What data do you need for a predictive model?

A data model for machine learning predictions is only as good as your data. Your modeling dataset must accurately portray how your business operates, and the historical data must know the outcome of each case or row.

What’s the first step in creating a predictive AI model?

Creating a predictive AI model starts by interviewing business experts and stakeholders about operations and challenges. Our team also generates hypotheses by combining machine learning expertise and customer experience knowledge.

How do you choose a predictive model?

We start with algorithms that provide explanations for predictions, avoiding black box solutions initially. Once validated and performing well, more complex models like neural networks can be explored.

How can you reduce churn with predictive modeling?

We collaborate with client experts to understand the business problem, select and preprocess the right data, train and fine-tune models, and deploy the ones that deliver the best predictions to generate real-world insights.

How do you plan a predictive AI project?

We start by defining the business problem and modeling target, planning solutions that align with how managers think about the industry to ensure AI delivers business benefits.

How do you select the right AI model?

We evaluate up to a dozen algorithms, combine them into ensembles if needed, and apply different algorithms to data subsets, creating hundreds of candidate models during the AI development process.

What are unstructured data sources?

Unstructured data sources include call center notes, survey responses, social media, maintenance logs, support tickets, and operational databases, all of which can provide valuable insights.