Research Outline

Predictive Analytics


To provide content on Predictive Analytics suitable for individuals with no technical experience or knowledge including an overview of the term, its common applications for non-technical business and marketing people, key fundamentals, how it works, and how to create a predictive analytics model, step by step.

Early Findings

Predictive Analytics - Overview

  • Predictive analytics is a "sub-field of Data Analytics and Business Intelligence" that involves "the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data." As the name implies, it involves predicting future occurrences based on previous statistics. It involves using advanced statistics such as "descriptive analytics, statistical modeling and large volumes of data."
  • Predictive Analytics uses "models to understand what is going on in specific processes and calculate what could happen when variables change." It is used by companies to "increase their bottom line, identify risks and opportunities and guide decision-making" and thereby optimizing processes.
  • Organizations now use predictive analytics to detect fraud, optimize marketing campaigns, improve operations, reduce risks, predict customer churn, plug revenue leakages, setting product prices, plan inventory, lowering operational costs, and reducing risks.

Applications of Predictive Analytics

  • Predictive analytics is used in the banking and financial services industry to "detect and reduce fraud, measure credit risk, maximize cross-sell/up-sell opportunities and retain valuable customers."
  • It is used in the retail industry for "merchandise planning and price optimization, to analyze the effectiveness of promotional events and to determine which offers are most appropriate for consumers."
  • In the manufacturing industry, predictive analytics can be used to "identify factors leading to reduced quality and production failures, as well as to optimize parts, service resources, and distribution."
  • The oil & gas industry uses predictive analytics to "predict equipment failures and future resource needs, mitigate safety and reliability risks, and improve overall performance."
  • Health insurance companies use predictive analytics to "identify patients most at risk of chronic disease and find what interventions are best."
  • Predictive analytics is used in medical services to diagnose asthma and COPD "using pattern-detection algorithms."
  • It can also be used to predict machine failures in industries.

Creating a Predictive Analytics Model

  • Before starting the process of predictive analytics, it is important to consider the problem to be solved and the available data.
  • The first step to creating a predictive analytics model is defining the business objectives. It is important to make sure that the model is addressing "a business question." This will help define the scope of the project and develop metrics for the success of the model.
  • Preparing historical data is the next step. The data may require cleansing and preparation or some form of transformation. This process will ensure the quality of the data.
  • Data Sampling is the third step. This step requires splitting the data into two sets, that is, training and test datasets. A test dataset "ensures a valid way to accurately measure your model’s performance."
  • The next step is building the model. The sub-processes in this step are dependent on business objectives and the "specific algorithm or model."
  • The final step is deploying the model. It is important to keep the "model up to date by refreshing it with newly available data."