1. DATA ANALYSIS &
RECOMMENDATIONS
Raj, Director of
Marketing

2. Product Catalog Management
(PCM)
Scope:
Catalog - To enrich the product information
or optimize the online catalog
 Attribute set creation/enrichment
 Aggregate attribute values
 Cleanse and Standardize values
Product with key and exhaustive information will make buyers to take a
quick buying decision, to improve sales and User Experience (UX)

3. Product with key and exhaustive information will make buyers to take a
quick buying decision, to improve sales and (UX) User Experience
• Aggregate data
from
manufacturers
and Flipkart’s
product data
• Cleanse and
normalize
product data as
per industry and
client standards
• Analyze
category, create
attribute set
based on
industry best
practices and
competitor
benchmarking
• Map products
into right
categories/taxon
omy
Product
Categorization
Attribute set
creation
Data
Aggregation
Data Cleansing
&
Standardizatio
n
Item Setup/Catalog Creation -
Methodology
Data Accuracy
Data Consistency
Data
Completeness
Data
Standardization

4. Faceted Navigation – Data Cleansing &
Standardization
Scope: To validate the values under all
facets, cleanse the junk values, maintain
data uniformity and also recommend the
facets to have a better competitive edge
Faceted Navigation or Refine Results or Filter Attributes always drive
consumers to land their required products easily which will improve the
shopping experience and conversions

5. Faceted Navigation & Recommendations
Analyze Facets
and values for
category
Cleanse facets
and values
Recommend new
facets based on
best practices,
client goals and
competitor
benchmarking

6. Faceted Navigation & Recommendations – Specific
Tasks
Brand verification, removal & standardization
Material, Color, Size and other facets - data
uniformity
Remove spell mistakes, duplicate data, etc.
Product de-duplication
To cleanse and normalize data; identify and remove “junk data” for data integrity and
usability purposes

7. Facet Recommendations – Mobiles (Link)
Existing Facets Recommended
Facets

8. Data Cleansing/Standardization – Mobiles (Link)
Bada
Blackberry
iOS
Symbian
WebOS
Recommended New
Values
Existing Values Existing Values
Value range should
not be overlapped –
For example:
products with 3.5
inch displayed in both
search

9. Data Cleansing/Standardization – Laptops (Link)
Duplicate of
same
facet/attribute –
needs to be
normalized

10. Data Cleansing/Standardization – Laptops (Link)
For
Dimensions,
height/depth/wi
dth to be
displayed to
make
consumers for
better
understanding

11. Data Cleansing/Standardization – Cameras (Link)
Duplicate of
same
facet/attribute –
needs to be
normalized
Value range should
not be overlapped –
For example:
products with 3.5
inch displayed in both
search

12. Data Cleansing/Standardization – Men’s Clothing
(Link)
Duplicate of
same
facet/attribute –
needs to be
normalized

13. Data Cleansing/Standardization – Induction Cooker
(Link)
Duplicate of
same
facet/attribute –
needs to be
normalized

14. Taxonomy Mapping -
Categorization
Scope: To validate the existing products
whether it has been mapped under
appropriate category and also to map new
vendor items under correct category

15. Mis-Categorization – Snapshot – Shirts (Link)
2 issues we
identified:
 Case 1: Casual
shirts has been
mapped under
Format Shirts
Case 2 : Product
name has been
updated with wrong
keywords
Example
for case 2

16. Mis-Categorization – Snapshot – Shirts (Link) Casual
Shirt
Formal
Shirt

17. Digital Asset – Images
Scope: To source images for products and
optimize the images as per standards
 Source images for products w/o images
 Optimize or enhance images – resizing,
white background, etc.
Product with consistent images will provide insights about the product to
consumers, which will improve buying decision and shopping experience

18. Images – Optimization - Snapshot
Background
to be
cleansed
Image shade
to be
removed

19. Consulting
Services
Images – Optimization - Snapshot
Background
to be
cleansed
Image shade
to be
removed

20. Taxonomy Building &
Assessment
Scope: To validate the existing taxonomy or
category structure, provide recommendations
to meet the competitive intelligence and also
par with customer expectations
A perfect taxonomy or category structure will always provide better shopping
experience (UX) and conversions (also effective utilization of search
keywords from the ecommerce platform)
Consulting
Services

21. Taxonomy Building & Assessment
Provide recommendations
and justifications for
taxonomy optimization
Apply taxonomy building
methodology
Analyze existing taxonomy

22. Taxonomy Building & Assessment – Quick
Reco
Computers,
Home
appliances,
Kitchen
appliances –
should be
maintained
separately to
improve the user
experience, to
meet the
competitive
intelligence and
industry
standards

23. Taxonomy Building & Assessment – Competitors
snapshot

24. Taxonomy Building & Assessment – Case
Study
Problem: One of the leading online retailers from Europe wanted us to assess their taxonomy whether
the current structure par with competitors.
Our Solution: GS1 taxonomy consultants provides the solutions for client problem and also
recommended best practices and also added more value proposition to problem statement
Best Taxonomy
Recommendation
Folksonomy
Competitive
Intelligence
Industry
Practices
Value Propositions we added:
1. Provided recommendations of
taxonomy based on
competitive intelligence
In addition, we ensured that the
taxonomy structure to par with
2. Industry practices
3. Consumer expectations (User
Experience)