The universal managerial imperative is clear: When something happens, you must do something. Still, while operational adjustments are relatively straightforward, even if potentially painful, it is hard to know whether you should you change your competitive strategy. It would help, of course, if we could run experiments. Unfortunately, there’s no such thing as a double-blind randomized controlled trial for competitive strategy. Even if there were, it would be too slow. Do something, after all, means now, especially in an urgent, widespread crisis, like a pandemic.
We can’t experiment on real-life strategists running real-life businesses, but we can experiment on real-life strategists running simulated businesses. I’ve run the ongoing Top Pricer Tournament for more than a decade. Over 2,000 people — managers, consultants, students, and professors from six continents — have entered the tournament, and from them we have learned a lot about strategy. The tournament quantifies the value added or subtracted by each entrant’s pricing strategies against two million combinations of the other strategies, in three-player markets where everyone starts from identical positions. It calculates and then ranks each strategy’s performance from 0 (worst) to 100 (best).
I recently adapted the tournament to experiment with competitive strategies during pandemic scenarios. Perhaps it can give us a clue about the value of changing strategies.
I ran 60 billion simulations from five sets of 12 billion simulations. One of those sets, the base case, covers the ordinary business conditions given to the tournament entrants as they developed their pricing strategies. The other four sets are pandemic scenarios. Simulating the entrants’ strategies under pandemic conditions shows what would happen if they stick with their original strategies.
Two of the scenarios affect only market demand: one with an abrupt decline and the other with an abrupt spike, each followed by a gradual return to normal. We’ll call those scenarios moderate decline and moderate spike. The other two pandemic scenarios are more severe, with a larger, prolonged drop or spike. The major-decline and major-spike scenarios also include impacts on price sensitivity (how much simulated customers care about price as they decide from which business to buy) and demand elasticity (how much overall market demand moves with prices).
The strategies’ performance scores, on the 0-100 scale, are directly comparable across the base case and the pandemic scenarios because they are rankings. A strategy that scores 100 in the base case and in a pandemic scenario means the strategy was best in both scenarios. It does not mean that profitability or market share would be the same in both.
Next, I calculated the correlations of the strategies’ scores in the base case to their scores in each of the four pandemic scenarios. High correlations would mean strategies ranked about the same in the base case and the pandemic scenarios. High correlations would suggest you should switch your competitive strategy if your strategy performed poorly in the base case, but you should not switch if it performed well in the base case.
Conversely, low correlations would mean base-case performance tells us little about performance in the pandemic scenario. Low correlations would suggest you should rethink your strategy, without prejudging whether you should keep it or change it, no matter how well it performed in the pre-pandemic environment.
We would expect high correlations if pandemic scenarios differ only trivially from the base case, and we would expect progressively lower correlations as pandemic scenarios differ more and more from the base case. That’s what happened in the tournament. But the correlations would not decline solely because the scenarios became less like normal conditions. They would decline also because of ripple effects: Changes in market conditions would cause strategies to behave differently in response to the conditions and the other strategies. The tournament simulates complex interactions among the 2,000 strategies that people entered. Belligerent strategies may trigger price wars. Shy strategies may do nothing unless others threaten their performance. Unpredictable strategies may sow chaos. Tit-for-tat strategies may retaliate, cooperate, or tailgate. Action and reaction, as in real life. The tournament is not a trend line or statistical forecast; it is a large game, in the game-theory sense, with numerous possible outcomes. Hence, the 60-billion simulations experiment.
One of the key insights from the 60 billion simulations is this: Do not assume you should change your competitive strategy only because something has changed and the knee-jerk, do-something alarm is roaring. Companies rightly seek agility and fear complacency. But consider the flip side: The more zealously you monitor the world, the more likely you will react to noise instead of signal. And this: The better your current strategy, the more likely a new strategy will be worse. Here are other factors to consider:
When I looked at individual tournament strategies to see why some worked much better or much worse in various scenarios it became obvious that business is not a sterile spreadsheet or abstract trend line; it is a living part of a churning capitalist ecosystem. Strategies’ performance changed in the tournament not only because the industries became temporarily bigger or smaller or because customers became more or less sensitive to price. Strategies’ performance changed because competitors did something different, as they behaved according to the strategies people entered, and their actions led to cascades of reactions.
Your competitors are on edge, just like you. They might make panicky moves or misinterpret perceived attacks, just like you. They might focus so closely on what’s changed that they might ignore what hasn’t, just like you. Consider how your company can lead, not just as the first to act but as the wisest.
Finally, those 60 billion simulations showed that the potential improvement in performance came far more from choosing a better base-case strategy than from fine-tuning a strategy for a pandemic. You don’t have to wait for a crisis to look for a better strategy.
This content was originally published here.