boosting consumer engagement at paypal

Boosting Consumer Engagement

at PayPal

Sujit Mathew, Goh Yew Yap, Chen Yanhui

PayPal

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

©2015 PayPal Inc. Confidential and proprietary.

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. Confidential and proprietary.

66 Million individual payments processed for charities via PayPal.

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

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

418,000 charities used PayPal to accept donations.

$5.7 Billion processed for charities by PayPal.

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

Bridging Consumers and Charities

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

Consum

er

Consum

er

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

Mapreduce Job

for Shard 2

Mapreduce Job

for Shard 3

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

03.05.0

15.09.0

2.11.0

3.001.001.03.12.07.0

VU T

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 Countr

y 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