A leading global pharmaceutical and medical device company needed to strategize their investments. This required them to forecast category growth for eight upcoming quarters across seven countries ̶ no small feat in this age of high-pressure decisions and huge amounts of data.

Delivering a statistically validated and consistent methodology across forecasts required us to apply several AI and machine learning techniques. And the result?

Thanks to a user-friendly AI forecasting tool, the client’s non-IT personnel could run their own forecasts. With just a few simple clicks, they had the power to:

Using Nielsen Retail Audit data for seven countries and a blend of regression-based causality and time series forecasting methods, our ensemble approach allowed the company to understand category movement in multiple areas.

First, we identified key macroeconomic and consumer indicators for each category. We then created a regression model based on these indicators and computed a time series forecast using Holt-Winters methodology. Finally, we added weighted averages to both the regression and time series models; this created a rounded and flexible ensemble model that users could easily fine-tune without recourse to the data science or IT departments.