Marketers today are dealing with a completely different type of customer - one that demands instant gratification, and has very little patience to wait. Customer journeys today are so dynamic that it is impossible to expect results using manual, rule-based marketing tools. In this webinar, we discuss how forward-looking brands are using data science to predict user intent to create the perfect marketing campaign for their target audience.

1. Align Marketing Results to Business
Outcomes Based on User Intent
With
Hiral Jasani, Product Marketing Manager at CleverTap
Jay Magdani, Product Manager at CleverTap
RETAIN YOUR USERS FOR LIFE

2. An Experience Optimization
Platform
We offer a customer data platform (CDP), real-time customer
insights, a segmentation engine and powerful engagement
tools into one intelligent marketing platform.

3. The CleverTap Experience Optimization Platform
CUSTOMER RETENTION SUITE
Marketing
Analytics
Omnichannel
Campaigns
Marketing
Automation
Personalization
At Scale
Geolocation
Targeting
Experimentation
& Testing
Drive Long Term
User Retention and
Revenue Growth
MobileDesktop CRM POS Call CenterUnify Data Across Silos
CUSTOMER DATA PLATFORM & REAL-TIME ANALYTICS
AI/ML Powered Customer Insights
Get Actionable
Data Driven Insights
ADVANCED SEGMENTATION ENGINE
Intent Based, Automated and Psychographic Segmentation
Orchestrate
Hyper Personalized
Engagement Strategies

4. Our Customers
Demand & FoodTech Ecomm & more
Telco & Finance Media & OTT Travel & Ticketing

5. $2 Billion
Combined Revenue Delivered$
25 Million
Campaigns Sent Per Month
1 Billion
Devices Reached
8000+
Customers Globally
Proven Performance & Scale
CleverTap Architecture solves for a variety of use cases without any assumption of domain

6. Agenda
● How Customer Segmentation Is Changing the Game for Marketers
● Rule-based Marketing vs. Goal-based Marketing
● Why User Intent is So Powerful
● Making User Intent Actionable - Use Cases
● Case Studies
● Q&A

7. Let’s Get Started!

8. Why Customer Segmentation Matters?
Most companies spend a
significant amount in app
development
Retaining users is more
challenging than ever
The window of
opportunity is getting
smaller
Mobile apps account for
57 percent of all digital
media usage
Attention span of today’s
mobile user has dropped
from 15 seconds in 2000 to
8 seconds since 2015.
Over 51% users
download less than 1 app
a month.

9. How Customer Segmentation is Changing the Game for Marketers?
76% of Users 3.64 devices84% of Users
Expect brands to
anticipate their needs
and make relevant
Say being treated like a
person, not a number is
the key to winning.
Every user is going to
interact with your
brand across 4 devices
Proactive Personalized Omnichannel

10. Types of Data to Segment
Age
Marital Status
Gender
Income
Ethnic background
Education level
Demographics
Activities
Attitude
Opinion
Personalities
Cognitive Attributes
Values
Psychographics
Technographics
Platform
Device
Browser
Geographics
Local
National
Regional
International
Climate
Behavioral
Benefit
Pattern
Usage Rate
MARKET
SEGMENTATION

11. Segmentation Strategies
● User Activity
● Segmentation based on business/domain knowledge
● Usage rates/App installs
● Retention rates/CLV
● Segment to segment comparison

12. Average User Acquisition Costs
Source: Liftoff

13. Average Email Marketing Benchmarks
INDUSTRY
OP EN
R AT E
CL ICK
R AT E
U NSUBSCRI BE
RATE
RETAIL 19.36% 2.24% 0.27%
TRAVEL &
TRANSPORTATION
20.03% 2.00% 0.27%
MEDIA &
PUBLISHING
21.92% 4.55% 0.12%
Source: Mailchimp

14. Rule-based Marketing vs. Goal-based Marketing
RULE-B ASED MARKETIN G GOAL-BASED MAR KETI NG
Based on hard triggers Based on probability
Driven by past behavior Driven by omni-channel customer interactions in real-time
Human bias and opinions Eliminates human bias
Scaling challenges Scales effectively

15. Why Use Intent?
Intent trumps loyalty, past behavior, or demographics.
Intent brings relevance, context, and immediacy.
• Run a huge discount promotion => most conversions from loyal users anyway
• Demographics, technographics, past behavior => still not a slam dunk
• Customer journeys look different every time => can’t predict what’s next?

16. Making User Intent Actionable
Start your Marketing Campaign with a Business Goal
• Which users are most likely to uninstall?
• Which users are most likely to upgrade their
subscription?
• How can I maximize revenue for weekend orders?
• What can I do to increase repeat purchases over
the next 30 days?

17. Pick a Segment: Users residing in California
Use Case: Food Tech
Increase Revenue Per Customer During the Super Bowl week

18. Create a Goal:
Maximize the number
of pizzas ordered
during the Super Bowl
weekend.
1
Use Case: Food Tech
Increase Revenue Per Customer During the Super Bowl

19. Let the Coeus Data
Science Engine Predict
the Outcome
2
Use Case: Food Tech
Increase Revenue Per Customer During the Super Bowl

20. Identify Intent Based
Segments
3
Use Case: Food Tech
Increase Revenue Per Customer During the Super Bowl

21. Create a campaign
using intent segments
4
Use Case: Food Tech
Increase Revenue Per Customer During the Super Bowl

22. Maximize Your Goal
with a Deeper
Understanding of User
Intent
5
Use Case: Food Tech
Increase Revenue Per Customer During the Super Bowl

23. Use Case: Food Tech
Increase Revenue Per Customer During the Super Bowl
• Number of campaigns = 3
• Actual number of orders increased by 4K
• Average Order Value (AOV) = $20
• => Additional revenue of $20*4K = $70K
Target Segment Size Orders Orders (%) Result Real Impact *
Before 1,26,53,285 54,841 0.43%
Increased
orders by 7%
43% boost in orders
with user engagement
(as opposed to Control
Group)
After 1,26,53,285 58,230 0.46%

24. IBS Size (in %)
Converting
Users (in %)
Most Likely 0.05 10
Moderately
Likely
0.1 15
Least Likely 93 75
With IBS, identify segments that convert substantially higher than your base conversion rate(1.5-4%).
As a marketer, you can concentrate your efforts on moderately likely or least likely.
0.15% of users = 25% conversions
Use Case: Food Tech
Increase Revenue Per Customer During the Super Bowl

25. Use Case: Food Tech
Increase Revenue Per Customer During the Super Bowl
RESULTS
Improve bottom line by reducing promotional spend
- Fewer special offer codes (50% off) to the moderately likely segment, (0.1% )
- Higher order value for each purchase
Optimize user experience with thoughtful engagement
- Moderate and low intent users typically comprise a majority (>95%)
- Spend less time on users already on the path to purchase
- Craft highly targeted messages to a smaller segment of users with variable
pricing and promotions.

26. Create a Goal:
Minimise App
Uninstalls
1
Use Case: General
Solving the Leaky Bucket Problem of App Uninstalls

27. Let the Coeus Data
Science Engine Predict
the Outcome
2
Use Case: General
Solving the Leaky Bucket Problem of App Uninstalls

28. Use Case: General
Solving the Leaky Bucket Problem of App Uninstalls
Identify Intent Based
Segments
3

29. Create a campaign
using intent segments
4
Use Case: General
Solving the Leaky Bucket Problem of App Uninstalls

30. Maximize Your Goal
with a Deeper
Understanding of User
Intent
5
Use Case: General
Solving the Leaky Bucket Problem of App Uninstalls

31. • Number of campaigns = 5
• Actual number of uninstalls decreased to 37K
Target Segment
Size
Orders Orders (%) Result Real Impact *
Predicted 5,02,627 65,780 13.09%
Decreased
Uninstalls by
43%
32% drop in uninstalls
with user engagement
(as opposed to Control
Group)
Actual 5,02,627 37,326 7.43%
Use Case: General
Solving the Leaky Bucket Problem of App Uninstalls

32. IBS Size (in %)
Converting
Users (in %)
Most Likely 92 84
Moderately
Likely
3 12
Least Likely 0.7 4
Base conversion rate = 2%
With IBS, identify segments that convert substantially higher than your base conversion rate.
As a marketer, you can concentrate your efforts on moderately likely or least likely.
3.7% of users = 16% uninstalls
Use Case: General
Solving the Leaky Bucket Problem of App Uninstalls

33. RESULTS
Improve retention based on likelihood to drop off
- Win back valuable users with timely content
- Attribute results directly to your marketing campaign — giving credit where it’s
due.
Optimize media spend on high value users before they lapse
- Recognize high value users before they uninstall to optimize your media
strategy
- Use the right channel to win back users in real-time
Use Case: General
Solving the Leaky Bucket Problem of App Uninstalls

34. Opportunity For Marketers
Predict results for any time-bound business goal and take the necessary steps to win.
Create tailored engagement strategies with highly relevant messaging, creatives, offers, and pricing options.
Save time and money on promotions and discounts to customers who would have purchased anyway.
Identify user micro-segments that you can actually influence, instead of spamming a broad database of users.
More accurately predict marketing campaign ROI and transform your marketing team from a cost center to a revenue
center.
Quantify the real value of your marketing team’s efforts so that you can give credit where it’s due.

35. Case Studies

36. Top Ecommerce Company In South America
Problem Statement
• Low NPS score
• Low conversion rates
• Marketing team always scrambling for the right one-size-fits-all content

37. Top Ecommerce Company In South America
For 4,091,068 users in the segment, ‘All Users’ as of Oct 29, 2018 who will do purchase by Nov 05, 2018
Customer Purchases
Conversion rate in Most Likely: 75%
Conversion rate in Moderately Likely: 30%
Conversion rate in Least Likely: <1%
This led to:-
- Improved NPS by 10 points
- Exactly 5 campaigns in the calendar
- Improved conversion rate by ~9%
Predicted Conversion: 45000
Number of campaigns: 5
Actual conversions: ~49000

38. Top Online Ticketing Company In Europe
Problem Statement
• High uninstall rates
• Low LTV due to high acquisition cost coupled with low retention rates
• Retention campaigns are hard as they require specific content and cannot be sent to all users
• Unsure of the expected uninstalls in the future

39. Top Online Ticketing Company In Europe
For 21,681,948 users in the segment, ‘All Users, Nov 1, 2018 who will uninstall by Nov 30, 2018
App Uninstalled
=> 25% avoidable uninstalls
This led to:-
- Know exactly what they are up against
- Already seeing improved November LTV
- Ran highly targeted uninstall campaigns
- Reduced uninstalls rate by ~13%
Predicted Churn: 8000
Number of campaigns: 2
Actual Churn: ~6900

40. Thank You!
clevertap.com/live-product-demo/

41. Questions?