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Analytics for Retail Banks
4.5( 4 REVIEWS )
169 STUDENTS
Overview Data analytics are driving retail banking worldwide. Modern banks believe that data analytics can be game changers to forester …
Overview
Data analytics are driving retail banking worldwide. Modern banks believe that data analytics can be game changers to forester competitive advantage. Undoubtedly, data analytics managers and professionals will be the backbone of any futuristic bank to convince customers to use their services. Understanding analytics for retail banks can make a difference by dropping banks’ costs tremendously to build higher revenues.
The game is simple! If you can master different data analytics software and concepts behinds successful data analysis, you can build a powerful career in the banking industry. This is where this course comes as your partner to guide you about analytics lifecycle and data trends to understand consumers’ lifecycle. If you are looking to hold a position of a top banking executive, then this course by Edureka will ensure your career progression amidst stiff competition. We encourage you to take a ride into Analytics for Retail Banks course and boost data management skills.
When fully explored, this course will solidify your key concepts with easy modules and exercises. This course will teach you a range of things such as upwelling and cross-selling in retail banks. The Analytics for Retail Banks course will steer you in the right direction to open new doors in the banking industry.
Why You Should This Certification Training at Study365?
Study365 is a leading online provider for several accrediting bodies and provides learners with the opportunity to take this exclusive course awarded by Edureka. At Study365, we give our fullest attention to our learners’ needs and ensure they have the necessary information to proceed with the Course.
Learners who register will be given excellent support, discounts for future purchases and be eligible for a TOTUM Discount card and Student ID card with amazing offers and access to retail stores, the library, cinemas, gym memberships, and their favourite restaurants.
- Access Duration
- Who Is This Course For?
- Entry Requirements
- Certification
- Awarding Body
- Method of Assessment
- Career Path & Progression
The course will be directly delivered to you, and you have 12 months of access to the online learning platform from the date you joined the course. The course is self-paced, and you can complete it in stages, revisiting the lessons at any time.
This course is recommended for:
- Analytics Professionals
- Data Analysts
- Analytics Managers
- Senior Managers at retail banks
- Aspiring professionals aiming to join banks
- Data-driven marketing experts
- Learners should have a basic know-how of English, ICT, and numeracy.
- Anyone above 16 years with a sound educational background can take this course.
Upon the successful completion of the course, you will be awarded the Certificate in Analytics for Retail Banks by Edureka.
Edureka is the fastest-growing online learning platform with a trusted name in the industry. The platform has the highest course completion rate and turns beliefs into realities by ridiculously committing to their students. Edureka collaborates with Study365 and many other educational bodies to provide guaranteed learning and success to global students & professionals.
The method of assessment includes learners completing an assignment. After completing the Analytics for Retail Banks, the learners will demonstrate their skills and concepts related to this particular subject. If you can apply them in the real world, you are well-prepared to earn a certification.
Analytics for Retail Banks certification from Edureka will enhance your status for getting several jobs required in data analytics departments. Moreover, you can study further advanced courses in the same domain to boost your academic expertise in data-driven marketing. These skills will have recognition worldwide to help you land well-paying jobs in many retail banks.
Here are a few job titles you can compete and search for in the industry. The average salary per annum in the UK according to https://www.glassdoor.com is higher than many other professions:
- Analytics Manager - £75,000 (Approximately)
- Data Analyst - £55,000 (Approximately)
- Senior Manager - £237,500 (Approximately)
Course Curriculum
| 1. Analytics Scope at a Retail Bank | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| Analytics Capabilities Required | |||
| Sample Usage Increase Programs | |||
| Offer Glut | |||
| Offer Fulfillment and Tracking | |||
| 7. Data Driven Customer Experience Management at Retail Banks | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
Cameron Dixon
The course teaches you all that you need to learn about analytics analytically.
Jamie Berry
Nicely structured and well defined.
Bret Cox
The course was very easy to understand and now I am much confident in my analytical skills.
Jordan Baxter
The course turned out to be an invaluable source of information that helped me understand the nitty-gritty of analytics.