Analytics for Retail Banks

The Analytics for Retail Banks provides in-depth knowledge about the analytics lifecycle, data infrastructure, customer lifecycle and digital trends. The delegates will understand about the analytics applications at a retail bank. The delegates will understand how incoming, outgoing and interactive channels helps the data driven programs. The delegates will also understand how to manage these challenges and how to use analytics to address these challenges.

Prerequisites

For attending Analytics for Retail Banks training, the delegates should have a basic knowledge of marketing processes, banking domain and high school mathematics.


Course Objectives

After the completion of Analytics for Retail Banks Training at Silicon Beach Training, the delegates will be able:

  • Understand the data infrastructure and the Analytics for Retail Banking Training set up required to carry out data-driven marketing
  • Get an overview of analytics best practices in retail banks
  • Understand the nuances of how incoming, outgoing and interactive channels impact data-driven programs
  • Understand and design event-based marketing programs and Contextual campaigns at retail banks
  • Understand the practical issues that one will encounter while implementing data-driven marketing programs at retail banks

Who is this course for?

This course is designed for everyone and who wants to understand the application of analytics especially in the retail banking context. The below professionals can attend this training:

  • Analytics Managers who are subject matter experts, leading a team of analysts
  • Senior professionals at Retail Banks who want to enhance their career prospects by moving into analytics
  • Business heads and CXOs to help understand the applications of analytics

Analytics scope at a retail bank

  • Introduction to Analytics objectives
  • Analytics data stack
  • Analytics lifecycle
  • Analytics process cycles
  • Analytics algorithms stack
  • Data visualisation
  • Context awareness
  • Analytics best practices
  • About CRISP-DM methodology

Marketing challenges across the retail banking customer lifecycle

  • Retail banking objectives
  • Customer lifecycle
  • Analytics applications across the customer lifecycle
  • Levers
  • Introduction to Analytics objectives and trade-offs
  • Segment marketing
  • Partner agencies
  • About ROI models

Data related Infrastructure at a retail bank

  • About the challenges of big data
  • Different types of data
  • Data lifecycle 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

  • Channel purposes
  • Types of channels
  • Channel infrastructure
  • Channel throughput
  • Campaign execution challenges
  • Omni-channel perspective
  • Use of social media channels

Data-driven customer acquisition at retail banks

  • Prospecting
  • Onboarding
  • Analytics capabilities for prospect analytics
  • Response models
  • Activation strategies
  • Digital activation best and worst practices

Data-driven usage management at retail banks

  • Analytics capabilities required
  • Sample usage increase programs
  • Offer glut
  • Offer fulfillment and tracking

Data-driven customer experience management at retail banks

  • Customer journey and analytics
  • Customer experience processes
  • Customer trust principles
  • Analytics capabilities required for customer experience and 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

  • 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

  • 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

  • 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

Keypoints

keypoints

Duration:2 Days

Analytics for Retail Banks Calendar

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