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How are Industries using Predictive Analytics Today?
How are Industries using Predictive Analytics Today?

February 14, 2022

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Predictive analytics is a combination of technologies and procedures to working with data. This analytics-oriented tool is widely used to detect hidden patterns and make predictions about the future. Almost industries use this method but there are only a few for which it’s highly beneficial.

Industry-specific predictive analytics examples

By implementing advanced technologies such as machine learning, data mining, statistics, and modeling, industries get insights, which can be used to solve business problems, reveal new opportunities, and forecast future outcomes. 

In this blog, we’ll look at the best examples of its uses in different industries:

  • Retail

Predictive analytics is used by retail companies like Amazon and Walmart to make buyer predictions. Companies use this technique to understand what motivates their customers to buy a product, and which offers are most likely to get the expected results. This is possible because of software developers who know how to use and implement predictive analytics.   

  • Healthcare

The healthcare industry is using data to enhance patient care, and minimize operational costs. This technique can examine the data of the previous patients with help of machine learning and AI. Predictive analytics helps physicians in making the right diagnoses to find out the probable treatments for patients with certain conditions.

  • Hospitality

You can find many benefits of predictive analytics in the hospitality industry like predicting the likes, and dislikes of returning customers, finding out how to increase direct bookings, how guests find the hotels, and improving customer retention.

  • Insurance

Predictive analytics in insurance uses IoT-enabled data to find out the needs, and desires of customers. It is used by insurers to recognize and target potential markets. With this advanced technique, insurers can make use of data to find out events, and information that could affect the results of the claims process.

  • Finance

With this ML-based technology, it’s possible to see the changes in the behavior of users and identify possible fraud by analyzing irregularities in their transactions. Also, it analyses the customers to recognize highly profitable customer segments.

  • Aviation

The predictive analytics engine is used in many ways within the airline sector. Airliners regularly use this ML-powered technology to read the weather properly to change the routes to prevent dangerous weather systems. Airliners analyze the data collected from weather forecasts to predict the impact of weather, and what’s happening on the ground.

  • HR

Predictive analytics is used in predicting the growth of employees, and show employee data to track their activities on a day-to-day basis. It aggregates data to manage workflows, and lessen turnover rates. It is used by recruiters to find out the right candidates for their job postings, and predict the performance of future employees.

Predictive Analytics use cases by industry

  • Churn Prevention: -

Predictive analytics identifies hints of dissatisfaction among your customers and recognizes those customers who are planning to leave. Using that insight, businesses can identify their pain points and work on them to keep them happy. 

It is applicable for retail, banking, insurance, and automotive industries.

Customer segmentation: -

This is called predictive segmentation, which is used to define customers by their tendency to take necessary action for a product or service. Here, PA is useful to determine the target market according to real data and find out the segments of those markets that are offering the same services or products. This is data is essential to classify segments in various industries like retail, automotive, banking and insurance, and telecommunications.

  • Quality Assurance: -

Quality control is the main platform for customer experience and operational expenses. Meanwhile, poor quality control will badly affect your customer satisfaction, their buying behaviors, and finally impacts market shares, and revenues. Inadequate quality control results in more warranty issues and repairs, and less structured manufactured.

Predictive analytics provide necessary insights to resolve issues related to potential quality, and trends. It is applicable for logistics & transportation, oil & gas, manufacturing, and pharmaceutical industries.

Conclusion

 

Today, PA methods play a key role in growing various industries, and their business operations. Applications of predictive analytics are taking business processes to the next level making the system more reliable on data and helping in making better decisions. 


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Caroline Smith
Marketing manager at Express Analytics

Passionate about writing articles on Customer Analytics, Artificial Intelligence, Marketing Analytics, and Digital Marketing.

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