Quant innovation and the 5 stages of grief

If you’re reading this, you probably know I use data and statistics to try and predict if innovations will survive or fail. It’s a world of probabilities, like what you’d find in any Wall Street firm or insurance company. In those cases you’d be shocked if an analyst or actuary wasn’t doing the math. Yet for some reason, using math to assess the odds of new innovations or startups has been surprisingly controversial. When I started this research nearly a decade ago, I knew it would raise eyebrows but wasn’t prepared for how much anxiety it could cause some people.

When they hear about my field, most people are a bit skeptical at first, as they should be with anything new. With sufficient evidence the lightbulbs turn on, or at least folks become more open minded. Still, there’s always a small percentage – the haters – who loathe the idea no matter what evidence there is. These folks seem physically unable to hear evidence because, in their case, the deeper objection is emotional. I find these reactions psychologically fascinating.

No matter where you land on the acceptance spectrum, I can’t help but notice how people – me too – go through something akin to the five stages of grief as we challenge closely held beliefs. Weird, I know. But sometimes it’s interesting to observe how our minds and bodies react to change, no matter how open minded we think we are.

The Five Stages:

1.  Denial. “Innovation will never benefit from data or statistics, it’s all about intuition, teamwork and perseverance.” Most of us resist big changes, at least at first. New information can take some getting used to, even if it obviously helps. It might feel daunting or overwhelming. We worry about the implications. Denial is the brain’s way of easing our transition from the old to the new, slowing change to a speed we’re more prepared to accept.

2.  Anger.  It’s okay. This happens. Anger can give our minds temporary structure during a transition – it’s a bridge between the past and the future. “How dare math tiptoe into the world of innovation! The naiveté! The audacity!”

3.  Bargaining. Bargaining is how the brain forms a temporary truce between the past and the present. “Okay,” you might say, “maybe data and statistics can help my innovation efforts, but only if I still have the final say… or if I can still do design thinking… or if my project still gets funded… or if I don’t have to do any extra work… etc.”  Bargaining let’s you adapt to a new reality incrementally and on your own terms.

4. Depression. What? Where did this come from? We were doing so well. After bargaining, your attention moves squarely into the present. “Oh crap. We’ve spent the whole year on this innovation portfolio and now the data suggests we made a lot of mistakes. This could take a lot of work. Ugh.” Depression is about learning to live with the implications of the change you just accepted, even if it’s an improvement. There’s a sense of loss.

5. Acceptance. Some people think acceptance is “feeling fine” with a change. You don’t have to feel fine. Some people – including me – may never be totally fine with abandoning old ideas, even in favor of obvious improvements. I still love my old Huffy dirt bike, even though it was a kid bike and I haven’t seen it in decades. Loving my Huffy isn’t about the actual bike, it’s about some sentimental notion I don’t want to lose touch with during my life journey. The reality is, switching to a grown-up bike, with gears, was a good thing for me.  I’ve accepted the new, without disrespecting the old.


Change happens, whether or not we’re ready for it. Sometimes it excites us, other times we go through the five stages of grief – which can span seconds or decades. Consider how a lot of taxi drivers feel about Uber (my last cab driver was definitely stuck on stage #2). Our brains have developed elaborate mechanisms to protect our psyches, even when the most rational thing is to embrace the new.

The world of innovation isn’t immune to change – it’s the epicenter of it. So it shouldn’t be too surprising to learn traditional innovation practices are being challenged at an accelerating rate by what we’ve begun to learn as quants. Yet as data and statistics continue to intersect with innovation, startups, M&A, corporate growth and venture capital, don’t feel bad if you need a little time to adjust.

This Post Has 4 Comments

  1. Miroslav

    Interesting, I have comments on 2 layers:
    a) Psychology of people who disagree! If someone fiercely disagree with you, he/she is labeled as a “hater”!? I think this is not a good basis for start the discussion!
    b) I can imagine that statistics can predict innovation success rate, so I am not the “hater”. But I am curious how it can function, because innovation means going in almost completely unknown territory. Let’s take one trivial example: people see liquid soap dispensers with expensive brand refills. They suggest to replace it with funnels cut from plastic Cola or mineral water bottles, nut and bolt is fitting! :-). This innovation saves money. How you will make statistical prediction whether this innovation will succeed or fail?

  2. Thomas

    Hi Miro, fair points, thanks for making them.

    I completely agree that anyone disagreeing or skeptical shouldn’t be dismissed as a “hater” offhand. That would be both unfair and counterproductive. In that example I was specifically referring to people who seem determined to viscerally object to a claim without acknowledging any evidence that is offered in support of it. Granted, I was rhetorically calling them haters as a literary shortcut, which could be reasonably seen as too dismissive or casual. Point taken. Still, I do feel it’s reasonable to single out and criticize opinions that refuse to moderate in the face of counter-evidence, or people who otherwise show wanton disregard in the face of legitimate data. That’s what I was getting at, I’m glad you gave me a chance to clarify.

    Part B of your question is fantastic, although there isn’t enough space in this comment (or in the blog itself) to do the question justice. There’s obviously no easy, half-baked way to predict innovation outcomes. I’ve been working on this for nearly a decade and it still feels like we’ve barely cracked the surface. However for a little better sense of what’s been done, I recommend these articles and links as a starting place:



  3. Anderson

    Transitions are hard, and few will have as big an impact on people as decision-aided technology and machine learning in the decades ahead.

  4. Brian Dorricott

    One thing I have noticed about people in general is that the more vehemently they are against the idea at first then, once they accept the idea, they are the greatest evangelists… so (assuming rational discourse) it is well worth spending time with the skeptics since they will become your greatest advocates.


Leave a Reply