Predictive Analytics
Predictive Analysis is the computational brain necessary for firms to extract information from their data, and find out how their business will do in the future.
Some of the main characteristics of Predictive Analysis:
- It’s based on the firm’s historical data
- It can be applied on both traditional data and unstructured data (such as images, tweets, videos)
- It can be used with both Big Data and smaller, more traditional data
- It generates rules that can be automatically applied to the business in order to make it more efficient
What is predictive analytics?
Predictive Analysis is the process of analyzing data through Machine Learning Algorithms in order to identify hidden, non-obvious patterns. The final aim of Predictive Analysis is to find relations within structured, and non-structured, data that can explain why certain things happen: why a machine breaks down, why clients of group A like a certain product while those of group B don’t, how we should price our goods next quarter, etc.
These types of analysis underline relations between different characteristics of our business that we couldn’t otherwise comprehend with traditional tools. The advantage of Predictive Analysis is to obtain rules that are directly applicable to the business, and can actively support the decision making process.
Some applications of predictive analysis
Marketing & CRM
- Advanced client segmentation
- Market-basket analysis
- Propensity analysis
- Sentiment analysis
- Churn analysis
Production
- Energy forecast
- Predictive maintenance
- Quality control
Forecasting
- Demand forecast
- Price forecast
Banking & insurance
- Fraud detection
- Accident forecast
Retail
- Trends analysis
- Competition analysis
Cross-sectors
- Resource allocation
- Document classification
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