Predictive Analytics

What it is and why it matters

What is predictive analytics?

Predictive analytics is the use of data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data.

The goal is to go beyond descriptive statistics and reporting on what has happened to providing a best assessment on what will happen in the future. The end result is to streamline decision making and produce new insights that lead to better actions.

Predictive models use known results to develop (or train) a model that can be used to predict values for different or new data. The modeling results in predictions that represent a probability of the target variable (for example, revenue) based on estimated significance from a set of input variables. This is different from descriptive models that help you understand what happened or diagnostic models that help you understand key relationships and determine why something happened.

More and more organizations are turning to predictive analytics to increase their bottom line and competitive advantage using predictive analytics. Why now?

  • Growing volumes and types of data and more interest in using data to produce valuable information.
  • Faster, cheaper computers and easier-to-use software.
  • Tougher economic conditions and a need for competitive differentiation.

With interactive and easy-to-use software becoming more prevalent, predictive analytics is no longer just the domain of mathematicians and statisticians. Business analysts and line-of-business experts are using these technologies as well.

More about predictive analytics

What can predictive analytics do?

A 2014 TDWI report found that the top five things predictive analytics are used for is to:

  • Identify trends.
  • Understand customers.
  • Improve business performance.
  • Drive strategic decision making.
  • Predict behavior.

Some of the most common applications of predictive analytics include:

Fraud detection and security – Predictive analytics can help stop losses due to fraudulent activity before they occur. By combining multiple detection methods – business rules, anomaly detection, predictive analytics, link analytics, etc. – you get greater accuracy and better predictive performance. And in today’s world, cybersecurity is a growing concern. High-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate occupational fraud, zero-day vulnerabilities and advanced persistent threats.

Marketing – Predictive analytics can help you better understand your customers. Most modern organizations use predictive analytics to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow the most profitable customers and maximize their marketing spending.

Operations – Predictive analytics plays an important role in operations for many organizations, allowing them to function smoothly and efficiently. Many companies use predictive models to forecast inventory and manage factory resources. Others use them for more specialized needs. Airlines use predictive analytics to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night to adjust prices to maximize occupancy and increase revenue. Predictive analytics are also used in human resources, asset maintenance, government and life sciences.

Risk – One of the most well-known examples of predictive analytics is credit scoring. Credit scores are used ubiquitously to assess a buyer’s likelihood of default for purchases ranging from homes to cars to insurance. A credit score is a number generated by a predictive model that incorporates all of the data relevant to a person’s creditworthiness. Predictive analytics has other risk-related uses, including claims and collections.

By using insights provided by predictive models, we can empower our nurse case managers to make a difference in people's lives.

Daryl Wansink
  Director of Health Economics, Blue Cross Blue Shield of North Carolina

Predictive analytics use across industries – real-life examples

Any industry can use predictive analytics to optimize their operations and increase revenue. Here are a few examples:

  • Credit card, banking and finan­cial services. Detect and reduce fraud, measure credit risk, maximize cross-sell/up-sell opportu­nities, retain customers and optimize marketing campaigns. Commonwealth Bank can reliably predict the likelihood of fraud activity for any given transaction before it is authorized – within 40 milliseconds of the transaction being initiated.
  • Governments and the public sec­tor. Improve service and performance; detect and prevent fraud, improper payments and the misuse of funds and taxpayer dollars; and detect criminal activities and patterns. The Hong Kong government visualizes and analyzes big, unstructured data to anticipate and address public complaints.
  • Health care providers. Predict the effectiveness of new procedures, medical tests and medications, and improve ser­vices or outcomes by providing safe and effective patient care. Taipei Medical University executives analyze and monitor performance across all hospitals in its system.
  • Health insurers. Detect and handle insurance claims fraud, identify which patients are most at risk of chronic diseases and know which interventions make the most medical and financial sense. Blue Cross and Blue Shield of North Carolina built a model to more accurately predict hospital readmissions and deploy nurse case managers to help patients most at risk.
  • Insurance companies. Determine insurance premium rates, detect claims fraud, optimize claims processes, retain customers, improve profitability and optimize marketing campaigns. Within two hours of an earthquake striking rural New Zealand, Farmers Mutual Group assessors were headed to affected areas. With SAS Analytics, they knew who their most at-risk policy holders were and chartered a helicopter to get to them quickly.
  • Manufacturers. Identify factors leading to reduced quality and production failures, and optimize parts, service resources and distribution. Lenovo detected a product issue 30 percent faster and reduced warranty costs 10 to 15 percent for previously hard-to-detect issues.
  • Media and entertainment. Deepen insight into audiences by identifying influencing attributes, trends, drivers and desires across properties, and score visitors to determine appropriate audience seg­ments and behavior value. How is the slot floor doing every day? How is the gaming floor performing? How are the nonsmoking tables compared to the smoking tables? The answers – which previously could take numerous weeks and many dollars to find out – are now coming in minutes and at a far lower cost for Foxwoods Resort Casino
  • Oil, gas and utility companies. Predict equipment failures and future resource needs, mitigate safety and reliability risks, and improve performance. Salt River Project is the second-largest public power utility in the US and one of Arizona's largest water suppliers. A sophisticated forecasting model helps them know the best time to sell excess electricity for the best price.
  • Retailers. Asses the effectiveness of promotional events and cam­paigns, predict which offers are most appropriate for consumers, deter­mine which products to stock where and how to build brand loyalty. Macy's increased its use of predictive analytics and reduced email subscription churn by 20 percent.
  • Sports franchises. Sports analytics is a hot area, thanks in part to Nate Silver and tournament predictions. The NBA’s Orlando Magic uses SAS predictive analytics to improve revenue and determine starting lineups.
  • Telecommunication companies. Segment customers, reduce customer churn, retain profitable customers and develop effective cross-sell/up-sell campaigns. T-Mobile is looking at new ways to retain valued customers through new insights it uncovers in massive volumes of customer data.

What do you need to get started?

The first thing you need to get started using predictive analytics is a problem to solve. What do you want to know about the future based on the past? What do you want to understand and predict? You’ll also want to consider what will be done with the predictions. What decisions will be driven by the insights? What actions will be taken?

Second, you’ll need data. In today’s world, that means data from a lot of places. Your transactional systems, data collected by sensors, third-party information, call center notes, web logs, etc. You’ll need a data wrangler, or someone with data management experience, to help you cleanse and get the data prepped for analysis. To prepare the data for a predictive modeling exercise also requires someone who understands both the data and the business problem. How you define your target is essential to how you can interpret the outcome. (Data preparation is considered one of the most time-consuming aspects of the analysis process. So be prepared for that.)

After that, the predictive model building begins. With increasingly easy-to-use software becoming more available, a wider array of people can build analytical models. But you’ll still likely need some sort of data analyst who can help you refine your models and come up with the best performer. And then you might need someone in IT who can help deploy your models. That means putting the models to work on your chosen data – and that’s where you get your results.

Predictive modeling requires a team approach. You need people who understand the business problem to be solved. Someone who knows how to prepare data for analysis. Someone who can build and refine the models. Someone in IT to ensure that you have the right analytics infrastructure for model building and deployment. And an executive sponsor can help make your analytic hopes a reality.

Learn more about analytics and the analytical life cycle


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