number of full-stack or managed marketplaces, or marketplaces that take on additional operational value-add in terms of intermediating the service delivery. While “Uber for X” models were well-suited to simple services, managed marketplaces evolved to better tackle services that were more complex, higher priced, and that required greater trust.
Managed marketplaces take on additional work of actually influencing or managing the service experience, and in doing so, create a step-function improvement in the customer experience. Rather than just enabling customers to discover and build trust with the end provider, these marketplaces take on the work of actually creating trust.
In the a16z portfolio, Honor is building a managed marketplace for in-home care, and interviews and screens every care professional before they are onboarded and provides new customers with a Care Advisor to design a personalized care plan. Opendoor is a managed marketplace that creates a radically different experience for buying and selling a home. When a customer wants to sell their home, Opendoor actually buys the home, performs maintenance, markets the home, and finds the next buyer. Contrast this with the traditional experience of selling a home, where there is the hassle of repairs, listing, showings, and potentially months of uncertainty.
Managed marketplaces like Honor and Opendoor take on steps of the value chain that platforms traditionally left to customers or providers, such as vetting supply. Customers place their trust in the platform, rather than the counterparty of the transaction. To compensate for heavier operational costs, it’s common for managed marketplaces to actually dictate pricing for services and charge a higher take rate than less-managed marketplace models.
Managed marketplaces are a tactic to solve a broader problem around accessing high-quality supply, especially for services that require greater trust and/or entail high transaction value. If we zoom out further, there’s many more categories of services that can benefit from managed models and other tactics to unlock supply.
What’s next: The future of Service Marketplaces (2018-?)
We think the next era of service marketplaces have potential to unlock a huge swath of the 125 million service jobs in the US. These marketplaces will tackle the opportunities that have eluded previous eras of service marketplaces, and will bring the most difficult services categories online–in particular, services that are regulated. Regulated services–in which suppliers are licensed by a government agency or certified by a professional or industry organization–include engineering, accounting, teaching, law, and other professions that impact many people’s lives directly to a large degree. In 2015, 26% of employed people had a certification or license.
Regulation of services was critical pre-internet, since it served to signify a certain level of skill or knowledge required to perform a job. But digital platforms mitigate the need for licensing by exposing relevant information about providers and by establishing trust through reviews, managed models, guarantees, platform requirements, and other mechanisms. For instance, most of us were taught since childhood never to get into cars with strangers; with Lyft and Uber, consumers are comfortable doing exactly that, millions of times per day, as a direct result of the trust those platforms have built.
Licensing of service professions create an important standard, but also severely constrains supply. The time and money associated with getting licensed or certified can lock out otherwise qualified suppliers (for instance, some states require a license to braid hair or to be a florist), and often translates into higher fees, long waitlists, and difficulty accessing the service. The criteria involved in getting licensed also do not always map to what consumers actually value, and can hinder the discovery and access of otherwise suitable supply.
Five strategies to unlock regulated industries We’re starting to see a number of startups tackling regulated services industries. As with each wave of previous service marketplaces, these new approaches bring more value-add to unlock the market, with variations in models that are well-suited to different categories.
The major approaches in unlocking supply in these regulated industries include:
Making discovery of licensed providers easier
Hiring and managing existing providers to maintain quality
Expanding or augmenting the licensed supply pool
Utilizing unlicensed supply
Automation and AI
1) Making discovery of licensed providers easier
Some startups are tackling verticals that lack good discovery of licensed providers. Examples include Houzz, which enables users to search for and contact licensed home improvement professionals, and StyleSeat, which helps users find and book beauty appointments with licensed cosmetologists.
2) Hiring and managing existing providers to maintain..
Growing startups and evaluating startups share common skills Earlier this year, I joined Andreessen Horowitz as a General Partner, where I focus on a broad spectrum of consumer startups: marketplaces, entertainment/media, and social platforms. This was a big moment for me, and the result of a long relationship that began a decade ago, when Horowitz Andreessen Angel Fund funded a (now defunct) startup I had co-founded. One of the reasons I’ve been excited about being a professional investor is the ability to apply my skills as an operator. The same skills needed to grow new products can be used both to evaluate new startups to invest in, and once we’ve invested; to help them grow.
The reason for this is that the steps for starting and scaling a new startup share many of the same skills as investing in a new startup: 1) First, we seek to understand the existing state of customer growth – including growth loops, the quality of acquisition, engagement, churn, and monetization. 2) Then, to identify potential upside based learnings from within the company as well as across benchmarks from across industry. 3) And finally, to prioritize and make decisions that impact the future. Of course, as an investor you can’t run A/B tests or analyze results directly, but you can form hypotheses, ideate, and apply the same type of thinking.
As part of my interview process at a16z, I eventually put together an 80 slide deck on how to use growth ideas to evaluate startups. In the spirit that this perspective can help others in the ecosystem, and to share my my thinking, I’m excited to publish the deck below.
Disclaimer: This was just one presentation in a 10 year relationship But before I fully share, I have a disclaimer. This is one presentation I made within a series of dozens of meetings and interactions I had with the Andreessen Horowitz team. It was just one ingredient. I’ve been asked by friends and folks on the best path into venture capital. From my experience, it’s a long, windy experience – others have written about their processes as well.
My journey took a while too:
10 years in the Bay Area (and blogging, building my network, etc)
Dozens of angel investments and advisory roles in SaaS, marketplaces, etc
Once kicked off, 6 months of interviews (dinners, sitting in pitches, analyzing startups)
100+ hours of interviewing and prep
This deck was just one step, but one that I’m proud of, and want to show y’all.
Above: I presented this deck as part of my interview to join Andreessen Horowitz to help demonstrate my expertise and “superpower” and how it might be used in an investing context.
As a result, it’s split into three sections:
About me and my superpower
How to apply user growth ideas in an investing context
My continuing leadership in the field
Let’s get started!
Above: When I first arrived in the Bay area, if you had searched for “growth hacking” – you would have gotten zero results. It wasn’t a thing. Some early companies like Linkedin and Facebook had started the notion of “growth teams” but this wasn’t a widely understood set of ideas in the industry.
While there were people thinking about user acquisition and ad tech, and some early consumer teams (like Eric Ries’s IMVU) thinking about cohort curves to mention retention, it hadn’t been centralized into a team that could execute against it.
I started my blog originally to write down everything I was learning. My previous background up to that point was in user acquisition and ad tech, and I was making the pivot to consumer products. There was a lot to learn.
As I learned from the best in the industry – in particular from the Paypal mafia who had employed a metrics-driven viral approach to build some of their most iconic companies – I started to write about what we’d now call growth.
If you look at Google Trends, you’ll see that “growth hacking” all of a sudden became a term people in the industry were interested, and were searching for, in 2012.
There’s a reason for that. I’d like to take some credit :)
I was lucky with the right timing, the right content, and with inspiration from my friend Sean Ellis to be able to popularize the terminology and ideas around “growth hacking” in an essay I wrote in 2012.
And these days, it’s spread and become its own ecosystem.
Teams focusing on user growth have spun up across some of the best companies in the ecosystem!
(As of early 2018, when I had presented this, these were some of the companies that had growth titles or formal growth teams)
Of course “growth hacking” has changed a lot – it’s no longer about hacks as much as a much bigger umbrella as it’s professionalized
One evolution is the number of books and conferences now dedicated to growth.
The other evolution in the ecosystem is that people are thinking about different things – about how to build growth teams, not just hacks. Thinking about new user experience, engagement metrics, and other important concepts.
I continue to contribute to this ecosystem by writing, being involved in social media, and press.
As part of that, as folks search for important concepts like “product market fit” and “user growth” – my essays are often on the front page. These are evergreen concepts and were relevant 5 years ago, relevant today, and will be important in the next phase of tech as well.
Beyond writing, I’ve also extended my efforts to bring together the high-end professional network of people working on startup growth. This hits a different part of my network as it’s a deeper relationship, and Bay Area focused, as opposed to my essays and social media which are global.
To accomplish this, I’ve been working with Brian Balfour (ex-VP growth from Hubspot) to start up Reforge which has educated 1000s of employees from top tech companies.
The flagship program on growth is 8 weeks and pulls together some of the foundational concepts.
The speakers include executives who run growth or related functions from across the industry. (Thank you to all the wonderful people who are involved with Reforge! Y’all are awesome and I’m happy to count you as my friends)
In the past few years, over 1500+ folks have attended the program from almost every company in the Bay Area and many F500 enterprises as well. This includes CEOs/founders, VPs, PMs, marketing folks, data science, engineers, and so on.
In the coming years, I want to stay as active as possible – to stay ahead of the curve by spending time with the smartest people from across industry, to bring communities together, and to continue to publish ideas. Establishing myself in the industry has taken a decade in the Bay Area and I intend to spend the next few decades at the same pace!
Next, let’s change gears. After all this talk about startup growth, how might you use this to evaluate new products in an investment context?
In this next section, I’ll present some of the central ideas in user growth and how you might use that to evaluate the quality of a startup’s growth as opposed to getting stuck on vanity metrics.
Above: To start, oftentimes you’ll find a new startup that presents their growth curve, which might look something like this – up and to the right! This is great. Time to invest, right?
The problem is, you don’t know where it’s going to go.
In the long run, over the course of an investment, you’ll find that this curve might go in a direction you may not want it to go – perhaps it’ll plateau. Perhaps it’ll even collapse. Or you may find that it’s going to continue going up, and even hockey-sticking.
How do you predict the future? Is it working and will it sustain? Will it even accelerate?
There’s a couple common frameworks to try to understand this, and one is the Growth Accounting Framework.
The Growth Accounting Framework looks something like this – within each time period (say a week, or a month) – you’ll add some users, reactivate some folks who had previously churned, and some go inactive. You add this up and it’s the “Net MAU” for a product – the difference between each time period.
If you positive terms (New+Reactivated) are smaller than your negative terms (the number who become Inactive) then you stop growing, and you get negative.
Let’s look at each term in isolation.
The New+Reactivated term tends to look linear or be an S-curve. The reason is that it’s really really hard to scale acquisition – only a few, like viral loops, paid marketing, and SEO can bring you to millions or tens of millions of users. And as the acquisition channel gets bigger, it tends to get less effective. Ads become more expensive to buy, viral loops end up saturating your target market, etc. This term dominates.
Reactivation tends to be hard to control. If someone quits your product, emailing them a bunch of times probably won’t help. (But if you have a network, something like photo-tagging or @mentions might!). But most products don’t have a network, and as a result, the acquisition term tends to be much bigger than the reactivation one.
Above: The Inactive curve is also an S-curve, but it lags acquisition. It’s simple to understand why, which is that until you have a base of active users, you can’t really churn. You can’t churn anyone when you have zero users. So it goes up over time. So usually your acquisition curve pushes you up, and then churn starts.
At the moment than your New+Reactivated is equal to your Inactive users, each time period, then you hit peak MAUs. This is the thing to watch for, because then it’s all flat or down from there.
I use MAUs in this example but you could also use active subscribers, or users who have bought something in the past 30 days, or some other definition. The underlying physics are the same.
If you’re following all of this, it’s already a pretty profound insight. We’ve moved from looking at a single curve that might have been growing and decomposed it into its underlying terms, and shown how a curve that’s been going up and to the right for a while might go flat the next month. And why. That’s important.
But there’s a problem.
The problem is that the Growth Accounting Framework provides for lagging metrics. It’s hard to predict the future. It’s the equivalent at looking at company’s current year P&L and its constituent parts – it’s useful, but not enough. It’s hard to be predictive. It’s also hard to be actionable for product teams.
That’s why for the growth and product teams I’ve advised over the years, this isn’t something you can look at every day or every week. It’s not helpful.
Instead, you need leading indicators and a more predictive conceptual model.
a16z Podcast: When Organic Growth Goes Enterprise - SoundCloud (1749 secs long, 89601 plays)Play in SoundCloud
The consumerization and developerization of B2B
Dropbox is the fastest SaaS company to $1B in revenue run rate with 600+ million users. This is just an example showing that companies are adopting software in a completely different way in recent years – we have individual users/developers picking out products that they want to use, and then it eventually spreads inside the organization.
This is the engine that powers Dropbox, Slack, Asana, and many other new companies. It brings together all the growth levers: Viral growth, performance advertising, consumer growth techniques – but also inbound marketing, enterprise sales, etc., etc.. It’s a great trend that brings together folks with consumery backgrounds (like myself!) and my colleague Martin Casado (prev Nicira, acquired by VMWare).
There’s a spectrum that goes from Atlassian (all self-serve, no enterprise sales team) all the way to a traditional enterprise company like Oracle. Startups have to choose where they want to play, and what organization they want to build. A lot of interesting nuances here.
Today, I want to share a new podcast on When Organic Growth Goes Enterprise – this is a podcast that includes Martin and myself, with DocSend CEO and co-founder Russ Heddleston, in conversation with Hanne Tidnam.
It was my pleasure to be on my first ever Andreessen Horowitz podcast! if you haven’t checked it out, you can subscribe here. I’ve linked to the Soundcloud and included a transcript below.
In the podcast, we cover a broad overview of growth/marketing topics, including:
The natural “gravity” that slows down high-growth businesses
What’s really happening beneath the surface of exponential growth curves
Organic, paid marketing, and LTV/CAC
Why blended CAC numbers are misleading
Why offline products are so compelling for acquiring customers
Cohort analysis and looking for “smile curves”
The Power User Curve aka L28
Why onboarding is so important for retention/churn
Phases of growth- why early companies focus on acquisition, but big companies focus on churn
High frequency versus episodic usage products
Why adding lots of spammy email notifications decreases your DAU/MAU
Network effects and why different products’ network effects are different from each other
Why Google measures many short sessions, versus other products focus on long sessions
Hope you enjoy it!
And thank you to my colleagues Sonal and Jeff for making this happen :)
Palo Alto, CA
a16z Podcast: The Basics of Growth 1 -- User Acquisition - SoundCloud (1232 secs long, 75785 plays)Play in SoundCloud
a16z Podcast: The Basics of Growth 2 -- Engagement & Retention - SoundCloud (1961 secs long, 84832 plays)Play in SoundCloud
Part 1: User Acquisition
Hi everyone welcome to the a16z Podcast, I’m Sonal. Today’s episode is all about growth, one of the most top of mind questions for entrepreneurs — of all kinds of startups, and especially for consumer ones.
So joining to have this conversation, we have a16z general partners Andrew Chen and Jeff Jordan. And we cover everything from the basics of growth and defining key metrics to know, to the nuances of paid vs organic marketing and the role of network effects and more.
Part one of this conversation focuses specifically on the aspect of user acquisition for growth, and then we cut off and go into the aspects of growth for user engagement and retention, in the next episode. But first, we begin by going beyond the concept of growth hacks — and beginning with the fundamental premise that businesses do not grow themselves…
Sonal: So the topic we wanted to talk about today is growth, which is a big topic. What would you say are the biggest myths and misconceptions that entrepreneurs have about growth?
Andrew: You know, not only is there the misconception that it happens magically, then the next layer I think is that it’s really just like, oh, a series of, you know, tips and tricks and growth hacks that kind of keep things going as opposed to like a really rigorous understanding of, you know, how to think about growth not just, as kind of the top line thing but actually that there’s acquisition, that there’s engagement, that there’s retention, and each one of those pieces is very different than the other and you have to like tackle them systematically.
Jeff: It is a scientific discipline, done right, because it requires you to understand your business and business dynamics at this incredibly micro level.
Sonal: I love that you said that because one of the complaints I’ve heard about “growth hacking” is that it’s just marketing by a different name, and what I’m really hearing you guys say is that there’s a systemic point of view, there’s rigor to it, there’s stages, there’s a program you build out.
Jeff: If you’re fortunate enough to achieve product-market fit and your business starts to take off, typically, you know, when in the wonderful situation do you get this hyper growth where you’ll grow year over year, you know, it’s triple digits. It’s just exploding. And then gradually the law of the large numbers starts to kick in and maybe the 100% growth becomes 50% growth the next year, and then the law of large numbers continue to kick in and there’s 25% and then it’s 12.5% and so growth tends to decay over time even in the best businesses. And so the–
Sonal: — Didn’t you use to call it like “gravity”?
Jeff: I called it gravity, you just would…it comes down to earth. And then the job of the entrepreneur is to be looking years down the road and say, “Okay, at some point growth in business A is going to stop and so I want to keep it going as long as I can and there’s a whole bunch of tactics to do that,” but then the other tactic, the other strategies, okay, I need new layers on the cake of growth.
At eBay the original business was an auction business in the U.S. and so, you know, some of the things we layered on early days we layered on fixed price in the U.S. — it’s not revolutionary but it really did grow then we went international. And then we layered in payment integration and each time we did that the total growth of the company would actually accelerate which is very hard to do at scale.
Sonal: That’s the whole point… like there’s intentionality to it. It’s not an accident that you guys introduce new businesses, new layers on the cake.
Jeff: Businesses don’t grow themselves, the entrepreneur has to grow them. And, you know, occasionally, you stumble into a business that seems to almost grow itself but they’re just aren’t many of those in the world and that growth almost never persists for long periods of time unless the entrepreneur can figure out how to continue its growth.
Jeff: And that’s the flip side of it. You know, early on you get this great growth, you had to keep it going. When it stops your strategic options had been constrained dramatically.
Andrew: A lot of times when you’re looking at what seemingly is an exponential growth curve. In fact, it’s really something like, oh, you’re opening in a bunch of new markets, right, so there’s sort of a linear line there, but then you’re also introducing products at the same time and you’re also reducing friction and, you know, sign-ups or retention or whatever, and so, the whole combination of those things is really kind of like a whole series of accelerating pieces that looks like it’s, you know, this amazing viral growth curve. But it’s actually like so much work underneath. <Sonal: Right.> You know, that makes that happen.
Sonal: I’ve also heard you [Andrew] talk about, being able to distinguish what is specifically driving that growth, so you don’t have this like sort of exponential-looking curve without knowing what that lever that you’re pulling to make that happen or knowing what’s happening even if it’s kind of happening naturally or organically. Can we break down some of the key metrics that are often used in these discussions including just what the definitions are and maybe just talk through how to think about them?
Andrew: Right. Yeah, so when you look at a large aggregate number like, you know, total monthly active users, right, or you’re looking at like —
Sonal: — “MAUs”
Andrew: –Yeah, MAUs, right. Or you’re looking at, you know, the GMV like all the…adding up all the transactions in your marketplace–
Sonal: — So, “gross merchandise value”.
Andrew: Yup. And so, you know, when you look at something like that and if it’s going up or down, you don’t have the levers at that level to really understand like what’s really going on. You want to go a couple levels even deeper: How many new customers are you adding? As you’re growing more and more new customers, a bunch of things happen. If you’re using paid advertisement channels, things tend to get more expensive over time because — you know, your initially super, super excited core demographic of customers — like they’re gonna convert the best and as you start reaching into different geographies, different kinds of demos, all of a sudden they’re not gonna convert as well, right?
Sonal: Just to pause in that for a quick moment, you’re basically arguing that growth itself halts growth in that context.
Andrew: Right. Yeah. So the law of large numbers means that you know there’s only a fixed number of humans on the planet, there’s only a fixed number of people that are in your core demographic, right? Once you surpass a certain point, it’s not like it’s it falls off a cliff, it’s just more gradual that you know that the customer behavior really changes.
Sonal: How do you determine what’s what when you don’t have product-market fit? Sometimes aren’t these metrics ways to figure that out or is this all when you have product-market fit… like is there a pre- and a post- difference between these?
Andrew: Very concretely, you want to understand how much of the acquisition is coming from purely organic (people discovering it, people talking to each other), as opposed to, oftentimes you’ll run into the companies that have over 50% of their acquisition coming from paid marketing and that tells you something that you’re, you know, needing to spend that much money to get people in the door.
Sonal: Yeah. So CAC, “customer acquisition cost”, that’s what you’re talking about when you talk about acquisition.
Jeff: CAC is what it cost to acquire a user, “blended CAC” is what it costs to acquire a user on a paid basis plus then also what free users you acquire. So if you’re acquiring half your users through paid marketing you’re paying a $100 to acquire a user but half of your users are coming at zero, paid CAC is 100, blended CAC is 50.
I think blended time is a really dangerous number. Most of the best businesses in the internet age of technology haven’t spent a ton on paid acquisition. And so the truly magical businesses, you know, a lot of them aren’t buying tons of users… Amazon’s key marketing right now is free shipping. And then, yeah, the economics of paid acquisition tend to degrade overtime.
Sonal: As it grows.
Jeff: As it grows and you just try to scale it and, you know, largely you’re cherrypicking the best users and then you’re trying to also scale the number you get to grow. I need twice as many new users this year as last year and you typically pay more so that magical LTV to CAC ratio which early on says, “Oh, we are three to one, you know, in two years it’ll probably be one and a half to one if you’re lucky,” or something like that. So we typically do try to look for these other sources of acquisition be it viral, be it, you know, some other form of non paid <crosstalk>
Sonal: I want to quickly define LTV — it’s “lifetime value” of the customer, but what does that mean?
Jeff: When you’re showing an LTV to CAC ratio you have no idea of what you’re seeing essentially given all the potential variations of the numbers. So we will almost always go for clarity. LTV, lifetime value, should be the profits, the contribution from that user after all direct costs.
Sonal: How do we define the LTV to CAC ratio? What do the two of them in conjunction mean?
Jeff: Well, let’s break them down. LTV is lifetime value. What you’re describing there is the incremental profit contribution for a user over the projected life of that user. So not revenue per CAC is that you know typically there’s cost associated to user. What’s the incremental contribution that the user brought from that <crosstalk> <Sonal: And that you mean the user brought to your company’s value.> To the company, yeah.
Sonal: So it’s a value of your customer to the bottom line?
Jeff: It’s the value of each customer to the bottom line, and then you compare that to the CAC or “cost of acquired customer” to understand the leverage you have between what I need to spend to acquire a customer and how much they’re worth. If your CAC is higher than your LTV you’re sunk. Because it’s costing you more to acquire a user…
Sonal: Than the value you get out of it. Now I get it.
Jeff: …then you’re going to get out of that user.
Jeff: If it’s the opposite, at least you’re in the game. You know, I get more profit out of the user than I get the cost to acquire that user. And then there’s this dynamics on how does it scale over time, CAC tends to go up, LTV tends to go down. Because you’re, on the CAC side, you’re acquiring the less interested users over time. So they cost more to acquire and they’re worth less, and so that the LTV to CAC ratio, in our experience, almost always degrades as over time with scale.
And so, you know, when you’re in that conversation, you’re in a very specific conversation of, “Okay, how much room do you have?” “How is it gonna scale?” “You know, what’s gonna impact your CAC like a competitive thing?” So there has to be a lot, it had to be like 10 to kind of get you over that concern that oh, my goodness, those two were so close, that you have no margin for error.
Sonal: Right. This also goes back to the big picture, the layers on the cake, because if you have other layers you don’t have to only worry about one layer CAC to LTV ratio.
Jeff: It really does affect the calculation. If it’s, I’m in a new business, and I have a whole different CAC versus, you know, LTV ratio then that’s a different conversation as well.
Sonal: And the big picture there, is that if you don’t know the difference of what’s doing what when you may get very mistaken signals, mixed signals about your business, and so you guys don’t want blended CAC because you want to know what’s driving the growth.
Andrew: I think what blended CAC gives you is it gives you a sense for at this particular moment in time, you know, what’s happening. The challenge is that when it comes to paid marketing, in particular, it’s easy to just add way more budget and a scale that than it is to scale organic or to scale SEO. So your CAC is giving you a snapshot, but then as you’re trying to scale the business you’re trying to increase everything by 100% over the next, you’re trying to double everything then all of a sudden, you know, your blended CAC starts to approach whatever your dominant channel actually looks like.
And so if you’re spending a bunch of money then it’ll just approach whatever is your paid marketing, you know, CAC. What entrepreneurs should think about is what is the unique organic new thing that’s gonna get it in front of people, without spending a bunch of money, right?
Jeff: A lot of the best businesses have this very interesting, I’ll call it a growth hack. I mean OpenTable, when I was managing it, did not pay any money at all to acquire consumers. Like how can you do that? You know, it had millions of consumers. The restaurants would mark it OpenTable on our behalf.
Jeff: They go to The Slanted Door website like when they were an OpenTable customer and you’d see, you’re looking…you go there to try to get the phone number to make a reservation and they’d say, “Oh, make an online reservation.” And we then got paid to acquire that user in its core form. But that hack was a wonderful thing. It scaled with the business and got us tons of free users.
Sonal: To be fair, and this is another definition we should tease apart really quickly before we move on to more metrics, that also had a quality of network effects which we’ve talked a lot about in terms of these things growing more valuable to more people that use it… is that growth? What’s the difference there?
Jeff: Well, the business grew into the network effect. The key tactic to build the network effect was that free acquisition of consumers that the more restaurants we had, the more attractive it was to consumers the more consumers who came, the more attractive it was to restaurants. So there is a wicked network effect.
Sonal: Like a flywheel effect, right.
Jeff: If you’re not spending anything on paid acquisition of consumers, how do you start it? And the placements that OpenTable got in the restaurant book both physically in the restaurant but particularly in the restaurant’s website was the key engine that got the network effect started. You had to manually sell some restaurants come for the tools, stay for the network, but then once the consumers got enough of a selection and started to use it, it was game over.
Sonal: Right, that was one way of going around the bootstrapping or the chicken-egg problem and seeding a network.
Andrew: Network effects have…there’s a lot of really positive things about them and one of the big pieces is that virality is a form of like something that you get with the network. You know, the larger your network is, the more surface area, the more opportunities you have in order to encounter it, right. And so, you know, in the case of Uber (where I was recently), by seeing all the cars with the Uber logo like those are all opportunities to be like, “Oh, what is this app? I should try it out.” And so it’s mutually reinforcing: then you get more riders and then you get more drivers that are into it and so, I think all of that kind of plays together.
Jeff: I’ll bring two examples up, the pink Lyft mustache when I first got to San Francisco.
Sonal: I remember that.
Jeff: You can see it once in the car and you’d go, “Oh, that’s pretty weird.” You see it twice in the car and you say, “Something is going on here that I don’t know about, and I have to understand what it is.” Lime is the same kind of thing.
Jeff: They’re bright green and they glow essentially. So when someone sees one in the wild, someone bolts by them in a glowing green electric scooter and you’re just like, “Okay…what is that?” And Lime hasn’t spent a penny on consumer acquisition. <Sonal: Right.> Because their model is such that physical cue in the real world leads to it.
Andrew: The other one I’ll throw in as well is within workplace enterprise products there’s a lot of kind of bottoms-up virality that comes out of people, you know, kind of sharing and collaborating.
Sonal: Like with Slack.
Andrew: Yeah, like for example Slack is a great, it’s an example of this. And so, these are all kind of really unique ways that you can, you know, get acquisition for free. And so then your CAC is, you know, “zero” as a result.
You guys have talked a lot, about organic. It makes it sound to me as a layperson that you don’t want paid marketing! Like what’s your views on this — is it a bad thing, is it a good thing; I don’t mean to moralize it but — help me unpack more where it’s helpful and where it’s not. Are they any rules-of-thumb to use there?
Jeff: I mean a lot of great businesses that have leveraged paid marketing. The OTA sites (online travel agencies – Priceline and Expedia) just spends, you know, they spend a GDP of many large countries in their acquisition; and then it’s often a tactic in some good business. But if it’s your primary engine, a couple of things happen: One is it tends…the acquisition economics tend to degrade over time for the reason we’re saying… <Sonal: Right this is…> And it leaves you wide open to competition.
Sonal: It gets commoditized basically.
Jeff: If you need to buy users, I mean if you’re selling, you know, the new breed of mattress and you need to buy users and early on, you’re the only person competing for that word, flas-hforward a year or two, they’re like six new age mattress manufacturers with virtually identical products competing for the same consumer. The economics are not going to persist over time. And so, you know, one of the key questions in businesses driven by heavy user acquisition is how does the play end? You know, it usually looks pretty good at the beginning of the play but in the middle it’s starts getting a little complex and there’s tragedies at the ends.
Sonal: There’s literally an arc.
Andrew: And I think, you know, if it is something that you’re using in conjunction with a bunch of other channels and you’re kind of accelerating things, that can be great. For example when Facebook in the past broke into new markets they started with paid marketing to get it going. And so in a case like that really paid marketing is a tactic to kind of get a network affect jumpstarted right? <Sonal: Gotcha.> And then you can kind of like pull off from that if you’d like. <Sonal: Right.>
Andrew: But if you’re super, super dependent on it and you don’t have a plan for a world that you know all the channels atregonna degrade [in] then you’re gonna be in a tough spot in a couple of years.
Sonal: Totally. Do you have sort of a heuristic for when to stop the paid? Is there like a tipping point, you know, THIS is when you move?
Andrew: I think in terms of how much paid should you do as part of your portfolio, I think that’s the right way to think of it is it’s one out of a bunch of different channels, right? And so I would argue the following: First is you really have to measure the CAC and the LTV and be super disciplined about not spending ahead of where you want it to be and not to do it on some, you know, blended number that doesn’t make any sense. <Sonal: Right.> And then I think the other part is you really want it to be kind of a small enough minority of your channels. Such that if you were to get to a point where it turns out to be capped that you’re okay, that you can live with that.
Sonal: Your business will survive and you continue to grow and be healthy.
Andrew: Right, exactly, and you can still get the growth rates you want and you can still, you have such strong product-market fit that you’re able to maintain that.
Jeff: Take a couple of sector examples. You know, ecommerce, a lot of companies..
[Today we have an essay on one of the common frameworks we use to analyze investments at Andreessen Horowitz: The Power User Curve. I worked closely with Li Jin, a partner on the investing team, to collect our ideas into this essay which she wrote. You can follow @ljin18 on Twitter for more thoughts. -Andrew]
The importance of power users
Power users drive some of the most successful companies — people who love their product, are highly engaged, and contribute a ton of value to the network. In ecommerce marketplaces it’s power sellers, in ridesharing platforms it’s power riders, and in social networks it’s influencers.
All companies want more power users, but you need to measure them before you can find (and retain) them. While DAU/MAU — dividing daily active users (DAUs) by monthly active users (MAUs or monthly actives) — is a common metric for measuring engagement, it has its shortcomings.
Since companies need a richer and more nuanced way to understand user engagement, we’re going to introduce what we’ll call the “Power User Curve” — also commonly called the activity histogram or the “L30” (coined by the Facebook growth team). It’s a histogram of users’ engagement by the total number of days they were active in a month, from 1 day out of the month to all 30 (or 28, or 31) days. While typically reflecting top-level activity like app opens or logins, it can be customized for whatever action you decide is important to measure for your product.
The Power User Curve has a number of advantages over DAU/MAU:
It shows if you have a hardcore, engaged segment that’s coming back every day.
It shows the variability among your users: some are slightly engaged, whereas others are power users. Contrast this with DAU/MAU: it’s a single number and so blurs this variance.
When mapped to cohorts, Power User Curves let you see if your engagement is getting better over time — which in turn helps assess product launches and performance of other feature changes.
Power User Curves can be shown for different user actions, not just app opens. This matters if the core activity that matters for your product is deeper in the funnel.
In other words, while the DAU/MAU gives you a single number, the Power User Curve gives entrepreneurs several avenues of analysis to assess their product’s engagement to the most addicted users — in a single snapshot, over time, and also in relation to monetization. This is useful. So how does it work?
The Power User Curve will “smile” when things are good The shape of the Power User Curve can be left-leaning or smile-like, all of which means different things. Here’s a smile:
The Power User Curve above is for a social product, and shows the characteristic smile shape that indicates there’s a group of highly engaged users using the app daily or nearly daily. Social products with frequent user engagement like this lend themselves well to monetization via ads—there’s enough users returning frequently that the impressions can support an ad business. Remember that Facebook would have a very right-leaning smile, with 60%+ of its MAUs coming back daily.
What matters is that, over time, the platform is able to retain and grow its power users: successive Power User Curves should ideally show users shifting over more to the right side of the smile. As the density of the network grow, and with stronger network effects, it’s expected that there’s more reason for users to return on a daily basis.
The Power User Curve can show when strong monetization is needed
Let’s look a different example, which doesn’t smile:
This Power User Curve of a professional networking product looks quite different than that of a social product. It’s left-weighted with a mode of just 1 day of activity per month, and decays rapidly after those few days. There’s no power users. But this light engagement can be okay — not every company needs to have a smile-shaped Power User Curve, just as not every product category necessarily lends itself to an ultra-high DAU/MAU.
When there’s low engagement, what matters is that the company has a way to extract enough value from users when they are engaged. Think about an investing product like Wealthfront or networks like LinkedIn — few users are likely to actively check it on a daily basis, but that’s ok, since they have business models that aren’t tied to daily usage.
CEOs of such companies should therefore,think about: Is there a way to create revenue streams where the business can still monetize effectively despite users’ infrequent engagement? Or, who are the users using this product more frequently, and how can I get more of them? Is there something about the product — e.g. onboarding, the core experience, etc. — where a significant chunk of the user base isn’t experiencing the ‘aha moment’ that makes them “get” the product, and therefore not getting value from it right now (and if so how to get there)?
Some products should be analyzed in a 7 day timeframe – like SaaS/productivity – and others on 30 days Another flavor of the Power User Curve is a histogram of users’ engagement for a 7-day period, also commonly called L7. The 7 day Power User Curve shows weekly actives, not monthly actives. Plotting this version can make sense if your product naturally follows a weekly cycle, for instance, if it’s a productivity/work-related product that users engage with Monday through Friday. B2B SaaS products will often find it useful to show this version, as they want to drive usage during the work week.
Note that using DAU/MAU wouldn’t be the appropriate metric for this product as it’s not designed to be a daily use product. You can also see there’s actually a smile curve through 5 days, but fewer users are using it 6-7 days, which makes sense for the power users of a workweek product like this.
CEOs of such product companies should therefore want to understand: Who are the users engaging just 1 or 2 days each week? Are there certain teams or functions within an organization that are getting more value, and how can I build out features to capture the teams with less engagement? Or, if the product is really driving a lot of value for specific departments — how can I understand their needs better and make sure we continue building in a direction that supports their daily workflow (and that we can upsell new features)?
The trend of over time can show if the product is getting more engaging over time
Plotting the Power User Curve for different WAU or MAU cohorts can also be very insightful. Over time, you can see if more of your user base are becoming power users, by seeing the shift towards higher-frequency engagement.
Here’s an example:
The Power User Curve for MAU cohorts from August through November shows a positive shift in user engagement, where a larger segment of the population is becoming active on a daily basis, and there’s more of a smile curve.
You can see when the line starts to inflect in order to see when a critical product release or marketing effort might have started to bend the curve. This might be a place to double down, to increase engagement. For a network effects product, you might expect to see newer cohorts gradually improve as you achieve network density/liquidity.
On an ongoing basis, you can measure the success of product changes or new releases by looking at different cohorts’ Power User Curves. If a product unblocks a bunch of features for power users, you might see a gradual increase in power users.
The Power User Curve can be based on core activity, not just app opens or logins
The frequency histogram can be keyed on actions beyond the visit — did someone show up or not — you can also go with deeper user actions. For instance, you may want to plot the core activity that maps closely to how your business is monetized… or that better represents whether users are getting value from your product. This is important because it forces you to think about what really matters to measure.
The above chart for a content publishing platform shows the total number of days in the month users posted content. A lot of products have smile-shaped core activity Power User Curves, because while most people tend to contribute lightly, there is a small contingent of users who are power users. Think of the distribution of Youtube creators, or Ebay sellers, or even how often you post on Facebook.
As the CEO or product owner of a platform like this, it’s important to design the platform such that the everyone has a chance to succeed. On Facebook, the news feed algorithm makes sure that if you feel strong affinity to a person or organization, you’ll still see their posts even if the sheer volume of other content (for instance, from more prolific media companies) would otherwise drown it out. On OfferUp, even if I seldom sell items, when I do list something, their algorithm makes sure that it’s surfaced to the relevant potential buyers.
Why does this all matter?
Not everything is a daily use product, and that’s okay.
Power user analysis allows you to get a better understanding of how users are engaging with your product, and make more informed decisions using that data. That might mean choosing an appropriate business model that works for your pattern of engagement, or designing better re-engagement loops for lower-engaged user segments, or doubling down on use cases that your high-engagement user base is already getting value out of.
The beauty of the Power User Curve over DAU/MAU is that it shows heterogeneity among your user base, reflecting the nuances of different user segments (and therefore what drives each of those segments). Creating versions of Power User Curve by various user segments can also be particularly insightful. For instance, for a business with local network effects (like Uber or Thumbtack), showing Power User Curves by market can reveal which geographies are developing density and strong network effects.
Power User Curves show if your product is hitting a nerve among a super engaged core group of users, even if perhaps the overall blended DAU/MAU is low. It also doesn’t have to just reflect app opens or logins — you can hone in on an action that maps closely to users getting specific value out of your specific product and plot the Power User Curve for that action. The key for founders is to know that there isn’t a single silver bullet to measure perfect engagement — rather, the goal is to find the set of metrics that are appropriate for their businesses. Comparing the Power User Curve of a social app vs. a work collaboration app doesn’t make sense, but looking at your own Power User Curve over time, or finding benchmarks for your product category, can tell you what’s working… and what’s not.
[Thanks again to Li Jin for pulling together this essay! Another plug for her Twitter account here. -A]