- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Predictive Analytics
- Equipment & Machinery
- Sales & Marketing
- Behavior & Emotion Tracking
- Livestock Monitoring
- Data Science Services
- System Integration
A large tech company was using legacy analytics platforms to support over 500 internal clients with predictive modeling projects. These projects were aimed at improving customer acquisition and retention, identifying up-sell and cross-sell opportunities, increasing revenue, and understanding customer lifetime value. However, the legacy systems were primarily used for data extraction, processing, analysis, and reporting, and had several limitations. The global analytics team was heavily dependent on these systems, and the idea of migrating to a new platform, the TIBCO® Data Science platform, was met with reservations. The team decided to conduct a feasibility analysis by creating a customer lifetime value model for one of the business units, a project they had undertaken multiple times before.
IT Infrastructure Company
About The Customer
The customer is a large tech company that supports over 500 internal clients with predictive modeling projects. These projects are aimed at improving customer acquisition and retention, identifying up-sell and cross-sell opportunities, increasing revenue, and understanding customer lifetime value. The company's global analytics team had been using other legacy analytics platforms for years, and they were heavily dependent on them for data extraction, processing, analysis, and reporting. The team was asked to migrate to the TIBCO® Data Science platform, a move that was initially met with reservations.
The team decided to use their existing Teradata Data Lab for data extraction and processing, and the TIBCO Data Science system for advanced analytics. The TIBCO Data Science platform proved to be an advanced analytics solution, making the project easier and delivering greater business value. The platform made data preparation an easy, menu-driven process, eliminating the need for extensive coding required by other legacy analytics solutions. The team used a year's worth of transaction data extracted from Teradata and TIBCO Data Science algorithms to identify natural product groupings and validate business assumptions about purchasing behavior. The TIBCO Data Science platform also provided excellent graphical capabilities, making it easy to visualize results. The team was able to use models supplied with the TIBCO Data Science system to determine both the predicted profit and the customer's probability of survival.
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