Demand Forecasting with Latent Matrix Factorization

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.

Readers will learn

- what latent matrix factorization is,

- how the models work,

- how it can be used to predict demand,

- what data is needed to use a latent matrix factorization model.

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Machine Learning Applied to Customer Targeting for Direct Sales

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.

COVID–19 Data Set Modeling and Analytics

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.

Machine Learning for Business Leaders

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.

Opening the Black Box - Learn how to think about AI and thrive in data-driven cultures of today and tomorrow

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.

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