PayPal has developed a behavioral tracking platform on Hadoop to understand customer behavior across its 110 million accounts. The platform collects metadata on customer interactions and sessions across channels. It uses a entity-event data model and performs attribution, sessionization, and metrics generation to provide insights. PayPal analyzes the data using Pig scripts to calculate metrics and dimensions like visitors, sessions, page views by page and browser. This helps PayPal improve customer experience and drive outcomes.

1. PAYPAL - BEHAVIORAL TRACKING ON HADOOP
ANIL MADAN
DIRECTOR OF ENGINEERING , MARKETING & ANALYTICS

2. PAYPAL'S VISION
Delivering the future of money today…
An essential part of our customer's financial and business
lives, enabling secure commerce anywhere, anytime, any way
110 million active accounts , 190 markets , 25 currencies
2

3. BEHAVIORAL TRACKING VISION
Understand our anytime, anywhere, any way to drive desirable
customer’s behavior outcomes for our
and experience customers and for PayPal.
Enable self-service
analytics for our
product and
Ensure marketing teams
Ensure privacy,
instrumentation
security and trust
standardization 3
for our customers
across channels
3

4. TRACKING PLATFORM OVERVIEW
Direct/ Transaction Email Display Search
Home Page Emails Marketing Advertising Engine
Marketing
Metadata Tracking Servers Real Time Systems
Tracking Metadata Marketing
Tracking Event Service
Tool
Segmentation
Tracking Validation
Tag
Taxonomy Service Experimentation
Catalog
Big Data
Reporting/Visualization Digital Metrics Attribution
4

5. METADATA - ENTITY MODEL
LAYOUT PAGE
ELEMENTS LINK
COMPONENTS
5

6. METADATA - EVENT MODEL
Tracking
Event
Impression Reaction Conversion
Event Event Event
Component Page Ad Click Click-Through Mouse-over
Impression Impression Impression Event Event Event
Event Event Event
Client Page Server Page Entry Exit
Impression Impression Event Event
Event Event
6

7. ATTRIBUTION MODEL
Channel Impression Click Open
Client Server
Direct ✓ ✓
Organic Search ✓
Paid Search ✓
Display Offers ✓ ✓
Onsite Offers ✓ ✓
Transactional Emails ✓ ✓
Marketing Emails ✓ ✓
7

8. LOGICAL ARCHITECTURE
Onsite Channels Marketing Channels
Mobile Search Display
Web Tracking Social Email Onsite
Instrumentation Engine Advertising
JS Marketing Marketing Marketing
API Marketing
Instrumentation
Tracking Tracking Message Delivery Services
Metadata Servers Service
Tool Marketing
Segmentation
Active MQ Offers
Service
Producer Service
Tracking
Metadata Active
Service MQ Hadoop Cluster
Tracking Active MQ Active MQ
Collector Consumer Consumer
Customer Operational
Intelligence Metrics
Metadata Tag
Repository Catalog NAS Filer NAS Filer Behavioral
Intelligence Reporting
Aggregation/ Sessionization Identity
Tracking
Compression Bot Flagging Mapping
Batch
8

9. DATA INGEST PIPELINE
Raw Event
PRE-PROCESS
Map Reduce Map Reduce
Gzip Text
Deduped Enriched
Validate/ Event Join Client & Event
Dedup Events Gzip block Server Events Gzip block
compressed compressed
Raw Event
SequenceFile SequenceFile
Gzip Text
CHAIN REDUCER
SESSIONIZATION
Map Reduce Mapper Mapper
Enriched Sessionization Geo Lookup Bot Flagging Sessions
Event
Geo Bot Data/
Data Rules
Map Reduce Map Reduce
Behavioral Reporting
GENERATION
Sessions Stage 1 Stage 2
Metrics MySQL
METRICS
Pig
Enriched
Event Adhoc Metrics

10. SESSIONIZATION
Events VisitContainer
Visitor Session Timestamp Event Visitor Session Payload
ID ID Payload ID ID
V1 S1 ie, winnt, {flash, quicktime},
V1 S1 2012-05-24 E1
{ca, usa}, 480 secs,….
05:12
E1
V2 S2 2012-05-24 E2
05:14 E3
V1 S1 2012-05-24 E3 E4
05:15
V2 S2 ff, winxp, {acrobat,
V1 S1 2012-05-24 E4 mediaplayer}. {wb, in}, 420
05:20 secs…..
V2 S2 2012-05-24 E5 E2
05:21
E5
V1 S3 2012-05-24 E6
07:25 V1 S3 sf, macos, {quicktime, java},
{on, ca}, 60 secs
V1 S3 2012-05-24 E7
07:26 E6
E7
• Chronologically sort events using secondary sort
• SortComparator on visitorid, sessionid and timestamp
• Partitioner & Grouping comparator on visitorid and sessionid
• Normalize data and store it against the session record 10
• Browser, os, plugins, geo-location, duration, bot-flag etc.

11. DIMENSIONS & METRICS
Dimension Metrics
Page Visitors
PageFlow Sessions
Country Bounce Rate
CountryRegion Page Views
Plugins
VisitDepth
VisitDuration Time Period
VisitByHour Hourly
SearchEngine Daily
OS Weekly
Browser Monthly
11

12. METRICS GENERATION
Mapper Input Mapper Output
Reducer Output
Visitor Session Browser Key Value
ID ID (visitorid, (#sessions) Key Value
browser) (visitorid, (#sessions)
browser) Compute
V1 S1 IE V1,IE 1
V1,IE 2
sessions sorted
V1 S2 IE V1,IE 1 by visitor,
V2,FF 1 dimension
V2 S3 FF V2,FF 1 STAGE 1
V3,IE 1 (browser)
V3 S4 IE V3,IE 1
V4,FF 1
V4 S5 FF V4,FF 1
Mapper Input Mapper Output
Key Value Key Value Reducer Output Compute
(visitorid, (#sessions) (browser) (#sessions, metrics
browser) #visitors) Key Value
(browser) (#sessions,
by
#visitors) dimension
V1,IE 2 IE 2,1
IE 4,3
V2,FF 1 IE 1,1
STAGE 2 FF 1,1
V3,IE 1 FF 1,1
V4,FF 1 IE 1,1 12

13. PIG – ADHOC QUERIES
/* EventLoader - custom loader ; Exposes correct data-types using metadata for each field*/
grunt> data = LOAD '/paypal/event' USING
>> com.paypal.EventLoader(
>> 'visitor_id, session_id, page_name, event_type, event_timestamp');
grunt> describe data;
data: {visitor_id: chararray, session_id: chararray, page_name: chararray,
event_type: chararray, event_timestamp: long }
grunt> events = FILTER data BY event_timestamp >= 1337583600000L and
event_timestamp < 1337587200000L;
grunt> grouped = group events by (page_name, event_type) parallel 20;
grunt> result = foreach grouped {
>> visitors = distinct events.visitor_id;
>> sessions = distinct events.session_id;
>> generate group, COUNT(visitors), COUNT(sessions), COUNT(events);
>> };
grunt> dump result;
((My Account Overview, im), 117875L,119343L,230216L)
((mktg:xsell:merchant::home-inside, im), 462L,466L,655L) 13

14. PIG – ADHOC QUERIES
/* VisitContainerLoader custom loader - Tuple ( Tuple, Bag (Tuple) )*/
grunt> data = LOAD '/paypal/visitcontainer'
>> USING com.paypal.VisitContainerLoader(
>> '{"visit":["visitor_id",”session_id","session_start", "session_end", "browser_type"],
"events":["page_name", "event_type"]}');
grunt> describe data;
data: {visit: (visitor_id: chararray, session_id: chararray, session_start: long, session_end:
long, browser_type: chararray),
events: {event: (page_name: chararray, event_type: chararray)}}
grunt> flattened = foreach data generate FLATTEN(visit), FLATTEN(events);
grunt> impression = FILTER flattened BY event_type == 'im' and session_start >=
1339045200000L and session_end < 1339063200000L;
grunt> grouped = group impression by (page_name, browser_type) parallel 20;
grunt> result = foreach grouped {
>> visitors = distinct impression.visitor_id;
>> sessions = distinct impression.session_id;
>> generate group, COUNT(visitors), COUNT(sessions), COUNT(impression);
>> };
grunt> dump result;
((Account History:Request Money Details, chrome), 522L,528L,726L)
14
((Account History:Request Money Details, msie), 706L,716L,967L)

15. REPORTING
15

16. THANK YOU
We Are Hiring!
• San Jose
• Boston
• Bangalore
• Shanghai

17. Sessions will resume at 4:30pm
Page 17