The abundance of data generated in recent years — by individuals and businesses — means we no longer have to make haphazard business decisions and ‘hope for the best’. Now, we can employ more systematic and deliberate measures.
At a point, the problem was how to access relevant data. Now, the challenge is what to do with it. Through people’s social media behaviour — their posts, comments, likes, and shares — their search engine and browser history, there’s so much we can learn about them. We can quantify all of this data and then use it to make predictions about future behaviours and tendencies. This is known as Predictive Analytics.
Predictive analytics is the use of present and historical data to forecast behaviour, trends, activity and outcomes. It is a systematic way of foretelling future events. It uses techniques such as predictive modelling, data mining, artificial intelligence, and machine learning to achieve its aims. Predictive models notice patterns in data and use them to identify risks and opportunities that can guide business owners towards better decision making and more predictable results.
Statistical techniques, analytical queries and machine learning algorithms are applied to data sets to create these predictive models. The models then predict the chances of an event happening by producing a numerical value. A good predictive model/software could significantly reduce your time-to-value cycle and your project failure rates.
For example, you run a fitness subscription app and you want to find out what the chances that a particular first-time customer will become a repeat customer. The customer has shown the tendency to renew her other subscription services in the past, the customer frequently visits fitness and healthy-living websites, and also follows several accounts of the sort on social media.
If all this data is run through a predictive model, you will find out that the chances of this customer renewing her subscription is high. Say 8/10. You run this on more customers and you can begin to anticipate their behavior and plan better customer acquisition campaigns. You can also use predictive models to determine the pricing of your monthly subscription rate and which class of people to target with your marketing efforts.
In the financial services industry, predictive analytics is used for credit scoring, to determine an individual’s creditworthiness. Predictive models analyse a person’s credit history, their loan application and customer data, and rank the person based on this. This ranking helps financial institutions determine whether a person will make future credit payments on time and whether or not their loan application should be approved. Insurance companies also use predictive models to determine a customer’s value to their business using data such as their driving record, age, occupation, income level, etc.
Another important way predictive analytics can help your business is through fraud detection. According to Business Intelligence experts, the prevalence of fraudulent activities is one of the major reasons why predictive analytics will continue to grow. As this article on TDWI says, “Insurance companies have long used data mining techniques to identify potentially fraudulent claims. The Internal Revenue Service (IRS) mines tax returns to refine its (non-published) Discriminant Information Function (DIF) system for identifying suspicious tax returns.” Also, the Securities Exchange Commission (SEC) “can mine stock market trades and personal associations to identify insider trading.”
The use of predictive analytics is boundless, it can be used in almost any industry in the world — marketing, advertising, public relations, insurance, micro-finance, travel, telecommunications, project management, supply chain management, and many more.
We are only just scratching the surface and it is exciting to know how much deeper we can go.
