For many pharmaceutical firms, trial recruitment forecasting plays a role in trial recruitment planning. However, these forecasts may be generated with relatively simplistic approaches based on only a small subset of available internal & external data.
Data Science, specifically machine learning forecasting techniques, enables decision makers across a spectrum of industries to set more effective sales targets and plan profitable customer routes that boost top and bottom line growth.
Unsupervised learning approaches are very powerful when applied to customer segmentation.
Hospitals can change the way they deliver care by understanding their communities through the application of machine learning and statistics.
Mosaic built a model that predicted signal disruption, up to 16 hours in advance, based on North American precipitation forecasts and historical signal quality measurements.
We designed and delivered a custom, extensive data science assessment to help this energy firm modernize.
We designed and deployed a custom deep learning based tool to extract images from millions of digital and scanned documents.
Mosaic helps a leading 3PL company apply machine learning to predict brokerage profit drivers.
Mosaic was engaged by a leading hotel chain to assess the best way to predict future demand for hotel rooms across their various properties.
We designed and deployed an AI model to predict shipments at risk of late fees for one of the world’s largest CPG businesses.