This document provides a summary of an analysis of discount trends for maintenance services conducted from 2013 to 2015. The analysis focused on the top three revenue generating product families. The methodology involved collecting data from various sources, cleaning and filtering the data, then segmenting and analyzing the data based on factors like product family, service level, territory, industry, and type of sale. Metrics like average and median discount percentages were used along with tools like Excel, Tableau and Rapid Miner. Correlation, regression and conjoint analysis methods were explored to understand relationships between variables and the influence of attributes on discount levels. Recommendations were developed for key stakeholders based on the findings.

1. Discount Analysis & Trends of
Maintenance Services
Kanupriya Dhiman
Summer Internship
May-August 2016

2. Analyze the trends &
variances of discount
for higher revenue
generating products &
services
Evaluate opportunities
to improve our pricing
model by leveraging
the discount analysis
To explore new
opportunities &
provide useful
recommendations to
key stakeholders if
current guidelines are
not optimal

3. Scope
Data collected for three years ranging from 2013 to 2015
Assessment for discount trend of products was limited to top three revenue
generating products families i.e. MX, EX & SRX
Data fields categorized as “Not assigned” were not included in the analysis after
careful consideration on various parameters
Most of the analysis comprised of both Capped & Non-Capped accounts as a whole but
the Recommendations phase reflected a fragmentation between these two types of
accounts

4. Methodology
Metrics &
Tools Used
• Average of discount percentages for fair representation of data
• Median of discount percentages to avoid the sensitivity towards
outliers
• Tools: MS-Excel (Pivot tables, v-look up, conditional formulas),
Tableau and Rapid Miner
Data
Collection
• Data on required fields collected as required
• Data extracted from various platforms like SharePoint, legacy ERP
and SAP BI
Challenges Solution
Attaining data for the year 2013 due to its
unavailability in new ERP system
Inconsistency of data fields among 2013 and 2014-
2015 information
Data moved in several parts
The data fields matched with one another and extra
information was removed
Limitation of MS-excel to manage large volume of
data
The data was split into several parts using EZ-split
software

5. Cleansing, Filtering & Validating the Data
Cleansing/ Scrubbing
• Similar information
erased to avoid duplicity
• “Not assigned” values for
Region data values were
optimized
• Identification of Software
SKUs
• Corrections in discount %
performed
• Conditional formatting of
data values
Filtering
• According to attached
product families*
• According to service
levels*
• * Validation from financial
dashboard
Validating
• List price
• Net price
• SKUs consisting of “cfts”
or “custom”
• Old & Obsolete SKUs
Data anomalies was filtered from the data post applying these conditions

6. Factors of Segmentation
By Product Families
- MX
- EX
- SRX
By Service Levels
- Core
- Core Plus
- Next day & Next day onsite
- Same day & Same day onsite
By Territory
- AMER
- APAC
- EMEA
By Industry
- Service Provider
- Enterprise
By Information System
- Hardware
- Software
By Type of Sale
- Partner
- Direct
By Type of
Contract/Agreement
- New
- Renewed
By Volume of Deals

7. Methods Exploration for Conducting Analysis
Correlation & Regression Modeling to know the relationship among variables individually
Conjoint Analysis to explore the influence of various attributes on discount
Performing manual output framing choosing possible combinations of factors as a bundle

8. Thank you