Despite the seeming ubiquity of Machine Learning, only 39% of firms embed ML in daily business functions. This white paper demystifies Machine Learning for non-technical business stakeholders to better collaborate in, and derive more value from, data science initiatives. This paper covers:
White Paper
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.
White Paper
For many businesses, demand planning is essential. Profitability, cash flow, and customer satisfaction and retention all hinge on getting this right. This white paper will introduce latent matrix factorization to model demand curves and discuss how it can be used to achieve these outcomes.
Solution Sheet
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.
White Paper
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.