Book review: “Competing against luck” – Clayton Christensen


I think it is no exaggeration to say that Clayton Christensen is THE management guru on innovation. His first book, “The innovator’s dilemma” is a must read classic management book.

I had linked already to his TED talk on his new concept “Jobs to be done”.

Christensen claims that most companies get innovation completely wrong. Most try to innovate without a clear strategy and worse, using some kind “big data” analysis trying to somehow identify what new product customers would like to buy.

This leads to many failures and very few new products are succesful. Instead of looking at (random) data patterns, Christensen advocates that companies should rather try to really understand what people try to achieve when the “hire” (buy) a product.

He calls this “understanding the job a customer tries to get done” when buying a product. Interestingly, the job to be done could be something completely different from what the company intends their product to be used for.

One example is the Milk Shake sold by a fast food chain. Management failed to increase sales on this product based on traditional analysis. When they really tried to find out who was buying their milk shakes and why, the found out that much of the business was done early in the morning.

Commuters would buy the Milk Shake on their way to work because they needed some calories on their 1 hour drive to work but also they want to have something which occupies them for some time. Plus, the milk shake was easier and cleaner to eat than a donut. Once they understood why customers were actually buying the milk shake, they then “innovated” along this “job” to make it easier and quicker to buy the shake in the morning and could increase sales significantly.

Trying to understand “jobs to be done” also enables to better understand who your actual competitors are. For the Milk Shake and the commuters, the primary competitors were for instance Donuts, Bananas etc. and not other Milk Shakes.

Another interesting example was Intuit, which had big success after they remooved many features from their software products, because they found out that people used it in a different way and were happy with less.

Interestingly, as simple as it sounds, the “job to be done” concept is not so easy to implement as it really requires deep analysis and deep insight into what customers really trying to achieve. The current hype about “big data” goes into the complete other direction. Big Data providers claim that you only have to collect enough data and then you will be able to understand what your customers want.

Personally, I find Christensen’s concept more realistic but for companies the “Big data” approach is clearly easier to implement. Interestingly, a post I linked to from the excellent Morgan Housel also mentions this as one important “competitive advantage”:

The ability to empathize with customers more than your competition

Forty-seven percent of mutual fund mangers do not personally own any of their own fund, according to Morningstar. That’s shocking. But I suspect something similar happens across most businesses.

What percentage of McDonald’s executives frequent their own restaurant as a legitimate customer interested in the chain’s food, rather than a fact-finding mission? Few, I imagine. How many times has the CEO of Delta Airlines been bumped from a flight, or had his bags lost by the airline? Never, I assume.

The inability to understand how your customers experience your product almost guarantees an eventual drift between the problems a business tries to solve and the problems customers need solved. Here again, a person with a lower IQ who can empathize with customers will almost always beat someone with a higher IQ who can’t put themselves in customers’ shoes. This was apparent in the recent election, when understanding the electorate’s mood far exceeded the power of traditional campaign strategies.

Similarities to current trends in investing:

IPerosnally, I also see some similarities in the investment space. Smart Beta or even “Big Data” and “Artificial Intelligence” funds  all claim that you just need enough data to create a great fund performance. Or you just buy an index fund and you will still do better than almost all “active funds”.

There is no need to understand how companies actually make money and what causalities drive stock returns in the long-term.

I think in investing, the big misunderstanding is that most people confuse “active investing” with fundamental stock analysis. Most active fund managers mostly try to guess what stocks will do the next quarter or 12 months, but very little time is pent on real long-term analysis.

That’s why I think that similar to Christensen’s “jobs to be done” approach, also in investing the “why and how do companies create value in the long-term” analysis will lead to much better results in the long term if done right. But that is maybe the topic of a future blog post.


As all of Christensen’s books, “Competing against Luck” is well written and quite easy to read. I found the underlying concept extremely helpful and intuitive. Indirectly, the book is also a slap into the face of the “big data” supporters.

For investment analysis I think it could be interesting to look for companies that are innovating in a way that Christensen describes as this seems to be a very efficient way to ensure continuous innovation without wasting a lot of money and time on stupid ideas.



  • It seems that the investing panorama is rather dull and unatractive: no interesting ideas, cases analyzed nor opportunities in perspective. Hence I guess that a patient, rational investor, should keep calm & carry on. Wait and look forward for better times. And just hope there are no “natural catastrophes” in the stock markets.

  • Great post. I couldn’t agree more that Jobs Theory can be used to identify investment opportunities, and I don’t think it’s necessarily harder to implement than Big Data practices.

    I’ve experienced situations where a couple of developers mess around with Hadoop for months to get big data in the right structure before anyone even begins thinking about what to do with the output. It’s can be really hard to get a useful outcome.

    Meanwhile, there is a playbook for implementing Jobs-to-be-Done in product development and I believe a similar approach can be used to identify investment opportunities:

    Clearly, both Big Data and Jobs Theory can lead to mediocre results if not implemented well, but I do think that Jobs Theory leads to more productive and reliable results when a team makes the effort to learn how to use it well.

  • Thanks for a great post.

    May I suggest better books: “Antifragile: Things that gain from disorder (luck/volatility/time)” and “Crossing the Chasm” which looks at marketing an innovation (a much more useful/repeatable task than studying doing post-mortums on innovations which necessarily cannot be predicted).

    Of course you try your best to experiment in the right direction, to maximize your gains from luck (e.g. allowing plenty of input from the lowest-level employees and customers who are more familiar with the product than simply from Board Members). Grassroots organizations succeed in the same way: Heads I win, Tails I don’t lose much. That isn’t competing against luck, that’s embracing randomness to the fullest. I worked at McDonald’s as a teenager. Anyone who thinks they do not listen to what the bottom level thinks about the food doesn’t understand the business. They’ve just been (wrongly) focused on innovating processes rather than the product. They’re getting back to their roots now with experimentation on different new products as Ray Croc intended (IMO).

    Come to think about it, I’m really not sure why anyone would ever want to “compete against luck.” This is where purist value investors on one hand and option trader-types on the other can’t or don’t want to reconcile Graham & Buffett and Taleb and see that the two philosophies are more similar than first meets the idea. Even though it couldn’t be more obvious.

    Graham said you buy with a margin of safety to guard against the “hazards of the future” and one’s own hubris (namely, attempting to “compete against luck”) which allows one to benefit from good fortune and, essentially, do “trial-and-error” of search, exclusion and selection with limited downside. Indeed, what is a special situation other than embracing luck to the fullest? No one really knows the odds of some deal going through (even the DoJ chief that’s making an antitrust doesn’t know) but if the odds are right, randomness is on one’s side.

    And it applies just the same to Viagara, Singapore (one of Lee Kuan Yew’s favorite dictum was “let’s try”), Amazon, Google, Roger Federer etc etc. When trial-and-error stops, you get “stock buybacks” or “cost reduction” types of over-optimization a la 3G (they may prove me wrong, . That’s not true innovation, that’s process improvement. The book had mixed signals and I’m not sure why. Taking randomness out of the picture? Maybe I need to read it again as I read it quite quickly when it came out and was largely disappointed.

    Christensen is brilliant, but the guy’s other book, Innovator’s Dilemma, is more satisfactory.

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