Predictive analytics
- 2. Predictive Analytics-Introduction Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast activity, behaviour and trends.
- 3. How it works? • Uses many techniques from data mining, statistics, machine learning & artificial intelligence to analyze current data to make predictions about future. • The patterns found in historical and transactional data can be used to identify risks and opportunities for future.
- 5. Predictive Analytics Process: (Contd.) • Define Project: Define the project outcomes, deliverables, scoping of the effort, business objectives, identify the data sets which are going to be used. • Data Collection: Data mining for predictive analytics prepares data from multiple sources for analytics. This provides a complete view of customer interactions. • Data analysis: Data analysis is the process of inspecting ,cleaning, transforming, and modelling data with the objective of discoverable useful information, arriving at conclusions. • Statistics: Statistical Analysis enables to validate the assumptions, hypothesis and test them with using standard statistical models.
- 6. Predictive Analytics Process: (Contd.) • Modelling: Predictive modelling provides the ability to automatically create accurate predictive models about future. • Deployment: Predictive model deployment provides the option to deploy the analytical results in to the everyday decision making process to get results. • Model Monitoring: Models are monitored and managed to review the model performance to endure that it is providing the results expected.
- 7. Predictive Analytics: (Applications) • Customer Relationship Management • Health care • Collection Analytics • Cross sell • Fraud detection • Risk management • Direct Marketing • Underwriting
- 9. Predictive analytics techniques that typically are applied: • Descriptive: This technique summarizes what has happened in the past and attempts to analyse and characterize it. • Correlation. Users can do correlation analysis to identify relationships and dependencies between different data variables to predict how they'll affect one another going forward. • Segmentation. This technique is a way to analyze a large collection of entity data, such as a customer database, and organize it into smaller groups.
- 10. Predictive analytics techniques that typically are applied: • Regression. This technique is designed to identify meaningful relationships among data variables, specifically looking at the connections between a dependent variable and other factors that may or may not affect it. • Association. One more technique for highlighting relationships between data elements for predictive purposes is to look for ones that demonstrate affinity. • Classification. Another means of separating different entities in a data set into related groups is to map them into predefined categories based on relevant characteristics or behaviours.
- 11. THANK YOU