AI/ML for E-Commerce in India

Transcription

1 AI/ML for E-Commerce in India

2 Why does India need homegrown AI solutions?

3 Many Indias - Require a differentiated approach 11% $30,000-35,000 UK Canada Mexico Brazil Kenya Egypt $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

4 AI For India

5 Data is the new oil! Period Visits Txn Users Daily ~13M ~600k Weekly ~85M ~2.7M Pageviews : 800+ mn Monthly ~400M ~8.8M Pageviews : 200+ mn Searches : 50+ mn Searches : 15+ mn Sessions : 100+ mn Sessions : 18+ mn Visits 13 + mn Typical Day Visits 30+ mm Average Big Billion Day

6 What data do we collect? Product searches Add to cart GPS location Mobile Device type* Shipping Address* Search Filters Sizes Ratings/Reviews Page scrolls Product Cards served Offers enabled Ship SLA promised Product clicks Category clicks ismarried? Gender Fake Detection

7 How do we process data, generate metrics & Insights? Ingested data Processed Insights & Metrics ML

8 What do we infer from all this data? Kids (& Age) Age Browse Location CLTV IsMarried, IsStudent Gender Address isstudent? Demographic Location ismarried? Behavioral Trust & Safety Store Affinity Brand Affinity Price Affinity Returns Fake & Fraud Detection

9 How do we scale our ML models? a. b.

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

11 AI, Knowing our Customers Case Study : Personalization

12 Understanding our Customers BROWSE & PURCHASE ACTIVITY on FLIPKART Home Page Visits, Searches, Product page views, Add to cart/wishlist, Orders, Store Visits etc. INDIVIDUAL ATTRIBUTES Gender, Location, Age, Spending Pattern. ENVIRONMENT Festivals, Weather, Natural Calamities, Seasons etc.

13 Optimised Merchandising and Ads Content Personalised Recommendations Personalised User State driven content Merchandising, Recommendations: 70% Clicks : We serve intent & enable Business goals. 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.

14 Search Landscape: Unique for Every Query Type, User, Category Category Brand Facet Fashion Shoes Nike Shoes Silk Saris Mobile Mobiles Mi Mobiles Line Product Galaxy S8 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

15 Watches: Low ASP users Vs Premium users Personalised Search Results: Driving consumers closer to their choice 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

16 Conversation Funnels : Glean User Intent

17 Conversation Funnels : Progressive Questions and Answers

18 Conversation Funnels : Customer Speak Single/Couple - 6 kg Family of 3-8 kg Family of 4-10 kg Large Families - 12 kg

19 Shaping Intent : Visual Curated Collections

20 Shaping Intent : Trending in India & Less Explored

21 AI, Human Moderation Case Study : Visual Similar

22 For customers, inspiration is everywhere FACT: A picture is indeed worth a 1,000 words No search query can capture a customer s inspiration

23 Previously, this is how Visual Search worked

24 Collaborative filtering is often not enough

25 AI methods produce better matches

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

27 AI, Progressive Innovation Case Study : Customer Reviews, Sentiment, Aspects

28 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! Accepted Please buy this product from my shop Rejected Flipkart service has always been great Discouraged

29 Aspect Reviews, Aggregate Opinion of our Customers - Input to Product Design

30 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.

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32 Real or Fake Set of randomly generated images Discriminator X Generator Z

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

34 Dresses Tops

35 Checks Stripes

36 Problem Statement(s)...

37 Problem Statement(s)...

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