Imagine a fashion retailer already knows what colors, cuts, and sizes customers will prefer next season. Imagine that workers on the shop floor in a production line know when and why a machine will fail before it actually happens and the production flow interrupts. Imagine an insurance company which can precisely estimate the likelihood that a potential new insurance customer will cause significant car damages in the future. All this knowledge would help companies to make even today good decisions for the future. But how to predict the future?
In the past, individual managers with experience and their intuition for trends and industry developments made the difference. Today, modern data analytics bring companies closer to that goal. By using historical data and statistical models such as regression analyses, companies can better forecast future developments. These data-supported scenarios can be used to optimize decisions in a variety of fields such as product program design, pricing, production maintenance, or customer acquisition. In Moneyball, a famous Hollywood movie based on a real story, the successful baseball team Oakland Athletics is formed out of players who scored badly based on traditional baseball metrics but exceptionally good based on predictive statistical analyses.
What makes predictive analytics a very hot and exciting topic in these days is a general trend called “digitalization” accompanied by some recent developments which allow companies to enlarge data opportunities more than ever before:
However, as with any technique, there is a risk. Predictive analytics can only work if historical data is available in sufficient quality and the past has predictive power for the future. For many day-to-day business decisions, this is fortunately the case. However, disruptive changes in industries are hard to detect and historical data can even be misleading here. The success of Henri Ford’s Model T or the coffee-to-go trend supporting Starbuck’s high growth would have been hard to predict by historical customer habits. In these areas, entrepreneurial intuition and early test market research will keep its importance.