Understanding Markets (and Marketing) as Complex Adaptive Systems
What do markets (and marketing) have in common with slime mold, beehives, and traffic? All are examples of what science calls complex adaptive systems. They share defining characteristics that make markets seem, well, a little wacky.
Markets only seem wacky when they are compared to the unrealistic model of a machine. Machines can be reduced to manageable parts. Improve the parts and the machine performs better. This paradigm, inherited from the 20th Century industrial era, underlies most traditional business practices. Viewed through the machine lens, it seems reasonable to believe that if marketing can’t be made predictable and reliable, then something must be wrong. But complex adaptive systems aren’t machines. Markets, cities, economies, traffic flows, stock exchanges, and social networks behave more like reefs than rockets, more like ant colonies than automobiles. When situations are relatively slow and stable, practices based on the machine-view produce efficiency and robustness. However, markets are what the U.S. Army calls VUCA (volatile, uncertain, complex, ambiguous). In complex situations, industrial structures and practices have serious limitations. They just are not agile and resilient enough to respond to reality.
Markets are more like living collectives than like machines.
A better understanding of markets as living collectives will improve marketing outcomes. Understanding how markets really work will pave the way for businesses to adopt more effective marketing methods. Navigating a VUCA environment requires techniques different from those that assume stability. Pioneering businesses are driving the popularity of complexity-wise practices such as agile marketing, integrated teams, more collaborative organizations, advanced analytics, and human-centered business culture.
Characteristics of Markets as Complex Adaptive Systems
With the help of analytics, we’re starting to see how human-social systems share characteristics with living collectives. Although we are in the early stages of learning about how complex systems behave, scientists have already identified fundamental properties. These characteristics are very different from machines.
Markets are comprised of multiple heterogeneous interdependent agents. Machines have parts, but they are limited, purpose-built and none act independently. Markets are tangled by the interactions of autonomous individuals (e.g., buyers, entrepreneurs, salespeople, marketers, influencers) and enterprises (e.g., corporations, media companies, regulatory agencies, supply-chain participants). Each agent adapts their behavior according to local motivation, context, and exposure to information and environmental signals. Feedback loops ripple through space and time. The longer the time between an action and an outcome, the greater the number of interactions that can occur, the broader the ripples, the more entwined the feedback loops.
Control is distributed. No one is in charge. People control machines. Outcomes can be specified. Behind traditional expectations about marketing is the belief (or wish) that companies are the “mission control” of their campaigns. But market behavior isn’t governed by any controller. Marketers can only intervene and influence. Distributed control makes marketing more like raising a child than flying an airplane. There is no one-size-fits-all manual.
Markets are subject to the “butterfly effect”. A machine’s performance can be replicated. The same starting conditions produce the same outcomes. Tap lightly on the accelerator and the car inches forward. Press pedal to the metal and the car races. In contrast, the nonlinearity (or “butterfly effect”) of complex systems means continual surprises. Flying an airplane is complicated and has many steps. But airplanes are predictable which contributes to their safety. Air traffic control is a complex adaptive system, constantly changing due to weather, aircraft downtime, etc. Non-linearity means that small changes, especially early, can have profound effects later. A single tweet in 2021 could initiate a huge deal in 2023. Alternatively, large actions may have little or no effect. Sales could take a downturn despite a large ad buy. Complex adaptive systems are semi-predictable and therefore can be modeled, but only within the bounds of probability and time.
Market-level behavior patterns emerge from the swarm of participant interactions. Knowledge of how machine components work enables you to predict machine performance. Machines are logical and reductive. What happens at a macro-level is directly caused by adding up all the component actions. The behavior of complex systems, on the other hand, comes about in an entirely different way. Complex systems have this weird propensity called emergence. Complex systems form higher-level patterns on their own – not directly related to the behavior of any individual and without the presence of any mission control. Traffic, for example, isn’t a “thing”. Traffic emerges from the interactions of cars, drivers, and the environment. Markets work the same way. Examples of emergent market patterns include innovation adoption curves, hype cycles, sales seasonality, popularity trends, and social media virality.
Markets have blurred boundaries. One of the biggest challenges for trying to calculate marketing return-on-investment (ROI) stems from a lack of a consistent definition of what’s in the ROI calculation. ROI calculation requires distinct category boundaries. Every machine component is distinct. But nothing about a market is distinct, causing organizations to disagree about the artificial perimeters that marketers must impose to attempt ROI. What does “return” mean? Is it only financial or should other important benefits such as customer loyalty or company reputation be included? Given the time lag between marketing actions and outcomes, where should measurement start and stop? Should the ripple effects of marketing actions be included? A study conducted by Proof Analytics of 400 business executives revealed that 95% of executive respondents doubted marketing leaders had the same understanding of value creation as business leaders. More likely, even the business leaders who responded to this survey would not agree on the definitions!
Digital technology and globalization mean that we encounter market complexity and its VUCA characteristics with greater frequency and often with greater consequences. To improve marketing effectiveness, it’s imperative that businesses adapt to the reality of today’s complex market.