In this case study learn how BRIDGEi2i helped a Fortune 100 Technology company to use SFDC pipeline data for better bookings prediction and delivered a method that necessarily accommodate for inn-accuracies in SFDC data

1. A Case Study in
SFDC in Demand
Planning
A Fortune 100 Technology Company
Quick Context
Objective
a. ~40,000 SKUs and a global dynamic demand scenario
b. Short product lifecycles and highly competitive landscape
• 15% higher
forecast accuracy
• ~$300mn business
impact
• NASSCOM “best 50
analytics case
studies” award
winner
Impact
• BRIDGEi2i understands SFDC data
and its pitfalls – but also knows how to
circumvent them with appropriate
statistical treatments
• Our prowess in identifying the right
algorithm for the right problem
Key Success Elements
Our Approach
5 Months
3 Years
Client
Project length
Length of relationship with client
• All data was securely accessed within
the client environment
• SAS was used for the predictive
algorithm development and deployment
• SFDC data was accessed through
Teradata queries written in SAS
• SFDC data has several inaccuracies –
regional adoption challenges, last-
minute updates, fat-thumbing numbers
• All of this was treated appropriately
• Expected Bookings from SFDC pipeline
with a 3 month offset was created as an
explanatory variable for Bookings
• A Generalized Additive Regression
(GAM) model was used with a self-
written Spline Smoothing function
• Outliers in the X variable were treated
with BRIDGEi2i’s own algorithm LOF**
• Model is non-linear and non-parametric
– learns very quickly
• A rigorously tested code was developed
and validated repeatedly on historical
Bookings prediction accuracy
• The final SAS code would fetch data
from SFDC, treat it appropriately, run
the GAM model, generate 24 months of
predictions and populate the forecasts
in Demantra – the single platform for
demand intelligence for the Planners
• Model has yielded great results
Data Management Algorithmic Play Operationalization
a. To use SFDC pipeline data for better bookings prediction
b. Method should necessarily accommodate for inaccuracies in SFDC data
* LOF – Local Outlyingness Factor – our own algorithm
developed for Fraud Detection in Credit Cards