Note to self: What I read about startups

DISCLAIMER: This text is plagiarized.

Definitions:

Startups that scale prematurely are classified as inconsistent and startups that scale properly are classified as consistent

Types of startups:

  • The Automator / Type 1: These startups are product centric with a self-service customer acquisition strategy, that focus on quick execution and often automate a manual process. The majority of them target consumers in existing markets.
    • Examples: Google, Dropbox, Eventbrite, Slideshare, Mint, Pandora, Kickstarter, Zynga, Playdom, Box.net, Basecamp, Kayak
  • The Social Transformer / Type 1N: These startups have a self-service customer acquisition strategy and often create new ways for people to interact. They are almost always confronted with the challenge of reaching critical mass. If they surpass this threshold they can often have runaway user growth in a winner-take-all market.
    • Examples: eBay, OkCupid, Skype, Airbnb, Craigslist, Etsy, IMVU, Flickr,
      LinkedIn, Yelp, Facebook, Twitter, Foursquare, YouTube, Mechanical Turk,
      PayPal, Quora
  • The Integrator / Type 2: These companies thrive on acquiring customers by generating leads with marketing and closing them with inside sales reps. They are product-centric and rely on early monetization typically through subscriptions in smaller markets. They often take innovations from Automator startups and rebuild it for smaller enterprises.
    • Examples: Intuit, Square, Adobe, PBworks, Uservoice, Mixpanel, Dimdim,
      HubSpot, Marketo, Xignite, Zendesk, GetSatisfaction, Flowtown
  • The Challenger / Type 3: These startups are focused on closing high paying customers in large but fragmented markets. They are highly dependent on a small number of deals being successful and usually operate in complex and rigid markets. To be successful they need to find a repeatable and scalable sales process.
    • Examples: Oracle, Salesforce, MySQL, Red Hat, Jive, Atlassian, Palantir,
      NetSuite, WorkDay, Zuora, Cloudera, SuccessFactor, Yammer

Lessons:

  • Startups that try to scale before they have reached product/market fit and streamlined their customer acquisition process don’t do very well. In fact, no inconsistent startup was able to get more than 100,000 users.
  • Inconsistent startups grow faster in the early stages, probably due to forcing or over-engineering growth and then relatively flatline by the scale stage. Meanwhile, consistent startups have slow growth in the beginning and take off in the scale stage in “hockey stick” fashion (a linear line on a log scale is a smooth exponential curve). By the scale stage, consistent startups are growing more than 20 times faster than inconsistent startups per month.
  • Inconsistent startups almost never reach a strong monthly run rate.
  • Startups that are inconsistent depict themselves as better than they are. The illusion holds up until the scale stage when consistent startups have a valuation that skyrockets and inconsistent startups have a down round.
  • Startups that scale prematurely has teams that are significantly larger than the consistent startups that haven’t scaled yet, but their team size also rarely gets as large as startups that do scale properly since they aren’t able to sustain their growth. Scaling the team prematurely is a problem because it’s very hard to align a large team if there are still frequent changes in direction of the company, as is usually the case in the early stages.
  • Raising too much money too early can be harmful to startups. It puts a company under pressure to scale even though they are not ready.
  • If startups raise too much money before the scale stage they have a very high chance of being inconsistent. Founders may think they can be disciplined if they raise too much money, but the numbers show that by and large they aren’t.
  • Before a startup can cost-effectively acquire customers they should not be spending a lot of money on customer acquisition. If you consider spending more than $15,000 on customer acquisition before you are ready to scale, then the data shows that Inconsistent startups are 2.3x times more likely to spend too much on customer acquisition.
  • All startups overestimate their valuation during discovery and then it drops once they actually start validating their product. Inconsistent companies have rose-colored glasses that are significantly darker.
  • Inconsistent startups over-engineer their products and spend too much time on building out features that are not absolutely necessary. We often see engineers that are entirely convinced that their product can only work if it has the same product complexity of a mature product such as Facebook or twitter. Most of the time this complexity leads to lower market adoption and eventually failure for the startup.
  • In the beginning, startups can get easily lost in building a product without validating the actual demand for it. Based on interviews most inconsistent startups are under the impression that they are an exception to the rule. They believe they have found a special insight for a disruptive startup that no one else has. Unfortunately, most of these startups fail.
  • Consistent startups spend more time discovering who their customers are, whereas inconsistent startups are focused on validating that customers want their product. Consistent startups are searching. Inconsistent startups are executing. It’s widely believed amongst startup thought leaders, those successful startups succeed because they are good searchers and failed startups achieve failure by efficiently executing the irrelevant.
  • Especially in an early stage, it is dangerous to outsource the product development. All dimensions: product, customers, business model, financials, and the team are typically changing at a high rate. If product development is not done in-house startups will have a hard time keeping up with the daily or even hourly feedback loop startups have at this stage.
  • Startups that are tackling new markets are more likely to be inconsistent because they have more uncertainty than existing markets. Tackling an existing a market where the product is differentiated by being cheaper has the highest certainty because people almost always prefer the same value at a lower price, whereas differentiation by better or niche rely on more subjective qualities.
  • Trying too hard to monetize leads to inconsistency. While money can be an important validation indicator, stressing it too heavily will lead startups to ignore opportunities and drift towards non-scalable opportunities that are likely to turn into small business or custom consultant shops.
  • The level of ‘difficulty’ or ‘uncertainty’ for the different types of startups is in the following order: Type 2, Type 1, Type 1N, Type 3 (see above for the description of types).
  • That consistency does not vary by estimated market size. There is an equal distribution of consistent and inconsistent startups across all the different market sizes.
  • The frequency of your product release cycle has no effect on consistency.
  • The education of the founder has no impact on whether a startup is consistent or inconsistent.
  • The gender of the founder has no impact on whether a startup is consistent or inconsistent.
  • The time the founders have known each other has no impact or little impact on whether a startup is consistent or inconsistent.
  • Age has no impact on whether you are consistent.
  • The amount of products a company is handling, therefore, does not influence the performance
  • Google Analytics, homegrown solutions, and spreadsheets are the top 3 three tools that are used by more than 90% of all startups to track their metrics and make decisions.
  • Geography has no impact on whether you are consistent

Other findings:

  1. Founders that learn are more successful: Startups that have helpful mentors, track metrics effectively, and learn from startup thought leaders raise 7x more money and have 3.5x better user growth.
  2. Startups that pivot once or twice times raise 2.5x more money, have 3.6x better user growth, and are 52% less likely to scale prematurely than startups that pivot more than 2 times or not at all.
  3. Many investors invest 2-3x more capital than necessary in startups that haven’t reached problem solution fit yet. They also over-invest in solo founders and founding teams without technical cofounders despite indicators that show that these teams have a much lower probability of success.
  4. Investors who provide hands-on help have little or no effect on the company’s operational performance. But the right mentors significantly influence a company’s performance and ability to raise money. (However, this does not mean that investors don’t have a significant effect on valuations and M&A)
  5. Solo founders take 3.6x longer to reach scale stage compared to a founding team of 2 and they are 2.3x less likely to pivot.
  6. Business-heavy founding teams are 6.2x more likely to successfully scale with sales were driven startups than with product centric startups.
  7. Technical-heavy founding teams are 3.3x more likely to successfully scale with product-centric startups with no network effects than with product centric startups that have network effects.
  8. Balanced teams with one technical founder and one business founder raise 30% more money, have 2.9x more user growth and are 19% less likely to scale prematurely than technical or business-heavy founding teams.
  9. Most successful founders are driven by impact rather than experience or money.
  10. Founders overestimate the value of IP before product market fit by 255%.
  11. Startups need 2-3 times longer to validate their market than most founders expect.This underestimation creates the pressure to scale prematurely.
  12. Startups that haven’t raised money over-estimate their market size by 100x and often misinterpret their market as new.
  13. Premature scaling is the most common reason for startups to perform worse. They tend to lose the battle early on by getting ahead of themselves.
  14. B2C vs. B2B is not a meaningful segmentation of Internet startups anymore because the Internet has changed the rules of business. We found 4 different major groups of startups that all have very different behavior regarding customer acquisition, time, product, market, and team.
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