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The Center for Business Analytics is pleased to recognize Verizon as an honoree of the 2023 Drexel LeBow Analytics 50 Awards. Read more about how Verizon used analytics to solve a business challenge.


Venkat Rangan Gangavaram, Director of Analytics and Insights




Technology and Communications

Business Challenge

Commercial leaders needed efficient end-to-end processes and tools for faster scenario planning and accurately forecasting outcomes, along with associated financials. Business-as-usual methods deployed traditional tools that did not effectively capture signals from changing market conditions, had limited ability to address complex scenarios and “what-ifs,” had longer planning cycles and yielded suboptimal solutions that were driving up costs. The business users needed more advanced capabilities with efficient financial planning and decision management tools for driving their go-to-market strategy.

Analytics Solution

The analytics and insights team embarked on a multi-year journey to embed in-house–developed, AI-driven “Challenger Models” for business users to augment current methods and accomplish the following:

  • Develop automated and interactive planning tools that leveraged the historical relationship between multiple drivers to generate granular outcomes and forecasts.
  • Generate “what-ifs” and multiple scenarios to evaluate optimal solutions to test a go-to-market strategy.
  • Begin automated business case generation, along with full-blown financials for a quicker end-to-end decision process.
  • Create full suite of business intelligence products to aid in demystifying AI outputs and provide confidence to decision-makers.


The AI-based scenario planning and forecasting tool has been running as a “Challenger Model” for over a year and is providing multi-fold benefits versus business-as-usual methods, such as:

  • Efficient planning cycles: There has been an 80 percent reduction in turnaround time from automated forecasts.
  • Improved accuracy: Results are closer to reality based on a 12-month mean absolute percentage error.
  • Enhanced complexity: The tool addresses complexity by generating granular forecasts across more than 10,000 combinations.
  • Adaptability: The tool integrates new data sources and drivers to identify complicated relationships.
  • Investment analysis and “what-ifs”: The tool is able to quickly identify and test win-win-win solutions.
  • Eliminated silos: The tool allows data from disparate sources to be assimilated for AI.
  • Successful adoption: The tool has resulted in change management and transformation due to cultural shift in driving AI adoption.