This presentation will cover how all aspects of marketing have evolved over the years. How AI will shape the landscape of marketing in the years to come and why marketers need AI to assist them for their jobs. The future lies in working towards better customer experience and especially customer retention seems to be the key. Marketers have to be on the lookout throughout - need to keep learning and keep a continuous tab on the customer’s pulse in order to deliver the best.

1. Almitra Karnick
HEAD OF MARKETING,
CLEVERTAP
SINGAPORE ~ SEPTEMBER 19 - 20, 2018
DIGIMARCONAPAC.COM | #DigiMarConAPAC
DIGIMARCONSINGAPORE.SG | #DigiMarConSingapore
How Artificial Intelligence
& Machine Learning Are
Transforming Modern Marketing
KEYNOTE

2. The Impact of Artificial Intelligence &
Machine Learning on Modern Marketing
By Almitra Karnik
Head of Marketing | CleverTap

3. With more than 10+ years of experience in the Bay
Area, drove all aspects of marketing for brands
having revenues ranging from 2M+ to 50Bn+.
Have led marketing efforts at various
companies such as Cisco, EMC, Splunk and Twilio.
Masters in computer science from the University of
Massachusetts and an MBA from Santa Clara
University
Almitra Karnik
About the Speaker

4. Agenda
1. How Marketing has evolved over a period of time
2. How AI will shape Marketing in the coming years
3. How CleverTap has used AI to enable the modern day marketer
5. Questions
A. Why Marketers need AI
B. How does it assist their jobs
• Better Customer Experience
• Remove the guesswork
• Customer Retention
• Better outcomes
• Keep a tab on customer’s pulse
• Data backed Suggestions
How a ticketing giant used AI to maximize ticket salesA.

5. How Marketing Has Evolved
Customer Centric MarketingProduct Focused Marketing
TeleSales
(Voice Marketing)
Website, Email
Marketing
Catalogue
Marketing
Inbound
Marketing
Powered by Data:
Age of AI/ ML
Print
Marketing
TVCs
& Radio Ads
Inbound
Marketing

6. How AI will
shape Marketing
in the coming years

7. In-depth User Insight and
Personalization
Why
Marketers
need AI/ ML
Predictive User
Behavior Analysis
Supplements User
Profiles with Data
Assist in all cycles
of Marketing
Clustering for Customer
Segmentation & Discovery
Dynamic Content
Optimization

8. How does it assist
a Marketer’s job

9. Better Customer Experience
Problem Statement
Improving user experience through hyper personalization
Solution Powered by AI-Using
In June last year, Starbucks moved to a to a real-time
personalization engine capable of producing hyper-personalized
emails each week. Its AI engine was able to launch real-time 1:1
personalized offers that are uniquely generated based on each
individual customer's behaviors and marked preferences.
Results
In the middle of 2016, they
have seen significant results
like a doubling of the email
redemptions and a tripling of
the revenue from individuals
who redeem offers.
Source

10. Results
Samsonite (which acquired
TUMI) saw e-commerce
revenue growth in 2016 and
increased six times over in
2017.
Remove the Guesswork
Problem Statement
How to target customers based on past purchase behaviour
Solution Powered by AI-Using
Tested out a product recommendation tool that surfaced items the
customer would be most likely to buy, based on the AI-built
dataset
Source

11. Customer Retention
Problem Statement
Complex Email Marketing Setup with limited Personalization
Results
Solution Powered by AI-Using
The team works on the algorithms for personalizing the Netflix
homepage, such as what artwork Netflix presents to depict each
movie or series it recommends for each user.
The algorithms help
Netflix save $1 billion a
year in value from
customer retention.
Source

12. Better Outcomes
Problem Statement
Helping subscribers find content that they enjoy
Solution Powered by AI-Using
AI email marketing solution Phrase, it began to test different
subject lines and text within emails to work out what was
most effective.
Results
Virgin Holidays saw a 66%
increase in email opens,
33% in web traffic, a 37%
rise in revenue and 38%
increase in calls to its call
centres.
Source

13. Keep a tab on customer’s pulse
Problem Statement
Recognize what makes a story appealing.
Solution Powered by AI-Using
Using data from Q+A site Quora, Disney researchers used the
site’s upvotes and downvotes to train the neural networks to
determine what makes some stories more popular than others.
Results
In the team's experiments, the
neural networks proved better
at judging stories than traditional
machine-learning techniques,
with the network judging the
story as a whole, registering an
18% improvement.
Source

14. Results
Judging by Amazon’s success, the
recommendation system works. The company
reported a 29% sales increase to $12.83 billion
during its second fiscal quarter, up from $9.9
billion during the same time last year. A lot of
that growth arguably has to do with the way
Amazon has integrated recommendations into
nearly every part of the purchasing process…”
Data-Backed Suggestions
Problem Statement
Increase conversions and revenues
Solution Powered by AI-Using
They have billions of data points to test so many different things
to find out what works and what doesn’t, quickly. Over the years
we’ve been watching the way Amazon does recommendations
both on-site and off, via email.
Source

15. How CleverTap has
used AI to enable the
modern day marketer

16. What is CleverTap: An Intelligent Mobile Marketing Platform
We combine real-time customer insights, an advanced segmentation engine and
powerful engagement tools into one intelligent mobile marketing platform.

17. 1. ANALYZE
Understand users as they move through your
app from onboarding to engagement to
uninstall to re-install
4. MEASURE
Evaluate how product & marketing efforts drive
growth & influence KPIs such as Retention,
Revenue, Customer Lifetime Value, etc
3. SEGMENT
Track how each group of users react to a new
feature or a marketing touchpoint over a period
of time
2.
2. ENGAGE
Trigger timely, contextual messages via the
optimal marketing channel (push, email, in-app,
sms,etc) to get higher conversions
3.

18. Our Analytics Stack
Funnels
Pinpoint exactly where users drop off
Retention Cohorts
Measure how many of your new users
come back
Flows
Visualize How Users Navigate Through
Your App
Pivots
Industry-first feature for better data
visualizations and customer insights.
Rich User Profiles
Rich user profiles to understand
users better
Device Crossovers
Get a single view of users as they move
from mobile to tablet to desktop.

19. Our Engagement Stack
Journeys
Visually build and deliver omnichannel
campaigns based on your users’
behavior, location, and lifecycle stage.
Triggered & Scheduled Campaigns
Schedule one-time, recurring, and
triggered campaigns based on user
behavior and profile
Clever Campaigns
Run predefined campaigns to retain
users, drive engagement, and
reduce churn.
User Segmentation
Group users based on their activity,
location, and profile info to engage them
in real-time.

20. Case Study: Movie ticketing giant of Asia
Problem: Maximize Ticket Sales in the next “N” Days
50 Million +
App Downloads
> 100 Million
Tickets Sold
250
Cities

21. Use a simple rules-based marketing strategy
Charged
(past 6 months)
Viewed
Synopsis
(past 30 days)
Selected
Showtime
(past 30 days)
10 Million Users Qualified
1.2% Conversion
Not Bought
(past 30 days)
OR OR
Send Push
$

22. But one size fits all has shortcomings
All 10 Million users got the same notification
Users who were going
to buy anyway now got
a nice discount
Users who were unlikely
to buy didn’t find the offer
compelling enough

23. Applying CleverTap’s Machine Learning
Predict each user’s likelihood to buy
Highly
Likely
Likely Less Least
Creating mutually exclusive audience segments so you can market to each segment differently

24. How it works?
Seen in app/website
(in past 7 days)
Model
discovered
for the
Most Likely
Segment
Transact every 15
days
(+/- 3 days)
Transacted > 10 times
(in past 6 months)
+ a few more things
and
andand
and
Simple Ingredients as inputs:
How frequently does a user visit,
convert, click on a campaign
How variable are these frequencies?
Plus a few other spices

25. And the results were astounding!
No Machine Learning
1.2% convert
all get
the same message
Very Likely
200K
Likely
400K
Less
2.8M
Least
6.6M
10M users qualify And I now know
that there are
500K additional users
likely to convert
but haven’t yet!With Machine Learning - I see the trees in the forest

26. Now we can market to each segment differently
Most Likely
Maybe nudge them
within 1 or 2 days of
when they’re supposed
to convert...but not
necessarily with a
monetary incentive
Hi Adam, want to have a good laugh AND
get in the spirit for Halloween? A Madea
Halloween is playing this weekend at your
AMC theater in Hollywood. Book today and
get a free popcorn on us!
APP

27. Now we can market to each segment differently
Least Likely
Probably won’t transact
anyway, so... send a
large discount for their
next booking
Hi Adam, want to have a good laugh AND
get in the spirit for Halloween? A Madea
Halloween is playing this weekend at your
AMC theater in Hollywood. Book today and
get a free ticket for a friend!
APP

28. We’re trusted by today’s top brands
8,000+
CUSTOMERS
1 billion
DEVICES REACHED
55 billion
EVENTS PROCESSED PER MONTH
10 billion
MESSAGES SENT PER MONTH

29. Questions
Thank you