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Fitness Landscapes for Product Management
How evolutionary theory can be applied to business
“ … the species that survives is the one that is able to adapt to and to adjust best to the changing environment in which it finds itself ” ― Leon C. Megginson (often incorrectly attributed to Darwin)
In nature, species either adapt to their environments or become extinct. Products are a bit like that too. They either thrive in the market or die.
Can we use insights from evolutionary biology to improve product design and management; to better understand why some products thrive and others fade away? I think we can. Let’s start by using fitness landscapes to study the phenomena of product/market fit.
What they are
Pioneering geneticist Sewall Wright devised the fitness landscape to help visualise all possible gene combinations graded with respect to their adaptive value under a particular set of conditions (1).
Plotted as a contour map, genetic traits that represent high-adaption to an environment are represented as peaks, while those with low-adaption are represented as lowlands. In a particular environment, there may be several adaptive peaks (local optima) separated by ‘valleys’, as depicted in the GIF below.
In Wright’s visual metaphor, adaption is depicted as a species moving towards high-fitness peaks over time. Sometimes this involves mutation paths that cross regions of low-fitness (adaptive valleys) before reaching an adjacent higher peak (2).
Often, just one or two traits can make an enormous difference to a species’ survival. Take fur colour, for example. A Polar bear’s white fur possesses high-fitness for the Arctic, allowing it to sneak up on prey. The same trait exhibits low-fitness in forests, where it would make the bear stand out. For the closely related Brown bear, however, it is exactly the opposite. Dark fur colour has high-fitness for the tenebrous boreal forests where Brown bears thrive, but low-fitness for the Arctic wilderness (3). The environment dictates fitness.
This is just like products. Certain key traits can make all the difference between a product becoming a hit or flopping. Take video conferencing, for example. Why did Zoom suddenly become the dominant video conferencing solution, rapidly overtaking incumbents like WebEx, GoToMeeting and Skype?
Two very important traits of video conferencing are interoperability (how well it works with other applications) and latency (the time it takes for a single frame of video to transfer from the source camera to the destination display). Eric Yuan, the CEO of Zoom, obsessed over them.
A visualisation of comparative fitness in competitors might resemble the image below, offering a quantum leap in meaning-making compared to a typical competitive analysis report (4).
Why they matter
There is nothing new about product owners and managers needing to focus on product/market fit. Indeed, Marc Andreessen calls it “the only thing that matters” However, the interpretative frameworks that we use to design and manage products are pretty lacklustre. Net Promoter Scores return only a trailing indicator of aggregate satisfaction. Customer surveys and focus groups are highly subject to bias.
With some careful consideration of variables (5), the fitness landscape model can highlight the relative impact of product traits. It can help product teams to focus on those key aspects of a product that will furnish evolutionary advantage. It can provoke them to ask questions such as:
what are the key fitness traits from the customer’s perspective?
what are the fitness criteria of different customer groups?
should we degrade trait x in order to optimise trait y?
should we explore neighbouring prototype space for possibly superior peaks?
Importantly, fitness landscapes can be highly dynamic, as depicted in the GIF below.
In nature, coevolutionary processes are always at work. In markets, customer expectations are always changing. WebEx was once dominant. When Zoom’s competitors catch up and also deliver low latency, what fitness traits will emerge as being critical?
By depicting products as agents in dynamic fitness landscapes, product teams are better equipped to consider the evolutionary forces at play, and the trade-offs required to navigate a complex product/market environment.
I will be expanding upon this model and exploring related theory-transfer from evolutionary biology in future transmissions. Please subscribe here.
To understand more about evolution from a mathematical perspective, these introductory videos by the Santa Fe Institute on fitness landscapes, the quasispecies equation and error catastrophe are excellent starting points:
Notes and References
1. Wright, Sewall (1932). The roles of mutation, inbreeding, crossbreeding, and selection in evolution (PDF). Proceedings of the Sixth International Congress on Genetics. 1 (8): 355–66.
2. Johnson, N (2008). Sewall Wright and the development of shifting balance theory. Nature Education 1(1):52
3. Cahill, James A et al. Genomic evidence of geographically widespread effect of gene flow from polar bears into brown bears. Molecular ecology vol. 24,6 (2015): 1205-17
4. This graph is just a mashup, no real data has been used. To create an accurate visualisation one would simply need:
a measure of fitness (e.g., customer satisfaction)
comparative interoperability data
comparative latency data
5. See the following paper for considerations regarding transferring fitness landscapes to other domains: Marks, P., Gerrits, L. & Marx, J. How to use fitness landscape models for the analysis of collective decision-making: a case of theory-transfer and its limitations. Biol Philos 34, 7 (2019).
GIFs of fitness landscapes courtesy of Randy Olson and Bjørn Østman, originally published in Visualizing evolution in action: Density-dependence and sympatric speciation