Category: data analysis

Posts

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07 October / fundraising / trends / data analysis
We've all seen the data on the average increases in round sizes over the last four or five years. Startups are able to raise larger early rounds because of the financial environment. One way of thinking about the early-stage fundraising market is as a collection of financial products. In 2008, there was a $5M series A product and a $10M series B product. Those were the most popular. As I've written about before, there's now a continuum of financial products available to startups at the early stage.
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30 September / data analysis / exits
Over the weekend, there was quite a bit of press about the challenged state of startup IPOs this year. I was curious about the real trends in share prices, so I gathered the data on some of the more salient IPOs. The chart above shows 11 IPOs at different points in their life cycles. The leftmost column is the price of the shares at IPO, followed by the share price on the first day close, followed by Friday's price.
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16 September / data analysis / exits
Last week, SaaS stocks fell by about 18% on average. The chart above shows the most recent enterprise value to forward multiple for a basket of next-generation software companies. The red line is the value and the blue line is the median over the same time frame. As of Friday, the median forward multiple is 9.3x which is a 11% drop from the previous high of 10.5x. The current valuation level is still top decile across this time period, despite the drop.
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There's a new debate about marketing efficiency recently, and it's an important one in the era of product-led growth. If a startup has great net dollar retention (NDR), should it be willing to increase its customer acquisition spend proportionately? I remain a believer that months-to-repay is the best metric for measuring customer acquisition efficiency for a single reason. You know the answer immediately and accurately. With the annual contract you have in your hand and the amount of money you spent in sales and marketing to acquire a set number of customers in a period, you know exactly your MTR.
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08 August / trends / data analysis
Which sectors see more startup company formation than others? The answer has changed quite a bit over the last 8 years. Some sectors have hit their apogee and are declining. Others have grown by more than 3x. Yet others are growing geometrically. Let's take a look. Hot Spaces Artificial Intelligence - yes, it's a buzzword but it's more than that. AI or Machine Learning is a new technology that will benefit nearly every type of sector and we're still in the very earliest innings.
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19 July / trends / data analysis
I've written before about the Jacob's Ladder of Fundraising. The Jacob's Ladder is a children's toy that flips over, and it's a great metaphor for the seed market. Seed rounds are rapidly approaching and now often equal to the sizes of Series As just five years ago. The chart above shows the mean round size in the US across. As the Jacob's Ladder flips, a series of important strategic questions arise in the market.
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15 July / data analysis / trends
Wing.vc published the Enterprise Tech 30 last week. It's a coaches poll of the top enterprise startups broken into early, mid and growth stage. Congratulations to all the companies and in particular, the 8 Redpoint companies on the list: Mattermost, Cockroach Labs, LaunchDarkly, Tray.io, AppZen, Snowflake, Hashicorp and Stripe. Coaches polls are fun because they provide a different perspective on the market. I analyzed the data set and added a few columns to it to see if there are any trends.
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12 July / data analysis
In Rethinking Customer Churn Rate & LTV/CAC, Thibaud Clement illuminates a counter-intuitive concept about churn. The faster you increase your growth rate (acceleration rate), the higher the churn rate. Consider the same startup under two scenarios: one in which the acceleration rate is 50% and one in which the acceleration rate is 0%. In the 50% scenario, churn will be 67% higher. A surprising result. Why does this happen? Because the odds of churn decrease with time, particularly for products with monthly billing.
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When I shared the Redpoint SaaS Metrics Template, I wrote about the difficulty I had identifying key engineering metrics. I was grateful for all the responses from leaders at many startups to share their expertise. I’ve updated the template with a few metrics. Reliability - percent of application requests that load. 1 minus reliability is the percentage downtime. This measures the durability of the application. Availability - percent of application requests that load within a certain latency.
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24 June / data analysis / saas
When we published the results of the freemium survey earlier this year, we noticed respondents targeting the enterprise observed higher net dollar retention and lower churn than those startups targeting other segments. I wondered if we could observe any other patterns about enterprise businesses, so I produced this analysis of public companies with ACVs (annual contract values) of $100k or greater. In the series of charts that follow, the red bars indicate the value of the metric during the year of IPO.
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20 June / data analysis
Every six months or so, I take a look at how the public markets are valuing next-generation software companies. There's been quite a bit of volatility over the last five years, and this update is no exception. As of mid-June, the public markets value software companies at all-time highs. The chart above shows the total enterprise value (TEV)/forward revenue multiple for the basket of public software companies. Just a quick reminder on these metrics.
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11 June / exits / data analysis
Yesterday, Salesforce announced it would acquire Tableau for $15.7B. Tableau sells data visualization software and the team has built an incredible business. We analyzed the S-1 in 2014. The company has grown since its public offering to generate about $1.1B in revenue, growing at 29%. Let's put this acquisition in context. First, it's the third business intelligence related acquisition in the past month. Google announced the Looker acquisition last week. SiSense acquired Periscope Data.
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10 May / data analysis / sales / saas
A public market investors asked me if there are any patterns in the list of recent software IPOs with the best sales efficiencies. As I looked through the list, I noticed one. All of these businesses sell bottom up with small initial ACVs that grow dramatically. Atlassian, Zoom, Twilio, Slack, New Relic, Elastic. All of them target small groups of users within larger organization who introduce the vendor. Over time, usage grows, accounts expand.
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As I looked through the list of public SaaS companies this morning, I read their forward multiples. ZScaler: 23.1x; Okta: 21.8x; Veeva: 18.8x; Coupa: 18.6x; Shopify: 17.0x. Those multiples are calculated by dividing the enterprise value today by its projected future revenue of the company. But what do they mean? What do they imply? First, we need to set some context. There are two kinds of companies: those valued on growth and those valued on profits.
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In 2014, I published a post called Do Startup Require Less Capital to Succeed than 10 Years Ago? It's been five years and time to see how things have changed. In the analysis, I created a metric, the return on invested capital (ROIC). ROIC is the number of revenue dollars that one venture dollar bought. In other words, at IPO, how much revenue per VC dollar did the company generate. In 2014 we saw increasing efficiencies over time, which was very exciting because it reaffirmed the efficiency of SaaS go-to-market.
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26 February / data analysis
Last week, the dynamic Harry Stebbings and I recorded episode 213 of the Saastr podcast, where we discussed the learnings from the free trial survey in a bit more detail. Harry's a wonderful interviewer, and moves effortlessly from topic to topic. I made him laugh once later in the show when I told him about the last book I read. Normally, he's the one making me laugh. Discussing the results with Harry, the data that is the most baffling to me remains the activity scoring data.
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11 February / data analysis / marketing
After publishing the survey last week, I received many questions. I've answered a few here. I'm happy the data has garnered so much interest and I hope it's helping with our two goals of sharing benchmarks and sparking conversations about how to optimize trial. If you have stories or data that buttresses or contradicts any of these findings, please share them. I'd love to publish them here. Also, if you have ideas for future surveys like this, send them my way.
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06 February / data analysis / sales / marketing / saas
At Saastr yesterday, I presented thetop 10 learnings from the Redpoint Free Trial Survey that we distributed in October. The data confirmed many rules of thumb but also raised some interesting new questions about the best way to use trials. When we distributed the survey, we never would have expected the response. About 600 companies submitted data. They span single digit ARR businesses to publicly traded SaaS companies. These businesses sell at every price point and sell to every operational buyer.
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01 February / data analysis
Late last year, my colleague Pat Chase and I announced the Redpoint Free Trial SaaS Survey. Over the course of a few weeks, we received roughly 600 responses from SaaS startups who use these marketing techniques. They span companies from $1M in ARR to more than $100M. The respondents sold into every key function of a business and at all different price points. On February 5 at 10am, I'll be sharing the top 10 learnings from the survey at Saastr.
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26 December / exits / trends / data analysis / saas
Reading the news in the past week made me wonder. Just where are we pricing SaaS companies today? The Nasdaq and the S&P have toyed with a bear market. Many stocks are down 10 to 50%. Absolute valuations are one consideration, but let's understand it at a deeper level. Have multiples compressed? The answer is yes, they have, but enterprise value to forward revenue multiples are still at some of the highest levels for SaaS companies in the past eight years.
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17 December / data analysis
The five most popular posts of 2018 were: The Strategic Shift in Revenue for SaaS Startups as They Scale - or popular by any other post by a factor of 10, this post discusses the importance of focusing on renewals as a business reaches $10M, $20M and beyond in ARR. The dynamics of retention meaningfully change the way the business ought to be managed. Ten Year's Worth of Learnings About Pricing - This post is a summary of a presentation I gave to the executive team at Twilio.
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28 October / exits / data analysis
Over the weekend, IBM announced the largest software acquisition of Red Hat, an open-source software company, for $35B. It is the largest software acquisition in history, and the third largest technology acquisition (Dell/EMC at $67B and JDS/SDL for $41B were both larger hardware mergers). IBM will spend 31% of their current market cap for Red Hat and pay a 70% premium to Red Hat's closing price on Friday. It's a triumph of the open source strategy.
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14 October / data analysis
Earlier this year, I wrote about MadKudu's analysis of free trials and asked if readers were interested in another benchmarking survey on the topic, and the response was overwhelming. Over the last few weeks, my colleague Patrick Chase, and I (along with help from many people in the community) have been busy putting the survey together. I'd like to thank Ryan Janssen for lending a hand. If you'd like to participate, please fill out the survey here.
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09 October / trends / data analysis
I've written quite a bit about the public market software multiples. They've increased to near historic levels with forward revenue multiples approaching 9x. As the public markets have appreciated, something has happened that I didn't expect. Some public companies are now fetching the mulitples of the most attractive private companies. I thought valuations between the markets would normalize because of a deflation in both public and private. Today, we have the beginnings of normalization by inflation.
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16 September / data analysis / benchmarks / saas
A founder asked me recently if there were any trends in professional services across public SaaS companies. I had examined the gross margins and share of revenue from professional services about 3 years ago. Professional services are consulting fees software companies charge to customers for software configuration, customization and education. What has changed over the past 3 years? First, we have more comprehensive data set, since many more companies have gone public.
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12 August / data analysis
How long should you let a customer use your software before they sign a contract? You could offer them a 7 day free trial. Or 14 or 21 or 30 or 90. Longer trials might be better. The customer could delve deeper into the product, become more committed and sign a larger contract. Shorter trials drive urgency, weed out the uncommitted, and result in shorter sales cycles. Both sides have compelling arguments.
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08 July / saas / exits / startups / data analysis
There's a theory to the idea that winner takes most in Startupland. The startup that grows a bit faster at the beginning demonstrates more momentum. The startup raises capital sooner, hires people, builds the product, markets and sells the product, grows more, and raises capital. Repeat the process for each round of capital. Is it borne out in reality? This theory suggests that irrespective of the category, the winner should capture most of the market value.
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2018 is a blockbuster year for software M&A multiples. The prices companies fetch relative to their revenues surpass any of those in the past 7 years. Billion-dollar plus acquisitions in 2018 have commanded a median 17.7x trailing enterprise value to revenue multiple. Nothing in the past seven years is close. In fact, there is not a single acquisition in that range. In 2018, three acquirers have paid greater than 14x trailing multiples, and two have paid greater than 20x trailing.
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Recently, people have been asking just where are we in the SaaS valuation cycle. I last updated the chart above more than six months ago. The answer is close to ten year highs. The chart above shows the median enterprise value to forward revenue multiple to multiple. Enterprise value is the market of a publicly traded company minus the available cash the company holds. Forward revenue is the sum of the next 12 months’ revenue.
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In the US, the median seed round has nearly quadrupled over the past seven years. In the mean time, seed investment has grown more than 7x and then fallen to a bit more than half of the high. As the market has grown and retrenched during that time period, I've been wondering about the geographic diversity of these seed dollars. Throughout these cycles, are startups in other states benefitting? Are they increasing their share of investment dollars?
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11 February / trends / data analysis
Much of the conversation today about crypto is about Bitcoin and currency. But that's myopic. Soon, we will be talking about how crypto will change the software world. In fact, many founders have already started that pursuit. More than 30% of the initial coin offerings (ICO) in 2017 target developers and businesspeople with their products. The numbers are still small. B2B crypto companies raised about $400M of ICO dollars in 2017.
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29 November / management / data analysis / benchmarks
How quickly do the fastest growing software companies build their teams? The answer is incredibly quickly. In fact, this data bolsters the notion that management team's top priority is recruiting, especially after the business has reached product market fit and capitalized itself well. Above, I've charted the headcount growth rate for 10 of the fastest growing software companies in recent history. I've normalized the years for when all the businesses were roughly at the same headcount - fewer than 50 people.
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03 May / data analysis
Earlier this week, a founder asked whether the fundraising market suffered from seasonality. Are there more prosperous months to raise than others? That's a simple question to answer - or so I thought. Ultimately, a dinosaur proved to me the answer is more nuanced. I plotted the mean round size of Series As from 2010-2016 in the bar chart above. You'll notice, as I did, a spike in June and November.
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16 November / data analysis / best practices
Imagine you've just been named the head of a bustling New York City restaurant challenged by one issue - customers complain about the customer service. A data-driven person, you search for a metric to evaluate the current customer service to validate the complaint and then track as you experiment with the restaurant's operations. What metrics would you employ? You might run a survey of customers at exit. You could follow with customers by telephone the day after the meal.
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04 April / fundraising / data analysis
Over the last six years, seed rounds have grown in size by 12% annually. Series As have grown by 14%, series Bs by 9%, series Cs by 14% and series Ds by 11%. In that same timeframe, the median series A and series C has doubled. Median seed rounds have more than tripled in size. This tripling of seed round sizes is a recent phenomenon, taking place in the first quarter of 2016.
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At SaaStr 2016 and SaaS Office Hours in New York, I shared an analysis of the fastest growing SaaS companies over the last 3 years. In particular, I benchmarked the revenue, growth rates and round size characteristics of these businesses at their Series A. I've embedded the slides here. These are the key bullet points from the deck about exceptional SaaS companies. Note: there are two key statistical biases in this analysis: survivorship bias and sample size bias.
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19 January / data analysis / management
Suppose you’ve been selected to participate in a game show. The game show host asks you to pick one of three doors. Behind one, the grand prize awaits. Behind the other two are goats. You choose Door 1. Then the hosts opens Door 3, revealing a goat. The host prompts you again, “Would you like to select Door 2?” Should you choose it? This statistics question rose to fame in 1990 when Marilyn Vos Savant asked it in Parade Magazine.
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11 January / fundraising / data analysis
For all the talk about late stage rounds, megarounds and unicorns, early stage startups are benefitting disproportionately from near-record years of venture capital investment. Of the $42B invested in startups in 2015, 34% or about $14B was raised in series A and seed rounds. That figure is up from 18% in 2005. The 35% attained in 2013 share for early investment ties the 1996 record. Both an increase in the number of investments and the average amount raised by early stage companies has contributed to this trend.
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23 November / office hours / data analysis
On December 2, SaaS Office Hours at Redpoint will welcome Maia Josebachvilli, VP of Strategy and People at Greenhouse, a fast growing recruiting software company. Before Greenhouse, Maia founded Urban Escapes, a DC-based startup she sold to LivingSocial. Maia is especially well known for her thought leadership in developing best in class recruiting metrics. She was also selected for Inc's 30 under 30. As we learned at SaaS Office Hours with Pete Koomen, after a startup is found product market fit, the company's most important initiative is building the machine that builds the machine.
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If there's one notion that will define the decade early 2010s in startupland, it's the Megaround, the investments of greater than $40M in private companies. Historically, startups needed to trade on public exchanges to access sums of money from $40M to several billion. But today, the private markets are providing this capital. These billions of dollars, which amount to about half of all venture investment, skew substantially towards consumer investments. The green bar chart above compares how private investors allocate their dollars.
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In Q2 2015, VC investment totaled $16.7B, about a 66% of the $28B deployed in Q2 2000 according to a new report. And the trends shows no sign of stopping. A big contributor to this growth are nontraditional investors including mutual funds and hedge funds, which now account for approximately 40% of dollars invested. And while the market is similar to the dotcom era in some regards, it is substantially different in others.
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Earlier this week, we examined the trends in the major categories of startup investment including eCommerce, Software, Social Networking and Education. But which lesser known startup sectors are starting to raise venture dollars? Where are founders finding unique opportunities to innovate? Bitcoin is the fastest growing sector followed by photo sharing and physical storage (which includes moving and self storage companies). Each year, starting in mid-2012 through mid-2015, these sectors have grown their investment dollars by more than 145%, according to Mattermark data.
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In the last six months, VCs have invested more than $57B according to Mattermark data, which puts 2015 on pace to exceed 2000 as the year the most venture capital will be deployed, ever. Which sectors are benefitting from all these venture dollars? The chart above contrasts the top 12 sectors receiving venture funding in the US, and plots the relative share of dollars invested by month. The red line indicates the monthly data point, the thin blue line shows a linear regression trend line, and the shadow around the blue line is the area of standard error.
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31 May / saas / data analysis
As the next generation of SaaS companies achieve maturity, they have begun to serve larger and larger customers, who in addition to demanding a great product, often request services. Professional services, as they are often called, entail training and customization. For product driven startups, the decision to offer professional services is a tricky one. On one hand, the customer is always right and services often enable substantially larger contracts. On the other hand, selling hours to drive revenue decreases the efficiency of the business, by hiring more people in order to grow revenue linearly.
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28 May / exits / data analysis / saas
Of the 43 SaaS companies to have gone public in the time period between 2006 and 2014, 60% are trading above their IPO pop price – the price at the end of their first day of trading. The median company has appreciated 69% since its IPO. The chart above shows the trends for each of the companies in this data set. Xero tops the list that more than 17x appreciation. This is an anomaly; the company went public right as it was founded and has grown to be worth several billion dollars.
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27 May / exits / data analysis
In 2000, the majority of tech acquisitions were primarily stock. One company would buy another using its own shares, instead of paying for the target business in cash. But since then, there's been a secular trend to cash deals. In 2014, 90% of the tech M&A transactions consummated by companies, and excluding private equity firms, in the US with disclosed deal values were cash deals. As the cash balances of large tech incumbents balloons (Apple is at greater than $30B, Google at more than $65B, Microsoft has $95B, etc), more and more M&A is primarily cash, because cash is cheap and interest rates are low.
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19 May / saas / history / data analysis
In the late 1990s, two of the dominant talent management platforms were founded. Taleo and SuccessFactors grew very quickly after they entered the market, bringing novel delivery to the human capital market. Both companies eventually offered talent acquisition, performance management, and learning tools for human resources teams. But they started in different places. Taleo initially focused on recruiting tools and SuccessFactors on performance management. As the chart above shows, both companies scaled revenue rapidly, reaching $100M in revenue 7 years after founding.
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14 May / data analysis / saas / history
Ariba went public in 1999 three years after having been founded. In its first year of selling, the company generated $800,000 in revenue. Then it ramped. $8 million, then $45 million, then $274M. In a three-year period, the company had grown 33x and achieved an astounding CAGR of 224% over the same period. Ariba shares increased 300% on its first day of trading at IPO, valuing the company at $6 billion.
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The rate at which startups are raising follow-on rounds is decreasing, and has decreased steadily from 2003 through 2013. Between 2003 and 2006, post-Series A startups raised series Bs about 57% of the time. However from 2011-2014, that figure fell to 28%. The same trend is true in series C rounds, where success rates fell from 43% to 35%. This decline in startup follow-on fundraising success is the result of an increased number of series A, which have been growing at a rate of 18% per year since 2009.
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07 May / data analysis / saas / history
In the late 90s, one company changed its name five times before they settled on one which today is a well-known brand. The business started as Silver Computing in 1995, then Stellar Computing in June 1997. Six months later, the company would rebrand as next ActiveTouch Systems, then six months later to ActiveTouch Inc., and finally, six months before IPO to WebEx. WebEx went public in June 2000 with $8.3M in revenue over the previous twelve months.
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An entrepreneur last week asked me if bottoms up businesses are more efficient software companies than top down sales processes. Enabled by web and mobile app distribution, the bottoms up software business acquires individual users, small teams and eventually departments. The top down model sells to a C-level executive (CEO, CIO, CFO) and captures the relevant part of the organization through one sales process. Because the bottoms up processes tend to rely on seemingly less expensive customer acquisition techniques like content marketing and in-product up-sell initially, this founder suggested, quite reasonably I thought, that bottoms up companies are more efficient.
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28 April / saas / data analysis / history
The first SaaS startup started as a packaged software company. After selling floppy disks and CD-ROMs of expense software in computer software stores, the company changed models for the first time, and sold software licenses directly to enterprises. The company went public on this model in 1998. But soon after the crash of 2001, the startup's market cap totaled only $8M. So the business evolved again and became a pure SaaS business, selling software accessible to anyone with a browser.
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27 April / data analysis / financials
The Information reported last week that in 2014, only 11% of tech IPOs in 2014 were profitable when they became publicly traded companies, an all time low stretching back to 1980, when the figure was 88%. This raises the seemingly absurd question, how important is it to be profitable for a startup? After all, growth is the largest determinant of valuation at IPO, not profitability. Only 19 of the 48 publicly traded SaaS companies in the basket I track have ever recorded a financial year with a positive net income.
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14 April / exits / data analysis
How many companies sell each year for $1B or more? In the last ten years, on average, 2.5 US venture backed IT companies are acquired for $1B+. In the last ten years, a total of 20 companies have sold themselves for greater than $1B. Over the past 20 years, that trend has been relatively constant, with the exception of the euphoria in 1999 and 2000. The typical unicorn acquisition generates $1.
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05 April / exits / saas / data analysis
In the Runaway Train of Late Stage Fundraising, I analyzed the growing disparity of the public and private markets. Ten years ago, we saw 2-10x as many IPOs as $40M+ rounds. Today, we see 16x as many $40M+ growth rounds as IPOs. There's no question that companies are waiting longer to go public, fueled by late stage private investment. I was wondering if as a consequence, we might see bigger IPOs.
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Every morning, it seems like a startup raises a massive growth round. In fact, the data proves the point. In 2014, there were 251 working days and 211 $40M+ growth rounds - just about one per day. In contrast to the frenetic private market, there were 15 US IT venture-backed IPOs with offerings greater than $40M last year, slightly more one IPO per month in 2014. Private market rounds were 14x as common as IPOs in 2014, compared to the 2004-2007 era, when IPOs were about as equally common as large private financings.
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13 March / fundraising / data analysis / saas
In a recent survey, 40% of VCs pointed to SaaS as the startup sector most likely to be impacted by a market correction. There's no question that the early stage SaaS founders are benefiting from substantial multiple expansion and pre-money valuation increases. But I was curious about how widespread aggressive investments are in software companies. As the data below shows, the seed and Series A markets have been relatively stable, but Series B rounds have seen a dramatic acceleration recently.
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05 February / data analysis / startups / saas / sales
SaaS companies are marvelous businesses. They are more predictable than most other kinds of companies and in addition they demonstrate leverage from technology. The best SaaS companies are able to build strong brands, develop scalable products and hire teams to bring those products to market effectively. To show the power of the convergence of these forces, I've analyzed the employee productivity patterns of the 50+ publicly traded SaaS companies. The chart above shows the headcount growth of the median publicly traded SaaS company from year four through year ten of the company's life.
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02 February / saas / data analysis
How much should your startup budget for its employee stock option pool? One way of answering this question is a blanket addition per year, say a 2% renewal. Another way is to look at the cash based cost of the stock based compensation. We're going to examine the second one today by looking at the basket of 50+ SaaS companies. The chart above shows the average stock-based compensation (SBC) per employee by years since founding across the basket of publicly traded SaaS companies.
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23 January / fundraising / data analysis / saas
In 2015, SaaS companies trade at a 30% lower multiple of revenue than last year. In early 2014, the typical SaaS company traded at about 9.2x their next-twelve-months of revenue. Since August 2014, that figure has dropped by about 30% to about 6.0x. Almost every public SaaS company has seen multiple compression. Only RealPage, Qualys, NewRelic, ConstantContact and Hortonworks are at highs in 2015 compared to 2014. The other companies in this basket have have all fallen between 1% and 60%+.
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22 January / data analysis / saas / fundraising
Figuring out how much capital your startup may need to raise will inform lots of different strategic decisions. A startup's growth rate is often highly correlated with the amount of capital it can invest in sales and marketing. More customers means more bookings, which means more capital and so on. The chart above shows the cumulative dollars raised across a basket of more than 50 enterprise software companies. The median company raises $88M before IPO in year six.
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19 January / data analysis / saas
SaaS startups are growing faster than ever before. Publicly-traded SaaS companies founded from 2008 through 2014 needed 50% less time to reach $50M than their counterparts founded between 1998 and 2005. I stumbled across this trend when looking at a different chart used in my S-1 analyses that compares the time to $50M for each of the 51 or so publicly traded SaaS companies. I've colored the companies founded in the last ten years in red.
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16 January / saas / data analysis
We've seen a sudden decline in SaaS pricing. In the past 3 years, the median Average Revenue by Customer of SaaS companies going public has dropped by about 70%. But has the shift towards smaller customers, shorter and faster sales cycles created less profitable businesses? Not at all. The chart above shows the gross margin trends of public SaaS companies broken down by their ACV (average customer value). At or close to IPO, the median company, irrespective of price point, operates at about 70% gross margin.
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05 January / saas / data analysis / sales / pricing
One of the most important forces in SaaS today is the Consumerization of IT. Instead of a centralized IT organization deciding which products to buy, product managers and marketers and engineers and data scientists determine which products they think would serve them best and buy them directly, often using a credit card. This movement is transformative and its impact is immediate. The chart above plots the median Average Revenue per Customer by Year of IPO for the 50 SaaS companies that have gone public in the past five years.
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16 December / data analysis / startups / fundraising
Are startups growing much faster than they have in the past? The chart above plots the time required for startups to raise rounds at $1B or greater valuation, over the past ten years. The blue line is a logarithmic regression demonstrating the decrease from about 7.5 years to less than 2.5 years. The answer seems to be an unequivocal yes. Let's break this chart down by type of company: B2B and B2C.
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11 December / data analysis / exits / saas / s 1 analysis
This post is part of a continuing series evaluating the S-1s of publicly traded SaaS companies in order to better understand the core business and build a library of benchmarks that might be useful to founders. Box is a 1000+ person company providing collaboration and document sharing software. We had previously analyzed the business when the company filed their first S-1. Yesterday, the company filed an updated version of their S-1.
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10 December / data analysis / exits
Growth is king in today's public markets. Most of the SaaS IPOs we've analyzed have traded growth for profitability and they have been rewarded handsomely for it. For the large tech companies, this trend is no different. The public market prizes growth. Some public tech companies sustain growth through internal efforts, but many use their cash reserves to acquire fast-growing startups. These public market cash reserves total $430B or so across the top 250 or so public tech companies, a massive war chest that will fuel startup M&A in 2015.
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08 December / data analysis
In which sectors have software companies created the most financial value? I asked myself this question over the weekend. I categorized the top 250 IT companies which spans $675B in market cap (AAPL) to $3B in market cap (ASOS) and created the chart above. B2B Software, which includes Microsoft, Oracle, IBM, and SAP among others represents about 30% of the total IT market cap today. The consumer web (GOOG, FB, Baidu, eBay) is second at 20% of market cap.
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05 December / data analysis / fundraising
When will the tech bull market end? It's a question that I'm asked with some frequency. There are three fundamental reasons for the bull market. First, technology is changing nearly every part of the economy. Consequently, there are many huge opportunities for entrepreneurs to seize. Our internal analysis shows that only 2% of IT budgets are spent on cloud today. Second, the capital startups require to pursue those opportunities is plentiful.
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04 December / data analysis / saas
2014 has been a great year for SaaS companies. By my count, 9 of them will have gone public. Meanwhile, SaaS companies in both the public and private markets continue to fetch premium valuations. To illustrate the rapid appreciation in the value of these SaaS companies, I've plotted the share price by round of each business. The color bars in the chart represent Series A, B… through to IPO. The last bar, called Q414, is yesterday's share price (if the company has already IPO'ed).
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21 November / data analysis / startups
At the DEMO conference, Danielle Morrill, the founder and CEO of Mattermark presented an impressive statistic. Seed, Series A, Series B and Later Stage startups employ 1M people, up from 650,000 just six months ago, according to Mattermark's data sources. While it's logical to think that the largest and fastest growing startups might employ the majority of startup employees because they hire at stupendous rates, this isn't the case today. Impressively, Pre Series A company employment has boomed, increasing by more than 2x in the past six months.
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20 November / saas / financials / data analysis
Hortonworks filed their S-1 last week. Reading through the document, I noticed the company had quite a substantial fraction of professional services revenue; 41% of trailing 12 month revenue is services. Of the companies we have studied in our S-1 analyses, Hortonworks generates more professional services revenue as a fraction of total revenue than any other company. But, many companies do book a meaningful amount of revenue from professional services. The chart above shows Veeva, Workday, Responses and MobileIron each generate 20% or more of their revenues from professional services (PS).
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17 November / data analysis / exits / saas / s 1 analysis
This post is part of a continuing series evaluating the S-1s of publicly traded SaaS companies in order to better understand the core business and build a library of benchmarks that might be useful to founders. New Relic is San Francisco based, 534 person company providing tools for engineers to understand how well their code is performing. The company operates in the Application Performance Management category, which New Relic calls Software Analytics.
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06 November / fundraising / data analysis / startups
Seed investments are booming. According to Crunchbase data, the number of seed rounds in US companies has grown by 10x in 6 years from 200 per year to more than 2,200 in 2013. This is driven by the expansion of the institutional seed investor and the tripling of seed stage capital available to founders. With all that capital entering the market, seed round sizes have also increased. The top quartile seed rounds have expanded by 44% in 8 years, and by 75% since 2008.
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04 November / compensation / data analysis / trends
The median equity stake of US venture-backed post-Series A CEO has increased from 15% to 21%, a 40% increase in five years. This trend is also manifested in Series Bs, but as the chart above shows, post-Series C and D, total founder/CEO equity positions have remained constant. Meanwhile the equity stakes of founding VP of Engineering and VP of Product have remained relatively constant throughout the same five year period across all stages of company.
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03 November / data analysis / fundraising
What a difference a few quarters make! In the past nine months, Series A valuations have skyrocketed. In fact, 2014 Series A pre-money valuations have surpassed median Series B valuations from 10 years ago, accounting for inflation. The same is true for Series B valuations exceeding Series C valuations. Cooley, a top tier startup law firm, reported this trend in their valuation quarterly report, which tracks these figures where they are counsel to either investors or founders.
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31 October / data analysis
It's Q3 earnings season and about half of the major public tech companies and recent startup IPOs have reported their figures. I keep track of earnings to get a sense for how these companies perceive their markets. Meeting or exceeding earnings indicates companies can forecast their growth and demonstrates how predictable these businesses are. The more predictable, the more stable the business environment and consequently, the fund raising environment for startups.
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When we analyzed the impact of location on a startup's ability to raise capital, we found no statistically significant difference. Startups in San Francisco, Seattle, Pittsburgh, Austin and many other cities all demonstrated similar ability to raise follow-on rounds. But is the same true for investors of various locations? Do investors across the US invest similarly across Seed, Series A and Series B? They do not. In fact, there is a statistically significant difference in investment patterns of investors depending on their location.
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20 October / benchmarks / saas / data analysis
Of the ten most important metrics on a startup's financial statements, revenue might seem to be the most important. But it isn't. Gross margin matters more because it is directly tied to a company's ability spend to grow and achieve profitability. Imagine two startups, both selling products at $1M price points. The first has 5% gross margins and the second has 95% gross margins. The first company will be able to spend about $50k per sale on Sales & Marketing, Research & Development and general operational costs.
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16 October / data analysis / benchmarks
In the past, we have benchmarked the revenue per employee of large publicly traded SaaS companies and determined that the average is about $200k of revenue per person. But, that analysis examined revenue per employee that only one point in time. As Jesse Hulsing pointed out to me, examining this figure over five years reveals quite a bit about the health of the business. Jesse was kind enough to provide data on a handful of category defining enterprise companies, which I've used in this analysis.
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The WSJ published a recent chart of the 49 startups with billion dollar valuations. According to their research, there have never been as many privately held companies with such high valuations ever. The absolute number of these massively valuable companies alone is amazing. Ten years ago, most of them would have gone public by now. But what other insights can we tease from the data about these very special businesses?
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I started working in venture capital three months before Lehman imploded. After the bankruptcy, the fundraising market contracted as investors internalized the new normal of the public markets. Over the past six years, the fundraising markets flipped from quite bearish to mildly bullish to extremely bullish. Or at least, that's the way it feels to me. I've often struggled to convey the magnitude of the change and its unevenness. So I thought I could do it with data.
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The market for startups raising capital has changed dramatically over the past few years. Round sizes have ballooned: startups raise 50%+ larger rounds than a few years ago. The looming Series A crunch never occurred. Instead, we've seen the bifurcation of the Series B market. Series Bs are the spring of hope for some startups who raise megarounds and the winter of despair for others who must compete for increasingly scarce Series B dollars.
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Bill Gurley and Fred Wilson have focused on burn rates as an important topic for startups. The immediate question that follows this commentary is: How much does the typical startup burn throughout its life? And what is a “risky” burn rate for a company? I use a rule of thumb to evaluate the burn rate of a Series A startup. I multiply the number of employees by about $10-12k, depending on the location of the company.
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16 September / data analysis / benchmarks / saas
After reading a few of the S-1 analyses on this blog, an entrepreneur asked me to look into the balance sheets of public SaaS companies. More specifically, how much cash should SaaS hold? How much equity do they raise? And do they employ debt to grow? The chart above shows the median cash on the balance sheet by year of founding for publicly traded SaaS companies. By its second year in business, the median SaaS company in this data has about $7M on its balance sheet at year two, from either the combination of a large seed round and Series A, or just a large A.
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09 September / data analysis / saas / startups / benchmarks
Is there an optimal price for a product to maximize a SaaS startup's sales efficiency? As I've been analyzing the S-1s of publicly traded SaaS companies, most recently of HubSpot and Zendesk, I've been asking myself that question. Do million-dollar enterprise price points and field sales people create more efficient sales organizations than content-marketing-driven SMB startups? Or are the high-velocity inside sales teams of the pursuing the mid-market, the most efficient?
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03 September / data analysis / exits / saas / s 1 analysis
This post is part of a continuing series evaluating the S-1s of publicly traded SaaS companies in order to better understand the core business and build a library of benchmarks that might be useful to founders. Zendesk is a 700 person company that builds customer support software. Zendesk went public earlier this year. It's a remarkable business primarily because the founders and the team have built an incredibly efficient customer acquisition funnel.
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28 August / data analysis / benchmarks / saas
Following this week's post Benchmarking HubSpot's S-1, Josh and Nikos raised an interesting question on Twitter. What are the right ways to benchmark SaaS companies from their early days through IPO? I have always used years-since-founding as the time axis to compare companies, because if I were a founder, that's how I might think about benchmarks. But after their comments I wondered if there were better ones. Some potential alternatives are:
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27 August / data analysis / exits / saas / s 1 analysis
One of the best ways I've found to understand SaaS companies is to pore through their public filings. A few months ago, I analyzed Box's S-1. In this post, we'll look at HubSpot's IPO filing and compare their journey to a public company with a basket of about 40 other publicly traded companies, in the hopes that this data will help other founders chart their path to IPO. In the next seven charts, we'll explore how HubSpot built their business.
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25 August / data analysis / exits
Does a startup's location impact its M&A prospects? We've already determined there is no material difference between the follow-on financing rates by geography. But do acquirers behave similarly to investors? To answer the question, I've prepared three charts used Crunchbase data and focused on the seven states with more than 20 acquisitions since 2010. In the first two charts, we'll compare the share of acquisitions by state to the share of financings by state in both number and dollar value, to get a sense of relative performance by state.
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19 August / saas / startups / data analysis
Consumer companies on the whole tend to grow faster and do so will less spending on sales and marketing, and research and development than SaaS companies. The chart above shows the revenue growth rates of 60 or so recent consumer and enterprise IPOs by years since founding. Enterprise/SaaS companies in the sample achieved very small revenue in their second year and grew consistently through year 8, at which point there's a decline.
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18 August / data analysis / fundraising
If you're a founder or potential founder and looking to raise seed capital, you're entering possibly the most attractive period in a decade to start a business. A few weeks ago,we analyzed the impact of Series A and later stage VCs in the seed market. In the past four years, traditional VCs began to invest in seed-stage companies, which led to a rise in the number and size of seeds. But there's another, more important force within the seed market: institutional seed investors.
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15 August / data analysis / exits
Earlier this week in a post on Quartz Mark DeCambre asks the question, Are IPOs Dying Because of Huge Growth Rounds? The chart above shows the 36 year trend in the number of tech IPOs. And as DeCambre points out, so far through 2014, the ten largest startup financings have yielded about twice as much capital as the ten largest IPOs. To paraphrase Mark's question, can startups raise just as much capital in the private markets as in the public market, without the hassle of public market regulation?
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11 August / trends / startups / data analysis
More than 100B mobile applications have been downloaded since the launch of the Apple iOS and Google Play stores. As the number of users, downloads and apps have exploded, the dynamics of the app store have also changed. During the past 18 months, the competitive behavior within the Free Apps section of these app stores has evolved substantially in four meaningful ways: First, the Android Play store has become substantially more welcoming to startups/new entrants than 18 months ago.
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04 August / data analysis / trends
Through last week, 80% of fast-growing technology companies who reported Q2 earnings had met or exceeded expectations, on average by 4%. As the earnings season has progressed, the tech sector is showing impressive signs of strength and predictability in the underlying companies’ business. Startups benefit from a booming public market for three reasons. First, strong earnings buoy public technology share prices which drives M&A. The chart above shows the relationship between beating earnings and Enterprise Value to Trailing-Twelve Month Revenue Multiple (EV/TTM Rev).
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28 July / trends / data analysis
I've never paid much attention to earnings reports in the past. But as I've been analyzing the tech industry more and more, and the stock market has begun to surpass record highs, I've been wondering whether the persistent bull market in tech is healthy. Or at the very least, whether the companies pushing the industry forward are able to understand the environment and their businesses well enough to meet the commitments they make to public market investors.
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Through the first six months of 2014, VCs have raised about as much as all of 2013. If this pace of fund raising continues, 2014 would mark the biggest year for VCs since 2001, when the industry raised about $38B. This new money hasn't yet hit the startup fundraising market in earnest, as the chart above shows. The second quarter of 2014 is the sixteenth largest by capital deployed sinced 1995, making it a top quartile quarter, but to break into the top five, that figure would need to triple.
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Last week, Twitter released a feature enabling users to download organic tweet data. Naturally, I put my data through its paces to see if I could find any best practices for this blog. Below are the conclusions, which are tested to 95% confidence. I've also linked below to the code for recreating this analysis for your audience. Engagement rate, defined by Twitter as clicks, highlights, and favorites of a tweet is relatively constant throughout the day.
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10 July / data analysis / exits
See an updated version of this post: Trends in the Startup Acquisition Market in 2015 The venture-backed startup IPO market has remained strong over the past five quarters, with 20 or more IPOs in each of those quarters. I was curious how the strength of the IPO market has impacted the acquisition market. In particular, how the number and value of startup acquisitions has changed, and more specifically, whether there are any trends in the sizes of acquisitions.
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08 July / data analysis / trends
It would seem hardware startups are booming. First, the amazing success of the GoPro business and IPO, which set a 23-year high-water mark for a consumer hardware company. Second, there seems to be a growing number of hardware startups bubbling in incubators like Lemnos Labs and Highway1. Third, Kickstarter and other crowdfunding sites have enabled hardware startups to mitigate one of the biggest risks in starting out: obtaining a reliable proxy for consumer demand.
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According to the WSJ, GoPro is the largest consumer hardware IPO in 23 years, though like most entrepreneurs, I don't remember the Duracell IPO. The last consumer hardware company IPO I remember is Tivo, which was in 1999. Because GoPro is the first sizable consumer hardware IPO in eons and because the startup world has a blossoming hardware segment, I thought it would be interesting to compare and contrast a top consumer hardware startup with the benchmarks of public SaaS companies using GoPro's S-1.
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Benjamin Morris, a writer for arguably the best computational journalism publication, fivethirtyeight, published “Lionel Messi is Impossible” which describes in words, statistics and charts why Lionel Messi is one of the greatest players in the world. Even if you're not a soccer/football fan, the article is worth reading because it's one of the finest examples of synthesizing data and a story to convey a point I've read in a very long time.
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I was eight years old and running with a dime in my hand Into the bus stop to pick up a paper for my old man I'd sit on his lap in that big old Buick and steer as we drove through town He'd tousle my hair and say son take a good look around. This is your hometown My Hometown by Bruce Springsteen Hometown investors, the local group of angels and VCs within a startup community, are an essential part of startup ecosystems.
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Last week, the team at Wharton in San Francisco invited me to speak at the Entrepreneurs Workshop. I chose the topic of the “Five Forces Shaping the Fundraising Market” and prepared a Mary Meeker style presentation, with a chart and a bullet point on each slide, to illustrate the forces in tension. It was great fun. I've embedded the slides from the presentation above and will link to the video once it's live.
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There's a cyclicality to fundraising. Certain sectors rise quickly and become competitive while others decline. I've been wondering about the state of the market. First, which sectors are in vogue now in Seed investing and Series A investing? Second, is there a delay between the sectors attracting seed capital and Series A capital? In other words, do seed investors see trends before VCs do? The chart above shows the trends in the seed investment market.
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13 June / saas / data analysis / benchmarks
Several weeks ago, I wrote a post about the Optimal Contract Value for a SaaS company. I wondered whether startups serving enterprises might be more or less valuable than those serving small-to-medium businesses (SMBs). Interestingly, the data showed there was no optimal customer value to build a publicly traded SaaS company. Having written that post, I began to wonder about other differences between different types of SaaS companies. In particular, do SaaS startups serving SMBs spend more or less than their counterparts in the mid-market and enterprise?
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Is it better to raise your startup's seed round from only angel investors, or is it better to include a VC or two? Several founders on the precipice of launching their seed fundraising processes have asked me this question. It's a very difficult one to answer hypothetically because there are many different variables to balance. For example, VCs may invest larger sums than angel investors. The imprimatur of a VC's investment in a company might help convince potential customers and recruits.
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For the past several years, early stage VCs have entered the seed market with vigor. VC's entry has resulted five different important trends in the past five years: The total dollars entering the seed market has increased by 132%. The mean seed round size has increased by 114% to $1.4M. VCs’ typical seed investment has grown by 50%. Mega-seeds, those seed investments over $2M, have reached historic highs exceeding 80 instances in 2013.
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Fenwick's report on the state of the venture market and I came across these three data points that summarise one facet of the market in Silicon Valley succinctly: 11 venture backed companies raised funds at a valuation of over $1 billion in Q114, more than did so in all of 2013. Hedge and mutual funds participated in 23 venture deals through mid-April, compared to 41 in all of 2013 Investment in later stage comprised 47% of all dollars invested in Q1, while Series A investment fell to a five quarter low at 15%.
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Has there been optimal time of year to raise a seed round? The chart above shows the number of seed rounds by quarter of the year from 2009-2013. At first blush, it would seem that the first quarter of the year is the most attractive period to raise a seed round. But that's a faulty conclusion. First, there's no statistical difference between the number of rounds raised in each quarter, according to a t-test on the four years of Crunchbase data I tested.
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Last week, we reviewed the state of the public SaaS market and observed the average company had lost 33% of its value from their highs. How have newly public consumer companies fared in the same environment and what does that mean for the tech industry broadly? I created a basket of most of the venture-backed consumer IPOs since 2010 and added bellwethers Facebook and Google. Above is a chart of these companies enterprise value (market cap minus cash) over the past six months.
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Earlier this week, I wrote about the increase in cash compensation and decline in equity grants to VPs of Engineering and Product in startups. I received a lot of comments about the analysis, and in particular hypotheses to explain the data. I dug a bit deeper into the data set to find an explanation. Founding employees keep more equity today than ever through the Series A and Series B. On average, founders retain 30-33% more equity than 4 years ago through those first two rounds of institutional investment.
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Since 2008, there has been a secular trend to increase cash compensation and decrease equity to startup management teams. Tho two tables below tell the story for VPs of Engineering (VPE) and VPs of Product (VPP) across the US broadly and in the SF Bay Area. In the past 5 years, VPEs have benefitted from a 10 to 16% increase in their cash compensation, but have seen their equity grants fall by 17-19%.
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15 April / saas / benchmarks / data analysis
Last week, we proved SaaS startups are raising more than they have in the past and newer SaaS companies seem to be generating more revenue per dollar invested. But do newer SaaS companies actually spend less on sales and engineering than their older counterparts? In fact, the 2014 cohort of public SaaS companies spend more on sales & marketing and engineering than previous IPO cohorts. But this increased spend results in faster revenue growth and consequent higher revenue.
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09 April / saas / benchmarks / data analysis
If you visit Yahoo Finance today, type in the ticker of every SaaS stock, copy and paste the image into a document, you might create a chart that looks like the one above. A cursory glance at the plunging lines in most of these names might send you into a panic, only to tweet in alarm that the bottom is falling out of the SaaS market. Chicken little. Chicken little.
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Last week, we analyzed the fund raising history of billion dollar SaaS companies and determined SaaS startups are raising nearly twice as much capital as 16 years ago before going public. Given that trend, I wondered if there is there any truth to the idea that startups today require less capital than before to succeed. To answer that question, I've taken the same basket of public SaaS companies and computed a revenue-on-invested-capital (ROIC) across the four 4-year IPO cohorts from 1998-2014.
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One of the cloud's great promise has been cost-reduction and for a while, we've chanted a mantra that startups require less capital than before to get started and ultimately succeed. As the number of publicly traded SaaS companies has grown with time, it's possible today to examine whether those statements are proven in the data, at least for those 41 publicly traded companies. I've gathered the financing histories of the 41 publicly traded SaaS companies and adjusted them for inflation.
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As I've described in a previous post, this blog's goal is to create and sustain relationships with readers across the startup landscape. Tuning the engine is proving much harder than I expected and I suspect that content marketers are facing similar issues. For example, over the past 18 months I've witnessed a halving of RSS subscribers to this blog. They have fallen from about 4,000 to about 2,000. I wasn't sure what the cause could be, until I compared the RSS data with email subscriber data.
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Yesterday, Box filed for its IPO and released its S-1. I enjoy going through S-1s because quite a bit about a private company is revealed and though only a subset of information is released, the S-1 discloses some very important details about the business operations. Over the past several months, I've analyzed the basket of the roughly 40 public SaaS companies many different ways. With the Box S-1 in hand, I can now benchmark Box's business against other publics, and in particular, SaaS companies nine years after founding.
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Each morning's news seems to bring another fund-raising announcement of ever larger scale. Just a few months ago, Pure Storage raised $150M in the largest ever venture investment in a storage company. These record financings certainly generate significant press interest. But how representative of the fund raising environment are these mega-rounds? The chart above breaks down fund-raising activity in US tech companies using Crunchbase data. Each chart shows the number of rounds raised bucketed by size from $0 to $5M and up to $150M to $200M from 2005 to 2013.
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Over the last 12 years, the number of startups founded has grown each year by 25%, according to Crunchbase data. That's quite an acceleration each year! See the chart here. As the number of companies in a sector grows, do the odds of successfully raising capital decrease? The chart above shows startup company formation rates, the number of new companies formed each year from 2004-2011 by Crunchbase sector. I didn't graph the 2012 or 2013 data because the Crunchbase team told me the data sets need about 2 years to mature.
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26 February / startups / data analysis / exits
A few days ago, Simply Business published an infographic and data on the acquisition patterns of Amazon, Apple, Facebook, Google and Yahoo. Looking at that data, I wondered which acquirers pay the most for startups. Ideally, this data provides some negotiating leverage to founders selling their businesses. I've prepared three charts and a table to tell the story. The first compares the average acquisition prices over the life of each of the tech monoliths.
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18 February / startups / fundraising / data analysis
Great entrepreneurs can come from anywhere. But do the locations of startups affect their ability to raise follow on capital? Do seed stage companies in the Bay Area face lower likelihoods of raising a Series A because of more competition? Or is it that New York based startups, because of a smaller ecosystem, face more difficulty? Using Crunchbase data, I charted the financing follow-on rates across the 12 US cities in which at least 10 seeds, 3 Series As and 3 Series Bs have occured in the Crunchbase data set from 2005-2014.
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13 February / data analysis
When presented with figures and numbers and statistics, it's easy to take the conclusions as fact. Numbers in a spreadsheet carry a finality, a exactitude that belies how inaccurate they can be. In 2005, Stanford professor Johannes Ioannidis turned the world of research and statistics on its head. He published “Why Most Published Research Findings Are False.” Ioannidis’ paper cast doubt on decades of research. More than 75% of experimental results published in the world's best journals couldn't be replicated.
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12 February / startups / fundraising / data analysis
Has it become harder to raise money? is a question I hear all the time. On one hand, the total dollars invested by VCs is relatively flat at just under $30B per year, according to the NVCA. On the other hand, the stories of difficulty raising series As and Bs have become a steady drumbeat. To get some sense of the patterns, I analyzed 917 companies from seed through Series B over the past 14 years, using Crunchbase data.
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09 February / startups / fundraising / data analysis
The average seed stage startup has a 20% chance of raising a Series A according to Crunchbase data for IT startups who raised seed and Series A rounds between 2006 and 2013. But this figure varies significantly sector by sector. Below is a chart of the different startups’ sectors and their rates of raising Series A capital net of the mean of 20%. To contrast two diametric examples, 40% of seed-stage search startups raised Series As, while on average only 10% of hardware startups raise Series As.
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07 February / startups / fundraising / data analysis
Naming your startup can be one of the hardest things to do when starting a company. Each founder must agree. The domain must be available to buy. Last and perhaps most importantly, investors need to like it because the first letter of startup's name has meaningful impact on how easily the company will be able to raise money. Whatever you do, don't pick a name that starts with the letter J.
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06 February / startups / fundraising / data analysis
How large of a seed round should founders raise to maximize their chances of raising a Series A? Smaller seed rounds are simpler and faster to raise because they typically require fewer investors. They may also require less dilution because of the smaller investment size. On the other hand, to raise a Series A, the startup needs enough runway to hire a team and prove certain milestones to Series A investors.
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04 February / startups / fundraising / data analysis
In What's Up with the Series A, Nikhil Basu Trivedi documents the bifurcation in the Series A market. While there are a handful of startups that raise blockbuster Series As of greater than $10M, the average Series A investment size remains relatively constant over the past 6 years just around $5.3M for US technology companies according to Crunchbase data[1]. After reading his post, I wondered if a big seed round is a leading indicator of a big series A.
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03 February / saas / sales / data analysis
What should the optimal revenue per customer be for a SaaS company? I could say million dollar contracts typical of enterprise sales provide more long-term stability and total revenue opportunity. On the other hand I might contend larger customer bases paying smaller license fees enable more predictable growth. Which is the correct argument? First, lets examine the relationship between average customer value and total revenues, to see if smaller customers create a glass ceiling for total revenue.
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Aside from a startup's internal considerations about the right time to raise money, founders should weigh the seasonality of the fund raising market when planning their raise. There's a rule of thumb batted around the valley that the worst times to raise capital are in the dog-days of summer and after Thanksgiving. As it turns out, this aphorism is only a half-truth. Below is a chart of the dollars VCs have invested by month of year.
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20 January / startups / exits / data analysis
Each year the National Venture Capital Association and Thomson Reuters release data characterizing the state of the startup market. I've analyzed the 2013 data and there are three important trends I observed. All in all, the startup exit market is quite healthy. Startup exit values are increasing more than 7% per year, on average. The number of exits is flat-to-down during the ten year period I studied. The public markets have opened to startups again, doubling their share of exits.
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16 January / startups / exits / data analysis
On the heels of last week's post about the Health of the Public Technology Market, Felix Salmon asked the thought-provoking question above. Despite the 68x growth in the value of technology market caps since 1980, are newer average technology companies worth less? Surprisingly, yes. The average public tech company value has falled by more than 2/3rds from $4.3B in the early 80s to $1.4B today, as measured in 2014 dollars.
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09 January / saas / sales / data analysis
For Google, seasonality is an important factor in forecasting quarterly revenue growth. In the advertising business, Q4 is always the strongest, followed by Q1. Q2 is the weakest. In Google's latest financial year, the difference between the weakest and strongest quarters was 22%: $14.4B in Q4 and $11.8B in Q2. I wondered if the same were true for SaaS companies. Should SaaS startup forecasts account for differences in underlying customer purchasing habits?
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06 January / startups / exits / data analysis
In the last 35 years, the tech industry has exploded in size from $62B in total market cap to more than $9.7T today, as the chart above shows. In that time, the tech industry has birthed some behemoths. In 2013, Apple became the largest publicly traded company, the first time a technology company held that distinction. Despite the number of massive companies built over the past three decades, these tech giants represent an increasingly small amount of the total value in the technology sector.
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17 December / startups / benchmarks / data analysis
One way of measuring the efficiency of a company's revenue model is to benchmark revenue per employee. Google and Facebook, the two most efficient companies, generate $1M per revenue per employee per year. Setting aside those exceptional cases and focusing instead on SaaS companies, the typical average revenue per employee is about $190k to $210k per year. The histogram above shows the ranges for publicly traded SaaS companies. In the scatterplot above, which compares revenue per employee to revenues (in log10 scale), the outliers pop out.
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10 December / startups / exits / benchmarks / data analysis
Raising money for a startup is expensive. The typical legal fees for a Series A are about 1% of the total money raised: roughly $40k on $4M. Of course, this doesn't factor in the time for the process and the dilution of the investment. But if your startup is considering an IPO be prepared to pay eight times as much in fees. Across 360 venture backed technology IPOs in the last 10+ years which on average raised $107M, 8.
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09 December / startups / exits / data analysis
When startups are acquired, there are many considerations in accepting an offer. Does the vision of the acquirer fit the startup? Will the startup operate independently or be integrated? What is the price and structure of the transaction? Most of these questions have to be answered through extensive conversations with suitors. As for the structure of the acquisition, there's data that can be used for benchmarking. I've assembled about 2400 M&A events of venture-backed technology companies since 2000 to compare the fraction of the total consideration which is stock and cash.
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22 November / best practices / data analysis
I first learned about Sankey diagrams in my thermodynamics class and they've since become one of my favorite data visualizations and analysis tools. Sankey diagrams, like the one above of visitors to this blog, show the flow of things. Originally created for measuring the flow of energy through powerplants, they are incredibly useful for content marketing analysis, visitor analysis or any other kind of funnel analysis. The best Sankey diagram ever created is Charles Joseph Minard‘s depiction of the Napoleonic War, which was made famous in Edward Tufte's Book, The Visual Display of Quantitative Information.
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18 November / startups / exits / data analysis
How much cash does a tech venture-backed company burn through before IPO? The median 2013 VC-backed tech IPO burned $33M and the average company burned $76M. The chart above shows the net income/burn rate of 2013 tech IPO by years since founding. Four categories of companies jump out in the chart: the profit leaders, the middle-of-the-pack, the negative hockey stick, and the go-for-broke. The profit leaders, Veeva(VEEV) and RetailMeNot(SALE), have generated tremendous profits from the outset.
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11 November / startups / benchmarks / data analysis / saas
Constrained by a limited budget while seeking to grow as quickly as possible, startup founders must decide how to balance growing their engineering teams with their sales & marketing teams. To help inform those decisions, I've benchmarked the relative sizes of the sales and engineering teams of the 36 publicly-traded SaaS companies from founding to IPO, typically 7 years later. The graph above shows the average Sales & Marketing allocation in turquoise and Research & Development investment in red.
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05 November / startups / data analysis / exits / benchmarks
In 2013, growth trumps all other considerations. For the average 2013 venture backed tech IPO, half of the startup's enterprise value is explained by its growth rate, while none of it is explained by profitability. The market has spoken and startups have responded. Of the 25 IPOs I surveyed, both pending and completed, only 20% are profitable. On average, these startups operate at about -20% net income margins but are growing at 162% annually.
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There's an interesting phenomemon occurring in founder compensation for post-Series A companies: founding CEOs are swapping cash for larger equity stakes in their companies. Founding CEO salaries, post Series A, have fallen by about 24% while founder equity has increased by 32%. This trend is broad. Each year, Redpoint portfolio companies participate in a compensation survey along with the portfolio companies of about 50 other firms, totaling about 800 startups. A third party pools the data to benchmark compensation trends across the executive functions in startups (CEO, VP of Product, VP of Marketing, VP of Sales, and so on) across the different financing series, locations, development stages and founders vs non-founders.
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There's a perpetual and roaring debate in Startupland about the ideal founding team. Should the ideal team be entirely computer scientists? How important to success is having an MBA/business person? What about the stories of billionaire dropouts? To answer that question, I've aggregated the academic backgrounds of 30 of the top startups of the past few years and analyzed the make up of each of those founding teams. Above is a chart comparing the number of “billion” dollar startups by the total number of founders and the share of technical founders.
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22 October / saas / data analysis / fundraising
In the past 24 months, something extraordinary has happened. The value of publicly traded SaaS companies has grown by 200 to 400% while the underlying customer unit economics of those businesses hasn't changed. Below is a chart of the ratio between enterprise value to revenue for two segments of SaaS companies. The All Segment contains 36 publicly traded SaaS companies. The High Fliers comprises the upper half. From about 2004 to 2011, the average publicly traded SaaS company held an EV/Rev multiple of 3 to 5x.
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