Source:- strategy-business.com
A large auto manufacturer asked a consulting firm to evaluate its competitive position in relation to ride-sharing startups building autonomous vehicles. Instead of viewing this as a classic strategy project, with a business case, PowerPoint decks, and five-year projections, the firm created a “game” that the automaker could “play” against its competitors. An artificial intelligence (AI) system modeled the voluminous individual choices available to customers, companies, and other entities as digital twins (a digital twin is a computerized replica of a physical asset, process, consumer, actor, or other decision-making entity). The hundreds of thousands of simulations suggested many strategic bets, option-value bets, and “no-regret strategies,” or moves that made strategic and financial sense in a multitude of situations. The selection of those strategies, in turn, made the AI system smarter through learning mechanisms called reinforcement learning, which then further empowered humans to make better decisions. As time progressed, the company was able to choose precise market approaches, pricing, advertising, and customer strategies for multiple cities and communities.
Taken together, these actions created a flywheel, a concept borrowed from the power industry to describe a source of stabilization, energy storage, and momentum, and that was popularized in the strategy context by the author Jim Collins. Executives, instead of trusting instincts and prior assumptions, were able to harness the power of this strategic flywheel to verify hypotheses in simulation and in the real world. Doing so exponentially expanded the array of strategic choices and reduced the cost of experimentation. Rather than paralyzing decision makers with the abundance of options they created, the simulations produced clarifying insights. The result for this auto manufacturer has been a multibillion-dollar valuation of its new services, achieved in less than two years.
Games. AI. Continuous execution and adjustment. Thousands of scenarios to consider. This is not how strategy at blue-chip companies has been done in the past. But it is how business leaders are starting to do strategy now, and how we will need to do strategy in the future — that is, if we are to develop strategies that can both withstand and adapt to the increasing pace of change and disruption that is evident in all industries.
Strategy, the way companies create competitive advantage, has traditionally been a deterministic, linear, and rigid undertaking. The idea is that strategists develop a perfect vision of the future demands of the market, pick a direction or position, invest the full set of resources against it, and execute relentlessly. Strategic planning came into vogue in the late 1960s, and in its pure form was an overarching plan for growth, usually written up in a formal document and endorsed by the CEO. Two decades into the 21st century, this 20th-century tradition continues to be propagated by business schools, by internal planning groups, and by strategy consultants. Even the strategies that seem to work set generic goals and position statements, typically allocate investments on the basis of linear priorities or success metrics (such as return on investment), and create five-year pro forma plans, which are rarely rethought deeply.
But this approach is problematic. The world today is not so deterministic, and the future is highly uncertain. Market and consumer demands, competition, technology, suppliers, and regulations change continually, and the levels and speed of change are intensifying. As a result, the traditional strategic planning process — deterministic, annual, and linear — needs to evolve to become more probabilistic, continual, and multidimensional. In a word, it needs to become more resilient. Organizations can make that shift by adopting a more dynamic approach that leverages AI and advanced analytical techniques. They can then be more sensitive to external market changes, be more rigorous and analytical in evaluating choices and portfolio investments, and make decisions with speed and confidence. In the process, they can develop strategic and growth flywheels that continually reinforce and recalibrate their approach to markets, innovation, and competition.
Clock speed and the flywheel effect
Charles Fine, an MIT professor, introduced the idea of clock speed, the rate at which products or capabilities or business models evolve in different industries. Changes in consumer preferences, technological advances, and regulation are radically accelerating the clock speed of all industries to some degree, and hence the degree of disruption they feel (see the chart in this article).
Capabilities-driven strategy suggests that companies that have a clear way to play (WTP) that aligns with market demands, and that invest in a system of four to six differentiating capabilities that enable the company to excel at the WTP, are better positioned for success. But increasing clock speed changes the calculation. Today, the half-life of a competitive advantage may be fleeting. As industries are disrupted, players that have been successful within the context of one business cycle might need to rethink their differentiating capabilities, their investment portfolios, and possibly even their WTP more frequently and dynamically. Ford no longer just makes cars; it focuses instead on mobility solutions. Big oil companies are investing in renewable energy as a hedge against constraints on emissions. Amazon is competing with…everyone. As a result, it behooves organizations and managers to continually assess competitive moves, regulatory and technology evolution, and consumer preferences — and to adapt decisions in a dynamic fashion.
Successful disruptors are able to exploit market trends by creating reinforcing feedback loops that give them an advantage over time. Consider the power of data network effects. The more data one has, the more one can personalize the customer experience; the more one personalizes the experience, the more customers are attracted; the more customers one has, the more data one gets. This effect leads consumers to flock disproportionately to a few leaders, thereby creating monopolies or oligopolies.
Let’s look at two remarkably simple examples of companies that have thrived in this age of higher clock speed. Jeff Bezos’s original “napkin” diagram (see “Constructing flywheels”), drawn well before Amazon became a leader in online retailing, describes a virtuous circle of broader product selection, better customer experience, more sellers, more traffic, lower cost structure, and lower prices, all reinforcing one another. A diagram describing Uber’s strategy shows a similar dynamic at work. Faster pickups generate more demand, which attracts more drivers, leading to better geographic coverage, less driver downtime, and lower prices. The components of the flywheels include positions or features that encourage reinforcement through causal effects, thereby increasing exponential and nonlinear adoption. One can assemble such flywheel approaches by thinking carefully about the most important features that are going to drive demand, and the causal linkages between them.
Resilient companies
A well-defined corporate identity, which includes a chosen way to play, associated capabilities, and a portfolio of operations, is helpful in creating boundaries and guidelines for focus. But to thrive through new and disruptive business cycles, companies must continually evolve their capabilities system to match — or even shape — the demands of the market. The secret to more sustained success involves three vital steps. First is adapting by continually sensing market variables, and experimenting with new ideas or bets using a clear mental model for the “spread” (or range of scenarios and possible outcomes) of the decisions being made. Second is developing reinforcing causal feedback loops that provide disproportionate advantage as disruptive market trends take off, and testing, killing, or modifying ideas against this framework constantly. And third is building focus on a WTP and scaling an associated capabilities system, as stated in the capabilities-driven strategy, informed by the needs of the dynamic feedback loop to scale and mature the business model.
Netflix, which our colleagues discussed in their 2017 s+b article on digital disruption, started out competing with Blockbuster Video on the basis of customer convenience and fees. It sent DVDs out by mail and didn’t require return by a specified date, and replaced individual rental fees with a monthly subscription model. In 2007, Netflix disrupted its own business (and killed the rest of Blockbuster’s) by introducing streaming video on demand, which pushed it into competition with cable television. As it amassed customers, Netflix continually refined its analytic capabilities, crunching data to offer more fine-grained recommendations, which consumers could explore more quickly with the flexibility of streaming. This analytic capability fed naturally into the creation of appealing original content, which began with House of Cards in 2013 and expanded to more than 350 original series released in 2017. Bird Box, a Netflix-produced horror film starring Sandra Bullock, was viewed on 45 million accounts in the first seven days after its release in December 2017.
Netflix’s culture and strategy allowed resilience and adaptation all along the way. It gave employees the freedom to explore ideas and learn, was willing to pay at the top of the market for talent, and openly eschewed conventional processes in order to provide room for agility. In effect, Netflix has built three virtuous circles that function as flywheels. There’s a personalization circle: better personalization with AI leading to more customers, more viewing, more data, and, in turn, better personalization. There’s a decision frequency circle: The subscription model leads to more decisions per time unit, which leads to more data and better personalization. And there’s a content creation circle: With more customers, Netflix has more viewings, and hence a better understanding of individual customer preferences, and becomes a more attractive partner for content creators.
Netflix also maintained its identity all along and focused on the capabilities system that enabled the business model to focus and scale within each business cycle. The unlimited subscription model, at the core of the company’s first disruption, was intended to position Netflix to offer streaming when the technology caught up. Once it did, Netflix focused on building world-class capabilities in subscription model administration, online streaming, customer insights, and tailored content production.
The focus on technology and logistics helped keep costs low, and enabled Amazon to vastly increase product variety. That, in turn, began to make it the go-to online shopping portal. But Amazon tapped into deeper capabilities to create a flywheel effect with consumers. It mined data to understand consumer preferences, and shaped buying behavior and convenience by introducing such features as one-click ordering and free shipping through Amazon Prime. Over time, the company also expanded into businesses that had nothing to do with retailing: online content streaming; Amazon Web Services, which provides cloud computing services; and new physical products such as Kindle (e-reader), Fire (digital media player), and Echo (smart speaker assistant). Echo’s Alexa has become the technology backbone for interoperability for countless devices exploiting the Internet of Things. This has created another causal loop, as making more devices interoperable on Alexa led to more integration and convenience, leading to higher customer sales, driving more suppliers to integrate with Alexa. Through these many evolutions, Amazon has retained its founding identity of being a customer-driven and technology-led retailer. Bezos wrote in a famous letter to shareholders, “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight.” Amazon’s capabilities system in supply chain and logistics; customer insights and preferences; and online, retail, and technology platform innovation have all been unparalleled.
Peloton, a fitness startup whose US$2,000 stationary bicycles and high-energy classes have gained a cult following since its founding in 2012, started out as a software player, but has since focused directly on every aspect of the value chain, including software, hardware, studio instructors, logistics, and even retail. As CEO John Foley tells the story of its evolution, it is clear that Peloton continued to adapt to market realities and opportunities against conventional thinking, creating a vertically integrated business that controlled all aspects of the customer’s experience. The dynamic feedback loops are evident in the fact that Peloton’s Net Promoter Score (its chief customer satisfaction metric) rose as every new aspect of the experience was controlled. Customer delight with the product and service led to highly efficient word-of-mouth marketing, which led to more customers and greater scale, which improved the company’s financial capacity to build an immersive experience. As it has grown and evolved, Peloton has built out its capabilities systems for software, logistics, and instruction.
Advanced strategy tools
As they work to build growth flywheels, companies can utilize the power of strategy flywheels. In fact, the forces that have led to disruption and accelerated clock speed are also providing businesses with the tools to develop resilient strategy. Automation, analytics, and AI have made huge advances in recent years. In business, we increasingly see machines perform manual or cognitive tasks; organize, analyze, synthesize, and act on large amounts of data; and make operational and management decisions, or at least recommend them. But what about strategy, which is generally regarded as a uniquely human endeavor? To be sure, AI alone can’t develop our strategy for us. But it can change the way we do strategy, and help organizations reimagine the future and develop their own flywheels. In fact, it is already starting to do so.
To be sure, AI alone can’t develop our strategy for us. But it can change the way we do strategy, and help organizations reimagine the future and develop their own flywheels.
It’s helpful to consider the process of strategy formulation, planning, and execution as a game. Games, in general, have a fixed set of rules, are played among a small and known number of players, have well-defined and agreed-upon outcomes, and have known uncertainty (that is, the uncertainty stems from the choices available within the constraints of the game and is not environmentally introduced). In contrast, strategy, or more broadly how businesses operate, is subject to changing rules, is often played against a large and unknown number of players (e.g., disruptors from brand-new industries), has an outcome that is not clear or agreed upon (e.g., maximizing profits or being a socially responsible organization), and is subject to both known and unknown uncertainty (e.g., the emergence of new technology). But the building blocks for games and strategy are actually quite similar, as both involve setting policies, grappling with dynamic models amid the backdrop of environmental assumptions, and coping with randomness.
Looking at strategy through the lens of gaming can lead to a new approach to building dynamic strategic plans that can embed foresight and resilience. As shown in “Sense, think, act” below, the dynamic and resilient flywheel strategy has three components.