Key Summary

Predictive analytics is an increasingly popular discipline of data analytics that identifies patterns in data sets to enable data-driven decisions and improved operational efficiencies. Historical data and statistical processes represent the backbone of predictive analytics and promise to guide the way to enhanced forecasting of future events and their outcomes.

Predictive Analytics: An Overview

Predictive analytics introduces an element of automation to data analytics. While data analytics typically focuses on the relationship and dependencies of data variables to result in insight of events in the past, predictive analytics is designed to predict what is likely to happen in the future.

By using techniques such as machine learning (artificial intelligence) algorithms to dissect and reassemble large data sets, predictive analytics can identify trending information as well as data patterns. These patterns can be aligned with future events and enable informed decisions that address business opportunities and challenges.

Benefits of Predictive Analytics

Predictive analytics is a quickly evolving discipline of data analytics and we may not have seen its entire capability yet. However, even today, there are several compelling benefits of predictive analytics and ways it can be used in various business scenarios, such as:

• Improve general decision making: The accelerated and automated extraction of patterns from data can lead the way to dramatically better-informed decisions. 
• Reduce risk: Pattern detection through predictive analytics can help organizations identify and mitigate risks by providing them with early warning signs and allowing them to take preventative action.
• Increase business efficiency: Information hiding in data can identify underperforming as well as overperforming business elements that can lead to greater business focus. 
• Improve customer service: Predictive analytics can be used to identify and validate customer needs and preferences that may result in stronger customer relationships. 
• Grow revenue and profitability: Better decisions and greater efficiency that address business challenges and opportunities may ultimately lead to higher revenue and profitability.
• Build a competitive advantage: Opportunities come in big and small packages, but predictive analytics may be able to identify opportunities that your competitor cannot and provide an unexpected and critical advantages that may have been overlooked otherwise.

Examples of Predictive Analytics

Some business areas are more aligned with the use and benefits of predictive analytics than others. Here are a few examples of business practices that are especially benefitting from the value of predictive analytics today.  

• Fraud detection: Predictive analytics can be used to identify patterns and trends that raise red flags and imply fraud.
• Customer churn prediction: A comprehensive automated analysis of data as well as cross-referencing of data may establish warning signs of customers who are at risk of leaving as well as necessary steps that increases the chance of retaining them.
• Credit risk prediction: Financial institutions use predictive data analytics to surface and identify customers who are likely to default on their loans.  
• Product and service maintenance: Predictive analytics can monitor the performance of products and services in a way that can provide guidance for maintenance such as necessary updates or changes prior to product or service failure.  
• Demand and product launch forecasting: Predictive analytics helps organizations plan a product launch, including distribution and promotion.

Insurers stand to significantly benefit from the capabilities of predictive analytics. Some of the use cases for insurance providers include:

• Underwriting: Predictive analytics can be used to analyze customer data and identify factors that are indicative of risk.
• Claims management: In predictive analytics for claims, the cost of claims can be predicted with greater accuracy and possibly fraudulent claims can be flagged.  
• Customer service: Insurers use predictive analytics to identify customers who are at risk of leaving early, establishing strategies how to convince them to stay and build opportunities to cross-sell or upsell products and services.
• Risk assessment and risk management: Insurance companies use predictive analytics to assess and price the risk associated with insuring against factors that may increase the risk of certain events occurring, such as natural disasters or epidemics.