Course Overview:
Business Analysis for Sales Growth Using Artificial Intelligence is a practical five-day Business Analysis Training programme designed to turn business information, customer intelligence, market intelligence, and sales data into measurable commercial action. Sustainable growth requires more than sales techniques; it depends on understanding business objectives, customer behaviour, competitive conditions, revenue performance, and the factors that enable or restrict growth.
This Business Analysis Course develops participants’ ability to diagnose commercial problems, distinguish symptoms from root causes, and identify evidence-based growth opportunities. Participants explore customer segmentation, customer analytics, competitor analysis, sales performance analysis, revenue analysis, and sales pipeline analysis before converting their findings into a focused sales growth strategy.
The programme also introduces AI for Sales as an advanced analytical capability. Participants learn how predictive sales analytics, AI customer segmentation, and AI sales forecasting can support faster pattern recognition, opportunity discovery, forecasting, and commercial decision-making. Attention is also given to identifying revenue leakage, improving sales optimization, and recognising suitable cross-selling and upselling opportunities.
Through business scenarios, sales datasets, analytical frameworks, and AI-supported demonstrations, participants learn to combine technology with professional judgement. By the end of the programme, they will be equipped to produce a practical, evidence-based sales growth analysis and action plan aligned with organisational priorities.
Target Audience:
- Business analysts and senior business analysts
- Sales managers and regional sales managers
- Business development managers
- Commercial managers and commercial analysts
- Revenue growth and revenue operations professionals
- Sales operations managers and analysts
- Key account and strategic account managers
- Marketing managers and marketing analysts
- Customer experience and customer intelligence professionals
- Market research and market intelligence analysts
- Product managers and category managers
- Strategy and corporate planning professionals
- Sales forecasting and demand-planning specialists
- CRM and customer data professionals
- Department heads responsible for commercial performance
- Senior professionals involved in data-driven decision-making
Targeted Organizational Departments:
- Business Analysis
- Sales and Sales Operations
- Business Development
- Commercial Management
- Marketing
- Customer Experience and CRM
- Strategy and Corporate Planning
- Finance and Revenue Management
- Data and Business Intelligence
- Product and Service Management
Targeted Industries:
- Banking, financial services, and insurance
- Technology, telecommunications, and digital services
- Retail, e-commerce, and consumer goods
- Manufacturing and industrial services
- Professional and business services
- Healthcare, pharmaceuticals, and medical services
- Energy, utilities, and natural resources
- Logistics, transportation, and supply-chain services
- Hospitality, tourism, and aviation
- Real estate and property services
- Automotive sales and distribution
- Media, advertising, and marketing services
- Government entities and public-sector organisations
- Education and professional training providers
- Wholesale, distribution, and international trading companies
Course Offerings:
By the end of this course, participants will be able to:
- Apply structured business analysis to sales and commercial situations.
- Connect business objectives with revenue and sales-growth priorities.
- Diagnose sales problems and distinguish symptoms from root causes.
- Analyse customers, markets, competitors, products, and value propositions.
- Use customer segmentation and customer intelligence to identify growth potential.
- Conduct sales performance analysis across products, accounts, teams, and territories.
- Interpret sales pipelines, funnels, conversion rates, and performance trends.
- Detect revenue leakage, stalled opportunities, and performance gaps.
- Use sales analytics to evaluate cross selling and upselling possibilities.
- Apply sales forecasting and scenario analysis to commercial decisions.
- Convert analytical findings into a prioritised sales growth strategy.
- Use AI for Sales to structure problems, analyse information, and generate hypotheses.
- Apply predictive sales analytics, AI sales forecasting, and AI customer segmentation.
- Validate AI-generated insights and combine them with professional judgement.
- Present evidence-based commercial recommendations to decision-makers.
- Develop a Sales Growth Analysis and Action Plan with priorities, KPIs, and implementation measures.
Training Methodology:
The course uses an interactive, application-focused methodology that connects Business Analysis Training with realistic sales and commercial challenges. Trainer-led sessions introduce essential concepts, analytical frameworks, and decision models, while facilitated discussions help participants connect these approaches to their organisations, customers, markets, and sales environments.
Case studies demonstrate how business analysis can uncover sales-performance gaps, customer opportunities, revenue leakage, and barriers to growth. Participants work individually and in groups to examine business scenarios, perform customer analytics, apply customer segmentation, conduct competitor analysis, and interpret market intelligence. Simulated sales datasets support sales data analysis, revenue analysis, sales pipeline analysis, sales forecasting, and root-cause investigation.
Structured workshops guide participants through the complete analytical journey: defining a commercial problem, collecting relevant evidence, interpreting performance, prioritising opportunities, and developing a sales growth strategy. AI Sales Training demonstrations show how AI can help organise information, generate analytical questions, detect patterns, support predictive sales analytics, and explore alternative commercial scenarios.
Reflection and feedback sessions are included each day to reinforce learning, correct analytical assumptions, and connect insights to workplace priorities. The programme concludes with an integrated AI-supported business case in which participants develop and present a practical Sales Growth Analysis and Action Plan based on evidence, commercial judgement, and responsible AI use.
Course Toolbox:
- Business Growth Diagnostic
- Business objectives and sales-drivers mapping
- Commercial problem definition
- Business model and value proposition analysis
- Customer segmentation and profiling
- Customer value and growth-potential analysis
- Customer portfolio analysis
- Customer and market intelligence
- Competitor positioning
- Sales performance analysis
- Sales data and revenue analysis
- Revenue leakage identification
- Sales pipeline and funnel analysis
- Win/loss analysis
- Sales forecasting and scenario planning
- Root-cause analysis
- Cross-selling and upselling analysis
- Sales-growth opportunity prioritisation
- Commercial strategy development
- AI prompting for business and sales analysis
- AI-generated insight validation
- Responsible AI and data confidentiality
- Sales Growth Analysis and Action Planning
- Sales KPIs and implementation priorities
Important note: Relevant tools are not provided. The course offers insights, demonstrations, and examples of suitable business analysis, sales analytics, and AI tools where required.
Course Agenda:
Day 1: Business Analysis and Sales-Growth Drivers
- Topic 1: Purpose and value of business analysis in commercial settings
- Topic 2: Aligning business objectives with sales and revenue priorities
- Topic 3: Business models, value creation, and commercial ecosystems
- Topic 4: Key drivers of revenue, profitability, and sustainable sales growth
- Topic 5: Defining business problems, symptoms, and growth opportunities
- Topic 6: Evidence requirements, analytical questions, and analysis priorities
- Reflection & Review: Applying a Business Growth Diagnostic to connect objectives, sales drivers, performance issues, and areas for analysis
Day 2: Customer and Market Analysis for Growth
- Topic 1: Understanding customer needs, expectations, and buying behaviour
- Topic 2: Customer segmentation, profiling, and portfolio analysis
- Topic 3: Customer intelligence and high-potential account identification
- Topic 4: Market intelligence, attractiveness, trends, and emerging demand
- Topic 5: Competitor analysis, positioning, and underserved market segments
- Topic 6: Cross-selling, upselling, retention, and customer-development opportunities
- Reflection & Review: Converting customer analytics and market intelligence into defined sales-growth opportunities
Day 3: Sales Performance and Revenue Analysis
- Topic 1: Historical sales data analysis and performance trends
- Topic 2: Revenue analysis across products, services, customers, and territories
- Topic 3: Sales pipeline analysis, funnel movement, and conversion rates
- Topic 4: Win/loss patterns, deal values, and sales-cycle performance
- Topic 5: Detecting stalled opportunities, revenue leakage, and performance gaps
- Topic 6: Root-cause analysis of sales problems and underperformance
- Reflection & Review: Completing a Sales Performance Diagnostic to identify what is happening, where, why, and with what commercial impact
Day 4: Developing the Sales Growth Strategy
- Topic 1: Converting business analysis into actionable commercial insight
- Topic 2: Evaluating and prioritising sales-growth opportunities
- Topic 3: Account expansion, acquisition, cross-selling, and upselling strategies
- Topic 4: Customer value propositions and competitive sales positioning
- Topic 5: Commercial strategy, resource allocation, and growth objectives
- Topic 6: Scenario analysis, risks, KPIs, and stakeholder recommendations
- Reflection & Review: Building an evidence-based sales growth strategy linking insight, opportunity, priority, initiative, and expected result
Day 5: Artificial Intelligence for Business Analysis and Sales Growth
- Topic 1: Capabilities, limitations, and responsible use of AI for Sales
- Topic 2: Using AI to structure business problems and analytical hypotheses
- Topic 3: AI customer segmentation, customer analytics, and feedback analysis
- Topic 4: AI-assisted market intelligence and competitor analysis
- Topic 5: Predictive sales analytics, AI sales forecasting, and anomaly detection
- Topic 6: AI-supported sales optimization and commercial recommendations
- Reflection & Review: Integrating business data, customer intelligence, market findings, sales performance, and AI-assisted analysis into a Sales Growth Action Plan
FAQ:
What specific qualifications or prerequisites are needed for participants before enrolling in the course?
No formal qualification in business analysis, data science, or artificial intelligence is required. Participants should have a basic understanding of business or sales operations and an interest in improving commercial performance. Experience in sales, business development, marketing, strategy, customer management, finance, or business analysis will help participants relate the frameworks to workplace situations. Advanced statistical or programming knowledge is not required.
How long is each day's session, and is there a total number of hours required for the entire course?
Each day's session is generally structured to last around 4–5 hours, with breaks and interactive activities included. The total course duration spans five days, approximately 20–25 hours of instruction.
Does the course focus on selling techniques or on analysing sales growth opportunities?
The course focuses primarily on business and sales analysis rather than traditional selling techniques. It examines customers, markets, competitors, sales data, revenue performance, pipelines, forecasts, and commercial opportunities. Participants then use these findings to develop an evidence-based sales growth strategy. Artificial intelligence is introduced as an analytical support capability, not as a substitute for professional judgement or relationship-based selling.
How This Course is Different from Other Business Analysis for Sales Growth Courses:
Business Analysis for Sales Growth Using Artificial Intelligence stands out by integrating three disciplines that are frequently taught separately: business analysis, commercial performance, and applied artificial intelligence. Instead of approaching business analysis as a general requirements-management subject, the course applies it directly to sales growth, customer development, revenue performance, and commercial decision-making.
The programme follows a clear analytical progression. Participants first examine the business model and growth drivers, then conduct customer analytics, customer segmentation, market intelligence, and competitor analysis. They subsequently perform sales performance analysis, revenue analysis, and sales pipeline analysis before transforming their findings into a practical sales growth strategy.
Its AI Sales Training component is equally focused. Rather than presenting artificial intelligence as a collection of disconnected tools, the programme shows where AI can support problem structuring, information analysis, predictive sales analytics, AI customer segmentation, AI sales forecasting, and sales optimization. It also addresses validation, confidentiality, inaccurate outputs, and the continuing importance of human judgement.
The final output is not simply a set of course notes. Participants bring the analytical components together in a Sales Growth Analysis and Action Plan covering business diagnosis, customer and market opportunities, commercial priorities, recommendations, KPIs, risks, and implementation priorities.
