Course Introduction
Customer analytics and business performance monitoring are essential for organizations seeking to understand customer behaviour, improve service delivery, strengthen relationships, and achieve sustainable business growth. As organizations collect increasing volumes of customer and operational data through sales platforms, customer relationship management systems, digital channels, and enterprise applications, SQL provides a powerful way to extract, organize, and analyze this information. This course introduces participants to the practical application of Structured Query Language (SQL) in transforming raw customer and business data into meaningful insights that support strategic planning, operational improvement, and evidence-based decision-making.
The programme examines how customer information is structured, stored, and managed across relational databases and business information systems. Participants will learn to work with customer profiles, transaction histories, product preferences, service interactions, marketing records, sales activities, and customer feedback datasets. Emphasis is placed on understanding customer data relationships, identifying relevant analytical variables, and combining information from multiple business sources. The course demonstrates how SQL can help organizations develop a more complete understanding of customer needs, purchasing patterns, engagement levels, service experiences, and long-term relationship value.
A major component of the programme focuses on SQL techniques for customer segmentation, behavioural analysis, retention monitoring, and sales performance assessment. Participants will develop skills in filtering, sorting, joining, grouping, aggregation, subqueries, common table expressions, and window functions. These techniques will be applied to practical business scenarios such as customer lifetime value analysis, repeat purchase behaviour, customer acquisition trends, churn identification, revenue contribution, product performance, and campaign effectiveness. Participants will also explore how SQL can be used to identify high-value customers, inactive accounts, emerging market segments, and unusual patterns that require management attention.
The course further explores the application of SQL in business performance monitoring through the development of structured queries and management reports. Participants will learn how to calculate key performance indicators, compare actual performance against targets, monitor sales growth, analyze customer acquisition costs, assess conversion rates, and evaluate service delivery performance. The programme addresses the development of reusable SQL queries, views, reporting datasets, and automated information extracts that support consistent performance measurement. Participants will also examine how customer and operational indicators can be organized into meaningful reporting structures for executives, business development teams, marketing managers, and operational leaders.
Modern customer analytics increasingly relies on integrated data environments, cloud platforms, automated pipelines, and advanced analytical technologies. This course introduces participants to the integration of SQL with customer relationship management platforms, enterprise resource planning systems, data warehouses, and business intelligence applications. It also examines emerging applications of artificial intelligence, machine learning, predictive customer analytics, and AI-assisted SQL development. Particular attention is given to customer data privacy, responsible analytics, data governance, access controls, data quality, and the ethical use of customer information, ensuring that analytical practices support organizational performance without compromising customer trust.
By the end of the course, participants will be better equipped to use SQL to investigate customer behaviour, measure business performance, and develop reliable analytical reports. They will understand how to retrieve and integrate customer information, identify important business trends, monitor customer retention, evaluate sales outcomes, and generate actionable management insights. The competencies acquired will help organizations strengthen customer engagement, improve marketing effectiveness, enhance operational accountability, and align business strategies with measurable performance outcomes.
Duration
5 days
Who Should Attend
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Customer analytics managers and specialists
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Business intelligence analysts and reporting officers
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Customer relationship management professionals
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Marketing managers and digital marketing analysts
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Sales managers and business development executives
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Data analysts and SQL reporting specialists
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Business performance and strategy managers
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Customer experience and customer service managers
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Market research and consumer insights professionals
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Revenue assurance and commercial performance analysts
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Database administrators and data engineers
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Product managers and product performance analysts
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Operations managers and process improvement specialists
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Management information systems professionals
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Digital transformation and business analytics consultants
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Customer retention and loyalty programme managers
Course Objectives
By the end of this course, participants will be able to:
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Explain SQL fundamentals and relational database concepts relevant to customer analytics, business performance monitoring, and organizational information management.
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Identify, extract, and combine customer information from CRM systems, sales databases, transaction platforms, marketing applications, and other business information sources.
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Develop SQL queries using filtering, sorting, joins, grouping, aggregation, and conditional expressions to investigate customer behaviour and business performance.
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Apply advanced SQL techniques, including subqueries, common table expressions, window functions, and reusable views, to conduct more sophisticated customer and commercial analysis.
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Segment customers using demographic, transactional, behavioural, geographic, and value-based characteristics to support targeted marketing and customer relationship strategies.
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Analyze customer acquisition, retention, churn, repeat purchasing, customer lifetime value, and engagement patterns to identify opportunities for sustainable business growth.
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Calculate and monitor key business performance indicators relating to sales growth, revenue contribution, conversion rates, marketing effectiveness, and operational productivity.
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Develop SQL-based performance reports that compare actual results against targets, identify deviations, and provide reliable information for management review.
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Apply data quality, validation, privacy, governance, and database security principles to maintain accurate and responsible use of customer and business information.
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Integrate SQL analytical outputs with spreadsheets, business intelligence dashboards, and modern analytics platforms to support timely and evidence-based business decisions.
Comprehensive Course Outline
Module 1: Introduction to SQL and Customer Analytics
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Understanding Structured Query Language, relational databases, and the importance of SQL in extracting and analyzing customer and business performance information.
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Examining customer data structures, including customer profiles, contact records, transaction histories, product preferences, service interactions, and marketing activities.
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Exploring the role of customer analytics in customer acquisition, retention, satisfaction, loyalty development, revenue growth, and organizational competitiveness.
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Understanding how SQL supports data-driven business functions, including sales management, marketing, customer service, product development, and operational performance monitoring.
Module 2: Customer Data Structures and SQL Fundamentals
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Exploring relational database components, including tables, records, fields, data types, primary keys, foreign keys, and relationships between customer information sources.
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Writing SQL SELECT statements to retrieve customer profiles, transaction records, sales information, service histories, and marketing campaign data.
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Applying WHERE conditions, logical operators, sorting, filtering, DISTINCT, and conditional expressions to identify relevant customer and business records.
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Using SQL date functions, numeric functions, string functions, and null-handling techniques to prepare customer datasets for accurate analysis.
Module 3: SQL Joins and Integration of Customer Information
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Applying INNER JOIN, LEFT JOIN, RIGHT JOIN, and other SQL joining techniques to combine customer profiles with transactions, products, services, and interaction records.
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Integrating customer relationship management data with sales databases, billing systems, digital platforms, marketing records, and operational information sources.
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Developing multi-table queries that provide a consolidated view of customer activities, purchase histories, service usage, and revenue contributions.
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Identifying duplicate customer records, missing information, unmatched transactions, and inconsistent customer identifiers through structured SQL validation queries.
Module 4: Customer Segmentation and Behavioural Analytics
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Applying SQL filtering, grouping, and aggregation techniques to classify customers according to demographics, purchasing behaviour, transaction frequency, and engagement levels.
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Developing customer segmentation approaches based on recency, frequency, and monetary value to identify high-value, active, emerging, and inactive customer groups.
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Analyzing purchasing patterns, product preferences, average transaction values, cross-selling opportunities, and customer movement between business segments.
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Using SQL queries to investigate customer journeys, channel preferences, service interactions, and behavioural trends that influence purchasing decisions.
Module 5: Customer Acquisition, Retention and Lifetime Value Analysis
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Analyzing new customer acquisition trends, customer growth rates, lead conversion patterns, and the effectiveness of different customer acquisition channels.
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Developing SQL queries to monitor repeat purchases, retention rates, customer inactivity, churn patterns, and changes in customer engagement over time.
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Calculating customer lifetime value using appropriate revenue, transaction, frequency, and customer relationship assumptions to support strategic planning.
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Identifying customer groups with high retention potential, declining engagement, or increased churn risk to inform targeted relationship management initiatives.
Module 6: Sales, Revenue and Commercial Performance Monitoring
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Developing SQL queries to analyze sales volumes, revenue growth, average order values, product performance, and contribution by customer, region, or business unit.
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Applying aggregation and comparative analysis techniques to evaluate monthly, quarterly, and annual sales performance against organizational targets.
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Monitoring product demand, customer purchasing frequency, revenue concentration, sales pipeline activity, and commercial performance trends.
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Investigating revenue variations, unusual transaction patterns, declining sales categories, and opportunities for improving business performance through data analysis.
Module 7: Marketing Analytics and Campaign Performance Measurement
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Using SQL to analyze campaign response rates, customer engagement, lead generation, conversion performance, and marketing channel effectiveness.
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Evaluating customer acquisition costs, campaign-generated revenue, return on marketing investment, and other indicators of marketing efficiency.
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Combining customer transaction data with campaign records to identify purchasing patterns, cross-selling opportunities, and the commercial impact of promotional activities.
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Developing SQL-based marketing reports that support customer targeting, campaign optimization, audience segmentation, and evidence-based marketing decisions.
Module 8: Advanced SQL for Customer and Business Analytics
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Applying subqueries and common table expressions to organize complex customer analysis and develop structured multi-stage business reporting queries.
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Using window functions such as ROW_NUMBER, RANK, LAG, LEAD, and running totals to analyze customer rankings, sales trends, and performance changes.
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Creating SQL views, reusable queries, stored procedures, and parameterized reporting structures to standardize recurring customer and business analytics activities.
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Applying SQL techniques for cohort analysis, period comparisons, customer movement analysis, transaction ranking, and identification of unusual business patterns.
Module 9: Business Performance Reporting, Dashboards and Emerging Technologies
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Defining and calculating key performance indicators for customer acquisition, retention, revenue growth, sales conversion, service quality, and operational efficiency.
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Developing structured SQL reporting datasets and automated extracts for management reports, executive dashboards, and departmental performance scorecards.
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Integrating SQL outputs with Microsoft Excel, Power BI, Tableau, and other business intelligence platforms to support interactive performance monitoring.
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Exploring cloud analytics, automated data pipelines, AI-assisted SQL development, predictive customer analytics, and machine learning applications in business performance management.
Module 10: Customer Data Governance, Practical Analytics and Implementation
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Establishing customer data quality standards, validation procedures, documentation practices, and data governance controls to maintain reliable analytical information.
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Applying privacy, consent management, access control, encryption, and responsible data handling principles when working with sensitive customer records.
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Conducting practical SQL-based customer analytics projects covering segmentation, retention, sales performance, campaign evaluation, and management reporting.
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Developing implementation strategies for sustainable SQL-based customer analytics, including reporting requirements, performance indicators, automation, user adoption, and continuous improvement.
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.
