Discount analysis & trends - Copy

Copyright © 2015 Juniper Networks, Inc. 1

Discount Analysis & Trends of Maintenance ServicesKanupriya Dhiman

Summer InternshipMay-August 2016


OBJECTIVES

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


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


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 SolutionAttaining 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


Cleansing, Filtering & Validating the DataCleansing/ 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


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


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


Thank you

Discount analysis & trends - Copy

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