Predictive analytics

Predictive Analytics is a branch of Advanced Analytics that uses both new and historical data to forecast activity, behavior and trends. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models that place a numerical value, or score, on the likelihood of a particular event happening.

Overview

Predictive analytics is used in Actuarial Science, Marketing, Financial Services, Insurance, Telecommunications, Retail, Travel, Healthcare, Pharmaceuticals and other fields.

One of the most well-known applications is Credit Scoring, which is used throughout financial services. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.

Techniques

There are a few key techniques used in predictive analytics:

• Regression Analysis: This is used to predict a number, like sales revenue, based on variables like advertising spend and price changes. It can also be used to understand the impact of these variables on the outcome.

• Decision Trees: These are used to predict an outcome based on different input variables. Each branch of the tree represents a possible decision, occurrence or reaction.

• Neural Networks: These are used to predict an outcome based on a large number of inputs. Neural networks are often used to predict stock market prices and are often used in advanced robotics.

• Machine Learning: This is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.

Benefits

Predictive analytics allows organizations to become proactive, forward looking, anticipating outcomes and behaviors based upon the data and not on a hunch or assumptions. Predictive analytics gives the power to predict what might happen in the future and allows the organization to make knowledge-driven decisions and to verify and refute existing theories or models.

Limitations

While predictive analytics can be an extremely powerful tool, it does have its limitations. It requires a large amount of data and the ability to accurately interpret the results. It also requires a clear understanding of the business problem and the data that is being used.

See Also

• Data Mining
• Big Data
• Business Intelligence
• Data Analysis
• Data Science

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