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Swings and misses: What can businesses learn from the “Moneyball” approach?

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In business, the approach is often to doing things that have previously reaped reward, and is therefore considered the ‘go-to’ for decision making. However, relying solely on the ‘known’ can stifle much-needed innovation — a blind reliance on doing things in the way they have always been done. However, as recent events have shown, the world of business can be subjected to the unexpected, and the organisations which are resilient to these new challenges are those that can adapt quickly.

The key to successfully adjusting to change, and getting ahead of it, lies in data. Having data sitting at the core of every decision a business makes, takes the guesswork out and grounds decisions in stats and facts. In fact, research from McKinsey tells us organisations that leverage customer behavioural data and insights, outperform peers by 85 per cent in sales growth and more than 25 per cent in gross margin.

But there is a natural fear of experimenting when it comes to business, so how can you take the right risks, that will reap rewards?

Scoring big when it comes to game-changing data

One of the best examples of risk and reward, based on data science, comes from the world of Baseball. Up until the late 1990s, player buying decisions were completely founded on the gut instinct of a few experienced scouts and the established reputations of ‘popular’ players. This was until Billy Beane, general manager of the Oakland Athletics team, used the ‘moneyball approach’ to recruit his team. Working with very little budget, Beane experimented by using previously unused data on players, to inform his buying decisions.

He took into account data on players’ past performance, injury, age, and experience, to create a methodology for picking the best player to purchase - and it wasn’t always the obvious or most ‘popular’ choice. This resulted in him building a team of previously undervalued players, who went on to routinely beat rival teams, even though their rivals had outspent Beane on recruitment fees, sometimes up to tenfold.

This method of using data analytics turned the game on its head, proving data is an essential ingredient for making consistently positive decisions. The success of the bestselling book and subsequent Oscar-winning film, Moneyball, based on Beane’s story, took data analytics mainstream.

Pitching until it’s perfect

It wasn’t all plain sailing though, especially in the early days, when many questioned Beane’s approach — fearing change. Beane was ridiculed if a player didn’t perform straight away, with critics blaming his controversial method of recruiting. However, Beane would hit back with “it's day one of the first week. You can't judge just yet.” He was ultimately proven right, going on to win many games with his carefully selected team. However, this resistance to change is not uncommon when making decisions to do things differently. Fear of failure often stifles positive changes, however, Beane’s ‘give it time’ approach should be applied across all businesses that are looking to experiment. Just because something doesn’t work straight off the bat, doesn’t mean it won’t. And if it fails, there are learnings that can be taken from that failure, which can be just as important. However, it would seem UK businesses are rather adverse to taking risks, and especially when it comes to the failure to learn approach. Our research found that failure is least accepted as an opportunity to learn in UK businesses (20 per cent), compared to all other regions. In fact, for a quarter (25 per cent) failure is not even considered as an option.

The Power Hitters of change

This is where Innovation Labs can come in. These business departments which are responsible for testing new methods and practices through experimentation have been growing at a prolific rate in recent years. Tasked with the job of pushing the wider business to take risks, putting frameworks and technologies in place to allow teams across the business to become more effective. Let’s take the financial service sector as an example, who have seen the highest rate of growth in Innovation Labs according to a survey by Capgemini. By the end of 2018, Singapore alone had 28 financial service-related Innovation Labs. Alongside this, research from Optimizely reports that 62 per cent of financial services companies plan to invest in both better technology and skilled workers for data analytics and experimentation. This is what all sectors should be looking to emulate.

The Innovation Lab team works with the business to understand how experimentation can best suit each department's needs. They can create best practice for tests and share learnings and wins with the wider business. Not everyone is going to understand when a business decides to make a change — much like Beane’s critics. The best way to convince people that your theory is correct is to show them, rather than tell them you're right. Experimentation initiatives in business allow new ideas to be proven right before they play out in front of your entire audience. Founded in facts and stats, experimentation promotes an ethos that is key in adopting new technologies and utilising data analytics to build roadmaps for the future. As the amount of data companies have access to increases, the ethos of experimentation will only become more important for predicting and changing the future for the better.

Being able to react quickly and respond to change will allow a business to identify that winning shot that stands them out from the competition. Today, businesses that apply a “Moneyball”, data-led, approach to the different areas of their business, especially when it comes to using data to aid experimentation, are the ones that will stay ahead of the curve.

Jil Maassen, Lead Strategy Consultant, Optimizely