Conventional wisdom suggests that the most important metrics for a startup – such as unit economics, cost of acquisition, lifetime value, churn rates – typically get better with time. I hear this asserted frequently by entrepreneurs who confidently project their businesses with increasingly improving metrics as they scale into the future.

The topic of scaling startups is one that I enjoy thinking, living and writing about (most recently, Scaling the Chasm).  In the class I teach at Harvard Business School, the first module of the course is dedicated to examining startups when they are pre-product market and struggling to find product-market fit while the second module is dedicated to what the challenges of scale post product-market fit.

One of the themes I explore in the class is the tough reality that many metrics can actually get worse over time for a startup. Take growth rate as a simple one.  The law of large numbers suggests it is easier to double in size when you are doing $1 million in revenue as compared to when you are doing $10 million, never mind $100 million. Thus, more mature companies naturally have slower growth rates than younger ones. Here are a few other key metrics that are hard to scale:

Customer acquisition. Most of the marketing techniques that look good in the early days cannot be scaled 10x, never mind 100x. For example, PR doesn’t scale. It seems like such an amazingly efficient source of customers, yet ask any marketing communications or PR professional to acquire 10x the number of customers that they did last year and they’ll look at you as if you have 10 heads.  Search engine marketing (SEM) and app store optimization (ASO) exploit arbitrage opportunities in keywords and placement, but those arbitrage opportunities are effective only for a moment in time and for a certain level of spend. When you spend more, you risk losing that edge. Similarly, if you try to scale email too much, you quickly risk fatiguing your list and spending money acquiring less valuable customers when compared to your core segment.

Customer acquisition is like drilling for oil.  A particularly successful tactic allows you to find a gusher, which you can take advantage of for a while, but eventually the well dries out and you have to find another well.  One of my CEOs pointed out to me at a board meeting last week:

“Our average customer acquisition cost (CAC) is irrelevant for the future. It is the marginal CAC that matters the most – that is, what does it cost to acquire the next incremental set of customers?”

Word of mouth, referrals, virality – these are all amazingly powerful customer acquisition techniques that hold the promise of scale, but they require you to have a great product, not just a great marketing plan, and a product that is elegantly design for virality.

Churn rates are another metric that can get harder with scale. When you expand your market, the next market segment may not be as perfect a “bullseye” market fit as the early segments and early customers. Even as the product matures, the customers that are recently acquired that represent newer segments can be less dedicated. A new battle for product-market fit must be waged – something that never ends – particularly as you expand into new customer segments and verticals.

Monetization can get harder with scale as well. Monetizing the initial user base – who is your most dedicated and often organically acquired – is easier than the more marginal users who you are spending incrementally more money to acquire from indirect channels that may not produce as loyal customers as the initial channels. Even a company as amazing and well run as TripAdvisor (who I once claimed had a better business model than anyone outside the mob) has seen average revenue per user (ARPU) decline over the years, from $14.10 in 2009 to $11.80 in 2012. During that period of time, their monthly uniques grew over 2.5x, from 25m to 65m. More recently, with the shift to mobile and the growth in emerging markets, this ARPU decline has become even more dramatic as mobile visitors and international visitors monetize at a lower rate than their earlier segments of online, US visitors.

The trick to keeping your metrics steady during growth, if not improving over time, is to find a series of techniques and keep improving on them as you go.  That’s why so many great entrepreneurs obsess over the details of landing page wording, button placement and color on a page, creative copy, etc. They know that being able to scale 10x from where they are has no silver bullet, but rather a series of tactics that need to be executed against. And they recognize that often times, as you are scaling 10x and 100x, your metrics may erode on the margin.

If your core metrics only erode 10-20% while you are scaling fast, like TripAdvisor’s ARPU, you are in pretty good shape. If they erode 50-75%, you are in deep trouble. Just remember, don’t project to investors that every metric is going to get better over time. Otherwise, you will be dismissed as naïve, out of touch, overly optimistic, insane or all of the above. Never a good combination.

One of my favorite business books of all time is Crossing the Chasm by Geoffrey Moore. It is a classic. My boss and mentor from Open Market, Gary Eichhorn, made the entire management team read it in the 1990s to hammer home its important lessons as we stumbled through the chasm on our way to scaling from zero to nearly $100 million in revenue in a few years.

I have been thinking about the challenges of crossing the chasm – that is, taking a cutting-edge product and selling it successfully to the mainstream, not just early adopters who are more tolerant of less complete solutions – and the challenges of scaling in general as many of my portfolio companies are dealing with these issues.  A few years ago, I wrote a few case studies on how some big players achieved scale – like Akamai, TripAdvisor and athenahealth - to help crystalize my thinking on the topic, but I thought it might be appropriate to write a more general blog post on the challenges that companies face at different points in the scaling process.  

Scaling up is becoming a hot topic lately, from non-profit Endeavor and the World Economic Forum focusing attention on the importance of scaling up companies in the Global Scale Up Declaration to The Economist pointing out that Israel's miraculous start up economy is seeking to transition from "Start Up" to "Scale Up".  I coined 2014 as the Year of Results, where the lofty promises would finally translate into real, tangible outcomes (and it was for us).  2015 may be the year of the Scale Up.

Dealing with scale up challenges is particularly important to me because of our firm's investment strategy.  We pride ourselves on being lifecycle investors, which means we invest very early on (typically at the seed or Series A stage) and then stick with a company through exit.  Some VCs prefer investing at the earliest stages and then cycle off the board of directors.  Others prefer to come in at the later stages, post product-market fit, and not have to deal with the risk and roller coaster of the early stages.  Gluttons for punishment, we prefer to start early, take the risk and stick around through the end.  As a result, I get to work with companies both during the search for product market fit and after they hit product market fit, and race headlong into the chasm.

For quick context, I sit on the board of eleven Flybridge portfolio companies and am an observer on two.  Each of us typically makes one or two new investments per year (I made one new investment in 2014).  With that rhythm, if things are going according to plan, I should have a spread of companies across a wide range of scaling stages, as measured by annual revenue.

If I plot my portfolio companies across a few broad revenue buckets, looking at 2014 figures, below is a chart.  They spread fairly evenly, although slightly more in the earlier stages as few companies achieve the kind of success that $> 50m in revenue entails.

At each stage, there are different problems.  Here are the patterns of issues I typically see at each stage – maybe you will recognize a few of them in your own companies:

$0-1 million

• People:  founder-run and trying to recruit amazing technical talent (the product development team is a huge priority at this stage) and integrate a few senior managers to help prepare the company for scale – which leads to cultural clashes and communication challenges. Also, the founders' roles' start to evolve (see:  "The Other Founder") as functional areas and responsibilities become more precisely defined.
• Product:  the product is buggy and incomplete – really more of a feature than a complete product – but it is past MVP.  Customers are using it and deriving value and now the challenge is how to complete it – fast, before running out of money. 
• Business Model:  running a lot of experiments – pricing, packaging, value proposition. Always testing and trying to run fast tests to put up some strong metrics before running out of money (did I mention we're running out of money?).
• Financing:  holy crap – we are running out of money in 6 months! Have we acheived enough value-creating milestones to raise an up round? who will lead it (insiders vs. outsiders) and on what terms?

$1-10 million

• People:  things are going well – everyone gets excited when the cash register rings after a few big sales. The first version of the go to market team is hired (i.e., first sales person, first marketing professional).  The founders are getting restless because they have been diluted, have less responsibility and realize that the company isn't going to reach $1 billion in 3 years. Also, that first VP you hired was great from 0-1 and good from 1-10, but you're afraid she can't scale to the next level.
• Product:  the product is better – A LOT better – but now we have technical debt thanks to our success. Anyone up for a rewrite? How much do we invest in a rearchitecture versus adding new features.
• Business Model:  Time to get some channel and business partners on board, because adding revenue by adding sales and marketing dollars is going to be expensive – no matter what the early LTV vs. CAC data shows.  The focus now is building a repeatable, scalable sales machine. 
• Financing:  The "hopes and dreams" financing stage is over. Nothing ruins a good story like numbers and now we have numbers so we better have them look good enough to support a strong expansion round. And why does it look so easy to raise $20m on $100m pre for companies at an earlier stage than I am every time I read TechCrunch, as my board reminds me every month (and can I stop having monthly board meetings already)? Do we take a little venture debt to get us to give us some cushion as we progress to the next valuation inflection point.

$10-50 million

• People:  The functional management team is running out of steam – do we need to roll up a few things and perhaps hire a COO? The board has too many investors on it (how did that happen?!) – can we add an outside director of two?  Most critically, is the CEO scaling or is time to replace them as well (ideally not).
• Product:  Now that we have a robust product and paid down our technical debt, we seem to have lost our ability to run experiments – how do we maintain that mission-critical quality for all these customers while remaining as nimble as we were when we were a startup? Also, customers are pushing us to provide a solution, not just a product, and so suddenly we need services and partners to round out our offering.
• Business Model:  Now that we are at a reasonable scale, why are our gross margins so low and what can we do to fix it?  Is it time to be profitable or should we continue to prioritize growth and invest ahead of revenue?  Should we pursue adjacent M&A or tuck in acquisitions to expand our market footprint?
• Financing:  Is our market big enough to support another round (which puts the exit bar even higher)? Is it time to consider an exit? Would an IPO be possible in the future if we can continue growing 50-100% per year?

$50-100 million

• People:  Should we have a business unit structure or retain the functional structure?  Do we have an IPO management team in place? Is the CEO still a single point of failure or can she delegate effectively in the event of a road show? Board committees start to really matter.
• Product:  With a robust product and complimentary solution in place, let's open this sucker up – let's build as many APIs as we can and evolve this thing into a platform. Time to enlist some 3rd party developers!
• Business Model:  If we're not profitable at this point, we better be growing > 50%/year. How profitable should we be? Are we seeing erosion in our LTV vs. CAC math or is it continuing to scale nicely? Where should our first international office be and how much should we invest?
• Financing:  Do we have the metrics to support a growth or mezzanine round?  Let's expand our debt capacity and put in place a working capital line and receivables facility.

>$100 million

• People:  The A team is in place at the top and now we have to focus on solidifying the next level and providing them with great training, career paths, growth and additional stock options (in the form of refresh grants) as they are all getting approached by pesky recruiters.
• Product:  We are in the midst of a feature war with competitors – how much do we invest in new product innovation versus continue to harden and prepare for scale. How can the product be changed to lower the cost of delivery for us and cost of ownership for our customers?
• Business Model:  Services revenue and services partners become more important. Investing more heavily in international.
• Financing:  Why haven't you filed the S-1 already?!

One of the things I've learned from my two decades in startup land is that it doesn't get any easier as you scale – the problems just evolve, but there are still problems.  And opportunities.  But I guess that is what makes the startup game so fun.

http://www.flybridge.com/newyear2014/

FlyBridge Infographic 2014

One of my favorite childhood books was SE Hinton's The Outsiders.  For whatever reason, I always related to this tough group of teenagers who felt like societal outcasts just because they were born on the wrong side of town.

I was reminded of the book the other day when attending the Unconference.  The Unconference is a Boston-based technology conference put on by the MassTLC that has no agenda.  Instead, the agenda is created dynamically the day of the conference by the attendees.  Sessions are created on the fly, led by whoever wants to lead a session.

At many conferences, there is a sense of "insiders" and "outsiders".  Insiders have attended the conference in past years, speak on panels, walk around with great confidence and poise because they "know everyone" and are sought after during the course of the conference.  They are the popular kids at the conference.  Outsiders come to the conference knowing no one else, are often lingering awkwardly on the periphery during networking time and struggle to gracefully secure air time with the very people they came to the conference to meet.

The Uncoference tries to break this paradigm with a more dynamic sesssion format alongside structured one on one sessions between well-known insiders with eager outsiders.  I try to sign up for these one on ones every year, which are essentially an extension of the offce hours concept that many VCs (including Flybridge) have been championing as a way to provide more accessibility and transparency between insiders and outsiders.  A few years ago, I was matched with a very tall, eager entrepreneur who shared with me his passion for private coaches for sports.  His name was Jordan Fliegel and, although his 6 foot 7 inches frame stood out amongst the crowd of nerds and middle aged investors, he was an anonymous outsider that day.

Since then, Jordan's company, CoachUp, has secured venture capital funding from a local big name firm (General Catalyst) and grown into a local success story.  In a few short years, Jordan has become the definition of an insider – he's now one of the best known figures at any conference and has even started an angel fund, Bridge Boys, with one of his childhood friends.

A few years ago, I met a student during office hours at HBS, who was embarking on a new company.  He was new to Boston, having grown up in Iowa, attended Brown and then worked in Chicago.  I was with him at a lunch at a conference and, sensing his discomfort as an outsider, started to introduce him around – endorsing him with the insiders around me, like reporter Scott Kirsner and serial entrepreneur Walt Doyle.  Before long, Brent Grinna (CEO/founder of EverTrue), blossomed into one of the local innovation community's strongest leaders and insiders, sought after as a mentor by others for his success with the company (backed by big time, insider firm Bain Capital) and within the community.  Brent reminded me of this story with this recent tweet.

Francis Ford Coppola turned SE Hinton's book into a move, released in 1983.  The movie starred a slew of young Hollywood outsiders – a remarkable number of whom became the ultimate Hollywood insiders, including Tom Cruise, Rob Lowe, Patrick Swayze, Emilio Estevez and Ralph Macchio.  That's the magic of a dyanmic, entrepreneurial environment – today's outsiders can become tomorrow's insiders.  That's why immigrants, students and other outsiders are such valuable members of the entrepreneurial ecosystem – and why we should be doing everything we can to encourage and support them.

Becoming an entrepreneur is illogical.  If you were to calculate the expected value (i.e., the probability-weighted average of all possible outcomes) of being an entrepreneur as compared to living the safe life of a traditional executive, it wouldn't even be close.  On a purely rational, probablistic basis, the math for entrepreneurship doesn't add up.

Despite this, entrepreneurship is on the rise.  For those of us who live in that world, we know that entrepreneurship is about passion more than rational thinking.  It inspires those who are crazy enough to believe that they can change beat the odds and succeed in changing the world, or at least their little corner of it.

That's why I love Linda Rottenberg's new book, Crazy is a Compliment.  First, I should admit a bias.  I deeply admire Linda and her non-profit organization dedicated to global entrepreneurship, Endeavor.  We first met in college when we volunteered together in an inner-city high school in Roxbury.  Although I don't get to see her as often as I would like, I've had such respect for Endeavor that I decided to donate the proceeds from my book to it.  Thus, I was positively inclined when I cracked open the binder.

But I still loved it.  It gives entrepreneurs a roadmap, plenty of fun war stories and (in typical Linda fashion) a very human angle.  For example, perhaps the most powerful part of the book is when she shares how her husband's bone cancer diagnosis forced her to be more vulnerable at work and let go of her perfectionist zeal.  She even dedicates a section of the book – "Go Home" – to addressing the importance of trying to "Go Big AND Go Home", i.e., pursue an ambitious career with passion AND at the same time live a balanced life (charmingly, she writes this section directly to her daughters – as if the reader is a bystander in the dialog).

Here were a few of my other favorite sections/lessons:

• Esta chica esta loca.  As an entrepreneur, there are many times when you need to do crazy things.  In fact, if you're not doing a few things that conventional wisdow would refer to as crazy, you're not thinking big enough.
• Fire your mother-in-law.  Sometimes, when you are growing and evolving the business, you have the courage to kill "sacred cows" (pun intended – but not all related to my mother-in-law, who is lovely)…
• Flawsome.  Effective leaders are very human – flawed AND awesome at the same time.
• Upside down mentoring.  I've written about Reverse Mentors and Linda's concept is similar – senior people should seek out junior ones to learn from them, not just mentor them.

The book is chock-full of funny, engaging stories and case studies as well – some familiar, but most unfamiliar and not your typical entrepreneur yarns (e.g., I never knew the story behind Maidenform Brands).  

If you're looking for a good read this fall, I highly recommend it.

Last week's successful IPO of e-commerce giant Wayfair (market cap $3B) and this week's impending IPO of Hubspot (if it prices in the range, market cap $600m) has many in the Boston tech community celebrating.  They are not alone.  2013 was the best year for IPOs since the tech bubble of the 90s and 2014 looks to wrap up even stronger this quarter.

I was an executive at a hot IPO company during the last big tech boom (NASDAQ: OMKT) and, like many who lived through that cycle, I gleaned a few important lessons. After the IPO party is over (and we had a great IPO party) and the euphoria wears off, you actually have to run a company and live up to the big expectations that you have just publicly set. Your venture capital investors and many early employees head for the door and you are left holding the bag. Here are a few things I learned after my 16 quarters as an executive post-IPO:

1) The Mission Continues.  On average, it takes 8-10 years for a start-up to go public. After a lot of ups and downs, twists and turns, it feels like a massive victory (aka "Mission Accomplished", as George Bush famously declared regarding Iraq in 2003). By that time, your team will be exhausted. Naturally, a huge let-down ensues, particularly after the first hiccup – and there will always be a hiccup:  a missed quarter, a departing executive or major customer, something. Recruiters and venture capitalists salivate over picking off executives at recently public companies with the siren song of "don't you want to do that again?". If the stock price flags, all the better. Executive teams need to focus their staff post-IPO on a new mission. Be clear that the end goal was never an IPO – that is merely a financing event, a means to an end.  The end goal is industry transformation, customer satisfaction, etc. Find that new mission – and make sure you get your team behind it. Give them more stock options, more incentives and more inspiration to go at it hard for another 8-10 years.

2) Don't Let The Turkeys Get You Down.  When Ronald Reagan left office, he provided a final note with words of wisdom for incoming president Geroge HW Bush:  "Don't let the turkeys get you down." And, believe me, when you're a newly public company executive, there are a lot of turkeys out there. Not only is there a risk that your company mood ebbs and flows with the daily stock price (your stock is down 10% thanks to Vladimir Putin – deal with it), but you are suddenly publicly castigated for every move. Investing an extra $1m in R&D in order to accelerate your game-changing new product? Pre-IPO, your board would have applauded. Post-IPO, you will get hammered. And if any insiders dare to divest of their shares, even in programmed trading batches, it will kill you. I remember delivering a (compelling, I thought) company presentation at a Goldman Sachs conference and, afterwards, the first question was, "Mr Bussgang. If your company is so great and the future so bright, why is your CEO selling stock?" Many Wall Street analysts are total turkeys. They build their reputation by tearing yours down. Be tenacious and true to your strategy and prepare your team to ignore the noise. Gail Goodman is one of the most tenacious, skilled public company CEOs I know. Many analysts hammered Constant Contact shortly after the IPO, complaining about churn rates and missing the social marketing window. The stock waxed and waned and Gail just kept executing. A few years later, the stock has nearly tripled these last two years and the market cap is near $1 billion. Watch her public presentations over the years and you'll see Gail kept telling the same story – making small improvements every quarter and showing the turkeys the value of the business. Care.com CEO Sheila Marcelo is in the midst of a similar situation. Her stock is down 3x from its post-IPO high with a market cap of a paltry $250 million. I'm rooting for her to prove the turkeys wrong, just like Gail did, but it requires a tremendous amount of patience and tenacity.

3) Wall Street Is Annoying…But Sometimes Right.  OK, I know this sounds like a contradiction to point 2, but it's the unfortunate truth. Wall Street analysts and hedge fund managers can be annoying, short-term minded turkeys, but they're smart and often right. Carl Ichan's recent battle with eBay/PayPal is a great example. The trick is to ignore the noise, but don't walk around with an arrogant attitude that you are always right and the critics are always wrong because they just "don't get it." Make sure you listen carefully to the smart Wall Street analysts and incorporate their feedback where appropriate. Make sure you have board members who make you a little uncomfortable because they hold you accountable. The cozy days of the VC-led board where everyone is trying to blow smoke and get you to help them with their next fund is over. Wall Street doesn't care about a long-term relationship. They demand results. And sometimes their cool, analytical distance can be very valuable. It can be painful and distracting, but sometimes very enlightening and helpful.

Ben Horowitz's book, the Hard Thing About Hard Things, is one of my favorite business books of the year. The best parts, in my opinion, describe Ben's struggles as a public company CEO trying to refocus and motivate his team, make hard pivots and hard decisions, while dealing with internal and external challenges. His case study is precious, because in my experience it plays out again and again and Ben's candor and authenticity allow us to peer into the raw emotions and feelings of riding through those ups and downs. Executives of these newly public companies should take heed. Linger on the champagne for a moment, but then quickly clean up and get everyone focused on what's next.

After the IPO bell has rung is when the hard work really begins.

Every September, I give a presentation at Harvard's i-Lab to provide a guide to the Boston start-up scene.  Students from around the world descend on Boston every fall to attend the amazing universities, but often fail to venture outside the ivory tower and explore the local start-up scene.  This guide is an attempt to inspire students to do just that.  This year, I added a number of updates and resources.  Enjoy!

According to Webster’s Dictionary, the word “programmatic” was first used in the late 19^th century.  Despite its long tenure in our lexicon, the word was an obscure one until recently.  If you aren’t familiar with it yet, if it hasn’t permeated your corner of the business universe, just wait.  Programmatic thinking might soon join the pantheon of 21^st century buzz words, alongside big data and cloud.

The current industry being transformed by programmatic thinking is the advertising industry.  A few years ago, software entrepreneurs began to realize that as advertising started to go digital, there was an opportunity to apply algorithms to media buying decisions.  Instead of having a 27 year old neophyte designing your media plan over a three martini lunch, have the world’s most powerful machines do it for you “auto-magically”, leveraging all your best data – and streams of other’s best data – to inform the decisions.  And the best part?  The machines learn how to make better and better decisions with every purchase.

The speed with which programmatic advertising has taken over the industry has been breath-taking.  From nowhere a few years ago, $12 billion of advertising was purchased programmatically in 2013 and the forecast for 2017 is $33 billion (Magna Global report).  86% of advertising executives and 76% of brand marketers are using programmatic techniques to buy ads and 90% of them indicate they intend to increase their usage by half in the next 6 months (AOL survey).  Companies like AppNexus, DataXu (a Flybridge portfolio company), MediaMath, RocketFuel and Turn are among the leaders in the field.

The next industry to be transformed by programmatic thinking is financial services.  Decisions to underwrite loans have historically been based on a few simple data points such as the lender’s zip code, credit score and job history.  With the application of big data techniques and sophisticated machine learning algorithms, underwriting decisions are becoming programmatic.  For example, Flybridge portfolio company ZestFinance evaluates thousands of data points in credit applications (even trivial ones, such as whether the applicant uses capitalization properly) to make loan underwriting decisions programmatically.  Like other programmatic-based businesses, ZestFinance sees a powerful network effect:   the more data they inhale and the more decisions they make, the smarter their decisioning algorithms become.

What other industries might see programmatic thinking ripple through?  Once I put the programmatic lenses on, I can see dozens of industries being affected.  Just think about all the decisions consumers and businesses make, and whether programmatic thinking could automate and enhance those decisions.  For example:

• Navigation decisions:  my navigation behavior follows clear patterns, as does that of millions of others.  Navigation software in cars and phones will soon become more programmatic in anticipating where I might be going and the best routes to get there based on real-time data and experience.
• Hiring decisions:  evaluate thousands of data points to evaluate the best candidates and then watch their performance and make better decisions next time.
• Security decisions:  evaluate thousands of possible threats and patterns, watch the outcomes, and design algorithms that learn from these experiences to reduce acts of fraud and terrorism.  
• Investment decisions:  One of our portfolio companies, MatterMark, evaluates thousands of data points to determine private company performance, and then seeks to tune those algorithms for more and more accurate predictive investment decisions.  Today, their service is being used by hundreds of investment firms.

Some might object that all this automation and machine learning designed to replace human judgment is going to be bad for society – making humans less relevant and eliminating jobs.  But in fact, many researchers believe the advent of machine learning will generate new kinds of jobs – where a hybrid of automation and common sense is applied.  MIT's David Autor presented a paper a few weeks ago that argued: 

Many of the middle-skill jobs that persist in the future will combine routine technical tasks with the set of non-routine tasks in which workers hold comparative advantage — interpersonal interaction, flexibility, adaptability and problem-solving.”

So don't be afraid to put those programmtic glasses on.  I think they're pretty rose-colored.

In 1998, Yom Kippur fell on September 30th. For most of the Jewish community, the date of the most important holiday of the year was no different than in other years. For me and my Jewish CEO boss, though, as officers of a public software company, September 30 was a tough day to be out of the office, sitting in synagogue atoning for a year full of sins. It was the last day of the third quarter of the year and we had more deals we needed to close to finish the quarter strong and report numbers to Wall Street that justified our high-flying profile as a recently public Internet commerce software company. By sundown September 29th, when we left the office for the onset of the holiday's traditions and presumably focused on higher order matters, we had not yet made the quarter. Going offline without knowing our fate resulted in one of the most miserable 24 hours in synagogue I can remember (and I am somone who usually enjoys being in synagogue!).

When my CEO and I got back online after sundown September 30th, it became evident that the final handful of deals that we needed to close to make the quarter had slipped out. A few weeks later, we "pre-announced" that we were going to miss the quarter – one of the worst speeches I ever remember being a part of.  Our stock naturally plummeted.

We were victims of a lot of problems, many of our own doing, and I can hardly blame Yom Kippur and the holiday's inopportune timing on our missing the quarter.  But many years later, I began to appreciate that one of our core flaws was our business model.

We priced our enterprise software in the form of a perpetual license.  As a result, the full revenue for each deal was recognized in that quarter as soon as the software was shipped.  This allowed our revenue to skyrocket from $1.8 million to $22.5 million in one year, the year we went public at a billion dollar valuation (ok, it was 1996; everyone went public in 1996 with a billion dollar valuation), and then $61 million the following year.  But the downside to our business model was that we did not have hardly any recurring revenue.  

I later came to realize that recurring revenue is magic.

Since my harrowing experience, I have become a zealot about recurring revenue.  When I discuss business models with entrepreneurs and investors, there is a varying appreciation for why recurring revenue is so special.  Recurring revenue business models are not a little bit better than non-recurring models.  They are 10x better.  At Flybridge, we have added "business model", with a particularly weighting towards recurring models with high gross margins, as one of the important evaluation criteria when we make investment decisions alongside market and team, which are the two canonical criteria for all venture capital firms. 

Before explaining why they are so magical, let me define a few types of recurring revenue models.  Many jump to the assumption that SaaS (software as a service) is the only recurring revenue model, but there are actually a few you can choose from when designing your business model:

1. Consumable - the classic recurring revenue business was invented by Gillette:  get cheap razors in the hands of shaving consumers and then perpetually sell them expensive razor blades.  Keurig has a similar beautiful model with its coffee machines – keep selling those consumable coffee containers and your business never loses its value.  3D printers, with their consumable resins, have a similar business model.
2. Subscription – this is when you have a subscription contract for a period of time, typically annualy, and charge yoru customers for the service or content pro ratably over the course of the period.  Magazine subscriptions and software subscriptions (often often called SaaS) fall into this category.  SalesForce.com basically invented this model for software companies.  Your cell phone provider, Netflix and Hulu are other examples of successful subscription revenue model businesses.
3. Transaction – this is where you charge for transactions that occur over and over again.  The credit card companies and other high-frequency payments-based businesses, such as PayPal or Stripe, are examples of this kind of recurring model.  Uber is another nice example of this since securing transportation tends to be a recurring transaction for many professionals.
4. Rental – finally, when you borrow an asset, such as an apartment or a car, you are signing up for a recurring charge so long as you continue to borrow that asset.  This creates a recurring model as well.  Data storage companies have this model as do many cloud services, such as Amazon's AWS.  Amazon is renting you their assets – powerful computers and endless data storage.  Amazon also has software and analytics that you are subscribing and so have a doubly powerful recurring model.

Here's why recurring revenue is so magical:

1. Predictability.  When you have a recurring revenue business model, you rarely miss your monthly or quarterly numbers by more than 10-20%.  Your forecasting process is much more accurate.  At the beginning of the quarter, you start with a base to grow from rather than begin at zero.  In a SaaS or subscription software business, you can predict your churn rate and new business closings to determine your growth rate.  The management team and the investors are thus rarely surprised by major fluctuations in your results.  As discussed below, this predictability has many downstream benefits.
2. Visibility.  Because of the nature of recurring revenue models, you have clear visibility into what is coming in the next few quarters.  You know where you stand well in advance.  In a recurring revenue model, if you take the last day of the quarter off, you will not tank the company because you have so much visibility into your business, you are rarely surprised about what happens on that last day.  For example, by mid-year, two of our portfolio companies with transaction-based recurring revenue models, Bluetarp and Cartera, have already confidently predicted they are going to meet or exceed their plan for the year and are working on what they can do to impact 2015.  If they are off, they will know it well in advance of any of our portfolio companies that are non-recurring in nature.
3. Expense management.  Predictability and visibility means you can manage your expenses more precisely relative to your revenue.  One of the hard things about lumpy revenue models is that until literally midnight on the last day of the quarter, you don't know how you did.  Which means it is hard to ramp up or down expenses smoothly to match revenues.  Ramping expenses up and down is a sticky process because it usually involves people and there are many friction points, delays and costs as well as externalities (such as morale) when you try to rapidly ramp down expenses in a quarter as a result of lower-than-anticipated revenue. 
4. Valuation.  Because of the predictability and visibility factors, valuation multiples are radically different for recurring revenue businesses than any other revenue model.  Terry Kawaja did a wonderful analysis of advertising technology company valuations and the positive impact on multiples that exist for SaaS and programmatic companies (such as our portfolio companies tracx and DataXu, respectively) as compared to non-recurring advertising technology companies.  When we analyze the public company comparables for our portfolio company, MongoDB, we are always amazed at how much higher those comparable companies (enterprise SaaS leaders, like Palo Alto Networks, Splunk and Workday) are trading as a multiple of revenue (often 8-12x) as compared to other public companies that are not blessed with such a magical business model.  A recent investment banking analyst report I read showed that companies with SaaS software models averaged a 6x revenue multiple, twice as high as the 3x revenue multiple that perpetual software companies average.

To be clear, recurring revenue models are not perfect.  It is harder to ramp to 10x year over year growth.  You do get plenty of lumpiness in bookings of new business, which translates into higher or slower growth rates over time, depending on performance.  

But despite these downsides, it is clear to me why there is such magic in recurring revenue models.  It looks like Yom Kippur once again falls on the last day of the quarter in 2017.  With the majority of my portfolio companies having recurring revenue business models, I am not going to sweat it.

My friend, Ed Zimmerman, wrote a terrific post for his WSJ blog – “Help Me Help You” – on soliciting him (and others like him) for investor introductions.

I wanted to add to Ed’s post and observe that not all introductions are created equal.  The source of the introduction matters a lot.  As a result, when the introduction comes in to the investor, judgment is applied based on the source.  Most investors apply a simple ranking algorithm against introductions which determines how they react to them in terms of prioritizing their time and the seriousness with which they approach the opportunity.  Here’s how it works in my experience:

1. Entrepreneurs who have made them money.  There’s no more powerful introduction to an investor than from an entrepreneur who has made them money.  Investors will drop anything to take a meeting with or seriously consider evaluating an entrepreneur recommended by someone who previously made them money.  No investor wants to hear feedback from a former moneymaking entrepreneur that they didn’t treat a friend of theirs respectfully.  The CEO of one of our top-performing companies made an introduction to a former technical colleague of his and we jumped all over it.  He personally invested (another huge positive signal in the ranking algorithm) and we ended up leading the company’s seed and Series A.
2. Entrepreneurs in their personal portfolio.  VC investors may have 8-12 actives investments at any time.  Each of those portfolio companies may have 6-8 executives that are senior enough to have board visibility.  These 50-100 executive represent the next rung in the ranking ladder.  Active angels might have twice this number.  Investors will take these introductions seriously, although may be more judicious depending on what they think of the executive making the introduction, how their company is performing and what their assessment is of the opportunity (all factors in the ranking algorithm).
3. Entrepreneurs they respect.  Generally, accomplished entrepreneurs are like soothsayers – if they’re a part of a successful company, then it is assumed that they have great insight into how to build other successful companies.  Thus, if an entrepreneur I respect sends me something, I always take a close look.
4. Service providers they respect (lawyers, bankers, accountants, headhunters).  Some service providers have very close relationships with investors and when an introduction is made, a rapid response and close look is taken.  Other service providers claim to have close investor relationships, but in truth merely are “friendly” with some VCs who may not think much of their investment judgment and sourcing suggestions.  Be careful with this category.  It can be gold (e.g., one of our best deals came from an introduction from a banker whom we respect greatly) and others are disregarded (e.g., the random investment banker / broker semi-cold emails).
5. Existing investors.  This is one of the trickier categories for introduction sources as there can be a wide disparity in how it is viewed.  All existing investors promote their portfolio companies – that’s part of their job.  Many have reputations for being indiscriminate promoters.  Others have reputations for being great at picking winners and thoughtful in who they expose their best companies to.  Before you ask your existing investors to fire off introductions, think through who has the best relationship with whom and what their impression of that investor is.  I’ve seen an existing investor who claimed to their entrepreneur to have a great relationship with a top-tier firm, but be dismissed out of hand as a small timer.  VCs, in general, are wary of the “buddy pass” – when one of their “VC buddies” (who isn’t really a close friend but rather a professional colleague) passes along their crappy portfolio company and tries to promote it aggressively.
6. Cold emails / LinkedIn messages.  Seriously?  This is the worst way to approach an investor.  In today’s transparent, super-connected era, if you can’t find a way to get to an investor through one of the methods above, you have failed a basic test.  This will result in a low ranking, for sure.
7. Other investors who are not investing.  After turning down an opportunity, I sometimes hear back from an entrepreneur a request to make an introduction to another investor.  Here’s why that’s a bad idea.  Imagine the conversation…VC1 to VC2:  “Can I intro you to this great entrepreneur raising money?”VC2 to VC1:  “Sure! Are you investing?”

   VC1 to VC2:  “No.”

   VC2 to VC1:  “Oh.  Well have you worked with the entrepreneur before in another setting?”

   VC1 to VC2:  “No.”

   VC2 to VC1:  “Well if it’s not good enough for you to invest and you’ve never worked with the entrepreneur, why should I bother spending time with them?”

I’m sure there are plenty of other permutations of the ranking algorithm, but you get the picture.  Think carefully not only about how you approach the introduction (as Ed recommends) but who you approach to affect it.