Business Analytics

Agenda 1. A Real life Digital Engagement 2. Demystifying Big Data Analytics

3. Capability requirements for Data Science & Analytics

Real Life Case study-1 : Online travel agency

3

The business ‘pain’ ? Objective-1 : Improve ‘Look to Book’ Objective-2 : Improve Rev/registered user

4

Decoding Travelers intent - Search data points

•Round/One-way/Multi •From •To •Depart on •Adults •Return on •Dates flexible flag •Children •Infants

•Class •Airline preference •Depart time •Return time

Those minor clicks matter !!! Micro digital events

A very important event which must be analyzed

Levers ? • Business Use Cases – “Valuable Vulnerable” : Decile-1 Travellers who searched but not booked – Hypothesis-1 : Is search response experience impacting booking revenue ? – Hypothesis-2 : Is price or timing is a concern on certain corridors ? – Property recommendation using collaborative filtering

• Technology – Hadoop based Sense n respond infra – Close loop on ‘lookers’ who have not booked – Multi channel & real time 7

High level Solution architecture

SENSE Mahout R

STORE

Columnar DB -Infobright etc SQOOP/FLUME

Hotel Search

Hive Hadoop cluster

Airline Search Express sentiment Payment Gateway drops Search Ordering & Filtering events

Map Reduce

Digital Traveller 360 Behavioural Model

HDFS

RESPOND Hbase Cassandra mySQL Log files

Sort events

Web Applications

S4- Real time response Inbound Digital events Outbound Email Queue Outbound Mobile queue Outbound Call centre queue

8

Digging deeper into Travel behavior - Tags  TRAVELS ALONE I/ FAMILY ?  PRICE SENSITIVE / VALUE CONSCIOUS ?

 ROADIE

?

 CASH COW

?

 BARGAIN HUNTER

?

 CAME FROM COMPETITIVE OTA ?  EARLY BIRD

?

 DESTINATION THEMES - BEACH / MOUNT ?  MILES TRAVELED PER QUARTER ?

Collaborative filtering algorithm to build a property recommendation engine

Scale !

1. CRUNCHED 100 million searches 2. 540 Terabytes logs ingested using a 12 node Hadoop cluster 3. 23 Behavioural Signatures Harvested 4. 6 million property recommendations using collaborative filtering algorithm made till date 5. Distilled Sentiment analysis done using R on unstructured

Business Impact !

12 X improvement in Look to Book in strategic corridors 8 % improvement in revenue ( Value tracker framework – Hit ratio calculation )

Challenges & Key lessons learnt • Lesson-1 : Dig Deeper – Turn data into dollars ! Lots of previously untapped data pools to monetize • Lesson-2 : Celebrate Jugaad ! Frugal Computing/ML algorithms can be a source of competitive advantage – Iterate a lot: Fail fast cheaply and early – New paradigms like Map Reduce / Hadoop / Complex event processing

Big Data Demystified !

14

“DATA” is the next “OIL” Industrial revolution Oil = Catalyzed manufacturing industry. Services revolution Data products = Fuelling services industry.

Dimension-1 : VOLUME RFID GP/Telematics

Auto Sensors

Medical sensors Telecom Switches

Blue tooth logs

Search Logs

Payment

Tower Data

Shopping Basket

User Generated Data

Word of mouth

VOLUME : Petabytes to flow thru ‘DATA PIPELINES’

Dimension-2 : VELOCITY Low velocity data

High velocity data

vs.

• • • •

Search logs Firewall events data Digital click events data Terabytes / day !

• • • •

Marketing spend Promo activations data Competitive data Megabytes/ month

Dimension-3 : VARIETY

Retina Scans

Unstructured call center transcripts

VARIETY : 80 % of worlds information generated is unstructured

“Data patterns fishing” in deep end data ocean is a different ball game EARLIER

GOING FORWARD

VS

Summarized analysis Ex: sales

Search based intent detection Ex: Daily click events

Flutura strongly believe in our Data Scientists being “T” shaped Formulation of ‘wow’ Questions/scenarios

Business Process Intimacy

Machine learning algorithms

Advanced Visualisation

Collaborative filtering

Unstructured text mining

Sequence Analysis

A/B testing

Clustering

Scoring models

Narrative story telling skills

A profound quote “Find something where you provide a scarce and complementary service to something that is getting ubiquitous and cheap.

So what’s getting ubiquitous and cheap? Data. And what is scarce and complementary to data? Analysis” - Hal Varian ( Chief Economist – Google )

3 parting thoughts !

Trend-1: Advanced Analytics pivotal to disrupting Business Models Trend-2: Analytical innovations happening outside than inside the organisation Trend-3: In a tough economic environment doing more with less is going to be mantra ! Constrain based innovation comes naturally to Indians Opportunity for India to change the global analytics game