A Case Study in
Revenue Planning &
Forecasting
The Software Division of a Fortune 100
Technology Company
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Revenue Planning and Forecasting (Software division of a Fortune 100 Technology Company)

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In this case study learn how BRIDGEi2i helped the Software Division of a Fortune 100 Technology company to develop an algorithm to forecast revenue from SW at a product level and to understand revenue forecasts from licensing and delivery dimensions.

Published in: Data & Analytics
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Revenue Planning and Forecasting (Software division of a Fortune 100 Technology Company)

  1. 1. A Case Study in Revenue Planning & Forecasting The Software Division of a Fortune 100 Technology Company Quick Context Objective • 7% higher forecast accuracy • ~$23mn business impact • Ability for BUs to forecast Revenues from SW Impact • BRIDGEi2i understands the SW industry and its challenges in revenue planning • Giving the problem an algorithmic treatment simplifies the know-how for the Revenue planners Key Success Elements Our Approach 6 Months 3 Years Client Project length Length of relationship with client • Data was securely accessed within Client environment • SW attributes such as Licensing terms by customer, renewal cycles and version changes accessed from Teradata • Order Data is merged with the SW attributes in SAS • Additional metrics such as ASP is estimated for future months • SW has 2 moving parts that makes Revenue Forecasting difficult – True Demand and the ASP** • ASP was estimated based on a multi- variate forecasting algorithm • Monthly demand quantities were aggregated and divided by ASP to create the Quantity demand timeseries • Advanced forecasting algorithms in SAS HPF used for forecasting • A rigorously tested code was developed and validated repeatedly on historical Bookings prediction accuracy for SW • The final SAS code would identify SW SKUs, fetch data from Finance BI and historical Bookings and generate 24- month forecasts for every planning cycle • Model has been deployed in Demantra Data Management Algorithmic Play Operationalization a. ~10,000 SW SKUs that usually ships with HW; variety of delivery models b. SW are exposed to licensing/ renewal – makes demand very volatile a. To develop an algorithm to forecast revenue from SW at a product level b. To understand revenue forecasts from licensing and delivery dimensions * ASP – Average Selling Price

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