boosting consumer engagement at paypal

Boosting Consumer Engagement at PayPal

Sujit Mathew, Goh Yew Yap, Chen Yanhui

PayPal

©2015 PayPal Inc. Confidential and proprietary.

Two decades ago, our founders invented payment technology to make buying and selling faster, safer, and easier—and put economic power where it belongs: In the hands of people.


Mass Adoption of Mobile Devices

Digitization of Cash

Transformation of Cards

Fragmentation of Payment Types, Technology and

Channels

Rise of Fraud and Cybercrime

Money is

changing

PayPal is leading the transformation


AT SCALE*

173 Million Customers**

$235 Billion TPV

$8 Billion Revenue

4 Billion Transactions

WITH MOMENTUM

+19 Million Customers Gained in 2014

+26% y/y TPV Growth

+22% y/y Transaction Growth

*Stats are full year 2014, unless otherwise noted. **Stat is Q3 2015


PayPal processed $46 billion in mobile payment volume in 2014,

up 68% over 2013.

In 2014, 20% of PayPal’s net Total Payment Volume was

from mobile payments.

Venmo processed $2.4B in Total Payment Volume in 2014.

In Q3 2015 Venmo’s Payment Volume was $2.11 billion – up 201% year over year.

In 2014, PayPal and Venmo combined handled billions in P2P payment volume globally and nearly half of that volume was international.

A leader in

person-person payments

Use Case

© 2015 PayPal Inc. All rights reserved. Confidential and proprietary. 8

“Boost consumer engagement by recommending merchants and products to

consumers.”

©2015 PayPal Inc. Confiden4al and proprietary.

66 Million individual payments processed for chari4es via PayPal.

36 Million consumers used PayPal to make a payment to a charity.

13% of dona4ons through PayPal in 2014 were made on a mobile device.

418,000 chari4es used PayPal to accept dona4ons.

$5.7 Billion processed for chari4es by PayPal.

65% YoY growth in PayPal’s total mobile payment value to chari4es globally.

Bridging Consumers and Charities

*Stats are PayPal full year 2014 reports

Modeling

Overview

Stack

Graph

Collaborative Filtering

Content Model

Deployment

© 2015 PayPal Inc. All rights reserved. Confidential and proprietary.

Technology Stack

12

Hadoop / MapReduce

Mahout Pig

Python / Shell

HDFS Cassandra

Titan

Gremlin

Graph Modeling


Build Property Graph Based on P2P transaction data.

14

Discover Communities within P2P Data


Discover Key Influencers

15

Eigenvector Centrality


Property Graphs

G = (V , E , λ)

V = vertices E = Edges λ = Properties

16


Property Graphs

17

User Type Age Influence Score

Charity Type Name

g.V[[type:"Charity"]] .inE("Donate") .filter{it.getProperty('Amount') > 25} .outV.filter{it.getProperty('Influence') > 0.5}

Amount

Recommend

Enrich the graph with Donations and Social data.

Key Interests within group

Charity

Send

Find all Key influencers who have donated more than 25 USD to the charity

Collaborative Filtering


Collaborative Filtering 101

19

Commerce Interaction Matrix

We want to model the affinity between consumers and merchants

More transactions occurred, more confident we believe the relationship

Consumer

nonprofits

Likeness Matrix

Confidence Matrix


Collaborative Filtering 101 A matrix factorization method

20

Data Fitting Regularization

Merchant

nonprofits

C

onsu

mer

C

onsu

mer

d

d


Alternative Least Square

21

Iteratively update Fix V and update U: Fix U and update V:

Regularization Data Fitting


Scalable Collaborative Filtering Improve the scalability of ALS

22

Shard 1 Shard 2 Shard 3 Consumer

Merchant

Commerce Interaction Matrix

Mapreduce Job for Shard 1



Stage 1: Compute the individual contributions of each rating

Stage 2: Aggregate all contributions for every user and update their models in parallel

Reference: http://www.slideshare.net/jekky_yiqun/scalable-collaborative-filtering-for-commerce-recommendation

Global MapReduce Job for ALS


What does CF Learn?

23

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡ −

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡ −

=⋅

!!!!

""""

03.05.015.09.02.11.03.001.001.03.12.07.0

VUT

Each vector of U model a consumer by d implicit attributes

Each vector of V model a merchant by d implicit attributes

Score = UiT . Vj = 1.162

Content Model


Data & Feature Dictionary cust_id string age range [10:20:30:40:50:] gender categorical [M,F,U] country categorical [NP, IN, CN] spend numeric nonprofits label

Cust ID Age … Gender Country Spend nonprofits

1 28 M NP 10.5 1

2 35 F CN 100 2

3 30 M IN 25.1 1

4 34 F IN 15 3

5 32 M IN 5 4

6 25 F IN 22.5 1

3 30 M IN 12 3

3 30 M IN 1 2

Feature Dictionary Dataset

Name

Type

Values


Training the model

Data Source

Featurizer

Business Logic

Feature Dictionary

Other resources

Features Learner Predictive Models

ML Algorithm


Logistic Regression


Recurrent Neural Network - LSTM

Source: http://colah.github.io/posts/2015-08-Understanding-LSTMs/


Combining the result

Teradata & HDFS

Interaction Matrix

Features

CF Model

Content Model

Hybrid Result

CF Engine

Ensemble

…

Deployment

•  Emails, Web, Mobile Applications

•  REST APIs Platform

• ML Models Data Science


REST API

The recommendations will

be stored.

MODEL

The recommendations will be generated

and uploaded.

FRONT-END WEB will query for

recommendations

Recommendations of respective

customer will be returned.

Web Emails Mobile Offers


Wrap Up

•  Approaches to modeling –  Graph Model –  CF Model –  Content Model

•  Deployment of models

Thank You

Sujit Mathew, Goh Yew Yap, Chen Yanhui

boosting consumer engagement at paypal

Presentations & Public Speaking