Analytics For Retail Banking Training in Bangalore

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Course details:
Course Code : MYT1629
Course Name:Analytics for Retail Banks
Course duration: Fast track – 4 weeks
Regular weekdays – 6 weeks
Week End – 8 weeks
Training mode:
instructor led class training | Live virtual training
Contact: +91 90191 91856
Email:info@mytectra.com
Web: www.mytectra.com
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Blog: http://mytectra.blogspot.in/

Analytics scope at a retail bank
 Learning Objectives - In this module you will understand the scope of analytics
applications at a retail bank and the underlying processes involved. You will also
learn about the various activities around analytics. Develop a sound foundation of
analytics frameworks. Learn about best practices in analytics and also
understand latest trends around analytics.
 Topics- Analytics objectives, Analytics data stack, Analytics lifecycle, Analytics
process cycles, Analytics algorithms stack, Data visualization, Context awareness,
Analytics best practices, CRISP-DM methodology.
Marketing challenges across the retail banking customer lifecycle
 Learning Objectives - In this module you will understand different stages of the
customer lifecycle, Marketing challenges across different stages of the customer
lifecycle, Best practices in managing these challenges, How to use analytics to
address these challenges and Undertake a case study of a Taiwanese bank.
myTectra Learning Solutions private Limited
Bangalore-BTM Layout/
+91 90191 91856/ info@mytectra.com / www.mytectra.com

 Topics- Retail banking objectives, Customer lifecycle, Analytics applications across
the customer lifecycle, Levers, Analytics objectives and trade-offs, Segment
marketing, Partner agencies, ROI models
Data related Infrastructure at a retail bank
 Learning Objectives - In this module you will understand the various types of data
needed at a retail bank, Infrastructure required to manage data and learn about
challenges and best practices in managing data.
 Topics- Challenges of big data, Different types of data, Data life cycle Logical data
models, Data cleansing, Unstructured data processing, Single view of the
customer, Single row per customer, Platform components required to process
data, Requisite processes.
Channel implications on data driven marketing at retail banks
 Learning Objectives - In this module you will understand the various types of
channels and their implications on data-driven marketing. Learn about customer
touch-points and how they can be leveraged. Appreciate best practices around
analytics and channel management.

 Topics- Channel purposes, Types of channels, Channel throughput, Channel
infrastructure, Campaign execution challenges, Omni-channel perspective, Use of
social media channels.
Data-driven customer acquisition at retail banks
 Learning Objectives - In this module you will understand how to run data-driven
acquisition programs, Best practices around analytics in the acquisition space,
understand the differences between prospecting and onboarding and also learn
about best practices around digital onboarding. Carry out a case study of an
Indonesian bank.
 Topics- Prospecting, Onboarding, Analytics capabilities for prospect analytics,
Response models, Activation strategies, Digital activation best and worst
practices.
Data driven usage management at retail banks
 Learning Objectives - In this module you will understand how to run data driven
usage management programs, Explore best practices around analytics in the
usage management space. Learn about challenges while implementing offers.
Perform a case study of a Thai bank and Chinese bank.

 Topics- Analytics capabilities required, Sample usage increase programs, Offer
glut, Offer fulfillment and tracking.
Data driven customer experience management at retail banks
 Learning Objectives - In this module you will understand the customer journey and
define customer experience. Learn about the benefits of having a good customer
experience, How to run data-driven customer experience management programs,
best practices around analytics in the customer experience management space
and also understand best practices of customer experience in digital banking.
 Topics- Customer journey and analytics, Customer experience processes,
Customer trust principles, Analytics capabilities required for customer experience,
Analytics capabilities required for customer satisfaction, Analytics for the end
customer, Personal financial management, Technology shifts, Design thinking,
Testing options, Digital customer experience sensors and actuators.

Data driven upselling and Cross selling at retail banks
 Learning Objectives - In this module you will understand how to run data driven
upsell and cross sell programs. Learn about best practices of analytics in the
upsell and cross sell space, tactics to increase customer penetration, approaches
to Bancassurance perform a case study of an Indian bank and Chinese bank.
 Topics - Upselling and cross selling processes, Tactics to increase customer
penetration, "Incoming call is your best bet", Next best offer analytics, Case study:
Card upgrade program, Case study: Cross selling credit cards to savings accounts,
Case study: Cross Selling mutual funds to savings account customers, Cross sell
between corporate and individual accounts, Bancassurance approaches.
Data driven retention and loyalty management at retail banks
 Learning Objectives - Understand how to run data-driven retention and loyalty
management programs, Approaches to building retention strategies, trends in
social media marketing. Learn about best practices of analytics in the retention
and loyalty management space. Undertake a case study of an Indian bank

 Topics - Retention and loyalty processes, Factors affecting, Customer loyalty,
Analytics capability for loyalty analytics, Attrition types and retention strategies,
Case Study: Attrition model, Advocacy analytics, Social Media Marketing.
Practical Implementation challenges for the data-driven market
 Learning Objectives - Understand practical challenges in implementing data
driven programs. Learn about basic principles driving IT infrastructure of digital
banking and also you will learn how to manage these challenges.
 Topics - McKinsey core beliefs on big data, Data privacy, IT principles for digital
banking, Architecture blocks for digital banking, "Know your business", Data
preparation groundwork, "Analytics is more art than science", Common
improvement areas at banks.

Analytics For Retail Banking Training in Bangalore

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