AI for Commerce: Personalise, Optimise, Outsell the Market

This course is designed for:

• CEOs, COOs, e-Commerce Directors
• Sales & Store Managers
• Customer Support / Live Chat Teams
• Marketing and Campaign Teams
• Product & Pricing Owners
• Inventory, Warehouse & Supply Chain Leads
• Data Analysts working on customer behavior and sales metrics

Objectives

• Understand AI, ML, and GenAI with a commerce mindset
• Apply AI to personalize customer journeys and increase conversions
• Use AI for pricing, demand, inventory and sales optimization
• Automate customer support, content creation, and internal workflows
• Adopt AI ethically and securely (GDPR + customer data protection)
• Build a 1-year AI competitive roadmap for the same company

Deliverables

• Three AI customer outreach journeys mapped to revenue growth
• Personalized campaign drafts (socials, SMS, email, offers)
• Dynamic pricing & demand prediction automation concepts
• Inventory cost-drain mapping + automation blueprints
• 1-year AI adoption & competitive roadmap
• ROI matrix (Revenue Impact vs Cost Savings vs Complexity)
• KPIs for conversion uplift, revenue scaling, and automation savings
• Responsible AI checklist tailored for retail customer data

Day 1

Module 1 — AI Fundamentals for Retail & e-Commerce

Purpose: Build AI literacy with a commerce and customer-growth mindset

Content:

• What AI means in retail: customers, behavior, demand, revenue
• Key components explained simply:
  • ML → demand and trend prediction
  • DL → customer pattern recognition
  • GenAI → content, offers, messaging, support automation
• AI vs Traditional retail systems (rules vs intelligence)
• Limitations: accuracy expectations, uncertainty, hallucinations

Activities:

1. AI literacy quiz tailored to retail business
2. Discussion: “Where AI adds profit vs where human expertise remains critical”

Module 2 — AI for Customer Personalisation & Conversion Growth

Purpose: Learn how to grow sales by personalising customer journeys

Content:

• Customer journey personalisation using AI:
  • Segmentation
  • Intent detection
  • Personalized offers
  • Conversion uplift strategies
• AI for campaigns, email/SMS/chat offers, product storytelling
• Personalization pipeline concept: customer data → AI → messaging → sales

Activities:

1. Create 3 personalized offer messages (email, SMS, live chat)
2. Build 1 campaign prompt template for conversion-focused marketing
3. Draft 1 product storytelling script for a retail item
4. Design 1 audience segmentation concept using AI-detected intent

Module 3 — AI for Pricing, Demand & Inventory Optimisation

Purpose: Use AI to predict demand and automate pricing & stock decisions

Content:

• AI for:
  • Dynamic pricing
  • Demand forecasting
  • Inventory cost-drain identification
  • Warehouse optimisation ideas
  • Sales volume prediction
• Pricing and inventory automation principles (no coding needed)
• ROI expectations: less waste, fewer manual hours, smarter stock

Activities:

1. Build 2 dynamic pricing prompt templates
2. Draft 2 demand-forecasting prompts
3. Design 2 inventory automation triggers (e.g., low stock, high demand)
4. Estimate cost savings (time + stock waste reduction)

Day 2

Module 4 — AI to Outsell the Competition

Purpose: Learn how to differentiate and win the retail market using AI

Content:

• How competitors use AI today in retail/e-commerce
• AI differentiators:
  • Better personalization
  • Faster support
  • Smarter pricing
  • Automated campaigns
  • AI-powered brand positioning
• Build the company’s AI competitive narrative

Activities:

1. Draft one AI competitive positioning statement
2. Define two to three retail differentiators for external use

Module 5 — AI Ethics, GDPR & Customer Data Security

Purpose: Adopt AI without violating customer data trust or compliance

Content:

• Bias & fairness in recommendations, pricing, and offers
• GDPR principles for internal retail AI usage
• Customer data protection boundaries
• AI security risks (data leaks, prompt injection, misuse)
• Responsible AI retail checklist

Activity:

1. Create one GDPR + AI security checklist draft for the retail company

Module 6 — Technical AI Demonstrations

Purpose: See real AI pipelines similar to retail organisations
Content:

• Recommendation engine concept demo
• Pricing AI pipeline demo concept
• Inventory AI pipeline concept
• Document AI demo concept (PDF → classify → extract → structured data)
• Explainability validation demo concept for auditing AI decisions

Module 7 — Internal AI Consulting Workshop

Purpose: Build a roadmap strategy tailored to the same retail company
Content:

• Assess AI maturity level
• Identify highest-ROI opportunities for:
  • Growth (conversion, sales, outreach)
  • Cost Cutting ( inventory waste, manual workload)
  • Market Dominance (competitors differentiation)
• Live production of strategy assets:
  • ROI Priority Matrix (Impact vs Cost vs Complexity)
  • 1-Year AI Adoption Roadmap
  • Department Ownership Plan
  • KPIs mapped to each business pillar
• Assign internal AI ownership per team

Module 8 — KPI Framework & Commercial AI Action Checklist

Purpose: Define measurable success metrics and implementation steps
Content:

• KPIs for Growth:
  • Conversion uplift
  • Engagement uplift
  • Sales response time improvement
  • Campaign speed improvement
• KPIs for Cost Cutting:
  • Hours saved per week
  • Inventory waste reduction
  • Manual tasks reduced
• KPIs for Market Winning:
  • Customer satisfaction
  • Competitor differentiation index
  • AI adoption maturity score
• Step-by-step AI adoption checklist with department ownership