Know your customers closely with Analytics, Building Data Products to power customer experiences!!

1. Know your customers closely
with Analytics
Building Data Products to power customer experiences!!
Future of Analytics – Summit 2018
~/Piyush Kumar - MakeMyTrip

2. ABCMS
§ A – Analytics / AI
§ B - BigData
§ C – Cloud / Docker/ K8s
§ M – Mobile / Machine Learning
§ S – Social / Networks
From Hype to Reality !
Advancements in the field of :

3. Data Platform
Better Data Architecture !
Layers:-
§ Data Capture
§ Client ( Apps / PWA / Web)
§ Server-Side instrumentation (_Kafka_)
§ Data Storage
§ Data Lake (_Raw & Processed_)
§ OLAP (_Data Model_)
§ Serving stores
§ Data Processing
§ NRT : Stream Processing
§ Batch Processing
§ Data Services / APIs (_low latency_)
§ Data Visualization
§ Notebooks, Jupyter, Zeppelin / Helium
Foundation:-

4. Segmentation+
Persona service
§ Enrich Customer data profiles to do better cohort
analysis
Not limited to:-
§ User search/ transaction history, time of the day, high
value customer
§ User’s device attributes , geo-location/base location &
affluence
practitioner's perspective:
§ CLV : Customer Lifetime Value / LTV Modeling using
Markov chains & decision tree learning
§ Customer Engagement Analytics : Retention Loops (
App stickiness / uninstall rates)
§ Customer Churn/ Lapser : Lapsing (predicting & doing
interventions to prevent churn)
Customer Data ~ Strategic Value

5. Personalization
§ Buyers rely on platforms to provide right personalized
recommendations – which are based on Real-Time
processed recent events.
§ Understand individual user/buyer journeys
(transactional / experiential) and generating intent
across channels
§ USER Context + User Segments + Business Priority
parameters
§ Boost revenue per customer ( X-sell/ Up-sell), Dynamic
Pricing
practitioner's perspective:
§ Concept of per user databases
§ Mappers / Graphs : Omni Channel
Reducing Information Overload!

6. Insights Engine
§ Persuasions powered by Data Insights Engine
§ Scarcity:
§ 80% hotels in Gurgaon are sold out. Book soon!
§ Booked by 10 family travelers in the last 30 mins
§ Discovery:
§ Great Choice! This is one of the highest rated (x) hotel near Qutub
Minar
§ Urgency:
§ Viewed by 45 people in the last 15 mins
§ Price Prediction - Fares are likely to increase by X in next Y days
§ Social Trust: 40% smart users now pre-buy meals/seats/Baggage
practitioner's perspective:
§ Feedback loops / re-compute ( Near Real-Time refresh)
§ User level insights, user activity metrics
§ Funnel metrics / supplier / inventory metrics
Metrics

7. Engagement
Platform
§ Growth Engine leveraging
§ Push / notifications / email / sms channels along with in-
app funnel flows
§ Relevance :
§ Intent identification across session chains before
Retargeting any customer!
§ Personalized Content / recommendation based on usage
history + persuasions play - when communicating to users
with right deep links
§ Retention Goals along with increase in visit frequency,
re-engagement ( reducing churn)
practitioner's perspective:
§ De-couple campaign management from the user
activity & persona layers
#martech

8. Riskprofile
§ BNPL: Book Now Pay Later / lending
§ For all customers :Trust Score, Credit Score
§ Use of alternate data sets / Data exchange
§ Detecting financial crime
practitioner's perspective:
§ Segmentation helps like travel agent detection
§ Risk scores : per user / per transaction
Know your customer !

9. PersonalizedAI
systems
§ Voice Enabled computing & services like Digital
Assistants / bots
§ Voice based search queries are fasted growing mobile
trend
§ Conversational Chatbots
§ Challenges:-
§ NLU ( Natural Language Understanding)
§ Vernacular Language
§ Informal conversational vocabulary
practitioner's perspective:
§ Use of Domain data to develop custom models rather
than relying on generic voice/chatbot models from
Google/MS/AMZ etc.

10. Data Quality § RuDRA : Read unveil Detect Report Anomalies
§ Given any dataset: Scan, compute aggregates, build meta
profiles, run business rules / test cases, outlier detection.
§ Quality Score of enterprise data assets
practitioner's perspective:
§ Dev/QA automation of test cases for logging APIs
§ Validation / Assertions at Data Capture layer

11. Thanks ! Q & A