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Thursday, October 13, 2011

Decoding what makes some startups successful

Ever wanted to know how some startups thrive and become successful and others struggle along, even if they do have good products and/or ideas? A report called the Startup Genome has made an attempt to figure this out. They studied data gathered from 600 startups who chose to fill up their rather lengthy survey (I got to the 75% mark and lost my patience!).

Anyway, they have divided the startups (only internet-based ones) along a development timeline - ie. the stages that all startups have to go through to become a full-fledged profitable player in the market. These stages were: Discovery, Validation, Efficiency and Scale. The report states that by the time a startup moves from the Validation to the Efficiency stage, they should be aware of a single fact - whether 40% of their customers can live without them, and if the answer is 'No', then that is great news!

The report then goes on to divide startups based on the service they provide and the market segment they are targeting. Here they are:

The Automizer
Common characteristics: Self-service customer acquisition, consumer focused, product-centric, fast execution, often automate a manual process.
Examples: Google, Dropbox, Eventbrite, Slideshare, Mint, Groupon, Pandora, Kickstarter, Zynga, Playdom, Modcloth, Chegg, Powerset, Box.net, Basecamp, Hipmunk, OpenTable etc.

The Social Transformer
Common characteristics: Self service customer acquisition, critical mass, runaway user growth, winner take all markets, complex user experience, network effects, typically create new ways for people to interact.
Examples: Ebay, OkCupid, Skype, Airbnb, Craigslist, Etsy, IMVU, Flickr, LinkedIn, Yelp, Aardvark, Facebook, Twitter, Foursquare, Youtube, Dailybooth, Mechanical Turk, MyYearbook, Prosper, Paypal, Quora, Hunch etc.

Click on the tables to view them better.

The Integrator
Common characteristics: Lead generation with inside sales reps, high certainty, product-centric, early monetization, SME focused, smaller markets, often take innovations from consumer internet and rebuild it for smaller enterprises.
Examples: PBworks, Uservoice, Kissmetrics, Mixpanel, Dimdim, HubSpot, Marketo Xignite, Zendesk, GetSatisfaction, Flowtown etc.

The Challenger
Common characteristics: Enterprise sales, high customer dependency, complex and rigid markets, repeatable sales process.
Examples: Oracle, Salesforce, MySQL, Redhat, Jive, Ariba, Rapleaf, Involver, BazaarVoice, Atlassian, BuddyMedia, Palantir, Netsuite, Passkey, WorkDay, Apptio,Zuora, Cloudera, Splunk, SuccessFactor, Yammer, Postini etc

Each of these types have their own challenges to overcome in each stage, as the table above shows, and the below table shows that once the startup reaches scale, some forms of revenue generation does not rake in as much money as it did earlier.

The report states, "Subscription and Transaction Fees are by far the most common type of revenue streams. It’s interesting to see what revenue streams startups think will work in stage 2 but have considerable drop off with startups that have actually made it to stage 4. Virtual Goods, Advertising and Data all have major drop offs."

After Page 30, the report has pie-charts which show answers graphically, to questions like these:
-Which experts (Paul Graham, Guy Kawasaki, Steve Blank, Eric Ries etc) were most looked up to.
-Did entrepreneurs start up ventures for money, self-growth or to change the world.
-Whether startups with mentors or without one, succeed in raising capital. The answer is obviously 'with mentors' but the numbers are there to back it up. (For answers to the first two questions, take a look at the report!). But the one main reason it gave, for so many startups failing was due to 'premature scaling'. So startups that did not do each stage properly, did many more 'pivots' (switches/turnarounds) and usually had to go back a step...to learn their lessons again.

The report is a good first attempt and the founders of the Startup Genome Project are looking to drill deeper into the statistics, and give more answers in their future reports.

Tables are taken from the report. Read the full report here:

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