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Predictive Analytics Resources

Learn predictive analytics best practices, watch thought leadership, and hear the latest breakthroughs from Pecan
BLOG | BY: LUCAS STEWART

Scaling Demand Forecasting with AI-Powered Predictive Analytics

Predictive demand forecasting understands and predicts customer demand patterns to ensure supply meets demand at all the critical points along the supply chain.

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Lucas Stewart
BLOG | BY: LUCAS STEWART

Scaling Demand Forecasting with AI-Powered Predictive Analytics

Predictive demand forecasting understands and predicts customer demand patterns to ensure supply meets demand at all the critical points along the supply chain.

LEARN MORE →
The Pecan Team
WEBINAR | BY: THE PECAN TEAM

What to Predict? The Most Effective Frameworks for Predictive Analytics

At Pecan, we’ve determined a large contributing factor to the high rate of failure in data science and ML projects is the disconnect from the business problem. By using predictive frameworks, you never lose sight of the business problem and can focus solely on the KPIs that product real business value.

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Lucas Stewart
BLOG | BY: LUCAS STEWART

3 Steps to Stop Daily Active User (DAU) Churn Using Predictive

In a constant battle for user time, developers need to keep their users engaged and happy while monitoring reduced engagement and churn rates.

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The Pecan Team
WEBINAR | BY: THE PECAN TEAM

Optimizing Mobile App Retention with Predictive Analytics

The mobile app is perhaps the most critical use case for getting predictive right. When a company’s future hinges on what happens in the first 90 days after user acquisition, descriptive analytics can be ineffective.

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The Pecan Team
WEBINAR | BY: THE PECAN TEAM

Predictive Analytics: Getting Actual Value from Machine Learning

In this webinar, we shared the essential components of predictive analytics to get value in the shortest time possible.

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The Pecan Team
BLOG | BY: THE PECAN TEAM

SAP Interview with Pecan’s CEO

Talking to SAP about the vast potential of predictive analytics—and how to get all the critical components right like time-to-model and accuracy.

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