The Carrot and the Stick: Leveraging Strategic Control for Growth

Game Theory, Signaling, and the Strategic Use of Information

How to Use These Concepts Strategically and Anticipate Competitive Response

So far, we have discussed the importance of strategic control points and how points of strategic control and vertical incentive alignment are crucial to success in markets today; however, implementing strategies by utilizing these principles is often easier said than done. Further, and perhaps more importantly, any plan to leverage strategic control points is likely to meet with a response from rivals in the marketplace. Fortunately, we have research, utilizing both mathematical and empirical models in the field of game theory, to guide us.

In emphasizing why you should be concerned not only about the “static” strategy of the carrot and the stick but also the “dynamic” strategy that incorporates competitive and market response over time, one of the more compelling stories is that of a consulting client of mine. In 2015, I was working with a company that was looking to obtain board approval for a brilliant strategy designed to steal share from rivals in what is essentially a zero-sum slow-growth market. The company’s board, which included the renowned business gurus Jack Welch and A.G. Lafley, pushed back. At a board meeting seeking board approval for this new strategy, the company’s CEO was asked, “Surely, any strategy designed to steal share from rivals will generate competitive response. Have you ‘gamed out’ this competitive response?” The leadership team had not – and based on this astute pushback by the board, we worked through a detailed game theory exercise in just over four months. At that stage, armed with a game plan based on competitive response, the team went back to the board and secured approval for their strategic plan. This strategy generated a 27 percent growth in revenue one year after implementation – inside of what was a relatively static and slow-growth market segment. It’s important to realize that the best-laid plans may not succeed if the competitive environment is such that you will be fought tooth and nail. Be ready and prepared. That is the idea behind the wisdom of Sun Tzu (quoted in figure 8.1) and what this chapter is about.

This is particularly true in today’s markets. Rita McGrath, in The End of Competitive Advantage,2 argues that, given how fast markets move today, competitive advantage is “transient.” In contrast, this book has argued that finding and securing points of strategic control is a key way to prolong competitive advantage and keep competitors at bay: when you use strategic control points to stay ahead of competitors in markets that are indeed transient, you can generate unique competitive advantages.

In the 1990s, Adam Brandenburger and Barry Nalebuff argued that “cooperating” with competitors and suppliers can analogously provide unique competitive advantages in many markets.3 As we saw earlier in the banking and “Fintech” example, this is a double-edged sword. On the one hand, cooperating has unique and distinct advantages. On the other hand, those initiating the cooperation (e.g., banks and “peer-to-peer” lending) do so only when it is to their own advantage. Although a strategy of cooperation fits neatly into the framework of this book, it should be used with caution. Game theory can help guide us in this respect.

The Halley’s Comet Effect

All comets, including Halley’s Comet, contain a center – a nucleus – that is usually only a few kilometers in diameter and is composed primarily of rocks and ice. What is less well known is that most comets actually have two tails, one of which is usually much brighter than the other. The tails form as a result of the sun’s solar wind – the stream of charged particles that emanate from the sun. The solar wind dislodges gas and dust from the comet and forces the material into very narrow (relative to their length) tails.

In business, we often compete (or end up) in the part of the market that is the metaphorical equivalent of a comet’s tail. We prepare our strategies for current market conditions and relative to the positions of our competitors; however, by the time we put these strategies and tactics into place, the market and our competitors have moved on, and we end up one step behind – in the market’s metaphorical tail. We then try to adjust, reacting to the new market equilibrium. By the time we respond anew, the market has moved again, and we are once again in the market’s “tail.” And so it goes. Over and over. The proverbial “Halley’s Comet effect.”

How do we stay at least one step ahead of the competition? How do we avoid remaining in the market’s tail, chasing a continually moving target? This chapter focuses on the use of a structured and disciplined approach for (i) establishing priorities, (ii) becoming more competitive, (iii) meeting customer needs profitably, and (iv) managing competition rather than just reacting to it. Tools and approaches are available to us that can be used in concert to avoid the Halley’s Comet effect – enabling us to always be ahead of our competition, customers, and market trends.4

The Story of the Girl on the Wing5

In a classic episode (“Nightmare at 20,000 Feet”) of Rod Serling’s Twilight Zone, Bob Wilson (played by a very young William Shatner) is on his first flight since his nervous breakdown six months earlier. At 20,000 feet, he repeatedly sees a creature on the wing, but whenever he points the creature out to someone else (his wife, the flight crew, fellow passengers), the creature has jumped out of sight. When the creature starts to tamper with the plane’s wing and wiring, he grows increasingly concerned for the plane’s safety. What should he do? If he ignores the creature, the plane might crash. If he continues to go on about a creature no one else has seen, he might likely end up back in the sanitarium. What would you do?

If you’ve seen the episode, you may recall that he steals a sleeping policeman’s handgun (it truly was a different time back then, before 9/11 and Transport Security Administration pre-flight checks) and proceeds to open the exit door and successfully shoot the creature. Since no one else has seen the creature, the end result is that Bob is taken off the plane in a straitjacket, back to the sanitarium, since for sure he was having yet another breakdown.

Spoiler alert: in case you haven’t seen the classic episode and plan to as a result of this narrative, the twist at the end is about to be revealed.

At the end of the episode, as Bob is being carted off to the sanitarium in a straitjacket, the camera zooms in on the wing to reveal the damage done to the wing and wiring by the creature. Bob wasn’t having a breakdown after all: in fact, he might have just saved everyone on the plane.

That was a Rod Serling fiction; the following account actually happened.

Recently, a very bright – and rambunctious – eight-year-old girl was traveling with her parents on a Boeing 747 from London to New York. They were sitting two rows ahead of me, and the girl was running up and down the aisle, reaching over seats to play with other people’s computers and generally creating havoc throughout the cabin. Her mother tried everything to control her – but to no avail. The father sat detached and oblivious to everything that was going on around him, head buried in an academic journal. After about twenty-five minutes of this, however, he had had enough. Kneeling in the aisle, he grabbed the girl by the arm and sternly said, “If you don’t behave, I am going to put you out on the wing!”

So, let’s examine the decision at hand for this astute young girl. Clearly the mother had little influence; but when the father became involved, the girl had two options: (1) ignore the father and keep on misbehaving; or (2) sit down and be quiet. Her choices are represented in figure 8.2.

The girl had to assess the probable outcome of her choice of options. What would the father do if she sat down and behaved? Clearly he would do nothing except go back to his reading; but she wouldn’t get to do what she wanted, namely wreak havoc on the rest of the passengers! On the other hand, if she continued, she would have to consider whether her father would follow through on his threat of putting her on the wing. She was a smart girl – it was clear he wasn’t going to follow through and put her out on the wing. Any eight-year-old child would realize that this is simply not a credible threat. Consequently, she continued to misbehave (much to the dismay of the other passengers), and the father went back to his reading. Smart girl.

Backward Induction and Tactical Moves

What the story illustrates is the power of backward induction – logic forward and reason backward. When we are faced with a strategic choice, we can use logic to play out all of the alternatives and work through the logical outcomes and responses related to each. This enables us to identify the most desirable outcomes. We can then choose the actions that lead to the most favorable outcomes and monitor the process so that we stay on the correct path. The first part requires the use of decision trees; the second part can be played out via a tool called “Bayesian updating.”

In the case of the little girl, she knew the outcome she wanted (continuing to misbehave). She quickly realized that she wouldn’t end up on the wing no matter what she did. But what could the father have done differently to have the girl choose a different path? Putting her out on the wing wasn’t feasible, which is precisely why his threat wasn’t credible; if he had played out the girl’s decision tree, he would have quickly realized that she wouldn’t listen based on this threat and that the only way to get her to sit down would have been to incentivize her – either positively or negatively. For example, he could have threatened to withhold her allowance for a week (and then follow through); he could have purchased an interesting movie for her to watch on the plane’s inflight entertainment system; or he could have suggested anything else that was (a) credible and (b) provided the proper incentive to move to the “right” path on the decision tree. Clearly, however, he wasn’t playing the game from her perspective.

Chess or Checkers Anyone? Sequential Games Involving Asymmetric Imperfect Information

We can use this same set of principles to analyze the logic of competitive moves in business – as we do when playing chess or checkers. Think about a contemplated price cut, impending capacity decisions (e.g., whether to build a plant, renovate an existing one, or take one offline), a potential acquisition, or a whole host of strategic decisions you might be considering for your business. Now, think back to the little girl on the plane. Imagine drawing a decision tree for your business and following these guidelines:

• List the set of potential options available to your business for the strategic issue at hand. Be complete.

• Draw one branch of a tree for each potential strategic move; each branch will represent one available option.

• One at a time, list the set of options available for each of your competitors for each branch of the tree you just drew. Imagine you actually did what was listed on each branch in turn – what is the complete set of feasible responses by rivals? Note that the tree is getting increasingly complex.

• For each and every branch, assess the outcomes for your firm – how good or bad would this be if the sequence of moves (first yours and then theirs) were actually to happen?

• Pick the best branches for your firm. What are the common elements of a “good” outcome? Is there a common first step? Are some of the outcomes particularly bad for your firm? If so, can you limit the likelihood of these bad outcomes by NOT doing the first step that leads to that bad outcome (by moving first, picking the strategic option for your firm first)? You can essentially take the bad outcomes “off the table” by choosing first, but this requires some work and forethought.

Decision trees can become very complex. For example, (i) probabilities and weights can be added to each branch; (ii) an outcome assessment (beyond simple “good” or “bad”) can be done for each final branch; (iii) simulations (e.g., Monte Carlo analysis) and probability distributions can be placed on each note and branch; and (iv) software programs (ranging from simple to complex) can aid in the analysis. Regardless of the sophistication and complexity of the analysis, decision trees share a common principle: logic forward and reason backward. We use logic to draw a comprehensive set of branches for a decision tree; thus, whether you use complex computer programs, a large whiteboard, or simple paper and pencil, the process is the same – “logic” all the sets of possible alternatives. Be comprehensive and complete. Once this is done, pick the outcomes that are desirable and then reason backward to initial actions that can maximize the likelihood of the best outcomes for your firm. Let this guide your initial actions. No matter how complicated or simple, the principles are the same.

For any of you who have played a game of chess or checkers, you already apply this process to the game. You can choose a current move to maximize the likelihood of a favorable end result when you think several moves ahead (i.e., for a current set of strategic alternatives) and reason backward. If you understand chess and/or checkers, applying decision trees to sequential games is something that should be quite intuitive. In addition:

• We can update the moves over time – as events unfold, we can remove (or sometimes add) branches, depending upon what is happening. We can even adjust the outcomes and probabilities as a result. This is a process that, when conducted more formally using certain principles, is known as Bayesian updating.

• We can use different metrics to guide what the outcomes will look like (e.g., in terms of our sales, market share, and profits). We can integrate this information into a comprehensive model of choice for each branch.

• We can bring in competitive responses and, at each node, assess the best and optimal rival response to get a more precise sense of likely rival actions. For example, for those familiar with simulations and Monte Carlo analysis, we can use such techniques to better gauge the likely probabilities across the branches.

From this, a complex and rich analysis of the competitive space can be detailed in such a way that no market outcome comes as a surprise and you can choose your initial actions to influence both your competitors and market outcomes. The order of actions matters; you need a structured approach to ensure that the order unfolds to your advantage.

Let’s think about the complex decision-making processes for a modern-day manufacturer selling through a channel. A firm needs to think about optimal tactical decisions based upon the market’s total or net response. Furthermore, typically tactical (e.g., pricing, promotion, and communication) decisions are made within the channel; however, actual buying behavior is determined at point-of-sale (POS) based, in part, on the “pass-through” from manufacturer to end users through the “waterfall” of decisions along the channel. The complex, interconnected set of moves, reactions, and equilibrium can be understood and influenced through such things as game theory, choice analysis, pass-through metrics, decision optimization, and Bayesian updating. Using knowledge from a these fields, we can now formalize strategic and tactical decisions.

The key is the importance of order. Order matters.

Order Matters. Setting the Game to Incentivize Your Rival to Do What’s in Your Best Interest (conditional optimality is not the same as unconditional optimality)

Game theory is, if used strategically, setting the game to be played to your advantage – getting your rivals to do what is in your best interest by incentivizing them to do what’s in your best interest. Thus, order matters – taking advantage of conditional (versus unconditional) decision heuristics is crucial to understanding how game theory can make a difference in strategic decisions.

To illustrate, imagine a hypothetical competition between two companies. Let’s call them Company Avocado and Company Paraná.6 They are both focused on producing the next generation of tablet devices, and they are deciding between producing a 10-inch or a 7½-inch screen (assume that they each can only produce one size of screen). We can represent the relative profits achieved by the two firms as a result of their choice of which screen to produce through something called a “payout matrix” (figure 8.3), where X\Y in each of the four quadrants in figure 8.3 denote the “payouts” (here in profits) for Company Avocado and Company Paraná, respectively. As an example, if both companies choose to produce a 10-inch screen, the figure shows that Company Avocado would earn $18 million in profits and Company Paraná would earn $12 million in profits; or, if Company Avocado decides to produce a 7½-inch screen and Company Paraná decides to produce a 10-inch screen, then Company Avocado will earn $40 million in profits and Company Paraná will earn $70 million in profits. The same logic applies to the remaining two boxes in the payout matrix.

Simultaneous outcome. If both companies decide which screen size to produce at the same time, the best outcome for each firm would be a profit of $70 million. For Company Avocado, this means producing a device with a 10-inch screen, hoping that the other company chooses to make a device with a 7½-inch screen. Conversely, for Company Paraná, this also means producing a device with a 10-inch screen and hoping the other company makes a device with a smaller screen.

Hence, if the game were played simultaneously, each firm would produce a device with a 10-inch screen. If this were to happen, Company Avocado would earn $18 million and Company Paraná would earn $12 million. That is, if each company acts according to its own best interest, both firms do not end up with optimal profits. In fact, both companies end up in the least attractive quadrant.

If you’re Avocado, how can you do better? The best outcome for Company Avocado is to produce a 10-inch screen with Company Paraná producing a 7½ inch screen. Here, Company Avocado would earn $70 million and Company Paraná $40 million. So, if you’re Company Avocado, how do you get Company Paraná to produce a 7½ inch screen? Do you call up their CEO and say, “It’s better for us if you make a 7½-inch screen, and so can you please do so?” Aside from antitrust reasons why you couldn’t/shouldn’t do that, the CEO certainly wouldn’t oblige simply because it’s in your best interest and because you asked!

The answer is that order matters. Going first turns a simultaneous game into a sequential one – you can choose your initial move in a way that alters the incentive of your rival to your advantage. Imagine that Company Avocado chooses first and recognizes that its choice will affect how Company Paraná reacts. If Company Avocado goes first, what should they do?

In order to answer this, we “unravel” the game as shown in figure 8.4 (where each pair X, Y represents the profits of Avocado and Paraná, respectively).

If Company Avocado were to commit to producing a 10-inch screen first (the circle in figure 8.4), we end up in a new equilibrium whereby Company Avocado ends up with its desired outcome (with $70 billion in profits, denoted by the arrow in figure 8.4). Think about it this way: by going first, Company Avocado has eliminated the entire right-hand side of the tree diagram, as shown in figure 8.5.

After Company Avocado announces it will be producing a 10-inch screen, the only two choices left for Company Paraná (see the branches in figure 8.5) would result in either $40 billion in profits (if they produce the 7½-inch screen) or $12 billion (if they produce the 10-inch screen). What would you rather have if you are Company Paraná, profits of $40 billion (by producing the 7½-inch screen) or $12 billion (by producing the 10-inch screen)? The choice is clear. By “unraveling” the game and going first, Avocado has ended up with the outcome that it wants – producing a 10-inch screen – with its rival, Company Paraná, voluntarily choosing to produce a 7½-inch screen.

The key here is that Company Paraná’s “unconditional” (in the original, simultaneous game) choice is very different from its choice of which screen to produce conditional on Company Avocado’s preemptive decision to produce a 10-inch screen. Essentially, by choosing first, Company Avocado has eliminated the entire right-hand side of the tree diagram (the only side with a potential bad outcome for Avocado). In short, Avocado moved first to eliminate their potential bad outcome.

If Company Avocado had simply waited for Company Paraná to choose first, it would not have controlled the market outcome to its advantage; in fact, by committing first, Company Avocado has forced Company Paraná’s hand to its own advantage.

In reality. Finally, note that in this stylized example, we assumed that we knew the outcomes; in practice, however, we can’t know these outcomes with any reasonable certainty in advance. However, we may know which is the preferred versus the less desirable outcome. Hence, in reality, instead of specific numbers in a payout matrix like the one just described, we might use a sliding “heat scale” that can vary from bright red (really bad) to bright green (really good), with yellow in the middle, allowing for a continuum of (subjective) outcomes in each quadrant.

In practice, I have worked numerous “game theory” sessions with various clients (e.g., Owens Corning, World Kitchen, Globe Union, and others), with each session mirroring the stylized example above. In these sessions, companies begin by simply listing (in some detail) all of the potential strategic options available to them. They then “game out” all of the potential sequences of competitive moves that could result from each of the potential strategic options, evaluating these one by one (with subjective probabilities for those moves). From this, they evaluate the outcome of each sequence (good or bad, typically using the sliding heat scale to evaluate the attractiveness of the outcome). They then identify their initial strategic move that eliminates all of the bad outcomes and try to select the options that result in the best outcome, as in the stylized example described for Company Avocado.

Order Matters. Use It to Your Advantage.

A real-life example of this played out in practice in the aviation industry as the major players struggled in the 1990s with the decision to build big versus small. The result of this strategic game was Boeing’s decision to produce what is now known at the 787 “Dreamliner” and Airbus’s decision to produce the A380. The 787, a smaller, more fuel-efficient plane, was designed to be efficient in “point-to-point” routes, carrying 200-plus passengers and generating fuel savings of approximately 20 percent because of its lighter-weight, all-composite design. By contrast, the A380, carrying from 500 to 900-plus passengers, was designed for long-haul, “hub- to-hub” routes.7 The history, as it played out, wasn’t unlike the stylized story told above:

• In January 1993, Boeing announced a consortium to build a “Very Large Commercial Transport” or “VLCT.”

• In June 1994, Airbus responded by announcing a commitment to build an “A3XX” as an answer to Boeing’s new large aircraft successor to the 747. The A3XX eventually became the Airbus A380.

• Shortly after Airbus staked its reputation on building the A380, Boeing announced plans to “drop the VLCT” in January 1995. Note that this happened only after Airbus’s commitment to build the A3XX.

Meanwhile, Boeing’s commitments signaled a very different strategy than Airbus’s super large “bus in the sky”:

• Boeing’s interest in producing a “sonic cruiser” – with speeds near the speed of sound (Mach 0.98) – was proposed in 2001, at about the same time that Airbus broke ground on the A380.

• The 787, originally the “7E7,” was announced in January 2003; the focus was on its smaller size (210 to 290 passengers), reduction in fuel consumption (by 20 percent), composite construction, lower humidity levels in the cabin, larger windows, and overall advanced technologies.

• The 7E7 was renamed the 787 “Dreamliner” in July 2005 as a result of a global contest started online (in July 2003). The first firm order was in 2003, and there were more than 500 orders in mid-2006. The A380 was certified in February 2006.

• The 787 Dreamliner was rolled out in a public unveiling on 8 July 2007 with 677 firm orders (and the first delivery to ANA in September 2011).

By most accounts, the Boeing 787 has been the most successful product in aviation history. Its success began with Boeing’s announcement of a decision to build and fund a “VLCT,” which pushed Airbus to beat Boeing to market. Push your competitor to where you want them, so that you can do what you really wanted to do all along. Brilliant. By contrast, in 2019, Airbus announced that they would stop manufacturing A380s, admitting stunning defeat. The origins of this outcome were set by the game being played in the 1990s.

In short, order matters.

In a similar vein, big box retailers like Home Depot can be quite effective with pitting suppliers against each other. The retailer creates “line reviews” whereby competing suppliers are invited, in real time, to compete on price. Home Depot, Lowe’s, and other firms know that the minute they get multiple suppliers competing against each other in the same location – or in real time online – they have already won. The trick for a supplier is to compete ahead of time (so that the decisions have already been made beforehand); the “trick” for the buyer is to force real-time competition (i.e., a “simultaneous game” across rival suppliers). If you are a buyer, you would generally prefer a simultaneous game; if you are a seller, you generally prefer a sequential game, where you go first. Indeed, Mike Thaman, recently retired CEO of Owens Corning, often told his people, “If we get to a line review, we’ve already lost.”

The key for firms, of course, is putting all this together – and forming a coherent strategy. In order to do this, we need the right priorities, set up in a way that incentivizes the competition to do what is in our best interest.

Commitment Matters – Use It to Your Advantage: The Story of Nike, Marathon, a Heart Attack, and the Persians8

Do you think U.S. politics is dangerous these days? Well, back in 500 BC, the Persian Empire was the greatest and most powerful of the time. However, in some cases, brains beat brawn. Just ask the Athenians.

In 504 BC, the growth and dominance of the Persian Empire had led the Athenians to reluctantly agree to a protection doctrine with the Persians. However, after the Athenians achieved a clever victory over another local foe, they became emboldened and decided to change their minds and tell the Persians, “We don’t need you anymore; we’re pulling out from the agreement.” This left a bitter taste in the mouth of the Persian ruler, Darius (known as “Darius the Great”) – so much so that he had an aide whisper in his ear every night at dinner, “Revenge to the Athenians!” When the Persians went to collect the taxes due under the agreement (that they still recognized), the Athenians killed the collectors and threw their bodies in a well. This further agitated Darius, who then sent an army of 20,000 men to Athens to burn the city to the ground. “Not so fast,” said the Athenians.

In the city of Marathon, 10,000 Athenian soldiers met the Persian army, and the Athenian general wisely and strategically surrounded them in a valley. In the first known executed “pincer” movement, the army from Athens killed more than half of the Persian army’s 20,000 men, while only suffering 198 deaths. (The names of these 198 are inscribed in the Parthenon in Athens to this very day.)

This huge victory led the Athenian general to send a messenger to Athens, 26 miles away to declare victory. Many runners today know that this is how the distance of the modern marathon came about: 26 miles and 385 yards (the distance from Marathon to the town square in Athens). What many don’t know, however, is that when the runner reached the town square and proclaimed “Nike, Nike” (for the Greek goddess of victory), he dropped dead of an apparent heart attack!

The Persians escaped to their remaining fleet of almost 100 ships and decided to make a beeline for Athens, 62 nautical miles away. The Athenian general then marched his troops to Athens, and when the Persian ships arrived in Athens and saw the troops waiting for them on shore, they decided to retreat back to Persia rather than fight.

This story illustrates two other key tenets of game theory – commitment (in this case, to fight on the part of the Athenian army) and a credible threat – in this case, the Athenian army’s readiness to do battle to back up its aggressive posturing. Sometimes having both can force your rival to retreat without so much as a fight. The lessons from all of this include the following:

• Make credible threats – given the defeat at Marathon, the threat that the Athenians would fight to the death was certainly very credible.

• Show commitment – they were there after all!

• Live to fight another day.

• Sometimes the best battle won is the one not fought – the power of game theory and credible commitment.

Order matters, commitment needs to be made, and a credible threat needs to be out there in order for you to be taken seriously. As an example, when my son was very young, he quickly learned not to negotiate with me on snacks or cookies. He once asked, “Dad, can I have four cookies?” I responded, “You can have two.” He next tried to compromise by saying, “How about three cookies?” My response was not the one he wanted to hear: “You get one cookie, then.” And he got one cookie. The next time I said that he could have two cookies, he said, “Thank you.” Credible threats.

Of course, now he’s twenty-two years old and does whatever he wants; I have no sway – cookies or otherwise!

Competitive Assessment – Measuring Competitive Response and Residual Demand Elasticities: Marshallian versus Residual Demand Elasticities

A theoretical construct illustrates the importance of “Marshallian” versus “residual” demand elasticities in a practical and business sense. Virtually all the measures of price responsiveness utilized in business today fit under the heading of “Marshallian” elasticities (named after the famous micro-economist Alfred Marshall, who came up with the concept in the 1920s). The concept of a Marshallian demand elasticity is something that you may remember from economics classes – namely, a measure of how responsive quantity demanded is to price (more specifically, the percentage change in quantity demanded divided by the percentage change in price).9 In the more technical, academic “industrial organization” literature, this is referred to as a “unilateral” price elasticity because it measures “ceteris paribus” (holding all other things constant) price responsiveness; thus, it captures the impact of a firm’s price change when its competitors leave their prices constant (hence the term “unilateral”). This measure does not consider rival actions; however, it is the form of demand elasticity that is used almost exclusively in business.

Enter the concept of “residual” demand elasticity.10 Residual (sometimes referred to as “partial” or “net”) demand elasticities consider competitive responses. You can think of this measure as reflecting the net result from a chain of events occurring after a price change and as a measure of what happens after (i.e., net of) a competitive response. To illustrate, imagine, for simplicity, that we have duopoly (i.e., two firms competing in a market), noting that the same process would indeed unfold with multiple competitors. In this duopoly, imagine that Competitor A reduces its price by 10 percent on a key product it is selling. As a result of the price cut, its customers respond, and the demand for its product increases by 15 percent (and revenue increases accordingly).11 This would suggest a standard “Marshallian demand elasticity” equal to −1.5 (i.e., a 15 percent increase in volume divided by the −10 percent price change). Competitor B sees the price decrease (which may very well have cut into its share); as a result, it decreases its price as well. The percentage change in Competitor B’s price, as a result of a given percentage change in Competitor A’s price, is known as the “reaction elasticity.” In turn, some of Competitor A’s customers may see that Competitor B has lowered its price and buy from Competitor B instead (which is called “cross-price demand elasticity”).

We can think of the chain of events as follows:

Competitor A decreases its price. Customers react to this price decrease by buying more.

Competitor B reacts by lowering price in response.

Customers react to Competitor B’s lowered price.

Ultimately, if we initiate a price decrease as Competitor A, we are concerned with the final or net response after this chain of events has concluded (not just the initial reaction of our customers).

Thus, in order to calculate residual demand elasticities, we need to know three things:

1 the regular or “Marshallian” demand elasticity,

2 the competitive “reaction” elasticity, and

3 the “cross-price” demand response.

Think of points 2 and 3 above as “feedback effects” – what happens to Competitor A’s demand when Competitor B responds.

In published papers in leading academic journals, we have estimated regular “Marshallian” as well as “residual demand” elasticities using advanced game theory models and advanced econometric estimation techniques for more than 200 fast-moving consumer goods (FMCGs) categories across the United States over a two-year period.12 The results for three representative categories (i.e., instant coffee, canned soup, and fluid milk) are shown in table 8.1; in the table, we show only the leading national brand and the leading store brand (i.e., a private label) in each category – although results were obtained across multiple brands in each category.

Table 8.1 Estimated “Marshallian” versus “residual” demand elasticities for national brands versus private label brands across three categories

	Instant Coffee	Canned Soup	Milk
Leading National Brand “Marshallian” Elasticity	−3.03	−1.39	−2.07
Leading National Brand “Residual” Elasticity	−0.12	−0.917	−2.05
Private Label Brand “Marshallian” Elasticity	−0.374	−6.38	−0.942
Private Label Brand “Residual” Elasticity	−0.314	−0.438	−0.878

These three categories are chosen, in part, to illustrate the differences we often see across categories. Here, in the canned soup category, there is a substantial difference in the Marshallian (holding all else constant) demand versus residual (net) demand response measure for the leading private label product. In fact, examining the competitive response using a traditional “Marshallian” demand elasticity would lead to an incorrect pricing decision: at first glance, a price cut would generate a large consumer response (vis-à-vis a “Marshallian” demand elasticity of −6.38), but once competitive reaction is accounted for, we realize that it would generate very little net response (a “residual” demand elasticity of −0.438). For a second category (milk), competitive response doesn’t matter at all (“residual” and “Marshallian” demand elasticities are almost identical for both national brand products as well as for both private label products). Accordingly, using the Marshallian demand elasticity would be just fine in this case. Unfortunately, differences between the two elasticity measures, holding all else constant (versus net of competitive response), are idiosyncratic to the category; thus, until the estimation is complete, one never knows whether or not there will be differences – or how important the differences will be.

Let’s quickly examine the three categories in a bit more detail since each has very different results:

1 Examining traditional ceteris paribus “Marshallian” demand elasticities would lead to erroneous conclusions and pricing decisions for one brand (i.e., the leading national brand) but not for the other (i.e., the leading private label) in one category. In the instant coffee category, there is a huge difference between the Marshallian and residual demand elasticity for the leading national brand (−3.03 versus −0.12) but not for the leading private label (−0.374 versus −0.314). This is not atypical (i.e., it is not unusual to see significant differences for one brand in the category but not the other). Sometimes this is because of lack of rivalry and competitive response by competitive brands in the category, and sometimes it can be the result of a lack of cross-brand customer response to rival price changes.

2 Examining traditional ceteris paribus “Marshallian” demand elasticities would lead to erroneous conclusions and pricing decisions across the board. Take a look at the private label elasticity measures estimated in the canned soup category from the table above (−6.38 Marshallian versus −0.438 residual demand elasticity); had you simply estimated a Marshallian demand elasticity (holding all else constant), you would have concluded that a 10 percent price cut, for example, would produce a whopping 63.8 percent jump in volume – a no-brainer price cut. However, net of competitive response, a 10 percent price cut would only actually have produced a paltry 4.38 percent increase in volume. Clearly, looking at the ceteris paribus Marshallian versus net residual demand elasticities can make a huge difference to what you want to do tactically!

3 Examining traditional ceteris paribus “Marshallian” demand elasticities would lead to the same results as the residual demand elasticities in another category. For example, the –2.07 estimate in the milk category indicates that, for the leading national brand (i.e., in the milk category), the “Marshallian demand elasticity” was −2.07, which suggests that for a given percentage price cut (increase), the demand for milk sold by the leading national brand would increase (decrease) by just over twice that percentage. For example, for a 10 percent price cut, the demand would increase by 20.7 percent, assuming that nothing else changes (“ceteris paribus”). In this category, it just so happens that after we examine the chain of events that follows this initial price cut, the net response is almost exactly the same (−2.07 versus −2.05). This may be for one of two reasons – either competitors didn’t respond to price cuts by rivals (which is indeed the case in the category and data studied here) or customers were extremely brand loyal and didn’t respond to competitor price moves (i.e., the “cross-price elasticity” was low, which, as it turns out, is not the case here).

Residual elasticities are constructs that are inherently estimable – do not accept simple survey “willingness to pay” (WTP) instruments or even traditional “Marshallian” demand elasticity measures using econometric estimation techniques – we can do better.13 Accepting even accurate customer willingness to pay or demand sensitivity measures will quite possibly put you in the position of having unreliable, or even wrong, information – getting accurate information about the demand response but missing the key chain of reactions that take place as a result. Again, we can do better. Demand it.

All of this has taken us into the realm of game theory: how do we consider competitive response in our strategic actions? How do we know the competitive “reaction elasticity”? This is where choice theory comes in. We can combine game theory with choice theory in a creative way to address these issues in practice. While much has been written herein about the “big-picture” view of a firm’s strategy, the notion of customer insight is a critical skill that every company (big or small, B2B or B2C, local or international) needs to master.

Some Concluding Thoughts: The World Is Changing. Learn from It. Use It to Your Advantage.

Just think of the pocket computer we carry around with us every day – the smartphone. In 2011, Eric Schmidt, the executive chairman of Alphabet, eloquently described convergences associated with the smartphone in a speech in Germany:

We have a product that allows you to speak to your phone in English and have it come out in the native language of the person you are talking to. To me this is the stuff of science fiction. Imagine a near future where you never forget anything. [Pocket] computers, with your permission, remember everything – where you’ve been, what you did, who you took pictures of. I used to love getting lost, wandering about without knowing where I was. You can’t get lost anymore. You know your position to the foot, and by the way, so do your friends, with your permission. When you travel, you’re never lonely. Your friends travel with you now. There is always someone to speak to or send a picture to. You’re never bored. You’re never out of ideas because all the world’s information is at your fingertips. And this is not just for the elite. Historically, these kinds of technologies have been available only to the elites and not to the common man. If there were a trickle down, it would happen over a generation. This is a vision accessible to every person on the planet. We’re going to be amazed at how smart and capable all those people are who did not have access to our standard of living, our universities, and our culture. When they come, they are going to teach us things. And they are coming.

There are about a billion smartphones in the world, and in emerging markets the growth rate is much faster than it is anywhere else. I am very excited about this.14

The book you have just read set out the premise that mobile, ubiquitous, always-on information transforms markets with unprecedented speed; furthermore, successful companies today must compete intensely via the utilization of key strategic control points to squeeze margins within their own value chains and across other value chains – something we defined as the competitive ecosystem. Firms must also find ways to align incentives throughout these interconnected value chains. Game theory teaches us how to do this by thinking ahead and redefining our rival’s strategic opportunity set.

Companies that typify these principles are Amazon, Apple, and Google. For each company, success depends not only on the acceptance of their products and services in the marketplace but also on how they exert control throughout their value chains and how they leverage success in one part of a business to extract margins in other parts. Think of the multidimensional brilliance of Jeff Bezos and Steve Jobs. Their obsessive need to control every aspect of their offerings (i.e., to coordinate, control, extract value, and leverage strengths not only within their supply chains but also away from their core businesses) has been a key strategic focus for both companies (e.g., Apple’s ecosystem margins and Amazon’s ability to extract margins through its Marketplace). As a result, suppliers, assemblers, manufacturers, and customers alike are often aligned with the interests of Amazon and Apple and provide deep, substantive, sustainable, and profitable competitive advantages for both companies. Thus, market success today depends not only on the products you deliver but also on how you deliver them. This is an important theme throughout this book – the motivation behind much of what we see in the strategic decisions that drive successful companies today, and what successful companies like Amazon, Walmart, Procter & Gamble, and Apple know at their cores.

Google – like Apple with the iPhone before it – has also leveraged its core business strength by utilizing the principles of game theory to its advantage. To illustrate, think for a moment about the obsolescence of mobile phones. What if, someday soon, we don’t need phones anymore? “Wearables” may replace our phones sooner than we think (e.g., within glasses, fitted contact lenses, or clothing) – with the potential to display weather forecasts, optimal routes (to get you where you want to go most efficiently), calendars, and/or video conferences. This isn’t science fiction but reality – complete with patent applications pending for embedding the technology in our contact lenses (brought to us by Google, Samsung, and Sony).15 This is what we call a paradigm shift – something that Apple mastered during the era of Steve Jobs.

By contrast, Nokia has seen significant market share declines in many of its major markets – in large part because it thought that it was in a handset (rather than an information and convenience) business. By the time it realized that it wasn’t, the smartphone revolution had left it in the dust. Ironically, Nokia had actually developed smartphone technology well ahead of the competition; however, it had decided not to take the technology to market – betting instead on the continued growth it was enjoying in the handset market.16 Growth can be a dangerous drug; it makes it easy to miss all of the warning signs. Good companies such as Apple and Google are relentlessly looking to displace current growth with new avenues of growth. In the past, Apple led the shift to new growth; Google is leading the shift today; and Nokia missed the paradigm shift.

Think ahead, plan ahead, use strategic control, align incentives.

Use the carrot and the stick to your advantage.

Chapter 8: Key Foundations and Business Principles

• Make information analytics a priority.

• Use information strategically.

• Think ahead rather than react – order matters.

• Think about drawing a decision tree for your business following these guidelines:

○ List the set of options for your business, for the strategic issue at hand. Be complete.

○ Draw one branch of a tree for each available option.

○ One at a time, list the set of options available for each of your competitors for each branch of the tree you just drew.

○ For each of this larger set of branches, assess the outcome for your firm – how good or bad would this be?

○ Pick the best branches for your firm. What are the common elements for a “good” outcome? Is it a common first step? Are some of the outcomes particularly bad outcomes for your firm? If so, can you limit the likelihood of those outcomes by picking the best strategic option for your firm first?

• Traditional demand elasticity only assumes a static market. Companies need to establish competitive assessment based on residual demand elasticity, which can be estimated using empirical analysis.

• Empirical analysis is even more important (and complex) in B2B markets, since both horizontal and vertical (such as “pass through”) moves need to be considered.

• Demand estimation of residual elasticities and don’t accept simple willingness to pay (WTP) measures and surveys; nor should you accept “Marshallian” elasticities without attention to competitive response.

• Require business-case justification for strategic decisions, and mandate financial justification of strategic choices.

• Most importantly, recognize that order matters – the unconditional does not equal the conditional. Use this knowledge to your advantage.

1 Translation by Linda Jin. Photo by William Putsis.

2 Rita Gunther McGrath, The End of Competitive Advantage: How to Keep Your Strategy Moving as Fast as Your Business (Boston: Harvard Business Review Press, 2013).

3 Adam M. Brandenburger and Barry J. Nalebuff, Co-opetition: A Revolution Mindset That Combines Competition and Cooperation: The Game Theory Strategy That’s Changing the Game of Business (New York: Doubleday, 1996).

4 Interestingly, it turns out that the comet’s tail always points away from the sun, which can produce a counter-intuitive event – when the comet is traveling away from the sun, the tail also faces away, so the comet is, in effect, following its own tail. Game theory, a concept underlying much of this book, enables us to know how to be in front of the comet (read: market) and when to lead in order to influence our competitor’s actions.

5 Many of you have seen and can recall this classic episode; the summary here comes from seeing the episode countless times: somehow, the ending is so chilling that it never loses its impact no matter how many times you’ve seen it.

6 This example is a classic one, originally presented in somewhat different form by Avinash Dixit and Barry J. Nalebuff, in Thinking Strategically: The Competitive Edge in Business, Politics and Everyday Life (New York: W.W. Norton & Company, 1993).

7 Specifically, the 787–8 was designed for approximately 210 passengers in a three-class configuration, and the 787–9 was designed for 250 to 290 passengers. The A380–800 was designed to carry 555 passengers in a three-class configuration and up to 850 passengers in a configuration that had only one class of service: economy. The A380–900 “stretch” was designed for between 650 and 950 passengers. Source: internal Boeing.

8 Source: History Channel Barbarians series.

9 This is technically a “point” elasticity measure, not to be confused with an arc or other related measures.

10 These studies generally fit under the heading of “Industrial Organization” in the academic literature, with the topic originally introduced by Baker and Bresnahan: Jonathan B. Baker and Timothy F. Bresnahan, “The Gains from Merger or Collusion in Product-Differentiated Industries,” Journal of Industrial Economics, 33 (1985): 427–44.

11 A price decrease for a product with elastic demand increases revenue. Think of it this way – revenue is simply price times quantity; if quantity increases by 15 percent with a 10 percent decline in price, then the product of price times quantity (i.e., revenue) must increase.

12 William P. Putsis, Jr, and Ravi Dhar, “An Empirical Analysis of the Determinants of Category Expenditure,” Journal of Business Research, 52 (3) (June 2001): 277–91. See also the elasticities reported in Ronald W. Cotterill, William P. Putsis, Jr, and Ravi Dhar, “Assessing the Competitive Interaction between Private Labels and National Brands,” Journal of Business, 73 (1) (January 2000): 109–37.

13 Examples of studies that provide detailed estimation of this sort include, but are by no means limited to: Cotterill, Putsis, and Dhar, “Assessing the Comparative Interaction”; Barry L. Bayus and William P. Putsis, Jr, “Product Proliferation: An Empirical Analysis of Product Line Determinants and Market Outcomes,” Marketing Science, 18 (2) (1999): 137–53; Putsis and Dhar “An Empirical Analysis”; Ronald W. Cotterill and William P. Putsis, Jr, “Do Models of Vertical Strategic Interaction for National and Store Brands Meet the Market Test?,” Journal of Retailing, 77 (1) (Spring 2001): 83–109; and Ronald W. Cotterill and William P. Putsis, Jr, “Market Share and Price-Setting Behavior for Private Labels and National Brands,” Review of Industrial Organization, 17 (1) (August 2000): 17–39, reprinted in Harry M. Kaiser and Nobuhiro Suzuki, New Empirical Industrial Organization and the Food System (New York: Peter Lang Publishing, 2006).

14 Source: Fred Vogelstein, Dogfight: How Apple and Google Went to War and Started a Revolution (New York: Farrar, Straus and Giroux, 2013), 135.

15 See, e.g., Heather Kelly, “iPhone Photography Is Cool, Eyeball Photography Is Cooler,” CNN.com, 12 May 2016: http://money.cnn.com/2016/05/12/technology/eyeball-camera-contact-sony/.

16 See Anton Troianovski and Sven Grundberg, “Nokia’s Bad Call on Smartphones,” Wall Street Journal, 18 July 2012: http://online.wsj.com/article/SB10001424052702304388004577531002591315494.html.