Tag - venture capital

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Anatomy of a Managed Marketplace
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Why the Micro-VC Surge Will Drive Innovation Across the US
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The Economics Underlying Chatbot Mania
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Ideas Matter
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Furnageddon: The Full Stack Attack on Home Furnishings

Anatomy of a Managed Marketplace

The following article was originally published on Techcrunch on May 25, 2017.

Managed marketplaces (also known as end-to-end or full-stack marketplaces) have been one of the hottest categories of venture investment over the past several years. Recent examples of high-flying managed marketplaces include The RealReal, Opendoor, Beepi, Luxe and thredUP, which have collectively raised nearly a billion dollars. They garner a lot of press because the consumer experiences are often radically different than what’s previously been available in the market.

But there is confusion over what a true “managed” marketplace is. It’s fairly easy to spot a true managed marketplace if you know what you’re looking for. Managed marketplaces typically adhere to the following characteristics:

  • A value-added intermediary (the “management” or “service”) that provides a superior experience versus more traditional peer-to-peer marketplaces, brick and mortar or even a legacy service provider.
  • An introduction of additional risk into the business model; examples might include pre-purchasing and holding inventory or via investing in services related to the buyer/seller that are an incremental, variable cost before any profits have been realized (money goes out before it comes in).
  • A take-rate (gross margin) that is a significant premium versus other buy/sell options in the market in order to offset the premium service level or risk transfer that has occurred.

It’s important to note that many of today’s ubiquitous marketplace companies such as Uber, Airbnb, Grubhub and others are “lightly” managed, by which I mean they invest resources in quality assurance, background checks and verifying reviews. But these services are typically a de minimis expense on the company’s overall operating cost structure — often even considered as part of the customer (or merchant) acquisition cost — and therefore do not classify as a fully managed service.

For Airbnb, these “light” costs might include the costs related to verifying a user’s home address, or the customer service costs of resolving disputes. For Grubhub, the light management might include the costs related to updating menus, but they are not fully managed in that they are not taking ownership of the food or food prep themselves (although Grubhub has begun rolling out delivery, which would qualify as a managed service). This infographic shows the primary marketplace categorizations:

In order to build a successful, sustainable managed marketplace, the take-rate margins must be high enough to support that value-added intermediary and the subsequent amount of services and risk the marketplace is providing. What makes these marketplaces so powerful is that they can drop a comparable amount of contribution margin to the bottom line while investing the higher take-rate revenues into customer experience, reducing friction and product improvements.

Additionally, if, over time, these marketplaces can develop technology that significantly reduces or eliminates the costs of providing these value-added managed services, they can continue to justify higher take-rates and build high-margin businesses that are worth a premium to their traditional service provider comparables or peer-to-peer businesses.

As a primer, here’s a quick chart of take-rates in the re-commerce industry amongst both managed marketplaces and traditional marketplaces. Can you guess which ones are actively managed?

Value innovation and risk

Now that we’ve identified that managed marketplaces are effectively business model innovations, it’s useful to go through a few on a case by case basis to identify each of these innovations and be able to properly identify managed marketplaces in the future.

Opendoor is a managed marketplace in the real estate industry that is an on-demand tool for selling your home. They utilize numerous data sources to offer real-time bids on a home, typically without ever stepping foot in it. Basically: Click a mouse, sell a house. They charge the typical 6 percent brokerage commission plus a risk-adjusted service fee (about 2-3 percent extra, on average, up to 6 percent).

Value-Add Innovation: A consumer no longer has to wait to sell their home. They don’t even so much as have to engage a real estate brokerage. Opendoor reduces the friction of selling a house from possibly months (and multiple showings) down to minutes. The company will also perform all maintenance/changes demanded by a licensed inspector.

Risk Innovation: Unlike brokerages such as RE/MAX or Century 21, which take zero capital risk on a transaction but collect 3 percent from each of the buy/sell sides of a transaction, Opendoor is buying inventory and holding it on their books. The effect of this is that they can offer an extraordinarily differentiated experience to their home sellers, who traditionally rely on peer-to-peer markets (typical MLS listings, with a realtor advising).

Take-Rate: To justify this level of risk (holding inventory) and service (managing maintenance), Opendoor charges a take-rate on average 50 percent higher than in a traditional real estate transaction. While a 50 percent premium may seem marginal given the delta in other categories, the large transaction sizes in real estate mean that the take-rate premium on a $500,000 house is $15,000 of incremental gross margin. That is a significant amount of money to manage maintenance and some risk.

Case in point is the below estimation of Opendoor’s revenue and cost structure on an average $220,000 home (their sweet spot) with a 9 percent brokerage fee, a 50 percent premium to market rates:

Source: Inside Opendoor: What 2 Years of Transactions Tell Us

For Opendoor, when all is said and done, their average net profit, $8,320, or 3.8 percent of the home’s original selling price, is still greater than the 3 percent an agent at a traditional brokerage earns. And, they are able to provide a substantially differentiated experience. It’s a powerful model.

TheRealReal is a managed marketplace in the luxury consignment space focused on clothing, jewelry, handbags, even art. The experience differs from eBay, for example, in that sellers need to provide zero effort other than sending their goods to a TRR warehouse (no photography, no descriptions, no customer interaction) and buyers take comfort in TheRealReal’s quality and authentication services, which they fully guarantee.

Value-Add Innovation: Rather than having to post online listings and photographs of items, pay for a third-party authentication or even deal with shipping, TheRealReal simply collects an item from a consignor and sends them a check once it sells. For sellers, it’s a true “set it and forget it” experience and is multiples more convenient than dealing with online auctions (or even price comparing between local thrift shops).

Risk Innovation: In order to provide a frictionless experience for the seller and an aesthetic, trusted experience for the buyer, TheRealReal is forced to frontload all those costs into their own overhead. They expense per-item charges for photography, copy writing and logistics before an item sells. If the item fails to sell, TheRealReal is forced to eat those overhead costs. Therefore, if they inaccurately forecast demand for certain items, they could end up burning more money than they’re able to recoup on sales.

Take-Rate: In order to justify its cost structure, TheRealReal (and other comparable marketplaces) command take-rates of 30 percent, effectively triple what non-managed, peer-to-peer marketplaces charge as a commission to sellers.

Luxe is a managed marketplace for drivers that reduces all friction associated with parking: finding a lot, searching for a spot, returning to the lot, paying the cashier and waiting to exit. Operating as an effective always-on, mobile valet service, drivers are met at their destination by a Luxe agent who takes the keys and parks a driver’s car. Upon leaving, the driver requests their car in-app and are met by a Luxe agent who delivers their car at their exit point.

Value-Add Innovation: Luxe fundamentally reimagines parking by providing any driver with an on-demand valet who will meet them across a large radius of major cities. Unlike traditional parking or even mobile parking marketplaces such as SpotHero* (which require a driver to park their own cars), Luxe reimagines driving to be destination-focused: drive to your ultimate end-point, not a parking lot. In theory, its product could save drivers time and enable them to avoid inclement weather.

Risk Innovation: In order to provide uninterrupted, on-demand service, Luxe is forced to employ numerous valets across each geography in which it operates. Irrespective of what these valets are actually paid, it is a considerable human capital cost that Luxe is forced to bear ahead of any realized demand. This is in contradistinction to a sharing economy marketplace model such as Airbnb or Uber who are not burdened with human capital costs, but rather pay transactional commissions on any given home-owner or driver.

Take-Rate: In order to counter-act the considerable human capital expense of staffing valets across a city, Luxe should be forced to charge a material premium compared to average hourly parking rates in a particular city. It is therefore quite surprising that they advertise an average of $5/hour for their service, especially when the average hourly rate in NYC, for instance, is $11-15/hour. Several months ago, reports from San Francisco suggested that prior rates of $5/hour have now increased to $15/hour or a $45 daily maximum and, as of this month, they have suspended the valet service. Given the considerable variance in parking costs by neighborhood, it is hard to assess their exact take-rate premium, but I’d estimate it would have had to have been about 200 percent of traditional parking take-rates to be profitable.

Each of these companies is built on the vision that technology will ultimately be able to deliver increased automation and better margins. For example, that one day TheRealReal’s item authentication will be entirely algorithmic or that Luxe will be able to predict the real-time flow of drivers, thereby reducing its human capital costs. Because these representative companies are all still relatively young startups, those tech-driven narratives have mostly only begun to play out.

Beepi versus Carmax

Beepi, a managed marketplace for used cars, recently closed its doors after burning through nearly $300 million in the span of two years. Unlike eBay Motors, which is a peer-to-peer experience, offering no concierge services (although it does offer some self-service options such as free Carfax reports), Beepi was a full-service platform promising rigorous inspections on cars, a 10-day no-questions return policy for a purchased car and, for sellers, a promise that if one’s car didn’t sell in 30 days, Beepi would buy it outright for the appraised value.

The seller fees for this risk-free service? Approximately 9 percent with Beepi… versus a $125 fee for eBay motors, or about 1.25 percent on a $10,000 car, nearly an 800 percent differential.

So with a premium 9 percent take-rate, how did Beepi fail?

The best insight into their failure may come from a similar model with considerable success. It turns out that the nation’s largest retailer of used cars is also arguably one of the most recognized managed marketplaces in the world: Carmax. Give Carmax 30 minutes to inspect your car and they will buy it, even if you’re not purchasing one of theirs, with a “no-haggle,” take it or leave it offer. Thirty minutes is pretty efficient, pretty darn close to on demand.

The extraordinary thing about Carmax is that the Company’s gross profit per used car sold basically doesn’t change even when the year’s average price per car sold moves up or down by 5 percent in any given year. Which means that Carmax is actually less focused on their take-rate per car, but instead focused on their profit per car; their commission is a function of the profit they expect to earn.

Rationally, this makes sense as well — consumers value convenience but have a cognitive dollar limit they are willing to trade for that convenience. By inverting their take-rate to be a function of their profit expectations, Carmax is able to offer more for higher-end cars where a 10 percent difference between the car’s BlueBook value and Carmax’s offer would likely be too extreme for a customer to accept. For high-priced assets, a flat tax is fundamentally unlikely to work.

Used cars are curious assets in that they depreciate so quickly that even 60 days can have a demonstrable impact on value. This is where Carmax excels. In their most recent annual reporting, Carmax notes having sold more than 600,000 cars in the prior year with (then) present inventories at about 55,000. That’s a retail turn of about 11, meaning that a car moves off Carmax’s lot every 35 days or so, allowing them to more accurately price cars andmake higher offers, being less exposed to the less predictable volatility of depreciation.

In a post-mortem on Beepi, Carlypso founder Chris Coleman suggests that in addition to the noted reasons (depreciation effects, and cognitive pricing differential), the approach was inherently flawed from a customer acquisition perspective. Specifically, that while there are customers looking to simply sell their car for cash, most car owners are looking to trade-in a car, because they still need a car and there are tax benefits to doing so; a platform that has to pay marketing costs for both the buyer and seller in all transactions is at a significant disadvantage.

At the end of the day, a managed marketplace model for used cars does work. Carmax is only one of thousands of proof points: tens of thousands of dealerships across the country hold inventory, inspect cars and reap a profit. Beepi’s failings appear to be the result of poor execution, mispricings and maybe even some bad luck around a financing that fell through.

Automation and Shutterstock

Because managed marketplaces involve a heavy “service” component to improve the overall experience, one of the expectations of the sector is that as artificial intelligence and automation continue to evolve, the human capital cost of providing the service will decrease if software can assume more of those responsibilities.

But an area of struggle with managed marketplaces is that very few digital managed marketplaces are actually public companies, reducing the visibility into their overall economics and processes — and making it hard to test the assumption that service costs should come down over time. Luckily, there’s at least one: Shutterstock (NYSE: SSTK), a marketplace for photographers to sell their images, bills itself as a “trusted, actively managed marketplace,” in that “each image is individually examined by [their] team of trained reviewers.”

On the spectrum of managed marketplaces, Shutterstock is undoubtedly on the lighter end — with the financial risk from its active management being only the human capital cost of its QA reviewers. Nevertheless, it would seem like this hypothesis is an ideal one to test on a company such as Shutterstock, which isn’t dependent on an unproven technology such as self-driving cars to reduce their cost of providing a service, but could presumably leverage proven, inexpensive image recognition technologies to do much of the quality assurance, copyright detection and tagging that the human reviewers do.

Yet, that doesn’t appear to be borne out by Shutterstock’s financials. To test the automation hypothesis, I decided to look at the company’s revenue versus the cost to generate that revenue. Shutterstock defines their costs of revenue as “royalties paid to contributors, credit card processing fees, content review costs, customer service expenses, infrastructure and hosting costs…and associated employee compensation.” I would assume that credit card processing fees as a percentage of revenue are relatively flat (if not slightly reduced year over year) and that cloud-hosting fees also scale mostly proportionately to demand (revenue). I’ve defined “True COGS” below as the aforementioned expenses to providing their service, minus the contributor royalties:

Surprisingly, rather than decreasing over time, these True COG costs appear to be increasing. Meaning that the same picture that used to take 10.5 percent of revenues to process now costs nearly 15 percent.

There are a number of possible explanations here. It’s certainly possible that these increased costs are because the company is investing heavily into automation, the effects of which simply haven’t been borne out yet while they streamline their QA process. It’s also possible that the number of photos the company maintains makes it more expensive to process each incremental picture — for any variety of reasons.

The learning from this Shutterstock case study, a company which is now 14 years old, is that it’s improper to simply assume that the substantial service-related costs that managed marketplaces incur in their early stages will decrease with “scale,” either through execution or software automation. As with any company, there is always room for improvement, but the above analysis of Shutterstock would imply that it’s nowhere near as easy as flipping a switch.

Takeaways

Managed marketplaces are a quintessential venture investment, allowing entrepreneurs to recast consumer experiences while leveraging venture capital subsidies to hold much of the risk inherent in these managed models.

From a unit economic perspective, the potential automation of much of the service labor that goes into these platforms could be significant. Investors and operators need to remain sensitive that it is ultimately the technology, not heavy services, that will long-term cultivate highly desired business models and margins. But, that future automation could also lower the barriers and defensibility of these companies, allowing peer-to-peer players to launch a comparable offering with similar software.

In my mind, the sustaining managed marketplaces will not only re-imagine the experience they’re approaching, but be focused from the outset on building a data moat around their product, thereby ensuring that they remain the platform of choice, even if software innovation begins to level the overall playing field.

Special thanks to Josh Breinlinger and Rebecca Kaden for their feedback on this article.

*Chicago Ventures is an investor in SpotHero.

Why the Micro-VC Surge Will Drive Innovation Across the US

The following was co-authored by Ezra Galston of Chicago Ventures(@ezramogee) and Samir Kaji (@samirkaji) of First Republic Bank.

Over the last several years much has been made of the opportunity, or perceived lack thereof in technology centers outside of the Bay Area and NYC. From Steve Case’s Rise of The Rest Tour, to Google for Entrepreneurs, to Brad Feld’s Building an Entrepreneurial Ecosystem , the discussion has consistently been overwhelmingly positive.

It’s easy to understand the stance as who wouldn’t want to support entrepreneurship, irrespective of geography? However, it’s hard to discern whether these opinions were borne out of a utopian desire or a sincere belief of true financial viability in markets outside of NYC and the Bay Area.

In Fred Wilson’s widely discussed (and debated) piece “Second and Third Tier Markets and Beyond,” he suggested that the opportunity outside of the Bay Area was significant, citing the successes of USV in New York, Upfront Ventures in LA and Foundry Group in Boulder:

“The truth is you can build a startup in almost any city in the US today. But it is harder. Harder to build the team. Harder to get customers. Harder to get attention. And harder to raise capital. Which is a huge opportunity for VCs who are willing to get on planes or cars and get to these places.

There is a supremacism that exists in the first and second tiers of the startup world. I find it annoying and always have. So waking up in a place like Nashville feels really good to me. It is a reminder that entrepreneurs exist everywhere and that is a wonderful thing.”

In an effort to move past anecdotes however, we wanted to explore one of the components that helps drive and catalyze early entrepreneurial activity in any localized geography — the availability of early stage funding.

Simply put, non-core US tech hubs are reliant on local early stage capital to subsist since seed stage fund sizes often make remote investing impractical (by contrast growth stage investors who manage large funds and have significant resources can easily invest in breakout companies outside their region).

With the hypothesis that quality local seed capital is needed to foster a strong entrepreneurial ecosystem, our analysis is centered on whether the MicroVC surge, has provided (or may provide) a material impact to these “2nd and 3rd” tier US geographies.

Fortunately, there’s good news for entrepreneurs everywhere. Of all of the Micro-VC funds raised since 2010 (this number includes firms currently raising funds), over 40% of Micro-VC’s formed were based outside of the country’s largest tech centers of SF, LA, NYC and Boston, a number we found quite surprising.

In total, those Micro-VC funds raised outside of the four core tech centers since 2010 represent $6.7B in investable capital, the vast majority of which have driven significant investment dollars in their geographies.

More important to note is that the opportunity in these secondary ecosystems is unequivocally noteworthy. Using M&A activity as an evaluation metric, these ecosystems, despite a relative dearth of funding, have performed quite well:

In each year dating back to 2010, the percentage of Micro-VC funds raised outside of SF, LA, NYC and Boston materially lags the volume of M&A activity, on % basis, in those same areas. This suggest that Micro-VC funds located in secondary markets face less competition — and proportionally more opportunity — for strong financial outcomes by betting on that delta. Now, it’s true that these opportunities are a bit geographically dispersed, however it’s clear that certain cities (Seattle, Boulder, Austin, Salt Lake, Chicago) have made great strides in developing great entrepreneurial talent.

This dislocation in M&A proportionality is of course amplified by the concentration of funds in the Bay Area and NYC. Because coastal deals are more competitive due to an oversupply of capital, they boast higher entry prices (valuations) than do deals in secondary or third tier markets — and the effect on a returns basis may also be material. Case in point: according to Angelist, the mean valuation for deals in Silicon Valley since 2010 is $5.1M. That compares to $4.5M in Chicago, $4M in Indianapolis, and $3.7M in Detroit — offering Midwest investors anywhere from a 10–30% discount at entry.

There are other ways of interpreting the data. One could argue that Bay Area deals deserve to be higher priced due to a premium in the quality of founding teams. Or that the pure volume of M&A in the Bay Area and Boston de-risk the level of returns variance for any particular fund. Those arguments may be with merit but are also balanced by data released by Pitchbook that show cities such as Chicago, Seattle and Washington D.C effectively comparable on a multiple of returns basis:

It is nearly indisputable that large technology companies are being built and enormous value is being created outside of the coastal venture markets: examples include Grubhub, Groupon, Domo, Qualtrics, ExactTarget and HomeAway. But these markets will require more patience for company maturity, a willingness by fund Limited Partners to accept greater short-term volatility, and conviction that key talent will stay in non-core markets due to a desire of staying local and the avoidance of the high cost of living present in the major US tech centers.

While the rhetoric around non-core markets has been historically positive, it appears that the early stage capital surge through Micro-VC funds may be a major factor in these areas actualizing on their potential.

Extra special thanks to Peter Christman for his tireless work in helping to analyze, aggregate and process the data underlying this article.

The Economics Underlying Chatbot Mania

Over the last several weeks, we’ve reached peak AI/Bot mania. Most of the conversation has centered around Chatbots and the potential emergence of a new platform/distribution layer. If you’ve been mostly ignoring the press, some good reads are:

Tl;dr – the major takeaways are as follows: given that consumers don’t really download apps anymore, brands & retailers have a new access point to end consumers, sitting on top of existing messaging platforms and leveraging chatbots to ensure mass scale. The truth is that the chatbot platform conversation is really just an extension of the one we had about a year ago during the emergence of Magic/Operator and SMS as the new platform, which we discussed in Are We Already Rebundling Mobile. An important extension given that such bots have been democratized and can now be spun up not just by tech companies, but by traditional retailers (on their own or within Messenger) or even by individuals such as you and me.

screen-shot-2016-03-24-at-09-50-46

All @TayTweets joking aside, the more intriguing aspect this time around is that Artifical Intelligence and Machine Learning have improved by leaps and bounds. A few articles that reflect this point are: Why AlphaGo Is Really Such a Big Deal, The Current State of Machine Intelligence, Can Machine Learning Predict a Hit or Miss on Estimated Earnings, and The Humans Hiding Behind the Chatbots

But again, the vast vast majority of analysis has focused on bots living within the worlds we frequent (messaging & SMS) and the platform implications. SO – I wanted to spend a few paragraphs to quantify and explore the effects of these advancements from a unit economic or business perspective. The big ones are twofold in my mind: (1) Properly executed AI can transform certain human capital marketplaces from operating as take-rate businesses and transition them into high gross margin software businesses. (2) Chatbots in their current function as customer service agents can make a material impact on contribution margin & overall EBITDA if they can successfully remove the customer service expense line.

20% Take Rate —-> 90% Gross Margins?!?!?

First, some context. About four months ago, writing in “The Middleman Strikes Back” I suggested:

“It’s clear why a hybrid AI/machine learning model is the holy grail for several verticals – replicating [a high] level of personalized service while minimizing overhead labor costs and maintaining extraordinary software level gross margins.”

But in fact, I was wrong. Dennis Mortenson, founder of X.ai in “The Humans Behind the Chatbots” believes that the hybrid in “hybrid AI /ML” should be minimized even further…to zero:

“The two scheduling e-mail bot companies have divergent plans for expansion. Clara, which is slowly letting people off its waitlist and said it currently serves hundreds of companies, charges $199 per month per user. X.ai, on the other hand, plans to move from limited beta to a public release later this year and wants to charge about $9 per month. Dennis Mortensen, its founder, wrote in an e-mail that “only a machine-powered agent can take on the 10 billion formal meetings that U.S. knowledge workers schedule every year.” Mortensen said the service will start asking e-mail senders to clarify when the computer can’t interpret an message—“Did you mean Monday, April 4?”—instead of having an employee read it and infer. “We want to give the job away for free, or for $9, which you can only do if it’s software,” he said.”

Executing on Dennis’ vision, by removing human labor in the middle, you have effectively transformed a take-rate marketplace into a high margin software business, while managing to provide a similar product. Here are examples of some of the service/agent businesses* that could see their economics transform towards 80-90% gross margins when fully leveraging AI:

The Unit Economics Underpinning the AI

Additionally, the company’s variable costs (ie the cost to provide each incremental production or engagement) will transition from significant (paying humans) to immaterial (software cloud hosting fees) enabling a much lower cost of servicing demand, should these companies choose to lower prices. Doing so could expand the audience for such products tremendously.

While I was initially a big skeptic of X.ai, the combination of a product that increasingly works with minimal human intervention and a product priced to undercut the market tremendously at $9/mo is a proof that this transition from human marketplace to pure software is already underway.

Further, there’s even an even bigger opportunity in play. If Matt Turck’s suggested “data network effects” take hold many of these service marketplaces – few of which are operating in actual winner take all markets – could be replaced by a software layer, leveraging data network effects so strong that those markets actually become winner take all.

$$$ Massive Value Creation in Public Markets

Again, this is mostly a theoretical exercise, but let’s imagine for a moment the value creation consequences of fully automated chatbots successfully managing 100% of a company’s customer service interactions.

Here are a handful of examples of some companies you might be familiar with showing their current enterprise value, approximately how much they spend on human capital customer service and the effective sensitivity in their valuation if they could maintain their current level of customer service via no-cost** chatbots:

The Unit Economics Underpinning the AI2

It’s not clear to me this is the optimal way to assess the economic value of chatbots – I kind of doubt anyone knows yet – but what is clear is that the effect to profitability would be tremendous. Massive. And that’s just by automating customer support alone.

Importantly, and intentionally, this exercise ignores the real, global cost of losing so many service level jobs from the economy. I’m not qualified to assess that cost nor its effect. There are many opinions in the market for what an AI economy might look like; Roy Bahat at Bloomberg Beta recently offered an intriguing one to The Twenty Minute VC – a futuristic marketplace actually placing a premium on any human manufactured products or human assisted services because of its rarity. There are others as well. Much discussion around AI is conjecture, but its economic effects are a very serious business.

* Certain of these companies do not publicize take-rate or offer non take-rate subscription models. In these cases, gross margins have been assessed from public interviews, or estimated based on reported hourly worker wages and expected throughput.

** This assumption is implausible as there would still be some associated costs related to the software itself and managing/customizing the software on a daily basis.

Special thanks to Dan Abelon for his feedback on this piece.

Ideas Matter

The debate amongst venture capitalists over whether to prioritize markets or people in investment decision making is as old as the industry itself.

Fred Wilson, famously authored an oft quoted 2004 blog – Execution Matters, Ideas Don’t – which referenced USV’s failed incubation of FaveMail leading to the following conclusion:

“The lesson i take away from the whole thing is great ideas don’t make great investments – great entrepreneurs do.”

Three years later, Marc Andreessen, co-founder of Andreessen-Horowitz, opined differently in “On product/market fit for startups,” noting:

In a great market — a market with lots of real potential customers — the market pulls product out of the startup.

The market needs to be fulfilled and the market will be fulfilled, by the first viable product that comes along.

The product doesn’t need to be great; it just has to basically work. And, the market doesn’t care how good the team is, as long as the team can produce that viable product.

Most recently, University of Chicago Professor Steven Kaplan in his 2009 study “Should Investors Bet On the Jockey or the Horse” in the Journal of Finance concluded:

The results for both of our samples indicate that firms that go public rarely change or make a huge leap from their initial business idea or line of business. This suggests that it is extremely important that a VC picks a good business. At the same time, firms commonly replace their initial managers with new ones and see their founders depart, yet still are able to go public, suggesting that VCs are regularly able to find management replacements or improvements for good businesses.

In spite of Andreessen’s comments, I would say that the working consensus in the early/seed stage venture world for the duration of my tenure has been to bet on special people and let the rest fall into place. That approach was verbalized in my interview with David Hornik on this blog where he outlined his investment approach as “people, people, markets, people.”

But the world is changing. The vast majority of companies I’m referred to are largely derivative ideas of larger tech/startup competitors. Problematically, this is actually fundamentally different than going after large monolithic incumbent corporates in a given space. For whatever reason I think it’s because entrepreneurs are trying to improve processes/better execute on problems rather than re-imagining the reasons those habits/processes even exist.

My sense is that the market is cycling back to the importance of bold, unique, creative ideas – above and beyond the obvious focus on “big markets” or “founder/market fit.” Of course people still matter, just as a large addressable markets have always mattered, but in my estimation there is a subtle yet undeniable shift of interest away from improved processes/products towards ideas that challenge the fundamental assumptions underlying the existence of those processes or products themselves.

In that vein, I was recently asked to prep some talking points for a firm offsite on the state and challenges of investing in consumer tech. I’ve attached those slides here. The theme was very clearly that we are in the “reimagination” stage of venture and my observation is that companies solving pain points but not re-imagining or re-conceptualizing consumer behavior are out of favor with top tier investors.

[Note: It’s always nerve wracking putting detailed thoughts into the market – I’m sure a lot of people disagree with me – but I do appreciate any feedback and counter-examples of places where I’m wrong.]

Furnageddon: The Full Stack Attack on Home Furnishings

As digital commerce has evolved over the past decade to penetrate nearly every element of our lives, one category has largely been left in the dust: furniture. But in the past twelve months disruption has accelerated exponentially – with the industry suddenly under a broad based assault from all key angles: manufacturing, delivery, assembly, and discovery.

A few months back, I noted that furniture was one of the remaining massive categories still struggling with finding a mass-market consumer fit online. In it I quoted a conversation I’d had with a well known venture capitalist –

“Similarly, [the investor] noted at the time, furniture – sofas, mattresses, tables, etc – were one of those categories that hadn’t been cracked by e-commerce. The unit economics made delivery expensive. And, like shoes, consumers wanted to try them on. Is it comfortable? Do the colors match up with room palette? What if, he proposed at the time, a furniture company offered the following value prop: We’ll show up at your home, for free, with ten different sofas of varying feels and colors, let you try them all out for free, and then just keep the one you want and send all the rest back for free?”

Under the legacy model, e-commerce furniture sales were simply a digital extension of traditional product purchasing and sampling. Further, the thinking continued, the legacy retailers were actually capable of avoiding disruption because of the complexity in the supply chain and delivery logistics that made it expensive, if not impossible, for startups to compete.

But this is all changing.

Furniture in a Millenial World:

To be fair, it’s still exceedingly early in the disruption cycle – two of the emerging brands, Campaign and Greycork for example, haven’t even begun shipping their product yet – but taking into account the strength of their pre-sales and the growing traction amongst other disrupters its hard to argue against there being a fundamental shift underway:

Slide1

At Chicago Ventures, we’ve categorized the disruption from these new, vertically integrated brands into four categories: Delivery, Assembly, Price, and Design.

  • Delivery: Arguably the most important angle, as evidenced by Casper’s runaway traction, millennial consumers are looking for experiences that provide for both immediate delivery and frictionless ease of return. Traditional retailers have long lead times on larger pieces (8-12 weeks) and they arrive via a 3rd party trucking provider (who disappear shortly thereafter) making it extremely inconvenient to return a product. The new disrupters are getting product to a customer’s door from time to purchase, in under a week and sometimes as little as 24 hours. They are also using novel shipping/packaging methods to enable accessible and low-cost (or free) returns.
  • Price: Typically the most important variable for consumers, it’s also one of the hardest to clearly differentiate in furniture as every retailer offers differences in quality, style, etc. One thing’s for sure: IKEA is the low cost provider in the space and despite the arguably miserable experience, they still move a lot of product. The new disrupters, by building a direct b2c brand are able to discount heavily on price while maintaining similar margins by removing some of the supply chain layers.
  • Assembly: The current status quo is either long lead times wherein items come fully assembled OR shorter windows/in-store pickup (think IKEA) where consumers are responsible for arduous assembly and installation processes (my wife loves doing IKEA assembly but she’s gotta be in the 1%). The new disrupters, focused on building an anxiety-free product, offer large items that can be assembled in minutes without tools. The benefit to the simplicity is that they can also be disassembled in minutes for moving, or for easy packaging to return items.
  • Design: If you’ve been to the big box retailers – from Pottery Barn to Arhaus to Restoration – you know each has its own unique personality. But the new designers believe those pieces aren’t being imagined with a millennial purchaser in mind. The new disrupters are attacking home, apartment and workplace furnishings and trying to reimagine product that stays sensitive to personal devices, new work habits, etc.

For us, we view Delivery and Assembly as the two most intriguing and sustainable angles being attacked. Although our internal consumer surveying has shown that price is ultimately the #1 most important variable for buyers, we fear that given the healthy gross margins in the category (42.9% at Ikea, ~38.3% at Pottery Barn*) and de minimus return rates that incumbent retailers will quickly be able to adapt on price undercutting or free shipping/returns offers.

What appears hardest for legacy retailers to respond to are fundamental re-imaginations of the production line, product design, and supply chain that enable immediate gratification or emerging models of fractional ownership/sharing.

Discovery

Outside of the millenial manufacturers, there’s an equal amount of momentum in the next generation of retail models. Right now, the process for actually purchasing a piece of furniture online is cluttered – as most product exists in an environment of millions of SKUs with minimal effective filters. And while the companies listed in the Pottery Barn graphic above are trying to reimagine the next generation of vertically integrated brands, that doesn’t solve the wider discovery problem for consumers.

The problem for consumers is that the increase in SKUs across all furniture categories has made shopping on the aggregators an utter nightmare. Comparing from vendor to vendor is also a time-consuming, high-friction experience. These are the reason that according to Greg Bettenlli the only signifcant growth driver at Wayfair is Joss & Main, their ultra-curated flash sales site.

Thus, the furniture category appears to be mirroring the quintessential e-commerce curve that I detailed earlier this year:

“Th[e] same catalog of infinite SKUs caused real pain for all but the most specific of product searches. This pain led to the birth of discovery- and push-curation focused platforms. On a macro level, the [present] move towards connection makes a lot of sense. Consumers are overwhelmed by email, social, and retargeted marketing, while at the same time flocking to platforms such as Uber and HotelTonight, whose focus is on constraining choice and cognitive noise.”

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For example, we’ve witnesses this process play out in the fashion industry – first with discovery focused platforms (Gilt, Fab, Wish, RueLaLa) and now personalization experiences (TrunkClub, Stitchfix, Wantable). The following chart illustrates how the same dynamic is now emerging in the furniture world as well:

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And yet in spite of the increasing focus on the sector, only about $20bn of the $160bn U.S. market for furniture and home accessories is being transacted online:

usfurniturehomefurnishings

Put together, the category represents a tremendous opportunity for venture investors: demonstrable consumer pain points, broken processes and experience, and a market size well over a hundred billion dollars. Few of the companies noted have material traction or brand equity – but it’s the early innings; it’s one of the most exciting categories to watch in consumer internet and is poised for outsized growth.

At Chicago Ventures, we’re definitely looking for entrepreneurs focused on reimagining the furniture space. If you’re one of them, please reach out!

 

* This is blended and includes results from Williams-Sonoma and a number of other properties. As PB sales have increased, GM has contracted, implying their GM on their PB business is probably closer to 35% than 40%.

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