Demand Forecasting with Latent Matrix Factorization

Using ML to effectively target the right customer at the right time with the right information can make a big difference in how effective your campaign is. This short white paper discusses some of the approaches and demonstrates how one company achieved a 6x uplift in conversions by apply ML to customer targeting.

During times of crisis, companies must look at the available data — both internal and external— and try to understand how that data can be used to determine how the business is currently being impacted, how it is likely to be affected in the future, what are most likely scenarios that will play out, what can be done to counter those scenarios and take advantage of hidden opportunities in this rapidly changing environment. The Starschema COVID-19 dataset ingests reliable data from multiple sources and makes it analytics-ready so it can be easily accessed and used.

From recommender systems on streaming services to personal assistants like Siri or Alexa, ML is nearly everywhere today. This white paper demystifies machine learning for non-technical business stakeholders to better collaborate in, and derive more value from, data science initiatives.

Machine Learning models can be so complex that they seem like a magical black box with inputs and outputs, but little understanding of how the outputs are derived. In this white paper,

Eszter Windhager-Pokol, Starschema head of data science, explains the concept of interpretability and several methods to address the problems posed by black box models.