The document discusses how businesses can transition from a traditional reactive customer approach to a more modern proactive approach using customer analytics and behavior patterns. It advocates understanding customer problems before they arise, segmenting and profiling customers based on their behaviors, and using real-time personalization based on analyzed patterns. The document also discusses considerations for ensuring systems and organizations are ready to support a modern customer analytics approach.

1. Md. Azimuddin Khan
Head, Business Intelligence &
Insights
AirAsia

2. Traditional
Customer = who initiated transaction.
Greet with a smile and sell your
product or may be listen a bit more
Reactive
Rewards only
Modern
Customer = Who is even looking for
similar product
Act based on Customer behavior
pattern using analytics
Mix of Reactive & Proactive
Not only rewards but also make them
comfortable

3. Understand the
problem before
customer tells you.
Customer
Segmentation based
on behavior.
Personalization
based on Pattern.
Customer Profiling:
Surprise!!!!

4. System Readiness: Is your system modern
enough?
Data Readiness: Does you system captures
all customer activities?
Organization Readiness: Is your project
objective clear? Are your people and process
ready?
Skill Readiness: Does your organization
depend on 3rd party too much? Is the right
skillset there?

5. Real Time
• Is it really necessary?
• Does it worth the investment?
Technologies
• Proper Plan for investment.
• Expensive generic tools vs customized tools.
Problem
Solving
• Detection rate vs Problem solving rate
• Does Acknowledgment improve customer experience?
Adaptability to
changes
• Importance of Machine Learning.
• Proper methodology for research and planning.

6. Events
• Call Drop
• Call Setup fail
• Connection fail
• Data Speed
KPI
• Call drop rate
• Setup fail rate
• Data Speed
rate
KQI
• Performance
index based
on KPIs
Dimensions
Location
Time
Device
Customer
Pattern Recognition and Enrich Model to do Proactive CEM

7. Objective: To identify prospective & existing petroleum consumers using
telecommunication and banking data
Telecom
Data
• Define area for customers based on
location update history.
• Define their stop point during travel to
locate appropriate fuel pump location.
Final
output
• Design offer for those who is around
existing fuel pump but not buying it.
• Plan for business expansion by
creating focused area.

8. Objective: To identify prospective airline passenger who flies to a
destination with competitor
Telecom
Data
• Prepare a subset of mobile subscriber based on browsing
history i.e. travel site, airline site and find out desired
destination.
• See the roaming history to see their travel and transit
areas
• Make a subset of those by identifying matching
destination.
Final
output
• Prepare a good offer for their frequent destination and
blast sms.
• Review the problem of the airlines by assuming market
share.

9. Objective: To improve conversion rate by Web performance Analytics
Web
Analytics
• Tools such as Google Analytics & Web Analytics can be
used.
• Identify customers transitions from different page.
• Identify customer search behavior.
Final
output
• Track the problem area in web and solve it to improve
conversion rate.
• Prepare product correlation.
• Do customer profiling and personalization.
• Design campaign based on customer search behavior.