What is Predictive Analytics?
Predictive analytics uses machine-learning technologies, data, and statistical algorithms to determine the possibility of an outcome based on the historical information. The aim is to best understand what may occur in the future based on the past.
It allows companies to be proactive and forward-looking to anticipate behavior and outcomes driven by historical information rather than simple assumptions. Here is the process of predictive analytics.
- Project definition to determine project goals and deliverables and identify data sets
- Collecting data from multiple resources
- Analyzing the information by transforming, inspecting, cleaning, and modeling the data to identify important information
- Statistical analysis for validating the assumptions and testing the hypotheses
- Predictive modeling to develop accurate future models
- Deploy the results to streamline decision-making procedure
- Monitoring to ensure performance meets expectations
More companies are using predictive analytics to resolve difficult issues and discover new opportunities. Some common uses include:
- Fraud detection
The combination of multiple methods may help identify behavioral patterns and prevent frauds. As cyber threats increase, predictive analytics is used to detect abnormalities to identify and prevent frauds.
- Marketing campaign optimization
Predictive analytics is used to anticipate customer behavior and purchasing habits. It is also used to promote cross-selling possibilities. Predictive models are beneficial to attract, grow, and retain profitable consumers.
- Improving operational efficiency
Some companies use this method to forecast inventories and resource management. Several airlines use it to determine air ticket prices. Hotels may try to forecast the number of guests on a particular night to maximize their occupancy rate and grow the revenues. Predictive analytics is beneficial in improving the operational efficiency of companies.
- Risk reduction
A common use of predictive analytics is to determine the potential risk of a consumer based on his credit score. It is a number that is generated by the predictive model and incorporates information about the consumer’s creditworthiness. Insurance companies and collection agencies also use this tool to reduce their risks.
Predictive analysis allows you to go beyond the past and discover new insights about what may occur in the future.