What is predictive analytics?
Predictive analytics combines advanced analytics, predictive modeling, data mining, real-time scoring, and machine learning to help companies identify patterns in data. Predictive analytics refers to using historical data to predict what will happen in the future. The historical data is fed into a model that analyzes it to identify patterns. The model learns on the data from the past and is then applied to current data to predict future outcomes. Predictive analytics is already widely used in many ways: customer lifetime value (CLV) measures predict how much a customer will buy from a company, a product recommendation system predicts what the shoppers will like. There are also sales forecasts, credit scores, fraud detection, optimizing marketing campaigns, and predictive maintenance. Some of them seem mundane and don’t make us think of AI, some are more “impressive” – and all are examples of predictive analytics in practice.
Why use predictive analytics?
Predictive analytics can be used for decision-making and solving business problems, as well as identifying new market opportunities, enhancing customer experience, optimizing processes, reducing operational costs, and mitigating risk by predicting problems that may occur.
So why use it?
Imagine a situation in your sales department: think about how the experts make decisions. We call it “based on professional experience” and it’s true, they make decisions with the use of their expert knowledge. But there’s a share of their work which is intuitive – a few simple rules, experience, gut feeling. They know which customer buys what products and what content the audience will be interested in. And while their knowledge comes from their experience, there’s still a large share of guessing or making intuitive choices. But what if you enhance such a team with AI? You use actual data to help them make much more accurate predictions. In some cases, they will be happy to see that what they’ve intuitively known to be true has been confirmed. In other cases, they will be fascinated to see how much potential data unlocks and how much more they can see.
And there’s another benefit here – an expert can leave your company. If you rely on the expertise of one person or a number of well-trained people, you need them to stay, and sometimes it’s just not possible. But the model stays. And the knowledge it unlocked is there to stay, too.
The use cases of predictive analytics are vast and tailor-made to each problem: when you know what question you want the model to answer, a data science team will help you identify the data needed for the training of the model and choose what model has to be built. With the use of predictive analytics, companies are able to analyze and manage pricing trends, making it possible to offer optimal prices at the right time. Businesses are also able to predict the behavior of customers, making it possible to target the right audience and identify users likely to abandon their online experience. What’s more, properly analyzed data gives companies better insight into the groups of their customers and identifying patterns helps create better, more personalized offers.