Understand your customers deeply ... Engage with a personal touch! #ItsPersonal

Debdoot Mukherjee

Personalization & Customer Insights @

Modeling

Personalized Customer Engagement

Data Driven Retail Functions

Product Listings Handpicked For Me

Notifications Fashion Feed

Offers & Promotions

Re-targeting

Marketing Research

Campaign Targeting Audience Monetization

Category Planning

Brand Benchmarking

Content Design

• Ephemeral and non-identifiable items unlike Books and Movies – Extremely sparse user/item matrix –Bias of products with higher inventory – Exploration versus Exploitation trade-off

• Diversity and Serendipity • Closest domain: News Articles

Fashion – What s diffe e t?

• Recommend based on user profiles stored as preference / weight vectors on item features, learnt from relevance feedback on items.

• Good vector representation for items? – Bag of product attributes does not work! Too many features,

s a e o se histo y fo a si gle use , so p ofiles do t generalize.

– Learning preferences along latent factor / topical dimensions or product groups (clusters) helps.

• Maintain two user profiles: long term (taste), short term (intent) – Incorporate time decay into browse history – Degree of personalization depend on the amount of browse data

• BUT, att i ute ele a e does t suffi e. The e is so ethi g e a t aptu e ia att i utes a out so e fashio ite s

that make them popular, others not. May be aesthetics.

Feature / Content based Approach

• Simple user-user, item-ite CF te h i ues do t o k ell e ause of extremely sparse user-item matrix

• Matrix factorization:

• Regularization is tricky and severe cold start. In practice, models are trained specific to each category of product, so maintaining separate models for cold start and warm start becomes difficult.

• Recent advances in Feature based Matrix Factorization address this - SVDFeature, Factorization Machines, RLFM, FOBM, fLDA, UFSM …

• Train model on snapshot of active products for recommendations

Collaborative Filtering Approaches

• A good vector representation for items would make si ila ite s eigh o s i the e to spa e. #di e sio s

should be not very high. • Co-browse of items in a session is (weakly) indicative of

si ila ity . “u h a sig al ei fo ed a oss a y sessio s becomes strong.

• Inferring substitutable and complementary products – Leskovic et al. KDD15 – Train a logistic regressor with features defined on the similarity

of item vectors represented as topics to predict whether two products are similar. LDA using the analogy (Item Æ Document, Item Attribute Æ Word)

– Core Idea: Joint training of logistic regressor and item topics by simultaneously optimizing both topic distributions and logistic parameters to maximize the joint likelihood of topic memberships and product similarity.

Vector Representation for Items

• We use this analogy so that existing models for finding representations in text / IR become applicable: – Browse Session Æ Document, Items Clicked Æ Sentences, Item

Attributes Æ Words • Evaluate LDA, Word2Vec, GloVe …

– Yields varying levels of topical and functional similarities along dimensions of the item vector • “ea h fo si ila te s fo nike :

– Topical Similarity: adidas, puma, sports, dry-fit, polyeste … spo ts elated te s – Functional Similarity: adidas, puma, fila, merrell, hrx …

– Mining interesting relationships between entities of interest viz. brand, price band, pattern, item collection etc.

• Spherical clustering to create product groups – a better unit of analyses than individual products.

• Create user profiles by aggregating their preferences on such item dimensions and product groups across all browsing sessions.

Vector Representation for Items (2)

• Explore/Exploit trade off – Popularity scoring of items (normalized for each

category / product group) • Use Thomson Sampling in a context free bandit formulation

that assumes Gaussian reward (CTR) • Adjust CTR with rank to formulate reward

– Contextual bandits can help in choosing the right recommendation strategy given page and session context

• Use of LSH to ensure diversity of recommendations

Explore / Exploit, Diversity ..

Personalized Product Listings

Devarshi – A Football Enthusiast Anand – Appreciates Value for Money

Personalized Product Listings

Devarshi – A Football Enthusiast Anand – Appreciates Value for Money

Handpicked For Me

Personalized page with different kinds

of recommendations: • Taste, Intent based • Cross sell based on

last purchase

Notifications

Drive a contextual conversation y i i g the use s shoppi g intent in real time

Customer Insights Platform A platform to slice / dice mined customer profiles has over 50K

different dimensions

Create a segment of loyal customers in Delhi who wear heels

Affinity toward heels

Highly loyal

From Delhi

Delhi women have a greater affinity for taller heel heights than Chennai women

A woman from Delhi is 2x more likely to be interested in stilettos than someone from Chennai

Brand A

Brand C Brand B

48.5% - 26 yr

27% - 28 yr 15% - 26 yr

1.8 % - 29 y

Loyalist Distribution

0.4% - 28 yr

Comparing 3 Men Shirt Brands and their loyalists

Compared to an average Myntra customer

What else do the loyalists buy?

Less likely

More likely

Personalization Services Myntra.com

Event Distributions

Event Processor

S3 Event Storage

Cassandra (Clickstream Aggregates)

Model Training

Serving Caches

Near-line Personalization

Online Personalization

Customer Profile ETL

Customer-wise Event Aggregator

Mongo (Customer Insights)

Insights Platform

Architecture

Transactional DW

Product Knowledge Graph

Ariana Grande

M perso al st le is a i ture of, like, girl , throwback, like retro '50s pin-ups, floral, like hippies, like a thi g fe i i e, a d like flirt .

Perso al “t le is about having a sense of yourself what you believe i ever da

Ralph Lauren

Ever o e looks at your watch and it represents who you are, your values and your personal st le

Kobe Bryant

And You Still Think I Would Know About Personal Style ?!!

Read more at http://sartorialscience.myntrablogs.com