Startups Unplugged

Category: startup data

Posts that include data about startups.

  • Decision Speed and Feedback Loops

    Depending on your work and life, you will have different situations in which to make decisions and to learn from them. Some of this is related to the speed at which you make decisions (lots of small ones versus few large ones) and the quality and also speed of the feedback you receive.

    In my roles with three startup accelerators and incubators I’ve been fortunate in that over the past five or six years I’ve been able to make five times as many decisions as I would in a VC role, as well as receive more rapid feedback than normal. Here’s a breakdown of what created opportunities for those decisions and that feedback.

    Create Opportunities to Make Many Trackable Small Decisions

    Hong Kong / AcceleratorHK. Chose 12 startups across two cohorts for the first funded startup accelerator in Hong Kong. Acceptance rate approximately 6%, which means 200 companies were evaluated to find the 12. $20K – $50K invested per startup.

    Rome/Vatican / Laudato Si’ Challenge. Chose nine startups in one cohort for an environmental-tech focused startup accelerator affiliated with the Vatican. Acceptance rate a little under 3%, which means over 300 companies were evaluated to find the nine. $100K – $200K invested per startup.

    University of Southern California in Los Angeles / USC Incubator / Marshall Greif Incubator. Across three years, 82 startups have participated across ten cohorts. I started the program three years ago and after the first year, the acceptance rate has hovered around 10%, which means approximately 700 companies were evaluated to find the 82. This program is different from the two above in that it is restricted to founders with a connection to USC, there is no financial investment in the companies, and that I sometimes get to know the founders before they apply and see the progress they make. I have declined to offer spots to founders who were too early only to accept them when they reapplied later on. This program is run non-stop with three intakes per year (unusual even for fast-paced accelerators).

    If I were at a “typical” VC firm, I’d be involved in decisions to accept (invest in) perhaps five companies per year. True, I’d look at more companies per year (perhaps 1,000 to get to that five) and the due diligence would be much more intense. Further, judging whether the decision was correct would be more difficult as well. If the VCs need to eventually exit their investment positions, in my case at the Incubator at least, I really just need to see signs of progress (anything from the company receives their first investment, increases their rate of growth, grows revenue, builds a team to things that were prevented, such as the reduction of co-founder problems). I average 30 companies accepted per year, at least over the past three years with 300 to 600 evaluated per year. The feedback loop cycle time for metrics I care about is also much shorter, at around one year rather than perhaps several years at a VC.

    As a result, I’ve improved my accuracy in founder and company evaluation.

    Feedback Loops in Other Places

    I never thought I’d teach at a university, but it has changed the way I think about learning and getting feedback. In my first year teaching I had a casual conversation with a colleague who admitted that all of the students failed a recent pop quiz that was based on what the class had discussed for the past month. I remember thinking “wow, you must be terrible…” I decided to try the same thing in a short quiz that included a couple formulas, a definition, and some basic business math. Result from that pop quiz in a class of 47 people: an average grade of 48%.

    I couldn’t get this out of my head.

    I provided the answer key and reviewed the questions with them. I told the class to expect another quiz like that one. One month later I gave the exact same quiz. Result: average grade of 70%.

    Again, I couldn’t figure this out. Why such low grades? I wanted to get to 100%.

    I provided the answer key and reviewed the questions with them. I told the class to expect another quiz like that one. In the last week of class I started by reviewing the same information. I gave the opportunity to ask questions. I cold called students to provide answers. I put the calculations on the board. I reviewed it all one more time.

    And I gave the exact same quiz.

    Result: average 95%. That meant that I still didn’t reach a couple people in the class.

    If you’re not used to receiving feedback that you don’t know something, that you must improve in some way, and it’s inescapable (the repeat quiz rather than a one-off unlucky quiz) it can be terribly upsetting. It was to a few students.

    Mostly we do not seek out opportunities to test our proficiency in subject matter and in different situations. When we do, it’s often a one-off test. We don’t revisit our proficiency again and again, forget, and fall into bad habits. Some professions, like medicine, require regular learning throughout a career, not only to review and test our retention of what we learned, but also to learn new techniques. We rarely do this in other fields. In some areas of our lives we may even actively fight the opportunity to learn how good we are.

    If you end up thinking about this and implementing ways of making more decisions and developing feedback loops, let me know what techniques you use.

  • A college level class in growth hacking?

    In a few days I’m teaching the first class in Growth Hacking at USC. I created this class because no matter what you feel about the “Growth Hacker” term, I find the role to be sought after by graduating students and the skill set to be appreciated by businesses. As far as I know, this is the first college class focused totally on Growth Hacking, so I thought I’d share the experience along the way. This is the first post.

    Framework

    I take Growth Hacking as understanding business fundamentals and then being able to make informed decisions using data, centered around orders-of-magnitude growth.

    Growth Hackers love data, are creative, and curious. Some are developers, some are designers, some are business oriented. Let’s dive into a way of looking at the world as a giant opportunity for growth.

    I call the framework I use to guide my thoughts CCARR: Collect, Clean, Analyze, Run, Repeat. That is, as a starting point, Collect data that is helpful, Clean up the data, Analyze the data to pull out insights, Run new experiments based on what you have learned, and Repeat the whole process as long as it makes sense.

    Tools

    Expanding on this framework, I use the following tools.

    Collect data that is helpful. This could be internal data you control, even using something as universal as Google Analytics. Data collection could also come from external sources, perhaps by web scraping other sites to save effort. I use Outwit Hub and SEOTools for Excel for basic off-the-shelf web scrapers, and Tamper Monkey’s Chrome extension for making and finding user scripts to save time.

    Clean up the data. The data you want, especially if it is from external sources, may not be in a format that you can easily use. I use Google Refine if there is a significant amount of work to do. Otherwise, I might just live with it and open up Excel.

    Analyze the data to pull out insights. This is business plus creativity. For this I use an expert level of Excel, which is just easier for me than using a database. This step is guided by knowing what business fundamental we want to impact. Eventually it all comes back to fundamentals like Life Time Value, Customer Acquisition Cost and cycle time (see next section). However, you may want to focus on one specific element of those.

    Run new experiments based on what you have learned. Here you might use ads on Facebook or Google, experiment with referrals, go to social platforms like Reddit, Product Hunt, or Twitter, or try something more guerilla. Just make sure you track what you are doing and your predictions for what will happen.

    Repeat the process as long as it makes sense.

    There are many tools that do these things. These above just happen to be the ones I’m using now.

    Fundamentals

    Adding more customers if you lose money on each one doesn’t make sense, at least not in the long-run. Eventually, your work has to come back to the fundamentals. That means if you’re trying to growth hack a business, you work on increasing Life Time Value (LTV), decreasing Customer Acquisition Cost (CAC), and speeding up cycle time. Let’s take those one by one. Simple to remember, but figuring out how to do it is the hard part.

    Ad hoc, I use a simple LTV formula: LTV = (Price per Unit – per Unit Costs) * Repeat Purchases. Those are the three triggers you have to increase monetary value to the business. As a growth hacker, the factors you can usually impact the most are Repeat Purchases and to a lesser degree, Price. It’s usually more interesting to also look at inputs to LTV over time. To do that, you can do a time series of what a customer generates in revenue and costs per time period.

    CAC is another area of focus for growth hackers. If you can figure out how to decrease your CAC, then you have more budget available to acquire more customers. The difficult thing about CAC is that different channels for customer acquisition constantly change in their efficacy. What worked yesterday may not work today, or may be too expensive to be worthwhile. That’s why many of the historical growth hacks you hear about are good for ideas, but may not be repeatable. The also means that another difficult thing is realizing that what’s working for you right now may stop working. To keep yourself honest, remember that there is no single CAC – every business has multiple channels to reach customers, each of them with their own associated cost and LTV. To make it easier to focus on growth, think of what will happen to each channel when you try to increase your numbers by 10 times. Some of the channels will not scale at all, some will scale, and some will only scale at a much higher cost.

    Rule of thumb: keep a healthy distance between your LTV and CAC, perhaps 3X or 4X, unless you are trying to take market share and have the reserves to play that game for a while.

    Cycle time is the delay between when you acquire customers and when you see the impact in the form of initial purchases. If this cycle is too long, even if you can decrease CAC and increase LTV, you’ll be out of business before you can benefit.

    The best tip is also the hardest: build a great product that your customers love. If you can do that, then growth hacking becomes a lot easier. Growth usually does not just happen all by itself or in a sustainable way. Keep the above CCARR framework in mind when you growth hack.

    (This post originally appeared in Jumpstart Magazine.)

  • Startup Sacrilege for the Underdog Entrepreneur

    The book is now available here. What do you think of the cover art?

    Chapter outline
    Context:  Fools Rush In; Why Read This; A Glance At the Seedy Underbelly.
    Sacrilege: Your Invisible Tribe; The Irrational Goal; Is There Enough Diversity In Tech?; Little Heroes; Investor Change; What You Can Control and Never Control; The Never Ending Accelerator Glut; Pitch Event Controversies; Idea Thieves.
    Action: Test Prep; Next-Gen Accelerators; Funding; Founder Immigration; Peer Pressure; Go Local.

    Thanks.

  • What’s the best way for a startup to measure its progress?

    Across public talks and internally in the accelerator I co-founded, I’ve taught and advised on metrics that matter for startups. I could add to the lengthy body of knowledge of startup metrics but there’s a qualitative metric that people don’t mention because it’s hard to measure and few see it in person. Startups in their first 18 months of operation will know what I’m talking about. I call this metric the beach day ratio.

    This is how it works. A particular startup is at a tough point: they’re late on a release, users are churning too quickly and recent investor introductions have gone quiet. Morale is low and people are exhausted. There are job offers in a couple team members’ back pockets. They might even be calling someone like me up to help take a look at their direction. In the midst of all of this, the team decides to take a day at the beach (or depending on where you live, a hike, a trip to the museum etc) to take a break and refresh themselves.

    The question is, now that they experienced that day of freedom — that space to think about what they want to do — what happens the next day?

    The teams that tough it out and go right back to work instead of continuing to escape to the beach (or take the new job, or give up etc) are the ones with the best chance of succeeding. If investors could watch their investments in this way, they could see that startups with high propensity to keep escaping (a high beach day ratio) are in bad shape and need more help than lean startup metrics could ever offer.

  • When Would You Give Up?

    If you’re a startup founder and you feel good about yourself, you just might justified. But you’re much more likely to be delirious, experiencing that temporary elation that comes from something that makes no difference to your actual business.

    For example, you’ll meet a lot of startups feeling good about themselves on the way home from another tech event (what I usually call startup entertainment). Or, on the other hand, you might possibly feel good because you have actual customer interest as demonstrated by engagement, referrals and purchases. If you’re going between these extremes and rarely experience justified elation, how do you know how long to keep working? The question of when do you give up on your startup is always a tough one.

    I was thinking about this because over time I’ve received this question from startups in tough places.

    The following quote is from a startup founder who recently contacted me. This founder is talented, but has tried two different startups in the past year and as you can see, is thinking about giving up. (Quotes used with permission):

    “One of the hard parts in a startup is after launching a beta product, nobody seems to care. I keep working on it but still nobody cares. In the beginning, I was highly motivated and kept pushing through. But at a certain point, I start to doubt, in my mind asking myself, ‘Am I heading to the right direction, or am I totally off? Should I persist in this domain, or do something else? What is the next right thing I should do?’ I don’t know the answer to any of the questions. To make things worse, I look at my own bank account and see the number there is constantly decreasing. I can’t bring anything to my family, and sometimes feel ashamed when facing my family members. Let me do some soul-searching about myself, what I really want to do with the startup. Hopefully I can give you a more solid answer in our next correspondence. Meanwhile, I am freelancing to get some side-income.”

    Many of the hallmarks of a founder in a tough spot. Because we’re often so passionate about our work, we think that others should be just as passionate about being customers. The encounter with the reality that isn’t so rosy leads to doubt, family pressures and turning to freelancing instead of working on the startup full-time. I can’t blame him for any of these feelings.

    Then this, from another founder.

    “I think I am just tired of coding. There are still times I try to brainstorm ideas, but then I quickly see myself being too emotional. Looking back, I sucked at approaching potential customer/discovery/validation, cause I don’t know much outside the technical world. Kinda sounds silly, but I have a feeling that if I can teach & observe better, I can tackle the customer process much easier.”

    The talented coder (he’s self-taught and for years supports himself by passive income from products he’s built) who gets tired of coding is a sad thing to see but I understand why. I’ve spent so much time getting coders to look up from their laptops and encounter meatspace that I’m surprised when one of them wants to dive right in. I just didn’t think that he’d want to set the code aside.

    And then this from a founder building for the industry he comes from:

    “Some things can’t be taught with precision, like when to quit working on a problem. There’ve been countless presentations on startup development (especially with respect to the Lean Startup movement), I feel, especially after having tried to build one myself, that many first-time founders still waste too much time in advancing concepts when… pursuing a pivot/concept any further won’t be productive (arriving at product/market fit, in other words commercial success)… It’s better to let go of an otherwise beautiful idea than waste another 3 months or more, this way we can work on other ideas that might prove to be more worthwhile.”

    The beautiful ideas are the most dangerous for entrepreneurs because they are the hardest ones to kill. You’ll have rooms full of people telling you that you must go ahead and build the thing, it’s such a great idea.

    Then this.

    “I think I just want to go and work at Facebook. They made me an offer and [colleague] is going to work at Google…”

    I can’t blame anyone for taking a job with a large organization. Those are the easy transitions to make, at least publicly. As above, the outside world will judge your decision as a smart one.

    Ultimately, whether or not to kill your startup, to let it stumble along or to keep charging ahead is a personal decision. Sometimes when I read posts on why you shouldn’t ever give up, or why you shouldn’t freelance, or why you need to learn early (fail fast) I feel like it’s just too easy to say.

    No one can give you a blanket answer to question of how long to work on something. It comes down to the stage you’re at in your life, whether you’re learning and how you feel when you get up in the morning. Founders at heart are not motivated by money. Steve Blank once told a roomful of 500 startup people that in five years two of them would earn $100M (crowd cheers) and the other 498 would earn less than if they worked at Walmart (crowd laughs). But of course, everyone thinks that they’re among the two fortunate ones.

    “We decided to sell to [acquirer]. I’ll tell you how much we’re getting. It’s [next to nothing]. But it’s an exit and who knows, maybe next time…”

    Years ago I was surprised to learn just how common it was for founders to agree to small exits. That is, small to the point that you might as well have worked at Walmart (see above). It saves face for the founders and maybe gives them an edge the next time around.

    What would it take for you to give up?

  • When Does a Startup Accelerator Succeed?

    There’s a lot written about startup accelerators. At least, there’s a lot written by outside casual observers; very little is written about them from the inside. There are reasons for that. In my experience running an accelerator, I wouldn’t share much about the startups until after demo day (more about why in a later post).

    Before we go any further, let’s define “startup accelerator” as a program designed to speed up the development of a new business (usually tech), accepting applicants usually of early-stage companies for a defined length of time and granting a seed investment in exchange for equity. Accelerators usually also include mentorship from an internal team and external subject matter experts and concludes with a demo day where the companies show what they have built and discuss their future plans. If the startups want, additional funding is often planned to be raised at or after demo day.

    Major themes for the last couple years are that there are too many accelerators and that they don’t produce results.

    This begs the question “What makes a successful accelerator?”

    Success factors usually mentioned for accelerators.

    There are many different types of accelerator programs. They are usually measured the same way, but in reality many of them have different goals and have different factors of success. But the two most common ways accelerators are measured are:

    1. Funding raised by, or shortly after, demo day. This is a problematic measurement, especially for programs that operate where there are small local investment communities or where they focus on very early stage startups. I’ve also seen startups who are not even looking for funding post demo day. When did amount of money raised become a measurement of success for a startup?
    2. Survival rate after demo day. The question is who reports these numbers and where do they get the data. It is very easy for a startup to appear live when the founders have given up even if it hasn’t closed down. Plus, almost no one does the work to check on the numbers. For example, my old review of TechStars’ original published success rates (which seemed amazingly high when they first published them in late 2011) led me to lots of “zombie startups”. And those were just the ones I found. The great thing is that TechStars periodically updates their data. Now that I revisited that old post and compared it to TechStar’s results page, most of the startups that I listed as possible zombies on my old blog post are now marked as failed. At a minimum, the results from the most recent one year of their startups should be removed from the statistics. Those startups haven’t yet had time to fail or succeed. From the 25 startups I had in my first year running an accelerator and bootcamp, only one shut down, so that means my “success” rate is 96% if I measure myself the same way. That’s not a helpful way to think about a success metric.

    These factors have something in common — they are seemingly easy to measure so they become the standard of measurement. But they don’t really tell us what’s most meaningful.

    Instead, the focus should be some other qualities that are harder to measure

    Time and money saved not building something no one wants, but which would have kept the would-be entrepreneurs busy for months or years. I’ve seen a lot of startups that are really just a couple of guys with a hobby and a devotion to writing code to serve that hobby. How much do the companies at start point develop and where are they by end point? How far did people get over the program?

    Strength and involvement of the mentors. Are the mentors celebrity mentors, too busy to take time with the startups or are they perhaps not famous but dedicated to the success of the startups?

    Involvement of the accelerator with the startups after the program ends. Are there follow-ups? What’s the activity of the alumni network? What do the entrepreneurs go on to do five or ten years later?

    The accelerator’s success exiting from portfolio companies years later. Again, hard to measure because of the time lag and since the exit numbers are typically private.

    So, leave all that aside and decide what accelerator, if any, works best for you.

    Update to the above post…

    Rereading this post after years, I notice the following:
    – I have given up on using “accelerator” and “incubator” as distinct descriptors. The reasons are there has been extensive change in the way these programs work, at least at the edges (the mainstream is often still similar) and in general few people ever knew the difference between the two terms.

  • The Recommended Daily Intake Approach to Startup Activities

    You’ve probably seen the “Recommended Daily Intake” used as a way to gauge how much of a particular nutrient or substance we should ingest in a given period. I was thinking about how startup people spend their time and ended up writing this table of “Recommended Startup Activity Intakes” for early-stage startups.

    • Reading popular tech blogs < 5 articles per week. In general, popular tech blogs preach an unhelpful message of  the “Hollywood” startup.
    • Reading articles in the industry that you’re focused on and on customers that you’re focused on > 5 times per week. You should know your industry, including the things that your customers are reading.
    • Meeting potential customers > 10 times per week. The Vitamin C of this list.
    • One-to-one coffees, drinks, meetings with other entrepreneurs, mentors or other people who can help > 3 times per week.
    • General startup events < 1 every two weeks. Less often if you usually talk to the same people each time at these events.
    • Attending events specific to your area of focus > 2 per month. In other words, these are not “startup events,” unless you are one of the few startups that sells to other startups.
    • Demo Days < 4 per year. I say this even though these are the only events I run. Go to see people who have built something talk about what they’re done. But realize that you don’t see the full picture at a demo day.
    • Pitch events < 1 per year. The processed sugar of the startup world. Go once to see how they’re run and to get a free drink. Then avoid them. The only exception is when their panel of judges includes people who have recently invested in early-stage startups in the location of the event.
    • Building your company = every damn day. OK, back to work!

  • Why Startup Lists Don’t Matter But You Read Them Anyway

    It just turned 2013 in Hong Kong and I feel like it’s 2008 in New York.

    Usually when I say that it’s because the startup growth I witnessed (and hope helped) in New York years ago reminds me of what I’ve seen in Hong Kong recently. This time, however, I’m talking about the latest in ecosystem envy. Startup reports are here again, this time in the form of Startup Genome Project and World Startup Report.

    Fact: Hong Kong made neither list. Question: does it matter?

    In the case of Startup Genome, I was one of the thousands of startups that contributed to their initial dataset in 2011. Their findings were actionable (though to be taken with a grain of salt), presented as applicable in different market types (more about that later) and changed the way we thought about survival probabilities.

    I was fascinated with their collection of data and suggestions for effectiveness of different models across market types. When they published their Startup Ecosystem Report I took it as another interesting data collection and not surprisingly, Hong Kong was not on their top 20 list.

    Likewise, when World Startup Report published their travel schedule for 2013, Hong Kong was not on the itinerary. Again, some people felt bad, seeing Hong Kong excluded. That’s on top of Dave McClure not stopping over on his way around the region recently. But really, we’re looking at this backwards.

    The goal of any city is not to get itself on a startup list. Being on a list or lobbying for a closer look doesn’t achieve anything meaningful. The goal is to do what’s best for your location and then maybe when you’re too busy to notice, someone mentions that you’re listed as a new top location. Hong Kong can’t mandate its way to global importance. The next list will be entrepreneur-led.

  • Are the TechStars Results accurate?

    I have total respect for TechStars and what I know about it tells me it’s a great program.

    So when I recently saw TechStars’ results page with a 80% success rate I had to learn more. I’m always very interested in “success/failure” at startups so I looked through their list of previous classes, going back to when they started in 2007. First thing I noticed was that the list might be out of date (ToVieFor from the most recent class is still shown as active).

    First a word on how I gathered the data.
    TechStars lists company status as either Active, Failed, or Acquired. But after I started to look, I saw a lot of companies that were listed as Active, but didn’t seem to be so. So I started a 4th category called “Inactive” and began to look at the list.

    Since I didn’t have any inside info and company founders don’t take kindly to strangers emailing and asking “hey, you still doing this startup or what?” I used publicly available information. I marked a company as inactive if they shared a couple of these traits: blog hasn’t been updated for over 1 year (many were more than 2 years old), has a Twitter account but no activity, has a Facebook account but no activity, the site doesn’t exist anymore (that was the easy one), the site is still a landing page just collecting emails even after more than a year.

    I also only looked at the first five TechStars classes, since any newer company hasn’t had a real opportunity to succeed or fail yet.

    I’m probably not totally accurate in the list, so take it as a list of who “appears” to be Inactive based on information online.

    This is TechStars data for the first five classes.

    And when I add the 9 companies (listed at bottom) that appear to not be Active, I get the results below.

    This takes the TechStars from an Active + Acquired average of 84% for those classes down to 66%. If you look only at Active companies, then it goes from 67% down to 49%.

    If anyone has better data, contact me and I’ll be happy to update this. To make it easier for you to make corrections, these are the 9 extra companies that seemed to be inactive: J Squared Media, BuyPlayWin, Peoples Software Company, LangoLab, TempMine, Mailana, Rezora, Monkey Analytics, TutorialTab, Sparkcloud. (Sorry for any mistakes.)

    I wrote this to learn about startup longevity rates, not to upset anyone. And I know that sometimes startups look inactive but there are other things going on.

  • Steve Blank quote

    Steve Blank quote from GigaOm video interview: “I did this at SXSW. I said ‘There are 500 people in this room. The good news is, in ten years, there’s two of you who are going to make $100 Million dollars. The rest of you, you might as well have been working at Wal-Mart for how much you’re going to make.’ And everybody laughs. And I said, ‘No, no, that’s not the joke. The joke is all of you are looking at the other guys feeling sorry for those poor son-of-a-bitches.’”

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