Problem-Solving: the value of Serendipity

I am currently reading ‘Enemy of all Mankind’ by Steven Johnson. It is a fascinating story of a pirate .. and in the introduction, he writes the main motivation behind the book: “we tend to think of grand organizations like corporations or empires coming through deliberate planning: designing the conceptual architecture for each imposing structure, brick by brick. But the shape of an institution ultimately takes is not so much designed in advance by a master engineer as it is carved away by challenges to its outer boundaries, the way a coastline is partly formed by the endless battering of much smaller waves.”. 1 In other words, he debunks the idea of determinism – the narrative arc of a clear, well-defined path from actions to outcomes. And elsewhere in his work, he offers up an alternative history with the notion of the ‘adjacent possible’, one of those ideas that seems, at first, like common sense, then gradually reveals itself as an entirely new way of looking at almost everything: Serendipity is way more important than we care to admit.

We all have seen this play out over and over again in professional settings: while you are trying to solve a specific problem, you stumble upon a related problem which, when solved, creates far more value than the original one you started with. Johnson documents many such situations in ‘Where good ideas come from’ another of his eminently readable books.

All this brings me to the main point: in our problem-solving arsenal, how can we create room for Serendipity?

What is Serendipity?

Serendipity is really the accidental discovery of information and can happen in two ways. At one end, it can be passive – the non-purposive kind where you are not explicitly looking for something but stumble upon an idea: one of the most storied examples being Isaac Newton’s motivation to study gravitation force triggered by an apple falling (although it is probably an urban legend). The other kind is active – looking for adjacencies as you are trying to solve a problem. One example that is often talked about of active serendipity is how Alexander Fleming ended up discovering penicillin while researching the properties of bacteria.

Why is it important?

In this very VUCA world that we are in, it is obvious that the expectation of driving progress in an organization by identifying well-defined problems that need to be solved to get deterministic results is to say the least, laughable. When the problem space is fuzzy, we all know that the path to a feasible solution needs to be discovery-led, and iteratively goal-seeking. And when you are stumbling along, it is important to keep an eye out for adjacencies – who knows what might turn up as an interesting insight or identify a related problem. I am reminded of a situation where we were trying to understand the propensity of enterprise B2B customers to renew their contracts – and during the course of the analysis, the team stumbled upon interesting insights based on the sequence analysis of customer engagement touchpoints from across the organization (Sales, Customer Support, activity on the self-service portal etc.) which in turn led to driving a ‘Next Best Action’ playbook for the account teams; which proved to be far more effective in driving towards contract renewals using multiple tactics as opposed to the default (often expensive) approach of price negotiation driven renewals.

How do you enable Serendipity?

This is probably the fuzziest and one of the most difficult to implement: while it is an alluring idea to allow your teams the room for the ‘slow hunches’, how do you make sure that the teams are not going down rabbit holes and compromising the project goals? There are no easy answers but here are 3 nudges (aside: these are methods that are used in recommendation systems where one of the goals is to encourage customers to explore new categories in addition to the main goal of relevance – i.e. engineer serendipity):

  1. Diversity: how diverse are the hypotheses, analyses and insights? For instance, during the Exploratory Data Analysis and hypothesis testing, does the team just churn through simple, univariate hypotheses or do they look for the non-obvious multivariate hypotheses?
  2. Novelty: how unusual or original are the hypotheses? While path analysis is fairly common in understanding consumer behavior online, how does it apply to multi-channel interactions in a B2B context?
  3. Unexpectedness: does the team deliver some unexpected insights or come up with unusual hypotheses? Here’s my favorite rant: while textbooks teach us that behavior tends to a normal distribution, which leads most rookie teams to assume just that despite evidence to the contrary: are the teams on the look-out for deviations from this simplistic view, especially when dealing with extreme events?

 And needless to say, all this is not pure science – there is art: from intuition developed by being around problems; from enabling connections between teams across functional boundaries; by building cross-functional, diverse teams that bring in different perspectives; and above all, enabling a culture of curiosity: one that encourages an exploratory mindset. As Steven Johnson said, ‘Chance favors the connected mind’2

Further reading:


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