Thread: “Fake it til you make it” is a key strategy for overcoming the chicken-and-egg problem of starting a new marketplace, popularized by companies like Uber and Yelp. The idea is to bootstrap one side of the marketplace inorganically in order to attract the other side.
There are a few tactics that fall under this umbrella of faking it in a marketplace–including paying guarantees or subsidizing transactions, managing supply, or producing the supply/demand yourself. Then, at a certain point, network effects kick in and organic growth takes over.
A few examples:
1) Uber launched by going to black car companies and paying drivers to be available on Uber during certain hours, ensuring that riders would be able to find a ride.
2) Relationship Hero, a relationship coaching marketplace, scaled to dozens of customers with just one coach–its cofounder! But the website listed 10 fake coaches, to give users the sense that it was a more active platform with diverse coaches who fit their particular situation. (Today, all the coaches listed on Relationship Hero are real)
3) Beepi, which was a used car marketplace, had a massive chicken and egg problem in attracting sellers and buyers initially. To solve this, the founders went out and purchased used cars to seed the supply side. After a few months, they moved to the marketplace model.
4) Managed marketplaces are also a form of “fake it til you make it,” where the supply side is employed or otherwise managed by the company. This model is frequently applied to complex services, to create a radical improvement in the user experience.
Sometimes these managed marketplaces preserve the managed aspect while scaling, while others open up more broadly (e.g. the Bird platform).
“Fake it til you make it” can be applied not just to building marketplaces, but all sorts of new networks, like dating, social, etc. Creating new marketplaces from scratch and overcoming the cold start is challenging. We love seeing creative strategies here.
My partner @cdixon has also written about the “come for the tools, stay for the network” approach. Let us know if any others come to mind!
[I co-wrote this essay with my a16z partner Andrew Chen. We’re excited about the future of marketplaces and the service economy. Hope you enjoy this glimpse into our thinking, and let us know if you have any feedback!]
Goods versus Services: The next trillion dollar opportunity
Marketplace startups have done incredibly well over the first few decades of the internet, reinventing the way we shop for goods, but less so for services. In this essay, we argue that a breakthrough is on its way: The first phase of the internet has been about creating marketplaces for goods, but the next phase will be about reinventing the service economy. Startups will build on the lessons and tactics to crack the toughest and largest 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 U.S. consumer service economy , through discontinuous innovations in AI and automation, new marketplace paradigms, and overcoming regulatory capture.
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 is 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.
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.
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.
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.
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.
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 (2009-2015)
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.
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 are 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 creates 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 translate 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 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 quality
Companies can raise the quality of service by hiring and managing providers themselves, and by managing the end-to-end customer experience. Examples are Honor and Trusted, managed marketplaces for elder care and childcare, respectively, which employ caregivers as W-2 employees and provide them with training and tools. In the real estate world, Redfin agents are employees whose compensation is tied to customer satisfaction, unlike most real estate agents who are independent contractors working on commission.
3) Expanding or augmenting the licensed supply pool
Expanding the licensed supply pool can take the form of leveraging geographic arbitrage to access supply that’s not located near demand. Decorist, Havenly, Laurel & Wolf, and other online interior design companies enable interior designers around the world to provide design services to consumers without physically visiting their homes (yes, in many parts of the US interior design requires a license!). With improvements in real-time video, richer telepresence technologies, and better visualization technologies, more synchronous services are also shifting from being delivered in-person to online. Outschool and Lambda School are examples of de-localizing instruction, enabling teachers and students to participate remotely while preserving real-time interaction.
Another approach is to help suppliers navigate the certification process. a16z portfolio company Wonderschool makes it easier for individuals to get licensed and operate in-home daycares.
Lastly, there’s the approach of augmenting certified providers so they can serve more customers. Fuzzy, an in-home veterinary service, uses AI and vet technicians to augment the productivity of licensed veterinarians; and a16z portfolio company Atrium builds automation and workflow management to provide efficiency gains in the legal industry.
4) Utilizing unlicensed supply
Some companies utilize unlicensed supply — notably Lyft, Uber, and other peer-to-peer rideshare networks. Another example is Basis, a managed marketplace for guided conversations with trained but unlicensed specialists to help people with anxiety, depression and other mild to moderate mental health issues.
In the pet space, Good Dog is a marketplace that brings together responsible pet breeders and consumers looking for a dog. Going beyond existing breeder licensing, which the company felt didn’t map to what consumers valued, Good Dog established its own higher set of standards and screening process in conjunction with veterinary and academic experts.
5) Automation and AI
Other startups automate away the need for a licensed service provider altogether. These include MDacne, which uses computer vision to diagnose and treat acne; and Ike Robotics and other autonomous trucking startups which remove the need for a licensed truck driver.
Opportunities for companies addressing regulated services
The last twenty years saw the explosion of a number of services coming online, from transportation to food delivery to home services, as well as an evolution of marketplace models from listings to full-stack, managed marketplaces. The next twenty years will be about the harder opportunities that software hasn’t yet infiltrated — those filled with technological, operational, and regulatory hurdles — where there is room to have massive impact on the quality and convenience of consumers’ everyday lives.
The services sector represents two-thirds of US consumer spending  and employs 80% of the workforce . The companies that reinvent various service categories can improve both consumers’ and professionals’ lives — by creating more jobs and income, providing more flexible work arrangements, and improving consumer access and lowering cost.
The companies mentioned in this essay just scratch the surface of regulated industries. You can imagine a marketplace for every service that is regulated, with unique features and attributes designed to optimize for the customer and provider needs for that industry. (A full list of regulated professions in the US can be found here.) We fully expect more Airbnb- and rideshare-sized outcomes in the service economy.
If you’re a founder who is looking to take on the challenge of tackling more complex services and bringing them online, we’d love to hear from you. Thank you for reading!
There are over 400 startups trying to be the next Warby Parker, but history shows that 90%+ of e-commerce companies will fail. What separates the successes from the failures? Here are 5 things I look for to figure out if an e-commerce startup is a good opportunity –>
1. Does it have defensible, scalable acquisition channels? 2. Is it operating in a category that is well-suited to brand building, and if so, has it built a brand that people love and trust? 3. Is it selling a unique product that not everyone can offer? 4. Does the business have network effects? 5. Are there economies of scale that can be captured ahead of later entrants?
Unpacking each one of these:
1. Customer acquisition: It’s unsustainable to rely heavily on paid acquisition channels to grow, as the margin ends up being bid away.
Instead, does the company have unique, scalable ways to reach new consumers, e.g. a devoted community who spreads the word, or exclusive distribution channels?
Dollar Shave Club’s launch video was a major accelerant for customer growth, and cost just $4500 to produce. While viral videos aren’t an uncrossable moat, it was an advantage that helped build up brand awareness cheaply, and enabled DSC to reach scale first.
2. Brand: Brand as defensibility exists for a lot of companies–but not all product categories are suited to building strong brands, and brand defensibility is also difficult to assess in the time frames that map to venture investing.
Brand as defensibility works better in categories where there is a big emotional component to the purchase, where a sense of identity or community is intertwined with the brand, e.g. categories like health, cosmetics, or anything aspirational.
Whitelabel a Glossier/Chanel/La Mer product, and chances are women won’t covet them nearly as much, even if the contents inside are the same. See my tweetstorm on the shifting power balance between brands & aggregators–and where the opportunities are:
3. Product: Does the startup have defensibility in terms of creating a proprietary product that others don’t have the ability to create, whether that’s derived from better design, deeper customer understanding, or manufacturing moats?
As an example, Hubble Contacts found that the vast majority of the contact lens market is controlled by a few manufacturers. Hubble was able to establish exclusive supplier relationships that blocked other contact lens startups from scaling in the US.
4. Network effects: There are various flavors of network effects in e-commerce. Marketplaces, obviously, improve with more buyers and sellers.
But even a single retailer or brand can have network effects. Stitch Fix’s algorithms for predictions and recommendations improve each time a customer reviews the items their stylist chose for them, leading to greater retention and LTV.
5. Scale effects: Amazon is the archetypal example of a retailer that thrives on economies of scale. As a startup example, Rent the Runway purchases expensive designer apparel and accessories, amortizing that cost over multiple rentals from different customers.
With greater scale, RTR can increase utilization, expand its inventory, and lower prices, making it harder for another women’s rentals startup to compete.
Not every company will be a ‘yes’ to all of the above, but more of these being true can indicate a stronger opportunity.
There’s also interactions between these: Strength in one regard can compensate for the absence of another. A manufacturing moat in a large category means establishing a beloved brand is less important. A strong distribution advantage can be investible in and of itself.
Obviously, building a great company and making investment decisions is more nuanced than just 5 factors. It’s apparent that there’s still tons of exciting opportunities left to build great e-commerce companies, and I’d love to chat if you’re working on one!
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 areengaged. 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.
1/ Consumers increasingly trust Amazon, Stitchfix, and other aggregators to sort and surface the best products, which diminishes the importance of individual manufacturers. This means the algorithm is the new consumer brand–signaling trust and quality.
1/ Consumers increasingly trust Amazon, Stitchfix, and other aggregators to sort and surface the best products, which diminishes the importance of individual manufacturers. This means the algorithm is the new consumer brand–signaling trust and quality.
2/ Brands used to play an important role in an offline world with limited access to information, acting as a proxy for preference, functionality, and quality. For instance: I need to buy detergent, and I trust that Tide will do the job well.
3/ But with ecommerce, algorithms incorporated user preferences, reviews, and product traits, and became smarter with more purchases & data. “Amazon’s Choice” or “recommended for you” fill the same need that brands used to, giving us confidence that we will like something.
4/ The decline in importance of brand is not limited to utilitarian, Amazon Basics-able categories. It’s also happening in categories where brand used to really matter.
5/ In fashion, personal styling companies like Stitch Fix capture data on users’ style and size, and bypass user choice to send them clothing they’re likely to love. If you ask a customer where an item is from, she’ll likely say “Stitch Fix,” not the actual underlying brand.
6/ In the travel vertical, Airbnb and other aggregators have also eroded the power of hotel brands. Hotels represented a certain experience and quality–the name was so important that most public hotel companies’ revenues come from franchising.
7/ But user reviews and algorithmic recommendations chipped away at this brand advantage, enabling independent operators to compete on the same footing as established hotel brands.
8/ As more verticals get “Stitch Fix’ed” by shopping platforms with data network effects and growing user trust, the winners are long-tail brands that can be discovered and build healthy businesses. But the losers are big brands who can’t win on the basis of brand alone.
9/ So what does this mean for founders who are aiming to build consumer companies?
10/ First, the platform always wins. If you rely disproportionately on digital platforms at any point in the purchasing funnel, it’s important to realize that your ability to reach consumers depends on a black-box algorithm.
11/ Secondly, not all consumer categories are created equal. Some are less prone to algorithmic disruption than others. In some categories, brand will remain important for years to come. More attractive categories for brand-building include:
12/ a) Categories where look and feel and emotion matters just as much, or more, as the underlying function. No one will ever say to Alexa, “Buy a smartphone” because they care too much about the intangibles of look and feel.
13/ Many women are happy to pay an extra $30 for a Dior mascara even though there’s widely known drugstore dupes, simply because of how it makes them feel.
14/ b) Categories where people’s taste is hyper-unique and specific. Food is a literal example of this: there’s actually preferences at the chemical level. Being a Pittsburgh native, I will only eat Heinz ketchup because it actually tastes better than other brands!
15/ c) Categories where brand is closely tied to a sense of identity. Strong brands can create a language for self-expression, where having their product feels like gaining membership in a club.
16/ I’d wager that ridesharing and AVs will be more disruptive to lower/mid-tier car manufacturers where the product is viewed as a utility, vs. luxury brands where ownership is just as much a signal of personal identity.
17/ Outside of the core product, there’s also a universe of other factors that can serve to shore up brand defensibility–for instance, the content and community that build user loyalty and foster an emotional connection. Or, a unique shopping experience that serves as a draw.
18/ And removing yourself from the algorithm entirely can also work, if you can get enough leverage. Existing outside of aggregators like Amazon, and creating a standalone presence for your brand online and offline is a sure way to own the end customer relationship and mindshare.
19/ Overall, the winner in this shifting power balance between aggregators and brands is the consumer. We’re lucky that instead of spending time & energy sifting through products, or relying on brands as shortcuts, we now have access to services that do the hard work for us.
1/ Kylie Cosmetics did $600m sales in its first 18 mos, driven by social media. But that’s just the beginning. To become an enduring, standalone business, it's necessary for all influencer brands to go beyond being tied to a single person, and create a "purpose brand.”
1/ Kylie Cosmetics did $600m sales in its first 18 mos, driven by social media. But that’s just the beginning. To become an enduring, standalone business, it’s necessary for all influencer brands to go beyond being tied to a single person, and create a “purpose brand.”
2/ To borrow Clayton Christensen’s concept, purpose brands are those that can powerfully drill into customers’ minds that their product/service is the best solution when they need to “hire” something to get a job done.
3/ E.g. IKEA exists to help me furnish my apartment quickly and cheaply. DryBar exists when I want to feel like I’m pampering and doing something for myself. Amazon exists when I need to purchase anything and want it to be priced competitively and delivered to me fast.
4/ Some of the strongest purpose brands even become verbs, inextricably linked with a specific job. It’s tough for brands to stick around without being tied to a specific job, and consumers’ jobs don’t often change.
5/ Martha Stewart Living Omnimedia can give a glimpse into the future for Kylie Cosmetics and other influencer brands.
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6/ In the 90s, Stewart leveraged her prominence from books and TV to create a business with publishing, broadcasting, and merchandising segments, all centered around her persona as a homemaking goddess.
7/ Its valuation hit $1.8B after IPO, but 16 years later, MSLO was de-listed and worth a small fraction of that.
8/ In today’s digital world, with compressed hype cycles and without the benefit of multi-year retail or broadcasting contracts, celeb-underpinned brands can fade even faster.
9/ There’s Preserve (Blake Lively), StyleMint (Mary-Kate & Ashley Olsen), and many other companies that may have gotten significant initial traction as a result of celebrity involvement, but that most of us have forgotten.
10/ To build a sustainable brand, companies need to tap into a deeper connection with consumers–whether that’s by creating a differentiated product or a superior user experience, or attaching to a larger movement that has longer-term appeal after the underlying celebrity fades.
11/ Social media has made it easier than ever to attract an audience and build widespread influence, and the barriers to entry to starting a new brand are lower than ever.
12/ The result is an unprecedented number of new influencer-driven media/product companies–but with potentially shorter lifecycles and lower defensibility.
13/ To ensure sustainability of her company, Kylie could aim to create cosmetics that deliver a real improvement from what else exists out there.
14/ Or, as is often the case in the beauty industry, she can align her brand with a broader shift in women’s attitudes: today, that could be makeup as self-expression and a celebration of individuality and diversity.
15/ This approach is in contrast to the outsourced R&D and manufacturing and heavy acquisition reliance on her own media channels today, the result of which is that her current customers purchase largely due to affinity with Kylie herself, not for the underlying products.
16/ Though she’s diversified her consumer touchpoints (reality show, social profiles, her own app, etc.), the underlying focus of her company is still the same–it’s on her.
17/ Stated another way, the major job that her products help consumers do now is to feel like they’re accessing a piece of Kylie. There’s nothing wrong with that, and the consumer affinity she’s built is incredible, but what happens when individual popularity inevitably wanes?
18/ Modern companies that have done well in transitioning from hyper-centered around an individual to purpose brands are GOOP (Gwyneth Paltrow) and The Honest Company (Jessica Alba). For these companies, celebrity served as a powerful force for initial distribution and traction.
19/ Their famous names imparted a signal of credibility, but to scale and endure, they aligned themselves with broader movements & communities around wellness, self-care, and organic/natural products. These brands are no longer about a single person, but rather tied to a purpose.