Travel & Transportation Ecosystem

The history and evolution of the travel and transportation industry is really quite fascinating. How we arrived at today’s mix of companies (both the incumbents and insurgents) is a story of technological innovation, savvy business positioning and a macro trend around increased travel and connectivity.

In today’s world, the range of different companies is quite impressive. On one end of the spectrum, you have the traditional, asset-heavy giants. In airlines there is United, Delta, American Airlines and many more. In hotels, Starwood, Hilton, Hyatt, and others dominate the scene. And in car rentals, Hertz, Avis, Enterprise and others have long been staples of ground transportation. Seeing the challenges that lie ahead, many of these travel/transportation behemoths are moving towards an asset-light approach and beefing up their digital and mobile offerings. Starwood for example has sold off nearly 85% of its properties in the last decade. They are moving more towards an approach that relies increasingly on a franchise / management fee model. Similarly Starwood has also invested heavily in their mobile app, which has paid off. In recent years, mobile bookings have increased 300% yoy and mobile revenue is a growing portion of total revenue.

In addition to the traditional players, you also have the .com darlings of the late 90s and early 2000s. These companies are particularly well represented among the OTAs. Oribitz and Expedia for example rose to prominence by helping consumers find and compare various booking options across the various legs of their vacation journey. Many of these companies have gone on to do quite well—though increasing competition has threatened earnings and growth. In addition to the OTAs, travel discount sites (such as Priceline) and review sites (such as Trip Advisor) were also largely born during this era and continue to do quite well.

Finally, there are the emerging companies of today that have successfully taken advantage of increased mobile connectivity, macro trends around the shared economy and advances is geo-location technology. In the taxi cab world, Uber, Lyft and others have built unicorn valuations in eye-popping speed. In vacation rentals, HomeAway and Airbnb are leading the charge to provide sellers with liquidity and renters with cost-effective options in those two-sided markets. And in mapping, Google Maps and Waze (acquired by Google), have greatly enhanced mapping capability. Not to mention the rise of ride sharing companies (Via, Sidecar, etc.) and parking services (Luxe, Zirx, etc.) which are just getting started. In the coming years, vertical cloud saas companies will only further enhance the options that consumers have today.

All in all, it’s a really interesting time to be in travel and transportation. I’ve put together a map of the travel and transportation ecosystem below. It’s certainly not all-comprehensive by any means, but it provides a general overview of these various players and trends. Hopefully it’s a useful guide to navigating the various sub-categories and players in the industry.

Travel & Transportation Ecosystem

P2P Lending: The Early Regulatory Environment & Today’s Emerging Landscape

Over the course of the last century government has always found a way to influence the course of business through regulation. Traditionally, however, the government has tended to focus regulatory efforts on large enterprises. The vast majority of startups often fly under the radar as they are too small to really create concerns for regulators. The select few that do balloon into bigger companies through super-sonic growth, i.e. the Ubers and AirBnBs of the world, often do encounter regulatory hurdles as they grow into large enterprises. The reality, however, is that government regulation, in certain industries, can be game changing even at the earliest of stages. We need look no further than peer-to-peer (P2P) lending as an example.

Going back a decade to the mid-2000s, P2P lending was a nascent industry that had begun to shape into a two-horse race between Prosper (founded in 2005) and Lending Club (founded a year later in 2006). At the time, U.S. consumers had about $880 billion in credit outstanding and the virtual marketplaces for P2P lending were thought to reach $300 billion in loan origination per year by 2025. It was (and still is) a huge and growing market with lots of potential. As the first mover in the space, Prosper had jumped out in front by the end of 2007, facilitating 10x the number of loan originations as Lending Club. But 7 years later, Lending Club went on to IPO in December of 2014 and now has an enterprise value of $9.6B. Prosper has fallen to a distant 2nd and has struggled to find demand for an IPO—withdrawing its filing process on numerous occasions. So what happened?

The answer is government regulation, which provided Lending Club with a huge competitive advantage over Prosper in the early days of P2P lending. The critical issue that came up in 2008-2009 was over whether the SEC would view the loan transactions Prosper and Lending Club were facilitating as promissory notes, without any filing requirements, or securities, which (at the time) had extensive and very expensive filing requirements.

Prosper took a more passive “wait and see” approach to this regulatory issue—hoping that regulators would simply view the underlying assets as promissory notes and leave them alone. Lending Club, however, had the foresight early on to see the danger with this approach. If the industry was forced to file with the SEC, everyone would have to shut down for 6 months and submit to the long and expensive registration process. As such, Lending Club chose to take a proactive approach in being the first movers to file. Moreover, they actually helped the SEC shape much of the regulation in P2P lending which provided them with a huge short-term advantage over the competition.

As soon as Lending Club finished their filing process, the SEC then ruled that every other P2P lending marketplace would have to do the same. This meant that they came out of the process right when everyone else had to temporarily shut down for 6-9 months. So for ~9 months (time it took Prosper to emerge from the process) they were effectively the only player in P2P lending, right as the market took off courtesy of the tailwinds provided by the financial crisis.

Many of the other early players essentially shut down due to the heavy costs of filing and the lost time. Prosper managed to hobble out of the process but by then the damage had been done. With 9 months of green field, Lending Club had built out both sides of the two-sided marketplace, enhanced their network effects and customer captivity and emerged as the #1 player in P2P lending. The last 5 years Prosper has been playing catch-up as best it can. So government regulation was game-changing early on in P2P lending.

However, the story doesn’t really end there. While Prosper and Lending Club continue to do pretty well, the challenge for both of them is that they don’t have much by way of long term competitive advantages. The image below shows their respective competitive advantages at 2 different points in time.


We’ve already discussed the early days of P2P lending, so let’s shift our focus on what is happening now in 4 cores areas: (1) government regulation, (2) scale, (3) customer captivity and (4) cost.

  • Government Regulation: Having cleared the SEC regulatory filing process, Prosper emerged wounded but alive. The problem with regulation now, however, is that in many ways Propser and Lending Club cleared the way for everyone else. The SEC filing process is now much faster and less costly. So there no longer exists a true competitive advantage for either from a regulatory perspective.
  • Economies of Scale: Neither Prosper nor Lending Club ever had an advantage in terms of fixed costs. These are software marketplace business that are not capital intensive; that basic principle remains true today. What has changed is the strength of the network effects.
    • Since dropping the auction-style borrower/lender matching process and migrating to the Lending Club method of assessing credit risk (through FICO scores and other metrics), the number of credit-worthy borrowers and therefore lenders willing to lend has increased tremendously on Prosper’s platform. The result has been a strengthening of Prosper’s network effects. They have now done $2B in total loan origination, which is a third of the $6B Lending Club had done.
    • The challenge though for both Lending Club and Prosper, however, is that the networks effects are not unique to their platforms. Lenders are simply in search of credit-worthy individuals in this low interest rate environment. As lenders on these platforms continue to move away from individuals and towards institutions, they will be increasingly more willing to multi-home across multiple platforms in search of the most credit-worthy borrowers and the highest yields. Borrowers will also multi-home in an effort to find the lowest interest rates as well as the most personalized / specific marketplace—hence the rise of P2P lending in specific verticals (more on this below).
  • Customer Captivity: Admittedly, I don’t have much by way of evidence but my belief is that as network effects begin to dwindle on both Prosper and Lending Club, so too will customer captivity. This will be most notable in terms of customer acquisition costs. Currently it costs Lending Club ~$40-$60 to acquire each borrower and ~$20 to acquire each lender. I am positive that this is higher than CAC in 2009-2010 when Lending Club barreled its way out of the SEC filing process as the only player and lower than what they will have to spend in years to come to acquire customers. Expect to see marketing and sales spends as a % of revenue to increase as well as higher churn and a lowered ARPU/CAC ratio.
  • (Supply) Cost: Early on, Lending Club and Prosper may have had a slight supply advantage in terms of proprietary knowledge and/or special resources. Their algorithms matching borrowers and lenders as well as probability of default may have provided them with a slight edge—particularly given the larger amounts of data they had as a result of being in business longer than others. Unfortunately, this too is not a sustainable competitive advantage. The technology in P2P lending over time has become increasingly commoditized—there are only so many ways you can assess credit-worthiness and, given enough brain-power and time, the technology can be easily replicated.

So the overall affect for both Prosper and Lending Club has been a weakening of their competitive advantages vis a vis the rest of the P2P lending market. This has resulted in a whole slew of new entrants fighting for share and cutting into the once high (as high as 40% for Lending Club) margins. Here’s a look at how the competitive landscape is shaping up today:


In particular, the last few years have given rise to vertically focused P2P lending marketplaces. These vertical P2P lending marketplaces focus on specific verticals like student lending (SoFi, CommonBond, etc.) or SMB lending (OnDeck, CAN Capital, etc.) Because they offer a specific focus or thematic lending opportunity, the network effects and customer captivity of these offerings tends to be high. While still mostly small and narrowly-focused, these vertical P2P lenders have broadened the P2P lending marketplace into more than just a 2-horse race.

Cap Table Modeling: Understanding the Mechanics of Equity vs. Convertible Debt

Cap tables are an important concept for entrepreneurs to grasp when taking outside financing. A cap table is a schedule that lays out the ownership stakes in an early stage company. They typically take the form of a spreadsheet that changes over time as more capital is raised and more investors become involved in the growth of a company. Cap tables can also vary based on whether the capital is raised through equity or through convertible debt (debt that converts to equity at a future point in time).

Much has been written on the merits and challenges of both equity and convertible debt. There are a number of great posts that explain each at a high level and then go on to take a stance on which method is preferred and when. A number of notable investors have weighed in on the topic through a variety of posts including: Fred Wilson, Mark Suster and Josh Kopelman. All of these posts do a great job of explaining the mechanics of each financing option and provide sound reasoning around when (and when not) to use convertible debt vs. equity.

The problem with these sources, is that rarely do they actually dive into the mechanics of building a cap table from scratch and modeling out the differences over time of equity vs. convertible debt. Of course, there are courses taught by organizations such as Wall Street Prep that do extensive training around cap table modeling. While these courses are great, they tend to be a) very expensive b) time-consuming and c) highly detailed-oriented (too detailed for what most entrepreneurs are looking for). So what do you do if you’re an entrepreneur who wants more than just a high level understanding of the pros and cons of various financing options but doesn’t want to pay a premium for a time-consuming, detail-heavy course?

I recently came across a great resource put together by my Professor at CBS and 37 Angels founder, Angela Lee. Professor Lee has built a step-by-step guide to modeling out cap tables for equity and convertible debt deals (both when the discount or cap come into play). The guide, which is posted below, provides detailed instructions on how to calculate the various components of a cap table (shares owned, share price, % owned, etc.,) across various rounds of fundraising. Although the tool is simplified, it provides an intuitive way to model various financing scenarios and their implications for your ownership over time. Hopefully this sheds a bit more light on the mechanics of how cap tables are put together. Big thanks again to Professor Lee!

37 Angels Cap Table Template

The Age of the Unicorn: Traits of Today’s Unicorns & Their Marriage to the IPO Market

A few days ago, Dan Primack and Erin Griffith from Fortune put out an article entitled “The Age of the Unicorn” along with a nice list of the 80+ unicorns currently in business. In addition to providing a working definition of the “unicorn” (essentially a pre-IPO tech startup that has reached a $1B market value), Primack and Griffith go on to describe some of the characteristics of today’s post-bubble unicorns and why these companies have become much more commonplace. I decided to spend a little time looking at their list and gathering a little bit more information on these companies.

The first trait worth noting about today’s unicorns has to do with their actual valuations. While the mean valuation of these companies is nearly ~4B, this average is heavily affected by a few of the upper outliers—companies like Xiaomi ($46B), Uber ($41B) and Palantir ($15B). There are only 8 “decacorns” (companies with a $10B+ valuation); the majority of these companies fall in the $1-2B range. In other works, the collection has a long tail of companies that are “just barely” above the unicorn threshold. Importantly, these valuations are all on paper. For the founders and investors involved, these numbers are largely irrelevant until there is an exit to provide liquidity to these valuations (more on this towards the end of the post.)

Another characteristic worth noting is when these companies were founded. The average company life of these unicorns is 8 years—not all that surprising until you consider the fact that many of these companies have been unicorns for several years before Fortune published this list. In fact, the speed with which some of these companies have reached unicorn status is unparalleled. 7 of the companies (~9%) were founded in the last 2 years and 31 (~40%) were founded in the 6 years since the financial crisis. A mere 12 unicorns (15%), are dot-com survivors (founded in 2001 or earlier).

A final characteristic of the unicorn list worth noting is where they were founded. The chart below shows a story that is not all surprising—namely that the Bay Area is still King when it comes to producing fast-growing tech startups. That being said, the Bay Area’s “share” of unicorns, at 44%, is certainly not what it used to be. China is clearly a major force in the production of unicorns as is NYC and Europe. The surprise from a geographic perspective appears to be Southern California in 4th place—bolstered by the likes of SnapChat, SpaceX and JustFab. While not shown in the chart below, Boston and India are both tied for a close 6th with 3 unicorns each. Interestingly, each of India’s 3 unicorns are in the online retail/commerce space: FlibKart, SnapDeal and InstaCart.

unicorn location

There has been much debate on the drivers behind the growth in the number of unicorns, the macroeconomic implications of more privately held $1B+ companies and the possibility of a growing bubble. Griffith and Primack’s article provides a great overview of these debates and other related issues. One implication that is pretty evident, however, is that the IPO markets need to continue to stay strong in the next few years or venture investors are going to face disappointment. A quick example will help illustrate this point.

Let’s say you’re a VC who recently invested $100M into the latest round of a fast growing startup at a $900M pre thus providing the company with unicorn status based on its $1B post. As a VC, you’re looking for at least a 3x cash-on-cash return on this investment. In order for you to realize that type of return, the company you invested in needs to exit for at least $3B. Very few companies in the F500 can afford an acquisition of that size. Thus, in order to realize that kind of return, you will push the company (and its management team) to go for the IPO. This push for an IPO may be aligned with the founder’s goals but it may also come despite the attractiveness of an acquisition from the founder’s perspective. Even if there is full alignment on exit strategy, the whole thing will unravel if the IPO markets cool and there is no demand for these assets. Thus, the Age of the Unicorn is strongly tied to the strength of the IPO market. In the coming months, I think we’re going to hear a lot more speculation from the venture community on this very topic.


Increasing Conversion along the mCommerce Customer Journey

Increasing sales on a mobile commerce (mCommerce) platform is often seen as synonymous with driving more traffic through mobile channels—whether through the mobile app, tablet or mobile site. Yet there are other ways for mCommerce startups to increase sales besides increasing app downloads. Chief among these methods is increasing conversion in the customer journey to levels that are on par or better than desktop conversion rates or any other benchmark a company is using.

Typically mCommerce platforms have customer journeys roughly similar to the desktop customer journey. Give or take a few steps depending on the product, industry, stored preferences, member vs. guest, etc. These customer journeys almost always (roughly) look something like this:


When scaled up to thousands if not millions of customers all going through this process, the customer journey in aggregate looks like a funnel. In early stages of the journey, the funnel is broad—there are many customers. Yet by the time the journey is at the “Confirmation” stage the funnel has narrowed, and there are very few customers remaining who actually convert into buyers. An example using Airbnb will help illustrate the concept of the funnel. All numbers are completely made up and used simply for illustrative purposes.

Let’s say there are 100 potentials customers who login to Airbnb’s iPhone app on Friday at noon. Of those 100, let’s say 80 proceed from the Login page to actually browsing the listings of sublets in the destination of their choice. Of those 80 who browse the listings, only 30 actually select a sublet that they are interested in. Of those 30 who select a sublet of interest, only 10 make it to the “Review” page where they review their listings and perhaps add any extra features they want. Of those 10, only 5 actually enter in their payment information. And of the 5 who enter their payment information, only 2 click submit and reach the “Confirmation” page. Thus of the original 100 who logged into the app, only 2 actually purchased, resulting in a final conversion rate of 2%.

There is clearly a big opportunity to increase conversion—particularly if Airbnb’s desktop conversion is higher than 2% or if their competition has superior conversion rates. Startups looking to increase conversion in the customer journey can target 2 different methods:

(1) The first method is to simply make it easier for customers by eliminating steps in the customer journey. A great example of this is how Uber has dealt with payments. By taking a photo of your credit card the first time a customer opens the app and then storing that information, they have effectively eliminated the payment step in the customer journey. Fewer steps in the journey, mean less opportunities to fall out and, ultimately, higher conversion rates.

(2) The second method is to simplify painpoints in the customer journey. In other words, increase the conversion rate of steps in the customer journey where customer fallout is particularly high. So if the conversion rate from “Browse Listings” to “Select Product” at Airbnb is currently 37.5% (30%/80%), focus on increasing that step’s conversion rate to 50% or 60%. This particular step in the journey has been mastered by many of the airlines and hotel companies (SPG and United in particular) with their unique mapping features, simplified browsing/sorting capabilities and sharp focus on UX. As can be imagined, increasing conversion early in the customer journey (when the funnel is still wide), should be prioritized as it has the potential to have the biggest impact on final conversion.

M&A Activity of Major Tech Companies

In the venture world, there are typically two ways VCs successfully exit the companies they invest in: (1) via IPO or (2) through acquisition by a larger tech company (think Google, Microsoft, etc.,). Of these two methods, an M&A exit has historically been more common. Nonetheless the literature within the venture community about why large tech firms acquire the specific targets they snap up is sparse. It seems odd that while ‘the acquisition’ is the main goal for most of the venture community, many VCs spend little to no time thinking about investing from the perspective of the firms doing all the acquiring.

This semester, I took Columbia Business School Professor Raul Katz‘s course on Developing Strategies for High Tech firms. In the process, I wrote my final paper on this very subject. The focus of the paper was to understand the recent (last 3 years) M&A activity of four of the largest global tech companies: Apple, Facebook, Google and Microsoft. Specifically the paper analyzed the implications the M&A activity of these four companies (and others like them) has for early stage VCs focused on investing in tech companies.

In building towards a hypothesis around the motivations for M&A activity, I examined the 7 motivational variables displayed in the table below. I focused solely on operational motivators and excluded non-value maximizing motivators such as management hubris or financial synergies like the desire to reduce the weighted average cost of capital (WACC). The rationale behind this focus is that operational synergies are the most relevant and identifiable variables for VCs to focus on as they think about M&A as an exit option. Operational synergies are also: specific, repetitive (allowing for pattern recognition), have predictive power and can be used to build an investment thesis.

The shaded rows represent new variables previously not looked at in the existing literature. I used S&P Capital IQ as well as a variety of analyst reports and news articles (VentureBeat, TechCrunch, etc.,) to populate the data used in the regression model.


The results of the regression analysis are displayed below:

Premium Paid = -$5,500 + $2,950(β1) + $324(β2) + $3,780(β3) + $109(β4) + $2,168(β5) + $4,193(β6) + $1301(β7)


There are several characteristics of the regression worth pointing out. First, the intercept (β0) is negative, which limits the full application of the regression equation. This is likely due to a small sample size and data that is not normally distributed. Because of this negative intercept, we cannot make a direct dollar connection between each M&A motivation variable and the premium paid by the acquiring firm. That being said, we can make some relative observations based on the size of each beta coefficient. Additionally we can make some important observations regarding statistical significance. As seen in the exhibit above the four M&A motivation variables that were statistically significant include: economies of scale, value chain integration, growth in new and existing markets and large network effects. The remaining three variables are not statistically significant according to our model.

The results can be broadly bucketed into B2C variables and B2B variables—although there is certainly some overlap. On the B2C front, unsurprisingly, tech firms like Apple, Facebook, Google and Microsoft place the largest premiums on startups with large network effects. Acquiring companies like Instagram, WhatsApp and Skype allows these firms to essentially acquire a massive customer base with a large customer life-time value. Because of the large network effects, these customers are unlikely to switch to substitutes. Big tech firms can then monetize these acquired customers over a long period of time as well as cross-sell products and services on their existing platforms.

According to our regression output, these big tech firms also place an important (though not nearly as large) premium on B2C companies that allow them to grow in new and existing markets. B2C companies like Snaptu (a mobile platform for feature phones in developing nations acquired for $70 million) and Oculus VR (a virtual reality and gaming device company acquired for $2.3 billion) allow big companies like Facebook to enter new markets—whether geographic, customer-segment specific or newly emerging industries.

When it comes to B2B acquisitions, the M&A model provides evidence that large tech companies place a heavy premium on value chain integration and economies of scale—both means to maintain a competitive advantage. Apple’s acquisition of semiconductor company Anobit Technologies for $400 million is a great example of value chain integration. Apple has slowly been moving away from hard drives to flash memory beginning with the iPod and most recently its MacBook Air. Flash memory allows Apple’s products to be thinner and run on less power. Acquiring Anobit allowed the firm to acquire the hardware component needed to complete value chain integration and transition fully from hard drives to flash memory chips.

Though not as important as value chain integration from a relative perspective, large tech companies also consider economies of scale when acquiring B2B companies. Within that realm, companies that provide a service or toolkit that enable a bigger tech company to take advantage of scale economies are also often worth acquiring. Microsoft’s acquisition of Pando, a file-sharing technology that works peer-to-peer like bit-torrent, is a great example of this. Pando’s technology can be applied to Microsoft products like Xbox and Windows Phone App Store, to reduce costs in these divisions and enable Microsoft to take advantage of its economies of scale.

For full analysis of the results of this study as well as a discussion of the implications for VCs, please email the author.

Google Glass Investment Thesis

About a year ago, I wrote a post about Google Glass and the possibility that Glass (and other wearable tech hardware platforms) would eventually give rise to the next generation of startups. It seems like the jury is still out on whether Glass will be the next iPhone, but it is certainly an area worth exploring further. This semester while interning with DFJ Gotham Ventures, I was charged with building out an investment thesis around the Glass ecosystem.

As a result, I did a deep dive analysis on the eyeware itself, the wearable tech industry more broadly speaking and the opportunities and challenges that currently exist for venture investors. In the process, I went as granular as focusing on specific industries and identifying companies within those industries. I was fortunate to speak with a wide range of VCs, entrepreneurs and industry experts – all of whom greatly contributed to the end product. Special thanks to Zak and Lucas on the Gotham team and Professor R.A. Farrokhnia for their guidance. Enjoy and feel free to drop me a line if you have any comments or suggestions. 

CareCloud: A Good Investment?

This past week, while applying for the InSITE Fellows program, I had to prepare a quick analysis of CareCloud (a healthcare IT company) based on a venture beat article that can be found here. Now that the application cycle is over, I thought I’d share my response and some general thoughts on the company. Admittedly, I know very little about healthcare companies but the industry is intriguing and very much ripe for disruption. Here is a first attempt at evaluating the company from an investment perspective.

In assessing CareCloud as a potential investment, I examined three core areas: the market, the technology and the team. While the technology is very sound and the team is promising, I would likely not invest in the company due to several significant issues in the electronic medical record (EMR) market.

The Technology

CareCloud’s medical practice management software is a great solution to a clear pain point in the market—namely that legacy vendor’s provide systems that are too bulky, inefficient and costly. Built on a nimble Ruby on Rails platform, CareCloud’s elegant design and user-friendly interface has been well received by physicians and other users. The product is completely cloud based making it easy for physicians to update and stay on top of complex regulations and compliance mandates. It also focuses on providing users with the flexibility to pick and choose components of the software rather than being forced to adopt an entire platform and abandon existing software. All these features result in a lowered cost to the physician and a better way to manage their practices.

The Team

The management team at CareCloud is also very strong—comprised of industry veterans and individuals who are experts at the given function they lead. This of course starts at the top with Albert Santalo—the founder and CEO of the company. Santalo has spent the last 12 years working in healthcare. He is a successful serial entrepreneur having co-founded and grown Avisena into one of the largest providers of revenue cycle management software and services for physician practices in the world. The rest of the team is likewise very strong and experienced. This is a team that has experienced a lot of success prior to starting CareCloud and in the first 4 years of the company have continued to be successful.

The Market

The biggest challenge with CareCloud is the market. At a high level, things look pretty good. Healthcare is the largest sector in the U.S. economy and set to grow from a $2 trillion dollar market to a $4 trillion dollar market in the next 10 years. In particular there are mounting cost pressures stemming from an aging U.S. population that will grow from 12% who are 65+ to 17% who are 65+ in the next 10 years. Those over the age of 65 tend to spend 4X as much on healthcare as the rest of the population. Against this backdrop, the EMR market is estimated to be a $6-10 billion dollar market—which would appear to be large enough to invest in.

However, the big problems with the market are the regulatory environment in the industry and the plethora of competition CareCloud faces. Regulations and mandates imposed by the federal government could easily destroy the industry and put CareCloud out of business—particularly since so much data is stored in the cloud where it is more susceptible to compromise. In terms of the competition, legacy vendors like Allscripts, Epic, GE and Siemens already control at least 75% of the market and almost all large hospitals use them because of the subsidies from the government—CareCloud is unlikely to take any of this market share away. The remaining niche of 25% or $1.5-2.5 billion of the original market is comprised of pysichians operating in small clinics with over 300 electronic vendors, including well established companies like Practice Fusion and AthenaHealth, competing for their business. Even if we assumed that CareCloud could capture 25% of that market (which it almost assuredly won’t), that would only be a total share of $375-$625m, which is too small to invest in especially since the company has already taken in $54 million in total venture funding and we are only at the Series B level. There is a lot of pressure to have a very high exit in situations like this. Because of these challenges in the market, I would be very hesitant to invest in CareCloud.

Additional Questions

Some additional questions I have that would be useful to know when investing include:

  • What was the pre-money valuation before the Series B round of financing?
  • What do the actual revenue and customer acquisition numbers look like?
  • What are the terms of the deal—what sort of exit size do we need to make a good return?


Market Sizing: How big is online video advertising?

Television advertising still dominates the scene when it comes to advertising revenue. Yet in the last 5 years, Internet advertising has nearly doubled proving that there is little doubt that advertising is increasingly going online. Within Internet advertising, the video advertising component, while still relatively small, has been growing steadily resulting in a tremendous opportunity for innovative entrepreneurs disrupting this emerging market.

But exactly how big is the online video advertising market? Applying a bottoms-up approach yields the following results:

Total # of Video Ad Views = U.S. Pop. X Average # of Video Ads viewed per person
Total # of Unique Video Ad Views = 315mm X *840
Total # of Unique Video Ad Views = 265B

Average Price per View =  CPM / 1000
Average Price per View =  $15 / 1000
Average Price per View = .015

Central Equation
Video Ad Market Size = Total # of Unique Video Ad Views X Average $ per View
Video Ad Market Size = 265,000,000,000 X .015
Video Ad Market Size = $4B

U.S. Market Size = ~$4B
**Global Market Size = ~$16B

*Based on ComScore 2012 U.S. data, market sizing estimates
**Applied a multiplier of 4 to get the global market size.

Doing a quick search through the Wall Street Journal – it appears that they agree with this market size of $4B for the U.S. market.

Picture 1

It is important to note two trends in the video ad market that matter and will significantly impact the size of the market.

1)   Contraction Force: The average price per video ad is decreasing. In 2011 at top tier sites ads were in the $17-$25 range. In 2012 that range fell to $15-$20. The WSJ argued that this price will only decrease further from here.

2)   Expansion Force: Video advertising may only be a $4B market as of 2012, but it is an increasing segment of the overall $42.5B digital media market—a market which is growing in size itself.

The net effect of these two forces is hard to determine as they act in opposite directions, but the overall affect will likely be an increase in the market size in the next 5 years, especially as the internet plays an increasingly important role vis a vis the television.

Google Glass

Since the beginning of the computing industry, it has been the case that hardware platforms produce software innovation. A single innovation in hardware can provide the base for a multitude of software applications. In the process, thousands of companies are created, millions of customers are acquired and billions of dollars in revenues are generated.

Hardware innovation in the 1970s and 1980s by IBM around the personal computer led to software innovation by now Fortune 500 companies like Microsoft, Oracle, Adobe, Symantec and SAP. In the mid 2000s, hardware innovation by Apple on the iPhone led to many of today’s rising stars: Twitter, Instagram, Flipboard and Waze are all built on mobile platforms.


It is still too early to tell whether Google Glass will be the next ubiquitously used hardware platform spurring software innovation. It looks like the product development teams have a ways to go to iron out some of the kinks and lower production costs to get the price down to what consumers would be willing to pay In fact, last week Forrester Report published survey results showing that only 12% or approximately 21.6 million U.S. online consumer would use Google Glass on an everyday basis.

Yet, if we looked back in time, I don’t think the early adoption numbers for the personal computer or iPhone would be all that different, especially pre-launch. Nonetheless here we are in 2013 and I can count on one hand how many people I know who don’t have a smartphone or a personal computer.

If Glass is able to capture broad consumer appeal, you can count on another big wave of software innovation. Already, Google has released parts of its developer API and the applications are limitless—everything from education to health to advertising. Smart entrepreneurs and VCs will already start thinking about software applications Glass could enable. It’s a great time for innovation.