November 18, 2020
OptioRx - Case study

Pharmacy Revenue Prediction for OptioRx with Azure Pipeline

The Problem OptioRx, a private equity-backed owner and operator of specialty compound pharmacies, needed to predict quarterly pharmaceutical revenue to improve revenue planning and predict fraud. The primary challenge was that the data contained significant seasonality with irregularities occurring in both doctor and pharmacy level data.  Blue Orange was brought in to create a production model that could be integrated into price forecasting. Due to […]
October 26, 2020
Point 72 - Case 72

Point 72 – Data Architecture

Client Point72 Asset Management is a global firm that invests in multiple asset classes and strategies worldwide. They operate across a few different business lines and handle multiple portfolio funds. The company prides itself on proprietary, state-of-the-art investing methods. Their quantitative investing solution designs and implements computer-driven trading strategies that are based on both publicly and privately available data. Challenge Point72 Asset Management needed a […]
October 22, 2020
Vetta - Case study

Business Intelligence Solution to Maximize an eCommerce Platform

Client Vetta Brands manages a portfolio of companies that innovate NCAA merchandise and provide partnership solutions in the imprinted apparel market. Being a leader in the US market, they possess NCAA licenses with more than 700 colleges and universities. Their strong business infrastructure allows them to manage major retail and whole distribution channels. Challenge Having done a series of acquisitions, the company faced an unavoidable […]
February 11, 2020
Supply Chain & Revenue Predictions for Pharmaceuticals

Supply Chain & Revenue Predictions for Pharmaceuticals

The Challenge A Private Equity firm needed to predict quarterly pharmaceutical revenue for the next quarter in terms of doctors and pharmacies. The problem here is that the data contains a lot of seasonality because some doctors are not regular in their services, the same happens at the pharmacy level. The challenge is to create a robust model to overcome these gaps in the information. […]
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