Over the past decade, the companies that make up the S&P 500 have spent an astounding 54 percent of profits on stock buybacks. Last year alone, U.S. corporations spent about $700 billion, or roughly 4 percent of GDP, to prop up their share prices by repurchasing their own stock.
The hegemony of science and scientific thought, in the developed world over the last century, is an indicator of the winner of the most recent battle for dominance among systems of inquiry. A belief in the scientific method, as the only and superior valid method of inquiry for describing, explaining, and interceding in the world, is a hallmark of our technological age. Science, as an activity of disciplined inquiry, has often been called the new religion of the contemporary age.
Nelson, Harold G., and Stolterman, Erik. The Design Way : Intentional Change in an Unpredictable World (2). Cambridge, US: The MIT Press, 2012.
To briefly recap, Aggregation Theory is about how business works in a world with zero distribution costs and zero transaction costs; consumers are attracted to an aggregator through the delivery of a superior experience, which attracts modular suppliers, which improves the experience and thus attracts more consumers, and thus more suppliers in the aforementioned virtuous cycle. It is a phenomenon seen across industries including search (Google and web pages), feeds (Facebook and content), shopping (Amazon and retail goods), video (Netflix/YouTube and content creators), transportation (Uber/Didi and drivers), and lodging (Airbnb and rooms, Booking/Expedia and hotels).
These slides are from Martin Weigel at Wieden+Kennedy Amsterdam, who writes a lot of great stuff over at his blog: https://martinweigel.org/. These are from his slideshare (link below).
They do a fantastic job of illustrating why targeting ‘light buyers’ is the primary route to growth for most brands. Most of this theory is built on Byron Sharp’s work, especially his book, How Brands Grow, which is required reading for any marketer.
The pattern is the NBD-Dirilecht pattern of brand choice. It shows the distribution of a brand’s customer base in terms of ‘loyalty’. This pattern has been found across brands, categories and countries, and illustrates why trying to create ‘passionate loyalty’ is not a great route to growth, nor targeting increased loyalty at all for that matter.
A lot of this aligns with what Roger Martin has advocated for in his writing on strategy and management (unsurprisingly, since ‘design thinking’ is essentially ‘social science thinking’):
- the blending of analytical and intuitive mindsets;
- the need for both analysis and synthesis;
- the need for choice-creating and choice-making;
- understanding quantities vs. qualities; and
- the difference between for reliability and validity.
Clayton Christensen also has a similar thread running through his work, from the harm financial tools do to innovation, to why numbers are the lingua franca of business, to the process of ‘theory building’ and associated ‘dumpster diving’.
Design thinking isn’t about design. At least not in the way we normally think about them. Instead its an abstraction of ‘how designers think when designing’. And the use of ‘designers’ here really refers to anyone skilled in the creation of new solutions, not only those with formal training design (though they are trained in some of the skills).
From this comment thread here:
I believe there is a form of thinking – analytical thinking – that dominates thinking in business. It is deeply rooted in the past; it seeks to extrapolate the past into the future using deductive and/or inductive logic. In opposition to analytical thinking is not design thinking but rather intuitive thinking – knowing without reasoning. Intuitive thinking imagines the future. It is about invention; the most disruptive and unsystematic form of thinking.
An organization that attempts to survive on analytical thinking will slowly die a death of stultification. An organization that attempts to survive on intuitive thinking will expire between inventions.
To me, design thinking is the productive combination of analytical thinking and intuitive thinking, a form of thinking that utilizes the deductive and inductive logic of analytical thinking and combines it with the abductive logic – the logic of what might be – from intuitive thinking. In combination, these modes of thinking enable an organization to achieve the reliability that permits survival and the validity that enables renewal.
Strategy development is a class of design process. Not only is the output a ‘design’ for a business, and a good business model having the attributes of being ‘well designed’ (focused, cohesive, elegant trade-offs, least possible parts), but the process itself is one of ‘designing’.
To this end, it requires both imagination and creation – abductive logic – to look into the future and imagine what might be, and inductive/deductive logic to validate that what we have created is feasible and viable.
Unfortunately this first part is poorly understood by most managers and consultants. Instead, they rely on the tools of deduction/induction to ‘analyse’ future opportunities. However, as Toby Golsby-Smith puts succinctly “we cannot analyze our way one inch into the future, for the simple reason that the future does not exist yet, so it is not there to analyze”.
There if a fundamental difference between optimising a system and designing a system. The first is what we often call ‘operations’, the second is the foundation of strategy work. But the tools we commonly use for the second are mostly built for the first.
Strategy work requires much more than rational, linear, quantifiable analysis. It requires looking into the future and inventing something new. It’s as much a creative process as it is analytical, and starts with a deep empathy for the customer, their world, and how it is changing – in all its messy, qualitative, emotional, inconsistent and human form.
The Moment of Clarity – Christian Madsbjerg and Mikkel Rasmussen is a great book all round, but especially in its articulation of this point.
Most of these strategies, created with a linear mode of problem solving, aim at getting the maximum growth and profit out of the business through rational and logical analysis. The ideal is to turn strategy work into a rigorous discipline with the use of deductive logic, a well-structured hypothesis, and a thorough collection of evidence and data. Such problem solving has dominated most research and teaching in business schools over the last decades and has formed the guiding principles of many global management consultancies. Slowly but steadily, this mind-set has gained dominance in business culture over the last thirty years. Today it is the unspoken default tool for solving all problems.
This linear mind-set borrows its ideals from the hard sciences like physics and math: learn from past examples to create a hypothesis you can test with numbers. As it uses inductive reasoning for its foundation, it is enormously successful at analyzing information extrapolated from a known set of data from the past. Default thinking helps us create efficiencies, optimize resources, balance product portfolios, increase productivity, invest in markets with the shortest and biggest payback, cut operational complexity, and generally get more bang for the buck. In short, it works extraordinarily well when the business challenge demands an increase in the productivity of a system.
How default thinking works
The default problem-solving model has its roots in what can be called instrumental rationalism. At the heart of the model is the belief that business problems can be solved through objective and scientific analysis and that evidence and facts should prevail over opinions and preferences. To get to the right answer, so the thinking goes, you should adhere to the following principles of problem solving:
- All business uncertainties are defined as problems. Something in the past caused the problem, and the facts should be analyzed to clarify what the problem is and how to solve it.
- Problems are deconstructed into quantifiable and formal problem statements (issues). For example, “Why is our profitability falling?”
- Each problem is atomized into the smallest possible bits that can be analyzed separately—for example, breaking down the causes of profitability into logical issues. This analysis would include “issue trees” for all the hundreds of potential levers for either decreasing costs or growing revenue (customer segments, markets, market share, price, sales channels, operations, new business development, etc.)
- A list of hypotheses to explain the cause of the problem is generated. For example, “We can increase profitability by lowering the cost of our operations.”
- Data is gathered and processed to test each hypothesis—all possible stones are turned and no data source is left untouched.
- Induction and deduction are used to test hypotheses, clarify the problem, and find the areas of intervention with the highest impact, or what is commonly called “bang for the buck.”
- A well-organized structure of the analysis is deployed to build a logical and fact-based argument of what should be done. The structure is built like a pyramid that develops the supporting facts, some subconclusions, and an overall conclusion and then ends with a prioritized list of interventions to which the company should adhere.
- All proposed actions are described as manageable work streams or must-win battles for which a responsible committee, or person, is assigned.
- Performance metrics and a proposed time frame with follow-up monitoring are put in place for each committee to complete the task.
- When all work streams have been completed, the problem is solved