It’s the array of ever-changing reasons and seemingly spontaneous nature of customer churn that make this problem so perfect for predictive analytics. If something seems random, that likely just means you need a machine to find the pattern.

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

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.

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

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.

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