How Predictive Analytics will Transform the CRM Experience

By Kuntala Sarkar | Technology

17 May 2022

In the last few years, predictive analytics has become the most discussed topic, as this is helping many businesses to predict their customer requirements. The experience of a well-designed predictive analytics tool can be very fruitful in terms of customer relationship management. Let us explore the different aspects of the predictive analytics and CRM dimensions.

Definition of Predictive Analytics

As the name suggests, predictive analytics predicts the future using data mining, machine learning, and predictive modeling; these predictions are based upon current and historical data. These forecasts usually help to understand future business requirements or customer demands and future risks.

Predictive analytics can be called advanced data analytics. It uses different techniques to study the data; four commonly used are decision tree, text analytics, neural networks, and regression model.

Predictive Analytics and CRM

The process of Customer relationship management has been using data analytics to understand their customer and business. Predictive analytics brings advanced techniques to analyze the data to the table, which provides predictions based upon the available data of different timelines. This will help the CRM module consistently monitor the customer activity and their experiences; this will help them make changes or advancements in their business module.

Examples of Predictive Analytics to improve CRM experience

Many business industries can be enhanced using predictive analytics; we will discuss four different types of sectors here –

Manufacturing with Predictive Analytics

Many major sectors, like the food industry, wood industry, paper industry, petroleum industry, or transportation equipment industry, qualify as manufacturing industries. In manufacturing, future demands in the project or equipment maintenance, which entails very high-cost margins in such sectors, can be predicted by predictive analytics. The use of the raw materials also can be monitored by analyzing the usage history. Using predictive modeling techniques, future product failure or risk involvement can be easily handled with the help of predictive analytics.

Healthcare with Predictive Analytics

In recent years the healthcare industry has benefited by using a predictive analysis integrated system, which has provided more significant outcomes in diagnosis and cure. By using predictive analytics, the results of patient outcomes have improved significantly. Predictive analytics algorithms can be programmed to provide insight into treatment methods that will work best for the current patients by studying patients’ history and past effects.

Banking service with Predictive Analytics

By Implementing an advanced algorithm to predict credit risk analysis. Predictive analysis can help understand the banking market and launch new ideas according to the prediction.

Customer service with Predictive Analytics

Businesses can be estimated better by using customer history and current data. The service provider can predict the user’s future requirements and, based upon that, can provide a service or solution. Let us say certain users have a history of forgetting credentials and want an alternate logging/credential recovery mechanism. Predictive analysis help business hypothesizes such use-cases and take preemptive steps, such as storing user contact to send OTP, to enable a seamless customer experience and provide greater customer satisfaction.

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