Predictive analytics model
Models are the foundation of predictive analytics. Because it is a template that allows users to turn historical and current data into actionable insights that generate positive, long-term results. Common types of predictive models include:
– Customer lifetime value model : accurately identifies customers who are more likely to invest more in products and services.
-Customer segmentation model : groups customers based on similar characteristics and purchasing behavior.
– Predictive maintenance model : predicts the possibility of failure of critical equipment.
Quality assurance model : finds and avoids defects when delivering products or services to customers, avoiding customer disappointment and additional costs.
Predictive Modeling Techniques
Decision trees, a common technique, are based on tree-like diagrams used to determine a course of action or to display statistical probabilities. Branching methods can show all possible outcomes of a particular decision and how one choice leads to the next.
Regression techniques are frequently used in banking, investment, and other financial models. Regression is useful for users to predict asset values and Italy Email List understand the relationship between variables such as commodities and stock prices. At the cutting edge of predictive analytics technology are neural networks. Neural networks are algorithms design to identify underlying relationships in a dataset by mimicking the way human thinking works.
Predictive analytics algorithms
Provides easy access to a wide range of statistical, data mining and machine learning algorithms designed for use in predictive analytics models. In general, algorithms are design to solve a specific business problem or set of problems, improve an existing algorithm, or provide a specific function.
For example, clustering algorithms are CH Leads suitable for customer segmentation, community search, and other social-related tasks. Classification algorithms are commonly used to increase customer retention or develop recommendation systems. Regression algorithms are utilized to create credit scoring systems or to predict time-driven event outcomes.