Discover how’s machine learning platform helps pharmaceutical companies leverage predictive analytics to drive operations and improve customer satisfaction.


Pharma’s Big Data Problem

Healthcare data represents a significant challenge for pharmaceutical companies because of its sensitive nature. Although there are many data analytics solutions available, most actors struggle to truly exploit their data for competitive advantage. This is mainly due to solutions’ inability to deliver insights in an interpretable manner and general lack of agility resulting in many failed opportunities for companies in the healthcare industry.


Applying Machine Learning Solutions

While classic analytics solutions simply lack the required skills and tools, machine learning solutions have proven useful to link vast amounts of data and drive better insights. Whether it concerns sales forecasting, inventory management, or product development, the use of adaptable predictive analytics represents a unique opportunity for pharmaceutical companies to optimize their operations and drive revenue.


Why for Healthcare?

We believe in building highly efficient predictive models that are immediately deployable, scalable, and accessible by users without a data science background. helps pharmaceutical companies accelerate their data science projects by empowering business analysts with machine learning models while reducing time spent in implementation.

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Sales Forecasting

Sales Forecasting

Business analysts are using machine learning models to improve their sales forecasting methods and obtain more precise demand forecasts. Thanks to our automated platform, models are built based on your enterprise data and can be refined over time. Analysts can spotlight key insights into customers’ consumption trends, maintain sales performance, and optimize their business strategies. View the use case

Customer Churn Prediction

Customer Churn Prediction

Client churn prediction models are designed to detect clients with risk behavior. provides understandable predictive models to effectively target ideal customer profiles, identify the main churn factors behind customer default, and develop an effective retention strategy. Models are continuously updated based on changes to the data. View the use case

Inventory Management

Inventory Management predicts the consumption trends of all your products and inventories. Thanks to advanced algorithms, you can predict which product will be more in demand at what time, and optimize your supply operations accordingly. Our automated machine learning engine analyzes your historical data and builds predictive models to help you better manage inventories. View the use case

In 2018, companies will focus on operationalizing AI, particularly in the cloud, to more easily build, refine, deploy and enhance machine learning environments.

– Scott Zoldi, Chief Analytics Officer at FICO (NYSE: FICO) –
– FICO Newsroom, Jan. 11 2018

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