In the past businesses had to almost predict what people wanted in an effort to attract people to their products. Nowadays, thanks to the internet and vast amounts of readily available research, those who are seeking to innovate can use big data to find out what their target audience wants. This kind of analytical data has also proven useful in sports for recruiting and tactical decisions. It is, therefore, no surprise that the predictive analytics market is projected to reach $6.5 billion by 2019.
Drawing upon analytical data can help companies make important decisions regarding how they push their merchandise. For example, games companies may look at how many people play different kinds of games and on which platforms before creating their products. Using the current stats, a console game developer may look to release on the PlayStation 4, as Sony sold 12.3 million units in 2016 compared to 5.83 million sales of the Xbox One. But they might also be intrigued to see that at the height of the Nintendo DS’s popularity it shifted over 20 million units for three years straight from 2008 to 2010. Couple this with the fact that the new Nintendo Switch has already sold over 2.74 million units in its first few months, and developers can make a solid prediction that the innovative console is a great option to make products for in the near future.
Analytical data has proven to be invaluable in some sports in recent years, as teams seek to recruit the best possible players while also staying within their budget. As the greatest players in sport come with increasingly inflated price tags this is more important than ever. In the English Premier League, for instance, Leicester City enjoyed success in the 2015-16 season due to shrewd recruitment. They picked up N’Golo Kante at the start of the season for £5.6 million, which is a meagre sum for a top-flight player in football terms. The Frenchman helped the Foxes win the league, which in turn led to his sale to Chelsea last summer for £32 million – a massive increase.
According to research from 888poker, Billy Beane of the Oakland Athletics Baseball team was the first to introduce the use of big data for player recruitment in American sports. He aimed to pick up players on the cheap who had been overlooked by other clubs, by analysing batting, pitching, and fielding measurements. This provided him with statistics referring to value over replacement player, wins above replacement, and batting average on balls in play. The method was so successful that other teams tried to emulate it and there was a popular Hollywood film based on it called Moneyball.
For businesses that want to start taking advantage of these advanced methods of gathering and evaluating statistics, there are a number of things you should know about and make use of. These include business experiments, visual analysis, correlation analysis, regression analysis, and scenario analysis. Companies that fail to realise the value of this kind of statistical data could soon get left behind.