Discount analysis & trends - Copy
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Transcript of Discount analysis & trends - Copy
Copyright © 2015 Juniper Networks, Inc. 1
Discount Analysis & Trends of Maintenance ServicesKanupriya Dhiman
Summer InternshipMay-August 2016
Copyright © 2015 Juniper Networks, Inc. 2
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
Copyright © 2015 Juniper Networks, Inc. 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
Copyright © 2015 Juniper Networks, Inc. 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 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
Copyright © 2015 Juniper Networks, Inc. 5
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
Copyright © 2015 Juniper Networks, Inc. 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
Copyright © 2015 Juniper Networks, Inc. 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
Copyright © 2015 Juniper Networks, Inc. 8
Thank you