This SlideShare will help you understand how CleverTap's AI/ML enabled features help brands convert, grow, and retain users. CleverTap's advanced features like Psychographic Segmentation, and Intent Based Segmentation use machine learning models to determine the propensity of users to perform an action or like a category of product. Similarly, CleverTap's Product Recommendations make use of sophisticated AI to recommend products to users based on their past behavior.

1. Analytics and AI based Retention
in e-commerce
Jay Magdani

2. We help Optimize Customer Experiences at Scale
Customer Lifecycle Management and Engagement Platform
Cloud Based
Digital Marketer
AARRR
10B+
App and Web
Analytics
Engagement
26M/min
CleverTap?

3. Analyst
Retention: How e-commerce plays the game
ATTRACT
CONVERTRETAIN
MarketerGrowth
Product

4. Day in a life of an e-commerce business
Strategic
Landscape
Retention: What the industry thinks
App performance?
How do I improve revenue?
How do I get stickiness?
Who is my User?
How do I reduce Churn?
What should I build next?
Tactical
Landscape
Who should I reach out to?
Why are my users exhibiting
said behavior?
Where should I target my Users?
What should be my message?
When is the best time to
reach my users

5. Martech Landscape
Day in a life of a marketer
Analytics Landscape
Make smarter decisions
Retention: What e-commerce demands from technology
How does my App perform?
How do I meet my KPIs?
Who?
Why?
When?
Where?What?
Descriptive Analytics
Prescriptive Analytics
Predictive Analytics
Diagnostic Analytics
C
L
E
V
E
R
T
A
P

6. Retention: What e-commerce demands from technology
SEGMENTATION
ANALYTICS ENGAGEMENT EXPERIENCES
Behavioral | Interest | Demographic | Geographic | RFM | Intent
Funnels | Cohorts | Flows Push | Email | WhatsApp Experiments | Triggers

7. But before that… Collect data
Profile
• Information about the user
• Geo, Demo, type, devices
Event
• Actions performed by user
• App Launch, Charged
Who I am?
End User
Profile
Event
Engage
Analytics
What I do?
Brand

8. AUTOMATED
USER SEGMENTATION
CHAMPION USER
OPTIMIZATION
• Analyze what the champion users do
• Track and compare behavior with other users
• Replicate the champion user behavior
1
2
How AI plays a role…
SEGMENTATION
ANALYTICS ENGAGEMENT EXPERIENCES
CONTENT
RECOMMENDATION
• What message should I send?
• Performance of campaigns based on message
• Track, measure and learn from ROI
3
OPTIMIZE NATIVE UI
EXPERIENCES
• Blue button vs Red button
• Run experiments
• Easily publish multiple app versions
4

9. History with Segmentation
Manual
Segmentation Tool
Automated
Segmentation
AUTOMATED
USER SEGMENTATION
1
Assisted Segmentation
Via RFM

10. Manual Segmentation Problems
AUTOMATED
USER SEGMENTATION
1
Intuition Based
Best users?
Users who did
 App Launch
and then…
 Searched
and then…
 Added to Cart
and then…
 Charged
Within 1 day

11. RFM Problems
AUTOMATED
USER SEGMENTATION
1
Single Event Analysis
Ignores Time Aspect
Low Predictive Power
Reactive
Split all users who made a Purchase into good and bad segments

12. Why Intent Based Segmentation
AUTOMATED
USER SEGMENTATION
Proactive
1
Users who will uninstall
in the next 30 days
Users
Most Likely
Moderately Likely
Least Likely

13. Productizing Predictive Segments
AUTOMATED
USER SEGMENTATION
1

14. Largest ticket booking app in LATAM
● Before
○ High uninstall rates
○ Reactive approach
○ Low conversions
Large food app in Asia
● Before
○ High marketing spend
Impact on Customers
AUTOMATED
USER SEGMENTATION
1
IBS Impact
Conversions
25%
Uninstalls
18%
Spend
21%
● After
● After
○ Optimized budget by selective, smart and
intent based engagement strategy

15. AUTOMATED
USER SEGMENTATION
• Real-time User Segmentation
• Customer Insights powered by ML
• Predictive modeling for better ROI
CHAMPION USER
OPTIMIZATION
1
2
SEGMENTATION
ANALYTICS ENGAGEMENT EXPERIENCES
CONTENT
RECOMMENDATION
• What message should I send?
• Performance of campaigns based on message
• Track, measure and learn from ROI
3
OPTIMIZE NATIVE UI
EXPERIENCES
• Blue button vs Red button
• Run experiments
• Easily publish multiple app versions
4

16. Compare Conversion Funnels
CHAMPION USER
OPTIMIZATION
2
App Launched
Product Viewed
Added to Cart
Charged
App Launched
Product Viewed
Added to Cart
Charged
1M 100K
500K
100K
25K
25K
10K
1K
Champion Users New Users

17. CLTV
CHAMPION USER
OPTIMIZATION
2
Customer Acquisition CostTotal Revenue generated Duration before churn
E-commerce Golden Ratio -> 3:1

18. AUTOMATED
USER SEGMENTATION
• Real-time User Segmentation
• Customer Insights powered by ML
• Predictive modeling for better ROI
CHAMPION USER
OPTIMIZATION
1
SEGMENTATION
ANALYTICS ENGAGEMENT EXPERIENCES
CONTENT
RECOMMENDATION
OPTIMIZE NATIVE UI
EXPERIENCES
• Blue button vs Red button
• Run experiments
• Easily publish multiple app versions
4
3
2
• Analyze what the champion users do
• Track and compare behavior with other users
• Replicate the champion user behavior

19. ● Poor user experience
● High churn rates
● Lost business opportunity
● Low LTV/CLV
CONTENT
RECOMMENDATION
3
● Category Based
● Same recommendations
provided multiple times.
REPEAT
RECOMMENDATIONS
● Using basic analytics like best
items, etc.
● Best guess intuition
MANUAL DISCOVERY
Issues with Manual Content Creation

20. CONTENT
RECOMMENDATION
3 Why Content
Recommendation Engine
Right Content
Vestibulum
congue
Vestibulum
congue
Vestibulum
congue
Vestibulum
congue
Relevance
SerendipityDiversity
Right User

21. CONTENT
RECOMMENDATION
3 How recommendation engine works
Upload
Catalog
Run
Recommendation
Model
Engage
Luke:
Views electronics goods
John:
Searches sports goods
Product
Name
Category Price
iPhone 11 Electronics $ 700
Tennis shoes Sports $ 400

22. ● Using Recommendations in Campaigns and Journeys
● Comparing performance of recommendations w.r.t Control Group
● CTR and Conversion performance Boost generated
CONTENT
RECOMMENDATION
3 Impact on Customers
40%
ARPU

23. AUTOMATED
USER SEGMENTATION
• Real-time User Segmentation
• Customer Insights powered by ML
• Predictive modeling for better ROI
CHAMPION USER
OPTIMIZATION
1
SEGMENTATION
ANALYTICS ENGAGEMENT EXPERIENCES
CONTENT
RECOMMENDATION
OPTIMIZE NATIVE UI
EXPERIENCES
2
• Analyze what the champion users do
• Track and compare behavior with other users
• Replicate the champion user behavior
4
3
• What message should I send?
• Performance of campaigns based on message
• Track, measure and learn from ROI

24. OPTIMIZE NATIVE UI
EXPERIENCES
4 What is it?

25. OPTIMIZE NATIVE UI
EXPERIENCES
4 What is it?
Purchase Now!!!
Offer available
30% off
Last day today
Buy
30% discount

26. OPTIMIZE NATIVE UI
EXPERIENCES
4 Why is it important?
money time
experience optimization

27. Prescriptive
Recommenders
Predictive
IBS
Diagnostic
Segment
Comparison,
Anomaly Detection
and Reasons, etc.
Descriptive
Events, Trends,
RFM, Flows, etc.
Data Science
Pillars
Summary: Driving Retention backed by Analytics and DS

28. Thank You!
jay.magdani@clevertap.com