This talk by Shreyas Shetty M, Rohit Yadav, and Mohit Gupta features the use of reviews and images to build successful products using actionable insights that have been extracted from said data.

1. Actionable Insight Extraction
from Reviews and Images for building successful
products
Shreyas Shetty M
Rohit Yadav
Mohit Gupta

2. Flipkart confidential - For Internal use only. Not to be shared externally.
Product Design Cycle
Voice of
Customer
Sales and
customer feedback
Products
on platform
Design
new products
Analyse
“voice of customer”

3. Flipkart confidential - For Internal use only. Not to be shared externally.
“Voice of customer”
Implicit signals
● Sales numbers
● Returns numbers
● Page impressions
● Add to cart
Explicit signals
● Product reviews
● Ratings
● Return comments

4. Review Analyser
Explicit Signals
such as product reviews and ratings

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Aspects from reviews

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Challenges in mining aspects
● Demographics e.g. lingo
● Review text is usually noisy e.g. spelling errors
● Conflicting signals - rating vs review
● Aspects vary with verticals

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Extract aspects
and opinions
Analyse
Sentiment
Cluster and
rank aspects
Pre-process
reviews
Product pipeline

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Pre-process
reviews
Extract aspects
and opinions
Analyse
Sentiment
Cluster and
rank aspects
Pre-process reviews

9. Flipkart confidential - For Internal use only. Not to be shared externally.
Extract aspects and opinions
● Dependency parser - extract grammatical relations
● Noun -> Aspects, Adjectives -> Opinion
Pre-process
reviews
Extract aspects
and opinions
Analyse
Sentiment
Cluster and
rank aspects

10. Flipkart confidential - For Internal use only. Not to be shared externally.
Analyse Sentiment
● Opinion words by themselves do not indicate sentiment
○ “I appreciate the fast delivery by Flipkart” - positive
○ “The phone battery drains really fast” - negative
● Context around the aspect is important
● fastText classifier trained on review snippets
● Review rating used as a proxy for sentiment label
Pre-process
reviews
Extract aspects
and opinions
Analyse
Sentiment
Cluster and
rank aspects

11. Flipkart confidential - For Internal use only. Not to be shared externally.
Cluster and rank aspects
● Different words for similar
aspect
● Similar aspects are grouped
● Aspects converted to
embeddings for clustering
● Ranking aspects for
prioritization
fabric
fabric material cotton cloth
Pre-process
reviews
Extract aspects
and opinions
Analyse
Sentiment
Cluster and
rank aspects

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Success story
● Mixer Grinders
○ Grinder jars being small was a concern identified
○ Led to ~15% of entire sales in the vertical

13. Fashion Intelligence
Implicit Signals
Visual understanding of catalog images
combined with sales numbers, page views

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Visual insights?
How Fashion designers identify trends. Trend statistics

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Challenges with catalog data:
Granularity of attributes Capturing Design Incorrect catalog
Mandarin
Collar
Solid or Stripes?Are all of them blue?

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Analytical Challenges:
● Confounders.
Both the
products are white polos,
Why one sells
more than the other?
Brand?
● Counterfactuals:
Changes
required to make it successful?
Color? Print?

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Color extraction
Correcting
attributes
AnalyticsCapture Design
Pipeline
Enriching
Catalog

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Design extraction and representation:
● Image segmentation - u-net deep learning models.
● Fine grained classification to identify design.
● Developed heuristics to measure design similarity between
products.
U-net Refined U-net
Color extraction
Analytics
Image segmentation Fine grained classification
Capture Design
Enriching
Catalog
Correcting
attributes

19. Flipkart confidential - For Internal use only. Not to be shared externally.
Color extraction and representation:
Image segmentation:
● Trained U-net image
segmentation model.
● 1200 images as tagged data
with augmentations.
Color representation:
● Histogram of 256 bins (color-
granularity).
● Primary colors
representation for search
Color extraction
Analytics
Capture Design
Enriching
Catalog
Correcting
attributes

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Correcting Catalog attributes
Vgg-16
Image
classification
Collar: Polo
Sleeve: Short
Pretrained VGG-16 tuned with 11400 tagged images
Color extraction
Analytics
Capture Design
Enriching
Catalog
Correcting
attributes

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Catalog Data Enrichment
Collar: round
Sleeve: long
Fabric: cotton
closure: zipper
Extracted Design Extracted color
Predicted
Catalog attributes
Additional
Catalog Attributes
Enriched
Catalog Data
Color extraction
Analytics
Capture Design
Enriching
Catalog
Correcting
attributes

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Analytics
● Statistics on last 1 month, 3 months and 6 months duration.
● Filtering based on MRP, RVP, discounts, and important catalog and
machine predicted attributes.
● Sorting of products based on rate of sales and release date.
Color extraction
Analytics
Capture Design
Enriching
Catalog
Correcting
attributes

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Fashion Intelligence website
Click

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Time for the results
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