Machine Learning And Artificial Intelligence 

Causal Inference

Prediction Models

Optimization

 

Scenario:

A Fortune 500 consumer products company needed to develop prediction models to forecast sales of thousands of products sold under various brand names, and each product had hundreds of features. Creating and maintaining individual models for each product required significant work and constant maintenance.

Result:

The Krenicki Center developed a robust clustering of interpretable prediction models. The solution included Shapley Values for rank-ordering the critical features to create clusters of prediction models.

 

Business Strategy And Communication

Organization-wide data strategy

Translating business problems into data questions

Dashboard communications

 

Scenario:

A large marketing and consumer e-commerce company needed to reevaluate its customer engagement processes based on changing market conditions. How will customer engagement incentives change the bottom line? Which specific customers could be influenced? How could the change work in other markets?

Result:

The Krenicki Center modeled the customers' lifetime value (LTV) calculation, investigated how LTV changed with engagement, and used this analysis to target specific customers and markets.

 

Data Acquisition and Engineering

Developing sensors for data collection

Scalable data infrastructure

 

Scenario:

A large industrial manufacturing company needed to expand its analytics and engineering capacity for R&D to develop a semi-autonomous robot.

Result:

The Krenicki Center formed and interdisciplinary teaam with data analytics, machine learning, and electrical and mechanical engineering expertise to design a robot with sensors to collect and interpret perception capture data, analyzing defects and infringements in the surrounding field.