Course Introduction
The Banking Data Analytics and Customer Intelligence Training Course provides a practical framework for transforming banking data into actionable insights about customers, products, channels, transactions, profitability, engagement, retention, and service performance. The course is designed for banking professionals who need to understand customer behavior, identify meaningful segments, measure relationship value, analyze customer journeys, and develop data-driven strategies using structured analytical methods and modern business intelligence tools.
Participants will learn how to work with diverse banking datasets, including customer profiles, accounts, transactions, deposits, loans, cards, digital interactions, branch activity, service requests, complaints, campaign responses, product holdings, balances, fees, and relationship information. The program emphasizes data quality, customer identifiers, dimensional modeling, transaction aggregation, time-based analysis, segmentation, and analytical measures that allow institutions to create a consistent view of customer activity across products and channels.
Customer intelligence is developed through practical analysis of customer acquisition, activation, engagement, product usage, retention, attrition, and relationship development. Participants will learn how to analyze customer segments based on demographics, behaviors, product relationships, transaction patterns, channel usage, balances, profitability, and lifecycle characteristics. These techniques can help banking teams understand different customer needs, identify behavioral changes, evaluate product adoption, and design more targeted customer-management processes.
The course also explores customer profitability and value analytics by connecting revenue, balances, fees, transaction activity, product holdings, service costs, credit relationships, and customer engagement. Participants will develop analytical approaches for understanding relationship profitability and identifying drivers of value across customer segments, products, branches, and channels. The emphasis is on interpreting data responsibly and distinguishing descriptive evidence from assumptions when using analytics to support customer and business decisions.
Modern business intelligence platforms provide practical capabilities for implementing these analytical workflows. Power BI can connect to numerous data sources, transform and combine data with Power Query, build semantic models with relationships and calculations, and create interactive reports and dashboards. Power BI also supports scheduled refresh, alerts, sharing, security roles, and other service capabilities that can support governed banking analytics environments.
By the end of the five-day program, participants will be able to develop an integrated banking customer intelligence framework covering customer segmentation, product analytics, profitability, engagement, retention, digital behavior, service performance, campaign analysis, predictive analytics, and executive reporting. Through practical exercises and a capstone project, learners will analyze realistic banking datasets, develop customer KPIs, investigate behavioral patterns, build an interactive customer intelligence dashboard, and present evidence-based findings for banking management and customer-focused teams.
Duration
5 days
Who Should Attend
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Banking data analysts responsible for customer, product, transaction, and performance analytics.
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Customer analytics professionals developing segmentation, behavioral, retention, and relationship intelligence.
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Retail banking managers seeking deeper insight into customer behavior, product usage, and relationship performance.
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Digital banking professionals analyzing online, mobile, and self-service customer interactions.
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Relationship managers seeking analytical techniques for customer portfolio monitoring and relationship development.
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Marketing and customer experience professionals working with campaign, engagement, and customer journey data.
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Product managers analyzing adoption, usage, cross-product relationships, and product performance.
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Business intelligence professionals developing banking dashboards, reports, semantic models, and management information.
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Customer retention and loyalty professionals analyzing attrition, engagement, customer lifecycle, and retention indicators.
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Financial analysts examining customer profitability, revenue, balances, fees, costs, and relationship economics.
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Banking operations managers analyzing service performance, branch activity, contact channels, and customer interactions.
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Banking executives and decision-makers seeking practical capabilities for data-driven customer and business intelligence.
Course Objectives
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Develop a comprehensive understanding of banking customer analytics and apply structured analytical techniques to customer, transaction, product, channel, financial, and behavioral data.
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Build reliable banking customer datasets by integrating customer profiles, accounts, transactions, product holdings, balances, digital interactions, service records, and campaign information.
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Develop customer segmentation models using demographic, behavioral, transactional, product, engagement, profitability, lifecycle, and channel characteristics.
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Analyze customer product adoption, cross-product relationships, balances, transaction activity, digital engagement, and service interactions to identify meaningful behavioral patterns.
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Calculate and interpret customer value and profitability indicators by connecting revenues, fees, balances, product usage, service costs, and relationship-level financial measures.
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Analyze customer retention and attrition patterns using historical behavior, engagement changes, product relationships, service experiences, and lifecycle indicators.
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Apply predictive analytics concepts to customer propensity, churn, next-best-action, product adoption, engagement, and other banking customer intelligence use cases.
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Design interactive banking dashboards that communicate customer segments, product performance, profitability, engagement, retention, digital behavior, and service indicators.
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Apply data governance, privacy, security, quality, and analytical validation principles to ensure banking customer intelligence is reliable, appropriately controlled, and responsibly interpreted.
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Complete an integrated banking customer intelligence project involving data preparation, segmentation, customer KPIs, profitability analysis, behavioral analytics, dashboard development, and management recommendations.
Comprehensive Course Outline
Module 1: Banking Data Analytics and Customer Intelligence Foundations
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Understanding the role of data analytics in retail banking, commercial banking, digital banking, customer experience, marketing, product management, and relationship management.
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Identifying banking data sources including customer information, accounts, transactions, deposits, loans, cards, channels, branches, service interactions, campaigns, and product systems.
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Designing customer intelligence structures using customer identifiers, account relationships, product classifications, transaction categories, channel dimensions, geographic attributes, and reporting periods.
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Establishing banking data-quality principles covering completeness, consistency, uniqueness, timeliness, reconciliation, lineage, business definitions, access controls, and appropriate customer-data governance.
Module 2: Customer Data Preparation and Analytical Data Modeling
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Preparing customer and transaction datasets by standardizing identifiers, dates, account relationships, product classifications, transaction categories, channel codes, and customer attributes.
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Integrating information from core banking systems, CRM platforms, digital channels, card systems, loan platforms, data warehouses, spreadsheets, and other analytical sources.
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Designing analytical data models that connect customers, accounts, products, transactions, balances, dates, channels, branches, campaigns, service events, and financial measures.
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Using Power Query and semantic modeling concepts to transform multiple banking data sources into reusable analytical datasets and reporting structures.
Module 3: Banking Customer Segmentation and Behavioral Analytics
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Developing customer segmentation frameworks using demographics, transaction behavior, balances, product holdings, channel preferences, engagement, lifecycle stage, and relationship characteristics.
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Applying descriptive and clustering concepts to identify meaningful customer groups and investigate differences in behavior, product usage, value, engagement, and service requirements.
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Analyzing customer lifecycle stages from acquisition and onboarding through activation, engagement, retention, expansion, inactivity, and potential attrition.
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Building customer segment dashboards that allow banking teams to compare customer populations, behaviors, product relationships, balances, engagement levels, and performance trends.
Module 4: Product, Account and Transaction Analytics
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Analyzing product adoption and usage across deposits, loans, cards, payments, digital products, savings products, and other banking services using consistent customer and product measures.
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Measuring transaction volumes, values, frequency, recency, average transaction size, account activity, balance trends, and channel utilization across customer segments.
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Investigating relationships between products to identify product penetration, customer-product combinations, usage patterns, and potential opportunities for relationship development.
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Developing product intelligence dashboards that connect customer segments with balances, transactions, adoption, activity, revenue indicators, and changing product usage patterns.
Module 5: Customer Profitability and Relationship Value Analytics
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Building customer profitability models that connect interest income, fees, balances, product activity, service costs, transaction costs, and other relevant financial measures.
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Comparing customer value across segments, products, branches, relationship types, acquisition channels, geographic markets, and lifecycle stages.
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Analyzing profitability drivers to understand how balances, transaction behavior, product combinations, service usage, and customer activity relate to relationship economics.
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Developing customer-value dashboards that provide consistent visibility into revenue, costs, profitability measures, relationship depth, and portfolio-level customer performance.
Module 6: Customer Retention, Churn and Lifecycle Intelligence
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Analyzing customer inactivity, attrition, account closures, product discontinuation, declining engagement, and other behavioral indicators associated with changing customer relationships.
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Developing customer retention measures using customer tenure, activity frequency, balances, product holdings, transaction behavior, service interactions, and digital engagement.
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Building churn-analysis datasets and exploring classification concepts for identifying customer groups with different patterns of retention or disengagement.
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Designing retention intelligence dashboards that monitor customer movement between lifecycle stages and highlight changes in engagement, activity, balances, and product relationships.
Module 7: Digital Banking, Customer Journey and Experience Analytics
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Analyzing mobile banking, online banking, ATM, branch, contact center, and other channel interactions to understand customer preferences and service behavior.
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Mapping customer journeys across acquisition, onboarding, authentication, product activation, transaction activity, service requests, complaints, and resolution processes.
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Measuring digital engagement using session activity, transaction frequency, feature adoption, channel migration, interaction patterns, and customer-service indicators.
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Developing customer-experience analytics that connect channel usage, service events, complaints, response times, customer activity, and relationship outcomes.
Module 8: Marketing, Cross-Sell and Customer Propensity Analytics
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Analyzing campaign exposure, customer responses, product applications, conversions, channel interactions, and campaign outcomes to evaluate customer engagement patterns.
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Developing analytical approaches for cross-sell and product-propensity use cases using customer characteristics, existing relationships, transaction behavior, balances, and product gaps.
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Applying classification, propensity scoring, segmentation, and response-analysis concepts to identify customer groups with different patterns of product interest or engagement.
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Measuring campaign and customer-development performance using response rates, conversion indicators, product adoption, incremental activity, relationship depth, and customer-value measures.
Module 9: Customer Intelligence Dashboards, KPIs and Advanced Analytics
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Designing banking KPI frameworks covering customer growth, active customers, product penetration, balances, transactions, engagement, profitability, retention, digital usage, and service performance.
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Building Power BI semantic models, relationships, measures, and calculations to support repeatable banking customer analytics and interactive management reporting.
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Creating interactive reports using filters, slicers, drill-through, navigation, segmentation, trend analysis, geographic views, and customer-level analytical pathways.
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Developing governed dashboards and reporting processes using refresh schedules, alerts, sharing, security roles, and appropriate access controls for different banking stakeholder groups.
Module 10: Capstone Banking Data Analytics and Customer Intelligence Project
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Preparing and validating a realistic banking dataset containing customers, accounts, products, transactions, balances, channels, service interactions, campaigns, and customer lifecycle information.
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Developing an integrated customer intelligence dashboard presenting customer growth, segmentation, product penetration, engagement, profitability, retention, digital activity, and service KPIs.
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Conducting a detailed customer analytics investigation using segmentation, behavioral analysis, transaction patterns, profitability measures, lifecycle indicators, and predictive analytics concepts.
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Presenting a final banking customer intelligence report that combines data-quality findings, analytical insights, interactive dashboards, customer patterns, management implications, and an actionable improvement roadmap.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
