What Are Some Examples of Predictive Analysis You Should Know about?

You cannot run a successful business without attracting customers. But equally important is establishing successful customer relationships, which leads to a high retention rate, repeated purchases, and brand loyalty. 

However, it isn’t easy to know which customers are interested in buying additional products (cross-selling) or expensive versions of the existing ones (upselling), even if you have access to enormous data.

The best way to predict customers’ behavior is to use predictive analytics software to identify meaningful patterns in buyers’ purchasing behavior. Although the process is complex, you could view examples to understand its working and how it might help in an industry you are involved in. 

Predictive analysis uses mathematical models to identify patterns in large data sets that the human mind can’t process. Customers’ data is available from CRM, ERP, marketing automation tools, customer experience management platforms, and other sources involved in daily transactions. 

But what makes it so efficient and accurate, and what are some industries which can benefit from predictive strategies? Read on to find out.

Why should you consider predictive analysis?

As mentioned above, it helps you identify a buyer’s future behavior. The process is highly complex because it involves plenty of customer data with accurate algorithms. 

A predictive analysis software takes the help of predictive modeling for cross-selling and upselling conversion. In this type of modeling, the software examines each row of data (the row representing the customer) and runs it through a complex mathematical process.

The result is a “class prediction,” which determines whether the customer is likely to convert into the cross/upselling offer or not. However, you will also come across software that tells the exact likelihood of a customer buying or choosing an offer and making decisions based on that. 

Some examples of predictive strategies

Here are some examples of predictive strategies that apply to some industries. 

• Retail

The use of prediction technology is widespread in the retail industry since businesses need to monitor their buyers’ behavior and track market trends consistently. It helps enterprises contact customers who are likely to respond to cross/upsell offers, and approach them accordingly. 

It enables retailers to know which buyer is likely to buy an improved version of a product with added features. Using intelligent analytics algorithms helps retailers identify the products in high demand, preventing overstocking or shortage of goods. 

• Insurance 

The insurance industry also stands to benefit a lot from prediction models. For example, insurers can take the help of advanced algorithms to determine which of their customers are likely to opt for additional services besides the existing coverage plan.

It also helps the insurance companies customize policy plans by studying the existing customer data and assessing their specific requirements. Insurers can also use analytics software in fraud detection by collecting customer data and identifying behavior associated with high risk or fraud. 

• Human Resources

Human resources departments in companies use analytics to know the attrition rate (number of employees leaving an organization), measure the impact of office activities on employee engagement, and boost productivity by focusing on workers’ performance and progress. They also use it to encourage skill development amongst the employees.

• Mobile apps

App development companies can benefit from predictive by offering those subscription opportunities to new subscribers, which they would usually reserve for premium subscribers. It is also helpful in showing new features of a game or app to its long-term subscribers. 

Organizations must choose reputed technical sites to get help on this particular subject. Understanding customers’ purchasing behavior is vital, and advanced algorithms enable you to do just that.