Analytics for Retail Banks

Course Features

Course Details

Course Objectives
After completing the Iteanz course, you will be able to:
1. Understand the applications of data-driven marketing across the customer life cycle at retail banks.
2. Understand the data infrastructure and the Analytics for Retail Banking Training set up required to carry out data-driven marketing.
3. Understand the nuances of how incoming, outgoing and interactive channels impact data-driven programs.
4. Understand and design event-based marketing programs and Contextual campaigns at retail banks.
5. Understand how to use Analytics for Retail Banking Training to improve campaign performance.
6. Get an overview of analytics best practices in retail banks.
7. Understand the practical issues that one will encounter while implementing data-driven marketing programs at retail banks.
8. Be aware of how data driven marketing is done across banks of different countries.

Who should go for this Course? 
The course is designed for all those who want to understand the application of analytics especially in the retail banking context.The following audiences could greatly benefit from this course:
1. Analytics Managers who are subject matter experts, leading a team of analysts.
2. Senior professionals at Retail Banks who want to enhance their career prospects by moving into analytics.
3. Business heads and CXOs to help understand the applications of analytics.

What are the pre-requisites for this Course?
The pre-requisite for this course includes a basic understanding of marketing processes, familiarity with the banking domain and high school mathematics

Which Case-Studies will be a part of the Course?
There are multiple case studies of banks across the globe as part of the course. Every module has assignments that help in understanding the applicability of analytics. These assignments would then be discussed in the class for peer learning to emerge.

1. 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.
2. 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.
Topics- Retail banking objectives, Customer lifecycle, Analytics applications across the customer lifecycle, Levers, Analytics objectives and trade-offs, Segment marketing, Partner agencies, ROI models
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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.
This course does not have any sections.

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