Connaizen is a Personalization Platform to optimize customer communication and increase lifetime value of customers. Connaizen works with players with rich and scarce customer data including banks and retailers. This deck is a an open pitch for banking customers

1. identify
incentivize
influence
Boost your Cross-Sell with
Next Best Action

2. Opportunity
51% customers want their bank to recommend products
and services for their financial needs.
55% customers who want proactive banking services say
that such services would strongly increase their loyalty.
By 2020, more than 30% of banking revenues would be
at risk owing to new competitors and trends.

3. Solution: Connaizen Next Best Action dynamically
delivers the Right Offer at the Right Time.
Decrease
Cost of Service
Increase
Wallet Share
Increase
Customer Loyalty
Become truly
omni-channel
Offer personalized
financial care
Fulfill every need
through your ecosystem

4. Transaction
Data
Identify Right Customers with Single View of Customer
Channel
Preference
Single View of
Customer
Demographic
Data
FI Products/
Services
Demographic data
– income, age, location, etc.
Transaction data
– number of monthly transactions, payment patterns, etc.
Customer Service
– complaints, inquiries, praise or suggestions, etc.
Online and mobile banking behavior
– the most frequent activities, visits history, etc.
Current/previous products and services
–e.g., open savings and credit card accounts, deposit, etc.
Channel preferences and usage
– e.g., customer rarely visits branch, receives both mail
and email communications, customer uses mobile app
Online/Mobile
Banking
Behavior
Customer
Service Data

5. Use-case breakdown
Use Case Input data stream Processing Model Final Result
Recurring Payments
Reminder
• Transaction Data • Time series analysis
• Reminders on next recurring
payment
Next Best Financial Product
(e.g. Card, Loan, Insurance)
• Demographic Data
• Geographic Data
• Financial Data
• ML based classifier model
• Recommending the next best up-
sell basis customer’s likelihood to
use a FI product
Next Best Retail Offer
• Demographic Data
• Geographic Data
• Transaction Data
• ML based hybrid
recommender system
• Recommending the next best up-
sell basis customer’s likelihood to
transact at retailer
Preferred Communication
Channel
• Customer engagement
stats across different
channels (SMS/E-mail/
Netbanking/App)
• Time series analysis
• ML based classifier model
• Best communication channel for
each customer-product/service
recommendation
Send Time Optimization
• Transaction Data
• Customer engagement
stats
• Time series analysis
• ML based classifier model
• Best suited time of communication
for each customer-product/service
recommendation

6. How would Next Best Action impact customers?
Dynamic Data
• Recent transaction at travel website
• Savings being decreased to nearly zero
• Another 20 days until his expected salary
payment
Next Best Action
• A short-term cash loan with an individual
interest rate that is lower than the
standard interest rate
• Increasing credit card limit for this month
to a newly calculated amount
• Two travel insurance options that take
into account extreme sports
John Doe
Demographic:
• Age: 35 years
• Gender: Male
Financial Products:
• Debit card
• Credit card
Transactions:
• Income of INR 60,000/month
• Spends primarily on shopping and bills
• Savings is normally equal to three
months’ worth of income.

7. We don’t just target offers, we prioritize them.
Offers are prioritized using
Customer Taste Graph.
[Likelihood score is generated
using ML-based algorithm]

8. Bank Firewall
API
Database
HDFS
Integration
Process Flow (On-Premise/Cloud)
Intermediate Database
Web and
Mobile Activity
Single View of
Customer
Communication Channels
Web/Mobile App
Call Center
SMS
Email
Transactional
Demographic
Marketing
Automation Tools
Customer
Profiling
Aggregate hidden
Customer Taste
Web and Mobile
Behavior
Campaign rules
and objectives
Decision
Database
Action
recommended
via preferred
channel
Connect Analyze Act
Micro-
segmentation
Customer
Matching
…
We never accept or aggregate Personally Identifiable Information (PII) and no data ever leaves bank server.
Primary Bank Database

9. Combined Active Card-base of
24 Million+
Transactions Analyzed
500 Million+
Clients

10. Nikhil Garg – CEO
Experience in Market Research and Analytics
Worked at The Smart Cube, Graduate from PEC, Chandigarh
Vikas Bharti – CTO
Machine Learning Expert, Holds Patent in Recommendation Systems
Worked at HDFC RED and InnovAccer, Graduate from IIT Guwahati
Investors
Sanchit Kapoor – CPO
Experience in IT Consulting
Worked at McKinsey and Amadeus, Graduate from PEC, Chandigarh
Team
Vikram Sud
Ex-O&T Head Citibank,
APAC
Umang Moondra
Ex-MD Citibank,
Singapore

11. Connaizen partners with banks and merchants to personalize customer engagement and enable targeting
Right-Offers-to-Right-Consumers.
With clients including India’s largest private bank, we have processed data for more than 45 million customers.
Identify, incentivize and influence your customers!
Do all this with Connaizen.
India
849-B, 8th Floor, JMD Megapolis
Sector 48, Sohna Road
Gurugram, 122004
Singapore
21 Woodlands Close
#09-30 Primz Bizhub
Suite #26359 737854
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