AI/ML for E-Commerce in India

Why does India need homegrown AI solutions?

“Many Indias” - Require a differentiated approach

$30,000-35,000

$10,000-15,000

$4,000-5,000

$3,000-3,500

$2,500-3,000

$2,000-2,500

$1,500-2,000

$1,000-1,500

<$1,000 Bangladesh Uganda

Kenya Egypt

Mexico Brazil

UK Canada

11%

AI For India

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‘Data’ is the new oil!

Visits13 + mn

Sessions : 18+ mn

Searches : 15+ mn

Pageviews : 200+ mn

Visits30+ mm

Sessions : 100+ mn

Searches : 50+ mn

Pageviews : 800+ mn

Average Big Billion DayTypical Day

Period Visits Txn Users

Daily ~13M ~600k

Weekly ~85M ~2.7M

Monthly ~400M ~8.8M

What data do we collect?

isMarried?Fake Detection

GenderCategory clicksProduct clicks

Add to cart

Product searches

● GPS location● Mobile Device type*● Shipping Address*● Search Filters● Sizes ● Ratings/Reviews● Page scrolls

● Product Cards served

● Offers enabled● Ship SLA promised

How do we process data, generate metrics & Insights?

Ingested data

Processed Insights &

Metrics

ML

What do we infer from all this data?

Age isStudent?

isMarried?

Behavioral

Fake & Fraud Detection

Brand Affinity

CLTV

Trust & Safety

Demographic

Location

Kids (& Age)

Gender

IsMarried, IsStudent

Address

Price Affinity

Browse Location

Returns

Store Affinity

How do we scale our ML models?

a.

b.

AI-First vision: Just tech is not sufficient...

AI, Knowing our CustomersCase Study : Personalization

Understanding our Customers

BROWSE & PURCHASE ACTIVITY

on FLIPKART

Home Page Visits, Searches, Product page views, Add to cart/Wishlist,Orders, Store Visits etc.

INDIVIDUAL ATTRIBUTESGender, Location, Age, Spending Pattern.

ENVIRONMENTFestivals, Weather, Natural Calamities,

Seasons etc.

SEGMENT SIMILARSRFM- Platinum etc., Segmentation: Visit & Purchase Cohorts

Merchandising, Recommendations: 70% Clicks : We serve intent & enableBusiness goals.

Optimised Merchandising and Ads Content

PersonalisedRecommendations

Personalised User State driven content

User cohort based on past behaviour. Current behaviour prediction done by ML model.

New clicks drive model recompute, serving up personal content in real-time.

Search Landscape: Unique for Every Query Type, User, Category

More than 2/3rd Searches are Broad (Eg: Mobile in Tier 2, iPhone X in Metros)

Semantics Differ Too: “Action” is a Brand, “Action” refers to sports shoes

Category Brand Facet Line Product

Fashion Shoes Nike Shoes Silk Saris

Mobile Mobiles Mi Mobiles Galaxy S8

Prediction based on user activity, Brand, Category, 6 views help

Using user’s device and location information to predict the price-affinity of new users

Watches: Low ASP users Vs Premium users

Personalised Search Results: Driving consumers closer to their choice

Conversation Funnels : Glean User Intent

Conversation Funnels : Progressive Questions and Answers

Conversation Funnels : Customer Speak

Single/Couple - 6 kgFamily of 3 - 8 kgFamily of 4 - 10 kgLarge Families - 12 kg

Shaping Intent : Visual Curated Collections

Shaping Intent : Trending in India & Less Explored

AI, Human ModerationCase Study : Visual Similar

For customers, inspiration is everywhere…

FACT: A picture is indeed worth a 1,000 words

No search query can capture a customer’s inspiration

Previously, this is how “Visual Search” worked

Collaborative filtering is often not enough

AI methods produce better matches…

Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce, Shankar, Devashish; Narumanchi, Sujay; Ananya, H A; Kompalli, Pramod; Chaudhury, Krishnendu

AI, Progressive InnovationCase Study : Customer Reviews, Sentiment, Aspects

● Mix of English and local languages (transliterated)

● Spelling and grammatical mistakes, internet abbreviations and slang

● 6 months of human-tagged data for training, though very noisy

Problem : Moderate Reviews, Understand Sentiment, Attribution

“Product is cheap, camera does not work!”

“Please buy this product from my shop”

“Flipkart service has always been great”

Accepted

Rejected

Discouraged

Aspect Reviews, Aggregate Opinion of our Customers- Input to

Product Design

Other Application:Auto Titling & Sentiment Analysis

Title suggested: Nice Product, good fitting

Use parts of speech patterns in text. Typical flow: Tag each word Superb_ADJ product_NOUN has_VERB great_ADJ camera_NOUN

Sentiment Suggested: Positive, Rating: 3.8

Used a variant of the Open AI sentiment nueron. The network can classify text into positive and negative sentiment. The sentiment score can be normalised to a star rating.

Generator

Z

X

Discriminator

Real or Fake Set of randomly generated images

Generator Losses Discriminator Losses

Makkapati V., Patro A. (2017) Enhancing Symmetry in GAN Generated Fashion Images. SGAI 2017.

Dresses Tops

Checks Stripes

Problem Statement(s)...

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Problem Statement(s)...

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