How Predictive Analytics Can Help Your Business

Predictive analytics has been around for decades, but as data storage has become cheaper and computers more powerful it is a strategy that can now be used by any company. In fact, I first remember being directly involved in this when I worked on a project to help an Indian credit card company back in 2006. They wanted to predict which customers would struggle to pay their bills. At the time it seemed like magic because the data analysts were so good at identifying which customers needed help.

Now it’s easier, faster, and you don’t need to be a mathematics graduate to explore the data and create ‘what-if’ scenarios.

In addition to my own credit card example, here are some scenarios where predictive analytics can play an important role:

Fraud prevention:

pattern detection can highlight unusual interactions or transactions by your customers. We have all been called by our bank warning about a suspicious spending pattern – this is how they create those warnings.

Optimize marketing:

predict who your best customers will be and focus more attention on them or determine where cross-sell opportunities might exist with existing customers. Using data to sniff out new opportunities.

Improve Operations:

airlines and hotels predict when they will be quiet and automatically offer reduced prices for those rooms or flights – and conversely increase the price when popular. A predictive analytics engine will create the insight that allows this automated pricing to take place.

Risk reduction:

credit scoring is a great example. By connecting together as much financial information about a potential customer as possible, such as income, bills, debts, and other liabilities, it is possible to estimate if you can extend credit to this customer.

One example that has become very popular in recent years is very similar to my experience in India and is focused on subscription companies. Subscriptions are no longer just for magazines – people are paying a monthly fee for their TV streaming service, mobile phone service, and the Internet at home. It’s now a very common business model to charge customers a monthly fee to access a service.

These companies can use predictive analytics to identify which customers are about to cancel their subscriptions. This can be based on behavioral insights – exploring how customers used the service just before they previously left and matching this experience against present customers. This is why you might be thinking about leaving your mobile phone provider and then they contact you offering a special price if you lock yourself to their service for the next year.

Optimization models work in a similar way, often in parallel with predictive analytics, but they are really a mathematical model of your business or a specific process. You can define input and outputs and can then experiment with scenarios that modify how the business operates. Rather than just predicting change, it allows for experimentation and then will create a prediction of the outcome of this change to the process.

Both these tools are extremely powerful and work across almost all businesses. Сompany leaders want to be able to predict what their customers will do next. With these analytical tools, you can almost get there.

Using special tools and systems, one can optimize business processes and predict customers’ behavior. Read more about the intersection between technology and business solutions, and discover how companies can benefit from machine learning.