Winning big often involves looking for the opportunities that are the opposite of what everyone else is doing
No rational person would ever enter the lottery. The chance of picking the right six numbers and hitting the jackpot in the UK’s Lotto is approximately one in 14m.
But even in something based purely on luck, a strategy can be found. If it was mandatory to play the lottery, how would you enhance your prospect of winning a bigger pay out? The answer is to always pick numbers above 31.
That is because analysis has shown that the majority of people choose numbers associated with their birthday or a family member’s birthday as their “lucky” numbers. So picking numbers above 31 will ensure that if your combination is the luck of the draw you will get a much larger slice of the winnings because other people are less likely to have picked the same.
This kind of contrarian thinking can be applied to business as well, where strategy and behavioural science can be combined to exploit the many seemingly irrational biases we all have. Smart traders have for centuries exploited “noise traders” who overreact to news events when making investment decisions. But my research shows this is also feasible beyond financial markets; recognising and fixing your own biases but exploiting those of rivals can be a successful strategy when it comes to business.
How exactly you do it needs solid evidence and analysis to provide a strong foundation for your strategy. So I call this approach “analytical behavioural strategy”. This is because it involves drawing on behavioural science to search for contrarian opportunities and then using data analytics to gain a competitive advantage.
Regression to the mean
For instance, most people don’t recognise something called regression to the mean, where very high or low results are usually followed by more average ones. This can be used to measure the impact of luck on performances.
Regression to the mean in business happens whenever results is not entirely under the control of the person or organisation, such as sales performance or a firm’s growth. A great performance suggests good management but also good timing or luck. By definition, good luck is temporary so future performance is unlikely to be as strong (it will regress downward to the mean). The good thing for a contrarian strategist is that many rivals will naively assume that the great current performance will persist.
Let’s look at the music industry. If a new band or musician has a top 20 hit, should a music label immediately try to sign them? My analysis of 8,297 acts in the US Billboard 100 from 1980 to 2008 would suggest not. Music label bosses should instead be looking to sign up those reaching positions between 22 and 30 in the charts.
Artists charting in the top 20 will likely see their next single achieve between 40 and 45 on average, regressing disproportionally more to the mean than their lower performing counterparts. Those charting between 22 and 30, meanwhile, have the highest predicted future rank for their next single. This is where music label bosses will find the hidden gems.
Most of their rivals will be bidding for those superstars who entered the top 20, which are both more expensive and, statistically, have lower expected future performance. In contrast, looking at the “second best” should unearth cheaper acts that are actually going to produce more impressive future successes.
Another question that we can tackle in this way is which markets to export growing businesses should export to. Many companies naturally head to Asian markets with a high GDP growth rate such as China or India. The problem with such a strategy is that most of their competitors will be heading for those countries as well.
A careful analysis of GDP growth between 1960 to 2017 around the world reveals that regression to the mean is also very strong in this area but has an asymmetrical effect, affecting countries at the bottom of the table more. This is where the hidden gems could be. If a country has a very poor growth rate – in the bottom ten countries – it will probably perform significantly better in the following year than the next ten worst performers.
Just as with the strategy for winning a greater share of the lottery jackpot, a contrarian company that enters new markets like this will benefit from being one of the few, if not the only, one investing in these countries. It is a brave move, but sometimes the wisdom of the crowd has to be balanced against the strong competition you will face in high growth countries or industries.
My research shows that countries in the bottom 10%, whose economies are currently shrinking by about 3% a year, are likely to improve their GDP growth rate substantially. In fact, in any year, the worst ten countries are more likely than not to move up the chart to perform better than 45% of all other countries in the following year.
This contrarian theory doesn’t guarantee success. Many countries will in reality have terrible prospects due to wars or other crises. Instead, this approach offers a search guide for looking for opportunities from the over or under-estimations of rivals. Some countries might be under the radar for political reasons but will still have a reasonable economic future.
A good example is China after the 1989 Tiananmen Square protests, which sparked worldwide condemnation and saw many Western companies pull out of the country. Instead of following this consensus, many Taiwanese and Hong Kong companies moved into China and their investment was welcomed with open arms. They gained first mover advantage, which has helped them keep ahead of a sway of Western firms ever since.
This shows how being aware of the biases discovered in behavioural science can help companies stay one step ahead of the competition and create new strategies to take advantage of the blind spots of rivals. Fortune favours the strategists who understand this theory. Following the evidence will enable you to see what others fail to see and do what others fail to do.
Associate Professor of Strategy and
Behavioural Science, Warwick Business School,
University of Warwick
* Published in print edition on 11 January 2019
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