Are Little Bets a recipe for better innovation?

A few weeks ago I reviewed Peter Sims new book on experimental innovation, Little Bets. Since then I’ve received emails from friends asking me for concrete examples of businesses doing experimental innovation as well as why this is a better approach to innovation.

For those of you who haven’t read Little Bets or want to familiarize yourself with the concept, Tim Kastelle wrote a great write up about how little bets work.

With that said, internet companies are exemplary of taking a little bets approach. Because of the nature of the net, internet companies can do trial and error in real-time. And because of the size of their user base, they can get results very quickly. 

Two of these companies are Google and Mozilla.

Google Labs

Google is probably the best known for their evolutionary approach to innovation. Their management philosophy is to drive evolutionary innovation through a combination of large mix of little bets and a small mix of big bets.

For example, Google Labs is a repository of ‘experimental ideas’ for new products and services which users can use before they are officially launched.

google labsOne of their most recent experiments is the +1 button you see on their search results. +1 is Google’s version of Facebook’s Like button, which will help Google further personalize your search results with the intent of using a users social network to present more relevant search results.

Google deliberately puts their experiments online and invites users to participate in these experiments because they understand that having such a huge user base enables them to see results and iterate very fast.

They know that most of these experiments will not work, but understand that most of them will end up ‘informing’ them about ideas that can then be used for other experiments. Tom Davenport did a thorough write up about how Google innovates for HBR a few years ago, you can listen to his interview below for more insights:

Mozilla Labs

Mozilla takes the same approach. With such a huge user base using their Firefox browser, Mozilla releases experimental features in the form of extension which users can install on Firefox. The capability this extension brings combined with user feedback, it may end up becoming a permanent feature in the next Firefox release.

For example, experiments that ended up becoming permanent features on Firefox are Personas (be able to change Firefox ui) and Sync (be able to sync browser across multiple computers and devices). Both of which started as extensions for Firefox 3.5 to 3.6 and became a couple of the main features in Firefox 4.

mozilla labs

An experimental mindset is crucial

As I said above, the open nature of the web makes evolutionary experimentation possible. Most, if not all, organizations are becoming knowledge intensive because the web is becoming a lot more pervasive in how they operate. Social networks are the most obvious drivers of this, and if looked through the lens of the internet, organizations are becoming huge intranets that don’t just include employees but also customers. That user base gives organizations the ability to conduct tests with users very fast and efficiently.

They key, is to bring these principles of testing and failing fast into your own environment. Having a high tolerance for failure, treating failure as a path to learning and using this knowledge to inform your thinking is crucial.

We could make the argument that most internet startups take a similar approach to developing their product or service. This approach is better known as agile, from software development, and embodied by the mantra: Fail often, fail fast, fail cheap.

Is this a recipe for better innovation?

I think so. It’s certainly a lot more intuitive and a lot more easier to understand and apply than the more analytical approaches. There are people who disagree with the Fail Fast mantra, arguing that it’s an intellectually lazy way to build businesses (again not very analytical). I think it holds true, but most big companies are driven by analysis. They don’t test their assumptions. If anything, they keep on confirming their assumptions.

Young startups are not driven by assumptions. They don’t have the time and resources to validate just about everything about an idea. Fail Fast is a way to test assumptions. To get out there quickly, test it with customers and see if whatever you have works and keep refining. Sure you want to have a financial model from the beginning or figure one out early but as we have seen, even that takes time.

The real challenge is in figuring out the business model.

What do you think about the Little Bets approach? What other examples would you add?

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  • Kevin Mcfarthing

    Hi Jorge,

    There are two ways to “fail fast”.  The first, which should be called market research, is to make your product, or at least a beta/prototype, available to users and to get their reaction.  Then you need to work out what works and what doesn’t, in order to launch with the most appropriate business model.  

    The second is to test market, in a way that mimics your go to market model and is ideally scaleable.  This tells you not only whether your product is appealing, but also whether you can make money.

    I think that companies with products and services launched on the “fail fast” principle should be crystal clear which of these approaches they’re taking, as a lot of good prospects can be lost if the wrong interpretation is made.


    • Hi Kevin (@innovationfixer:twitter) ,

      Real-time market research. Agree on your last point, users can be put off by what they ‘think’ of the initial version and not come back.

      What do you think of this ‘experiment’ by BMW:

      From afar it looks like a ‘fast fail’ approach.


      • Kevin Mcfarthing

        Hi Jorge,

        The BMW example is great, it looks like a classic test market.  If it works, they seem to have built enough measures in to ensure it is scaleable.  If it fails, they’ve lost very little.  It’s also interesting that they’ve publicised it.  Even though it gives competitors a “heads up”, they also ensure they get local traction for potential customers.  Good for them!


        • @9c48014272e6dc60ae401afcbe70023a:disqus,

          Yeah this was way back in October. Haven’t heard much from that since then. Like you, I think it’s a great move.



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  • I think the term ‘experimental innovation’ (new to me, btw) also fosters the meaning of a part of the term ‘incremental innovation’. Sorta like the software development philosophy of code, test, code some more, test, etc. etc. Could all experimental innovation be ‘incremental’ by very nature? Innovate some, test some, innovate some more, test…

    • Hi Mohan,

      You are right. The Fail Fast approach comes from software development and does seem like ‘incremental innovation’. I do think that experimental innovation more often than not leads to incremental improvements, that’s one of the reasons I said it’s a lot more intuitive than the more analytical approaches. It’s based on minimizing risk and reducing complexity.

      Everybody focuses on BIG innovation, but incremental innovation is better than none at all.

      I still think there’s no clear recipe to Big innovation though.



      • Jorge,

        Thanks for the response. I agree with you that experimental innovation leads to incremental improvements. Can the terms ‘incremental innovation’ and ‘experimental innovation’ be used interchangeably?

        What are the progress indicators for ‘incremental innovation’? How can people realize that a tangible progress has occurred, an innovation has been made?
        Incremental Innovation Indicators: Something is now better, faster, simpler, more intuitive, etc. Are there others?


        • Hi Mohan,

          I think both terms mean practically the same. We could say that a better approach to achieve incremental improvements is to make Little Bets.

          As far as your other questions go, I think they’re open for discussion. I opened it up at Quora:

          Thanks again,


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  • Wojtek Ozimek

    I agree only to a certain level. It can be a great way but there are better…

    First of all, experiments and trial and error is always about wasting resources. Of course, huge corporations can afford spending more time and money, but ideally it would be better to spend less. Frost and Sullivan research indicated that only 1% of innovations generate growth so 99/100 are not game changers and so in other words professional innovation should be about minimizing this risk. There are analytical methods of modeling that can dramatically reduce risk..

    Secondly, there is a reference to the evolution. The evolution has been already studied by the General Theory of Innovation (GTI) and it says that man-made systems do not evolve randomly and there is the Law behind. This enables designing products that have competitive advantage, moreover, applying Evolutionary templates (special tools) enables to predict future problems of a system, thus, you can design or at least understand what problems to solve next so you can be few steps ahead of your competition. There are multiple examples of the companies having few generations of products prepared to release so every time competition was trying to catch up, they were releasing a better version.

    GTI has been for used by various companies from different countries and sectors (including a design of IT systems – I recently completed a project for Tauron, the biggest energy supplier in Poland) and I am amazed (and clients too) that by applying analytical thinking and studying of customer behavior you can design amazing, disruptive products and product strategies.