flipkart pre sales_analysis
Post on 14-Dec-2014
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DATA ANALYSIS & RECOMMENDATIONS
Raj, Director of Marketing
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)
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 Arnotts product data repositories
•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/taxonomy
Product Categorizatio
n
Attribute set creation
Data Aggregation
Data Cleansing & Standardizati
on
Item Setup/Catalog Creation - Methodology
Data Accuracy
Data Consistency
Data Completeness
Data Standardizatio
n
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
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
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
Facet Recommendations – Mobiles (Link)
Existing Facets Recommended Facets
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
Data Cleansing/Standardization – Laptops (Link)
Duplicate of same
facet/attribute – needs to
be normalized
Data Cleansing/Standardization – Laptops (Link)
For Dimensions, height/depth/width to be displayed to
make consumers for better
understanding
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
Data Cleansing/Standardization – Men’s Clothing (Link)
Duplicate of same
facet/attribute – needs to be normalized
Data Cleansing/Standardization – Induction Cooker (Link)
Duplicate of same
facet/attribute – needs to
be normalized
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
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
Mis-Categorization – Snapshot – Shirts (Link) Casual Shirt
Formal Shirt
Digital Asset – ImagesScope: 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
Images – Optimization - SnapshotBackground
to be cleansed
Image shade to be
removed
Consulting Services
Images – Optimization - SnapshotBackground
to be cleansed
Image shade to be
removed
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
Taxonomy Building & Assessment
Provide recommendations and
justifications for taxonomy optimization
Apply taxonomy building
methodology
Analyze existing taxonomy
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
Taxonomy Building & Assessment – Competitors snapshot
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)
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