"Information is the oil of the 21st century, and analytics is the combustion engine."
- Peter Sondergaard, SVP, Gartner Research
There’s a great little mom-and-pop ice cream shop where I grew up. An old Korean couple runs the place, and every time I stop by on a nostalgia visit, the same smiling grandma is there asking how I’m doing and knowing what I’m going to order by heart. This is the kind of old fashioned place where they do everything by hand, right down to jotting down their sales in a hand written notebook at the end of the day.
Recently, I asked the owners what they actually do with that data, and I was pleasantly surprised by their response. Sure, they look at monthly sales, inventory numbers, and that sort of thing.
But they had also looked deeper and identified trends that affect their business. When it’s raining, customers tend to come in less frequently. Starting in May, sales tend to shoot way up (kids are out on vacation… and Georgia summers are no joke). The ice creams that are prominently featured in the display case tend to be sampled and then bought the most.
While these conclusions intuitively make sense, the coolest part is that they were actually able to put some numbers behind these insights to influence decisions on staffing and product placement.
Although they would never have called it such, the old Korean couple with the ice cream shop had been practicing predictive analytics.
What is Predictive Analytics?
At its core, predictive analytics is about identifying and quantifying patterns in data, and then using those patterns to make predictions about future events.
For example, if sales are consistently low during the winter months, there’s a pretty good chance that they’ll be low again next winter. If customers are canceling subscriptions after several complaints with customer service, a customer with a similar experience is also at risk to cancel.
Sometimes these patterns are easy to discern; yet more often than not, there are so many variables at play that it quickly becomes impossible to judge on feel alone. For example, consider how many factors contribute to the value of a home -- the size and features of the property itself, nearby schools, the crime rate in the surrounding area, and more. Through a variety of statistical methods, predictive analytics attempts to use available data to scientifically quantify these patterns.
Of course, the fun part is applying these insights. Having a strong predictive model for key metrics is like a real life cheat code. Imagine how empowering it is for ANY business to be able to make confident projections about future trends.
A business can plan for the future and potential trajectory, identify risk factors and opportunities for growth, and consider the effects of different scenarios on key metrics. If that sounds too good to be true, well, there is one catch: predictive analytics can only find patterns in the data to the extent that there are patterns to find.
At the same time, that very principle gives assurance that this isn’t some magic 8 ball solution – these are data-driven insights born from the rigor of statistical analysis.
Predictive analytics aims to take the guesswork out of predicting, and allows businesses to move from being reactive to proactive.
And that’s something that everyone, from Fortune 500 CEOs to sundae-slingin’ grandmas, can appreciate.