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.

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