Keiter Technologies deals with dozens of tools and technologies to find the best solution to your company’s unique challenges.
What is Predictive Modeling and how can it be applied to your company or industry?
Predictive Modeling uses statistical algorithms and machine learning techniques to predict future outcomes based on the underlying patterns in historical data. There are a huge variety of applications across industries, including:
- Churn prevention
- Customer lifetime value
- Customer segmentation
- Sales projections
- Inventory management
- Equipment maintenance
- Market analysis
- Marketing campaign efficiency
- Content recommendation
- Process optimization
- Clinical trials
- Risk modeling
- Quality assurance
Several predictive modeling techniques based on statistical algorithms identify how strongly individual factors contribute to the outcome, known as feature importance. For example, in a statistical model predicting churn, feature importance can identify the most influential factors to inform future customer retention efforts. If that model identified customer engagement and sales amounts as the most significant factors contributing to churn, the business could focus on increasing customer engagement and incentivizing larger individual sales.
Contact us to learn more about using Predictive Modeling as a potential data solution for your company >
See our Glossary of Data Science Terms for more examples of how data solutions can be applied to any industry.
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About the Author
The information contained within this article is provided for informational purposes only and is current as of the date published. Online readers are advised not to act upon this information without seeking the service of a professional accountant, as this article is not a substitute for obtaining accounting, tax, or financial advice from a professional accountant.