Predictive Analytics

A different form of machine learning than artificial intelligence or robotic process automation is predictive analytics.

It is a form of machine learning that uses current and historical statistics and models to determine future performance. The technology actually looks for patterns in the data and decides if they are likely to reappear. The concept allows companies or investors to adjust their resources to take advantage of potential future events. This happens in a multi-stage process.

  1. Define the project.

  2. Gather the data.

  3. Analyze the data

  4. Analyze data using models.

  5. Use the results for daily decision-making.

  6. Monitor the model.

Who benefits from predictive analytics

Predictive analytics is just a decision making tool and is used in a variety of trades. For example, an insurance company can use the tool to determine the likelihood that it will have to pay for a future claim. Probabilities are determined based on a current risk pool of similar policyholders and past events that resulted in payouts.

Marketers use the tool to determine how consumers are responding to the broader economy. They use the result when planning new campaigns. They also use this tool to determine changes in demographics to see if their current product mix is ​​encouraging customers to buy.

In addition, retailers can use it to gain competitive advantages. For example, predictive analytics can help companies that offer many products sell additional products to specific customers. It helps retailers retain customers. One study found that a 5% increase in customer retention rate results in 25% to 95% more profit. Additionally, it can help businesses engage with customers.

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Stock traders use predictive analytics to examine a range of metrics based on past events so they can decide whether or not to buy a stock. In addition, the tool assists traders in forecasting future price movements based on historical data.

It is also an ideal tool for credit scoring used in the financial services field. In this case, predictive analytics uses a customer’s credit history, loan application, and customer information to rank customers’ likelihood of making future loan payments on time. In addition, financial institutions use it in their collection activities. They know that many of their resources are wasted on customers who most likely won’t pay their bills. Predictive analytics can help financial institutions develop appropriate strategies for each customer to increase payments while reducing collection costs. It also helps financial institutions identify high-risk fraud targets.

The predictive analytics tool can also be used in telecom, travel, healthcare, child protection, pharmacy, planning and other fields.

For example, in the child protection field, child protection agencies have begun using technology to identify high-risk cases.

Healthcare uses predictive analytics to determine who is at risk of developing certain diseases, such as diabetes, asthma, heart disease, and more. It is also used to aid in decision making when treating a patient.

The telecom industry uses the tool to better understand customer behavior, improve customer experience and proactively address customer issues.

Predictive analytics is helping the travel industry make recommendations to customers including which plane tickets to buy and which hotels to book, and sensors on planes can anticipate upcoming problems that can then be addressed before they become catastrophic.

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Pharmaceutical companies use predictive analytics to help them discover new drugs and minimize adverse outcomes.

It is evident that machine learning can offer a wealth of help to any type of business, regardless of the industry.

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