How to build a Single View of Customer using his Digital Journey across multiple channels & multiple assets? Presented at Big Data & Analytics Innovation Summit, Singapore, 2018

1. Revealing 360
view of
Customer Digital
Journey in
Astro
Nitin Khattar
AVP, Big Data Architect
Astro Malaysia

2. About me
Currently working as Big Data Architect for Data Lake
Platform & DMP projects
Worked on Open Source, AWS, Azure
Data + Strategy = Success

3. – Jason Miller
“The key ingredient to a better customer
experience is Relevance”

4. Astro Malaysia
Astro is Malaysia’s No. 1 digital-first media and lifestyle
company in the Digital, TV, Radio and eCommerce space.
7.4 million unique visitors per month across the digital
platforms of its entertainment and lifestyle brands.
Reaches 21 million individuals in 5.3 million households, or
73% of Malaysian households
Astro Radio includes Malaysia’s highest rated stations
across key languages, available on both terrestrial and
digital channels, reaching 16.5 million weekly listeners.

5. Digital
DTH, VOD, OTT
E-Commerce

6. What is Single Customer
View?
Single Customer View is
a holistic, consolidated
and consistent
representation of an
organisation’s customer
data
Multi-Channel
Cross-Sell
Multi-Asset

7. Why Single Customer
View?
Identify new targeted sales opportunities
Discover faster answers
Increase Customer Lifetime Value
Build meaningful segments that support targeted
marketing campaigns.
Better Personalisation
Make smarter, more effective decisions.

8. The Goal

9. Customer Traits
Transactional - Viewing Logs, Purchase Patterns
Interaction - Voice calls, Email/SMS, Web/App Data
Behavioural - Upgrade/Downgrade, Feedback
Demography - City, Age, Contact Details
Analytics - Ethnicity, Customer Segments

10. How?
1. Analyse Data Sources
2. Consolidate into Data Lake
3. Unified Metadata Repository
4. Build Processing Platform
5. Analytics
6. Targeting

11. 1. Understand the nature
of Data Sources
Identify traits or data points coming from each
data source

12. Ecommerce
Periodic spends
Favourite Categories
Favourite Payment Mode
Shipping + Billing Details
Loyalty Spends
Returns frequency
Reviews/Comments

13. Content/Media
Favourite Genres
Preferred Language
Average Time Spent
Number of Articles read
Average Visit/Click count
Preferred Geographical Location
Reviews/Comments

14. Entertainment
Average Viewing Duration
Fav. Categories/Genres
Average Downloads
Periodic Spend on Subscriptions (New,
Renew, Add-on)
Preferred Language
Geographical Location
Fav. Actors/Directors

15. 2. Consolidation of Data
from Multiple Sources
Build a data lake to consolidate data from various
sources. Cleanse, Standardise & Validate data at single
place.

16. Data Lake
Within an organisation, there are plenty of systems
which are continuously running, reading data from
one end & writing data at another end. To consolidate
the entire data (produced in whichever format – raw,
structured or semi-structured) at one place, there is a
need to built Data Lake.
It is then used for various tasks including reporting,
visualisation & analytics.

17. Advantages of Data Lake
Scalability
Convergence
AS-IS Data Format
Unlimited Query options
Low Cost
High Speed

18. Data Lake Architecture
Stream

19. Batch

20. 3. Unified Content
Metadata Repository
Build a unified repository for storing metadata of
different content types - text, image, video &
audio.

21. Content-based Metadata
Repository

22. 4. Build Self-Serviced
Processing Platform
Build highly accessible platform for all user types -
Business, Data Scientists, Data Engineers,
Marketing

23. Platform Architecture
Data
Lake
Hive Metastore
backed up on
AWS RDS
Automated Deployment
Alerts
API Gateway
Web/App users
BI users
Data Scientists/
Engineers

24. 5. Powered by Analytics
Using multiple approaches of Analytics to draw powerful
insights. Descriptive, Diagnostics, Predictive &
Prescriptive

25. Descriptive Analytics -
What?
Using Business
Intelligence reports or
dashboards, KPI
Tracking
Periodic & Ad-hoc
Reports
Using Power BI, Tableau
or other BI Tools

26. Diagnostics Analytics -
Why?
Using Search, Query tools, Indexes
Data Exploration, Pattern Detection
Using Elastic Search, SOLR, Hive, etc

27. Predictive Analytics -
What may happen?
Predictive Modelling - using AI/Machine Learning Techniques for
following use cases :
Demand Forecasting
Fault/Anomaly Detection
Optimised Media Scheduling
Price Optimisation
Targeted Content Generation
Ethnicity Engine

28. Predictive Analytics -
What may happen?
Build Customer Personas
Heavy Watchers - Linear+Recording+OTT, >12hrs
viewing
Ultra Premium - Linear + Recording, 8-12 hrs viewing,
high on subscriptions
Shopaholic - Linear + Recording, 2-5 hrs viewing,
shopping channels
Weird - Linear, < 2hrs viewing, No subscriptions

29. Knowledge Graphs are large networks of entities, their
semantic types, properties and relationships between entities.
Graph = flexible
Semantic or self-descriptive
Smart
Alive
OLAP/OLTP Graph Databases(Cosmos, OrientDB),
TinkerPop(Gremlin), Spark GraphX
Predictive Analytics -
What may happen?

30. Prescriptive Analytics -
What Action?
Automation
Recommendations
Rules

31. Prescriptive Analytics -
What Action?
Recommendation
Single System for different content types - text, images,
video
Item-Item based - Content Similarity Analysis
User-Item based - Using Correlation between content
metadata & user interactions
User-User based - Similar User analysis using ratings,
feedback

32. 6. Precise Targeting
Based on the Insights, Knowledge base created
using Analytics, target the right customer with right
means

33. Seamlessly integrated
Components
Data Management Platform
Campaign Management Platform
Experimentation
Media Activation
Click Stream Analytics

34. Architecture

35. Result = Increase in Customer Lifetime
Value

36. Thank You
Nitin Khattar
nkhattar88@gmail.com
@khattarn