Predictive Analytics

Another form of machine learning other than artificial intelligence or robotic process automation is predictive analytics.

It is a form of machine learning in which current and historical statistics and models are used to determine future performance. The technology actually looks for patterns in the data and decides whether they are probably going to emerge again. The concept allows businesses or investors to adjust their resources to take advantage of potential future events. It achieves this in a process that involves a number of steps.

1. Define the project.
2. Collect the data.
3. Analyze the data
4. Analyze data through models.
5. Deploy results into every day decision-making.
6. Monitor the model.

Who Benefits From Predictive Analytics

Simply a decision-making tool, predictive analytics is used in a variety of trades. For example, an insurance company may use the tool to determine the probability that they will have to pay out for a future claim. The determination of probabilities is based on a current risk pool of similar policyholders and past events that have led to payouts.

Marketers use the tool to ascertain how consumers react to the overall economy. They use the result when they plan out new campaigns. They also use this tool to determined changes in demographics to determine if their current mix of products will induce customers to make a purchase.

In addition, retailers can use it to achieve competitive advantages. For example, predictive analytics can assist companies that offer many products to sell additional products to certain customers. It helps retailers retain customers. One study found that a 5% increase in customer retention rates translates to 25% to 95% of increased profits. Moreover, it can help stores target customers.

Traders of securities use predictive analytics to study an assortment of metrics based on past events so that they can decide whether or not to buy a security. In addition, the tool assists traders to forecast future price movements based on historical data.

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

The tool of predictive analytics can also be used in telecommunications, travel, healthcare, child protection, pharmaceutical, planning and other fields.

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

In healthcare, predictive analytics is being used to determine who is at risk of developing particular diseases including diabetes, asthma, heart disease, and more. It is also used to support decision-making when treating a patient.

The telecommunication industry is using the tool to better understand customer behavior, enhance customer experience, and be proactive in handling customer issues.

Predictive analytics helps the travel industry offer customers recommendations that include what airline tickets to buy and hotels to book, and sensors in planes can anticipate upcoming problems that can then be repaired before they become catastrophic.

Pharmaceutical companies are using predictive analytics to assist them in discovering new drugs and to minimize negative outcomes.

It is obvious that machine learning can provide a plethora of assistance to any type of business regardless of the field.