Mastering AI-Driven Pricing Strategies for Competitive Advantage
Pressure is mounting. You're expected to deliver pricing decisions that drive margin, volume, and market share - but legacy models can't keep up with real-time demand shifts, competitor moves, or customer segmentation at scale. Manual approaches leave money on the table. Static price lists erode profitability. And without AI, you're reacting - not leading. The risk? Losing market position to agile competitors who've already embedded intelligent pricing into their DNA. But here's the opportunity: companies using AI-driven pricing report 2–5% margin increases within the first quarter, with some seeing double-digit revenue lifts from optimisation alone. This isn't speculation - it's measurable, repeatable, and within your reach. Introducing Mastering AI-Driven Pricing Strategies for Competitive Advantage, the only structured, implementation-ready programme that turns pricing theory into profit-engineering practice. This is not a conceptual overview - it’s your 30-day roadmap from uncertain assumptions to a board-ready, AI-powered pricing model with documented ROI. One pricing analyst at a global CPG firm used this exact framework to redesign their regional price architecture. In under six weeks, she deployed a dynamic pricing layer that increased gross margins by 3.7% - real money, validated by finance, and credited in her promotion review. You don't need a data science degree. You don't need a massive budget. What you need is a battle-tested system - one that eliminates guesswork, integrates seamlessly with your tech stack, and delivers audit-proof pricing decisions. Here’s how this course is structured to help you get there.Course Format & Delivery Details Designed for Real Professionals, Real Constraints
This is a self-paced, on-demand learning experience with immediate online access. No fixed start dates. No live sessions. No schedule conflicts. You progress at your speed, on your time, from any device. Most learners complete the core framework in 21 to 30 days, applying each step directly to their business. Many report seeing initial pricing improvements - such as margin lift flags or competitive gap identification - in under 10 days. You receive lifetime access to all course materials, including every future update at no additional cost. As AI pricing models evolve, so does your training - automatically. All content is mobile-friendly and built for 24/7 global access. Whether you're working from a laptop in London or reviewing pricing logic on your phone during a commute in Singapore, your progress is always synced and secure. Expert Guidance Without the Hype
You are not alone. Throughout the course, you’ll have direct access to instructor support via structured feedback channels. Need clarification on elasticity modelling? Guidance on feature engineering for price sensitivity? Ask. Responses are delivered within 48 business hours, grounded in real-world pricing operations. Your success matters. That’s why every concept is reinforced with templates, diagnostics, and diagnostic checklists - not just theory. This is learning by doing, with professional oversight. Prove Your Mastery with a Globally Recognised Credential
Upon completion, you earn a Certificate of Completion issued by The Art of Service - a credential trusted by professionals in 147 countries. This is not a participation badge. It’s validation that you have mastered the frameworks, tools, and decision logic required to design, validate, and deploy AI-driven pricing strategies. The Art of Service certifications are cited in LinkedIn profiles, performance reviews, and promotion packages. HR and finance leaders recognise them as proof of applied competence. Transparent, Fair, and Zero-Risk Enrollment
Pricing is straightforward with no hidden fees, subscriptions, or upsells. What you see is exactly what you get - lifetime access, all materials, full certification, and ongoing updates. We accept all major payment methods, including Visa, Mastercard, and PayPal. Transactions are secured with bank-level encryption. If you follow the course steps and do not gain actionable insight into AI-driven pricing optimisation, submit your completed work for review and you’ll receive a full refund. This is a satisfied-or-refunded guarantee - because we know the value you’ll receive. Real Results, Even If You’re Not a Data Scientist
This works even if you’ve never built a machine learning model. Even if your data is incomplete. Even if your organisation moves slowly. The framework is designed for real-world conditions - patchy datasets, legacy systems, cross-functional resistance. A senior pricing manager at a European telecom provider used this programme to overcome stalled AI initiatives. With no prior coding experience, he applied the incremental integration model to launch a customer-tier-based dynamic pricing pilot - now being scaled across three markets. After enrollment, you’ll receive a confirmation email. Your access details will be sent separately once your course materials are fully prepared - ensuring everything is optimised and ready for your first session. No risk. No guesswork. Just a clear, proven path to pricing mastery.
Module 1: Foundations of AI-Driven Pricing - The pricing challenge in a high-velocity market environment
- Why traditional price optimisation fails in dynamic conditions
- Core principles of AI in pricing decision-making
- Distinguishing rule-based, statistical, and AI-driven pricing
- Understanding price elasticity in a machine learning context
- Key differences between forecasting and predictive pricing
- The role of real-time signals in pricing AI
- Common misconceptions about AI and pricing automation
- Defining competitive advantage through pricing intelligence
- Aligning AI pricing with corporate strategy and margin goals
Module 2: Data Requirements and Preparation for AI Pricing - Essential data types for AI-driven pricing models
- Internal data sources: sales, inventory, CRM, and POS systems
- External data integration: competitor pricing, market trends, weather
- Data quality assessment and gap analysis
- Feature engineering for pricing algorithms
- Handling missing or inconsistent pricing data
- Time-series data formatting for pricing models
- Creating customer segmentation variables for model input
- Normalising currency, units, and regional variations
- Building a pricing data pipeline checklist
Module 3: AI Models for Pricing: Selection and Application - Overview of regression models in price optimisation
- Using decision trees for pricing rule derivation
- Random forests for handling categorical pricing variables
- Gradient boosting for high-accuracy price predictions
- Neural networks and deep learning in dynamic pricing
- Clustering models for customer-based price grouping
- Reinforcement learning for adaptive pricing
- Model selection framework: accuracy vs. interpretability
- Choosing models based on business size and data maturity
- Matching AI techniques to pricing goals: margin, volume, or share
Module 4: Building Predictive Price Elasticity Models - Understanding elasticity beyond textbook definitions
- Historical price change impact analysis
- Quantifying cross-product cannibalisation
- Estimating customer price sensitivity by segment
- Incorporating promotional lift into elasticity models
- Dynamic elasticity adjustment using AI feedback loops
- Short-term vs. long-term elasticity prediction
- Threshold detection for price breakpoint identification
- Validating elasticity model outputs against real sales
- Deploying elastic zones for automated pricing guardrails
Module 5: Competitive Pricing Intelligence with AI - Automated competitor price monitoring systems
- Web scraping ethics and legal considerations
- Natural language processing for promotional text analysis
- Image recognition for in-store price validation
- Real-time competitor response modelling
- Predicting competitor reactions using game theory AI
- Building a competitive pricing heatmap
- Identifying pricing leaders and followers in your category
- Detecting predatory pricing patterns with anomaly detection
- Automating competitive price matching rules
Module 6: Dynamic and Real-Time Pricing Systems - Defining dynamic pricing in AI contexts
- Event-triggered pricing adjustments
- Demand forecasting integration with pricing engines
- Inventory-level sensitivity in pricing decisions
- Time-based pricing optimisation: hourly, daily, seasonal
- Geolocation-based dynamic pricing models
- Personalised pricing without violating privacy
- Balancing personalisation and fairness in AI pricing
- Real-time pricing in e-commerce and subscription models
- Latency requirements for live pricing engines
Module 7: Ethical AI and Regulatory Compliance in Pricing - Understanding algorithmic bias in pricing models
- Avoiding discriminatory pricing by protected class
- Transparency requirements under GDPR and CCPA
- Explainable AI methods for pricing decisions
- Building audit trails for AI price changes
- Price fairness assessment frameworks
- Documentation standards for regulatory review
- Handling customer complaints about AI pricing
- Designing opt-out mechanisms for personalised pricing
- Aligning AI pricing with corporate social responsibility
Module 8: Integration with ERP, CRM, and Pricing Software - Common enterprise systems used in pricing operations
- API integration strategies for AI pricing modules
- Syncing AI outputs with SAP, Oracle, and NetSuite
- CRM integration for customer-tier-based pricing
- Feeding AI recommendations into Salesforce CPQ
- Middleware solutions for legacy system compatibility
- Data flow architecture for bidirectional pricing updates
- Version control for pricing model deployment
- Integration testing protocols for pricing systems
- Monitoring live integrations for data drift or failure
Module 9: AI Pricing in B2B vs B2C Environments - Key differences in B2B pricing dynamics
- Contractual pricing and AI override mechanisms
- Handling negotiated discounts in AI pricing
- Volume-tiered pricing model optimisation
- AI in distributor pricing governance
- B2C challenges: flash sales, bundling, and promotions
- Subscription pricing with churn prediction
- Freemium conversion optimisation using AI
- Cross-industry pricing patterns: retail, SaaS, telecom
- Customising AI models by customer acquisition cost
Module 10: Implementation Roadmap and Change Management - Assessing organisational readiness for AI pricing
- Identifying key stakeholders and decision gatekeepers
- Building a business case with quantified ROI projections
- Phased rollout strategy: pilot, expand, scale
- Change management communication templates
- Training sales and finance teams on AI pricing outputs
- Handling resistance from legacy pricing managers
- Creating feedback loops between field teams and AI
- Establishing governance for AI pricing approvals
- Developing escalation protocols for model anomalies
Module 11: Model Validation and Performance Monitoring - Defining success metrics for AI pricing models
- Accuracy benchmarks for price prediction
- Backtesting pricing models against historical data
- Holdout sample validation techniques
- Monitoring model drift over time
- Alert systems for statistical degradation
- Profitability tracking post-price change
- Attribution analysis: isolating AI impact from other factors
- Confidence intervals for AI-generated price recommendations
- Monthly model health dashboard template
Module 12: Advanced AI Pricing Techniques - Predictive cannibalisation modelling across product lines
- Basket-based pricing optimisation
- AI-driven price bundling strategies
- Loss leader identification and optimisation
- Psychological pricing thresholds and AI
- Sentiment analysis for pricing perception tracking
- Generative AI for pricing scenario simulation
- Multi-objective optimisation: margin, volume, share
- Fuzzy logic for edge-case pricing decisions
- Monte Carlo simulation for pricing risk assessment
Module 13: Pricing AI in Subscription and SaaS Models - Churn prediction as a pricing input variable
- Customer lifetime value optimisation
- Tiered pricing model elasticity testing
- Add-on and upsell pricing with AI
- Trial-to-paid conversion price sensitivity
- Downgrade prevention pricing strategies
- Usage-based pricing optimisation
- Regional pricing in global SaaS deployments
- Competitive response in freemium markets
- Renewal discount optimisation with AI
Module 14: AI for Promotions, Discounts, and Markdowns - Optimal timing for promotional price changes
- Promotion lift prediction models
- AI-powered A/B testing for discount levels
- Preventing discount dependency in customer behaviour
- Markdown optimisation for excess inventory
- Network effects in bundled promotions
- Dynamic coupon generation based on customer value
- Post-promotion dip forecasting
- Balancing short-term gains with long-term margin
- Automating approval workflows for discounts
Module 15: Pricing Strategy Simulation and Scenario Planning - Building digital twins for pricing environments
- Simulating competitor price wars
- Demand shock modelling with AI
- What-if analysis for new market entry
- Scenario planning for supply chain disruptions
- Stress-testing pricing models under volatility
- Generating executive briefing reports from simulations
- Interactive dashboards for leadership review
- Exporting simulation results for board presentations
- Aligning simulations with strategic planning cycles
Module 16: Pricing Governance and Risk Management - Establishing pricing oversight committees
- Defining AI pricing authority levels
- Automated compliance checks in pricing workflows
- Maximum price change tolerance thresholds
- Blackout periods for price changes
- Handling regulatory audits of pricing algorithms
- Insurance considerations for AI pricing risk
- Vendor risk assessment for third-party pricing tools
- Contract clauses for AI pricing service providers
- Incident response plan for pricing errors
Module 17: Hands-On Implementation Projects - Project 1: Build a price elasticity model from sample dataset
- Project 2: Design a competitive response algorithm
- Project 3: Create a dynamic pricing rule set for e-commerce
- Project 4: Integrate pricing recommendations into a CRM
- Project 5: Simulate a pricing strategy shift for market entry
- Project 6: Audit an existing pricing model for bias and risk
- Project 7: Develop a board-ready pricing proposal with ROI
- Project 8: Build a pricing health monitoring dashboard
- Project 9: Optimise a promotional campaign using AI
- Project 10: Draft a pricing governance policy for AI use
Module 18: Certification Preparation and Career Advancement - Review of key AI pricing concepts and frameworks
- Practice exercises for certification assessment
- Case study analysis: real-world pricing failures and wins
- Building a portfolio of pricing projects for LinkedIn
- Using your certification in performance reviews
- Networking strategies with AI and pricing professionals
- Positioning yourself as a pricing innovator internally
- Salary negotiation leverage with certification
- Preparing for advanced roles: pricing director, revenue officer
- Next steps: continuous learning and specialisation paths
- The pricing challenge in a high-velocity market environment
- Why traditional price optimisation fails in dynamic conditions
- Core principles of AI in pricing decision-making
- Distinguishing rule-based, statistical, and AI-driven pricing
- Understanding price elasticity in a machine learning context
- Key differences between forecasting and predictive pricing
- The role of real-time signals in pricing AI
- Common misconceptions about AI and pricing automation
- Defining competitive advantage through pricing intelligence
- Aligning AI pricing with corporate strategy and margin goals
Module 2: Data Requirements and Preparation for AI Pricing - Essential data types for AI-driven pricing models
- Internal data sources: sales, inventory, CRM, and POS systems
- External data integration: competitor pricing, market trends, weather
- Data quality assessment and gap analysis
- Feature engineering for pricing algorithms
- Handling missing or inconsistent pricing data
- Time-series data formatting for pricing models
- Creating customer segmentation variables for model input
- Normalising currency, units, and regional variations
- Building a pricing data pipeline checklist
Module 3: AI Models for Pricing: Selection and Application - Overview of regression models in price optimisation
- Using decision trees for pricing rule derivation
- Random forests for handling categorical pricing variables
- Gradient boosting for high-accuracy price predictions
- Neural networks and deep learning in dynamic pricing
- Clustering models for customer-based price grouping
- Reinforcement learning for adaptive pricing
- Model selection framework: accuracy vs. interpretability
- Choosing models based on business size and data maturity
- Matching AI techniques to pricing goals: margin, volume, or share
Module 4: Building Predictive Price Elasticity Models - Understanding elasticity beyond textbook definitions
- Historical price change impact analysis
- Quantifying cross-product cannibalisation
- Estimating customer price sensitivity by segment
- Incorporating promotional lift into elasticity models
- Dynamic elasticity adjustment using AI feedback loops
- Short-term vs. long-term elasticity prediction
- Threshold detection for price breakpoint identification
- Validating elasticity model outputs against real sales
- Deploying elastic zones for automated pricing guardrails
Module 5: Competitive Pricing Intelligence with AI - Automated competitor price monitoring systems
- Web scraping ethics and legal considerations
- Natural language processing for promotional text analysis
- Image recognition for in-store price validation
- Real-time competitor response modelling
- Predicting competitor reactions using game theory AI
- Building a competitive pricing heatmap
- Identifying pricing leaders and followers in your category
- Detecting predatory pricing patterns with anomaly detection
- Automating competitive price matching rules
Module 6: Dynamic and Real-Time Pricing Systems - Defining dynamic pricing in AI contexts
- Event-triggered pricing adjustments
- Demand forecasting integration with pricing engines
- Inventory-level sensitivity in pricing decisions
- Time-based pricing optimisation: hourly, daily, seasonal
- Geolocation-based dynamic pricing models
- Personalised pricing without violating privacy
- Balancing personalisation and fairness in AI pricing
- Real-time pricing in e-commerce and subscription models
- Latency requirements for live pricing engines
Module 7: Ethical AI and Regulatory Compliance in Pricing - Understanding algorithmic bias in pricing models
- Avoiding discriminatory pricing by protected class
- Transparency requirements under GDPR and CCPA
- Explainable AI methods for pricing decisions
- Building audit trails for AI price changes
- Price fairness assessment frameworks
- Documentation standards for regulatory review
- Handling customer complaints about AI pricing
- Designing opt-out mechanisms for personalised pricing
- Aligning AI pricing with corporate social responsibility
Module 8: Integration with ERP, CRM, and Pricing Software - Common enterprise systems used in pricing operations
- API integration strategies for AI pricing modules
- Syncing AI outputs with SAP, Oracle, and NetSuite
- CRM integration for customer-tier-based pricing
- Feeding AI recommendations into Salesforce CPQ
- Middleware solutions for legacy system compatibility
- Data flow architecture for bidirectional pricing updates
- Version control for pricing model deployment
- Integration testing protocols for pricing systems
- Monitoring live integrations for data drift or failure
Module 9: AI Pricing in B2B vs B2C Environments - Key differences in B2B pricing dynamics
- Contractual pricing and AI override mechanisms
- Handling negotiated discounts in AI pricing
- Volume-tiered pricing model optimisation
- AI in distributor pricing governance
- B2C challenges: flash sales, bundling, and promotions
- Subscription pricing with churn prediction
- Freemium conversion optimisation using AI
- Cross-industry pricing patterns: retail, SaaS, telecom
- Customising AI models by customer acquisition cost
Module 10: Implementation Roadmap and Change Management - Assessing organisational readiness for AI pricing
- Identifying key stakeholders and decision gatekeepers
- Building a business case with quantified ROI projections
- Phased rollout strategy: pilot, expand, scale
- Change management communication templates
- Training sales and finance teams on AI pricing outputs
- Handling resistance from legacy pricing managers
- Creating feedback loops between field teams and AI
- Establishing governance for AI pricing approvals
- Developing escalation protocols for model anomalies
Module 11: Model Validation and Performance Monitoring - Defining success metrics for AI pricing models
- Accuracy benchmarks for price prediction
- Backtesting pricing models against historical data
- Holdout sample validation techniques
- Monitoring model drift over time
- Alert systems for statistical degradation
- Profitability tracking post-price change
- Attribution analysis: isolating AI impact from other factors
- Confidence intervals for AI-generated price recommendations
- Monthly model health dashboard template
Module 12: Advanced AI Pricing Techniques - Predictive cannibalisation modelling across product lines
- Basket-based pricing optimisation
- AI-driven price bundling strategies
- Loss leader identification and optimisation
- Psychological pricing thresholds and AI
- Sentiment analysis for pricing perception tracking
- Generative AI for pricing scenario simulation
- Multi-objective optimisation: margin, volume, share
- Fuzzy logic for edge-case pricing decisions
- Monte Carlo simulation for pricing risk assessment
Module 13: Pricing AI in Subscription and SaaS Models - Churn prediction as a pricing input variable
- Customer lifetime value optimisation
- Tiered pricing model elasticity testing
- Add-on and upsell pricing with AI
- Trial-to-paid conversion price sensitivity
- Downgrade prevention pricing strategies
- Usage-based pricing optimisation
- Regional pricing in global SaaS deployments
- Competitive response in freemium markets
- Renewal discount optimisation with AI
Module 14: AI for Promotions, Discounts, and Markdowns - Optimal timing for promotional price changes
- Promotion lift prediction models
- AI-powered A/B testing for discount levels
- Preventing discount dependency in customer behaviour
- Markdown optimisation for excess inventory
- Network effects in bundled promotions
- Dynamic coupon generation based on customer value
- Post-promotion dip forecasting
- Balancing short-term gains with long-term margin
- Automating approval workflows for discounts
Module 15: Pricing Strategy Simulation and Scenario Planning - Building digital twins for pricing environments
- Simulating competitor price wars
- Demand shock modelling with AI
- What-if analysis for new market entry
- Scenario planning for supply chain disruptions
- Stress-testing pricing models under volatility
- Generating executive briefing reports from simulations
- Interactive dashboards for leadership review
- Exporting simulation results for board presentations
- Aligning simulations with strategic planning cycles
Module 16: Pricing Governance and Risk Management - Establishing pricing oversight committees
- Defining AI pricing authority levels
- Automated compliance checks in pricing workflows
- Maximum price change tolerance thresholds
- Blackout periods for price changes
- Handling regulatory audits of pricing algorithms
- Insurance considerations for AI pricing risk
- Vendor risk assessment for third-party pricing tools
- Contract clauses for AI pricing service providers
- Incident response plan for pricing errors
Module 17: Hands-On Implementation Projects - Project 1: Build a price elasticity model from sample dataset
- Project 2: Design a competitive response algorithm
- Project 3: Create a dynamic pricing rule set for e-commerce
- Project 4: Integrate pricing recommendations into a CRM
- Project 5: Simulate a pricing strategy shift for market entry
- Project 6: Audit an existing pricing model for bias and risk
- Project 7: Develop a board-ready pricing proposal with ROI
- Project 8: Build a pricing health monitoring dashboard
- Project 9: Optimise a promotional campaign using AI
- Project 10: Draft a pricing governance policy for AI use
Module 18: Certification Preparation and Career Advancement - Review of key AI pricing concepts and frameworks
- Practice exercises for certification assessment
- Case study analysis: real-world pricing failures and wins
- Building a portfolio of pricing projects for LinkedIn
- Using your certification in performance reviews
- Networking strategies with AI and pricing professionals
- Positioning yourself as a pricing innovator internally
- Salary negotiation leverage with certification
- Preparing for advanced roles: pricing director, revenue officer
- Next steps: continuous learning and specialisation paths
- Overview of regression models in price optimisation
- Using decision trees for pricing rule derivation
- Random forests for handling categorical pricing variables
- Gradient boosting for high-accuracy price predictions
- Neural networks and deep learning in dynamic pricing
- Clustering models for customer-based price grouping
- Reinforcement learning for adaptive pricing
- Model selection framework: accuracy vs. interpretability
- Choosing models based on business size and data maturity
- Matching AI techniques to pricing goals: margin, volume, or share
Module 4: Building Predictive Price Elasticity Models - Understanding elasticity beyond textbook definitions
- Historical price change impact analysis
- Quantifying cross-product cannibalisation
- Estimating customer price sensitivity by segment
- Incorporating promotional lift into elasticity models
- Dynamic elasticity adjustment using AI feedback loops
- Short-term vs. long-term elasticity prediction
- Threshold detection for price breakpoint identification
- Validating elasticity model outputs against real sales
- Deploying elastic zones for automated pricing guardrails
Module 5: Competitive Pricing Intelligence with AI - Automated competitor price monitoring systems
- Web scraping ethics and legal considerations
- Natural language processing for promotional text analysis
- Image recognition for in-store price validation
- Real-time competitor response modelling
- Predicting competitor reactions using game theory AI
- Building a competitive pricing heatmap
- Identifying pricing leaders and followers in your category
- Detecting predatory pricing patterns with anomaly detection
- Automating competitive price matching rules
Module 6: Dynamic and Real-Time Pricing Systems - Defining dynamic pricing in AI contexts
- Event-triggered pricing adjustments
- Demand forecasting integration with pricing engines
- Inventory-level sensitivity in pricing decisions
- Time-based pricing optimisation: hourly, daily, seasonal
- Geolocation-based dynamic pricing models
- Personalised pricing without violating privacy
- Balancing personalisation and fairness in AI pricing
- Real-time pricing in e-commerce and subscription models
- Latency requirements for live pricing engines
Module 7: Ethical AI and Regulatory Compliance in Pricing - Understanding algorithmic bias in pricing models
- Avoiding discriminatory pricing by protected class
- Transparency requirements under GDPR and CCPA
- Explainable AI methods for pricing decisions
- Building audit trails for AI price changes
- Price fairness assessment frameworks
- Documentation standards for regulatory review
- Handling customer complaints about AI pricing
- Designing opt-out mechanisms for personalised pricing
- Aligning AI pricing with corporate social responsibility
Module 8: Integration with ERP, CRM, and Pricing Software - Common enterprise systems used in pricing operations
- API integration strategies for AI pricing modules
- Syncing AI outputs with SAP, Oracle, and NetSuite
- CRM integration for customer-tier-based pricing
- Feeding AI recommendations into Salesforce CPQ
- Middleware solutions for legacy system compatibility
- Data flow architecture for bidirectional pricing updates
- Version control for pricing model deployment
- Integration testing protocols for pricing systems
- Monitoring live integrations for data drift or failure
Module 9: AI Pricing in B2B vs B2C Environments - Key differences in B2B pricing dynamics
- Contractual pricing and AI override mechanisms
- Handling negotiated discounts in AI pricing
- Volume-tiered pricing model optimisation
- AI in distributor pricing governance
- B2C challenges: flash sales, bundling, and promotions
- Subscription pricing with churn prediction
- Freemium conversion optimisation using AI
- Cross-industry pricing patterns: retail, SaaS, telecom
- Customising AI models by customer acquisition cost
Module 10: Implementation Roadmap and Change Management - Assessing organisational readiness for AI pricing
- Identifying key stakeholders and decision gatekeepers
- Building a business case with quantified ROI projections
- Phased rollout strategy: pilot, expand, scale
- Change management communication templates
- Training sales and finance teams on AI pricing outputs
- Handling resistance from legacy pricing managers
- Creating feedback loops between field teams and AI
- Establishing governance for AI pricing approvals
- Developing escalation protocols for model anomalies
Module 11: Model Validation and Performance Monitoring - Defining success metrics for AI pricing models
- Accuracy benchmarks for price prediction
- Backtesting pricing models against historical data
- Holdout sample validation techniques
- Monitoring model drift over time
- Alert systems for statistical degradation
- Profitability tracking post-price change
- Attribution analysis: isolating AI impact from other factors
- Confidence intervals for AI-generated price recommendations
- Monthly model health dashboard template
Module 12: Advanced AI Pricing Techniques - Predictive cannibalisation modelling across product lines
- Basket-based pricing optimisation
- AI-driven price bundling strategies
- Loss leader identification and optimisation
- Psychological pricing thresholds and AI
- Sentiment analysis for pricing perception tracking
- Generative AI for pricing scenario simulation
- Multi-objective optimisation: margin, volume, share
- Fuzzy logic for edge-case pricing decisions
- Monte Carlo simulation for pricing risk assessment
Module 13: Pricing AI in Subscription and SaaS Models - Churn prediction as a pricing input variable
- Customer lifetime value optimisation
- Tiered pricing model elasticity testing
- Add-on and upsell pricing with AI
- Trial-to-paid conversion price sensitivity
- Downgrade prevention pricing strategies
- Usage-based pricing optimisation
- Regional pricing in global SaaS deployments
- Competitive response in freemium markets
- Renewal discount optimisation with AI
Module 14: AI for Promotions, Discounts, and Markdowns - Optimal timing for promotional price changes
- Promotion lift prediction models
- AI-powered A/B testing for discount levels
- Preventing discount dependency in customer behaviour
- Markdown optimisation for excess inventory
- Network effects in bundled promotions
- Dynamic coupon generation based on customer value
- Post-promotion dip forecasting
- Balancing short-term gains with long-term margin
- Automating approval workflows for discounts
Module 15: Pricing Strategy Simulation and Scenario Planning - Building digital twins for pricing environments
- Simulating competitor price wars
- Demand shock modelling with AI
- What-if analysis for new market entry
- Scenario planning for supply chain disruptions
- Stress-testing pricing models under volatility
- Generating executive briefing reports from simulations
- Interactive dashboards for leadership review
- Exporting simulation results for board presentations
- Aligning simulations with strategic planning cycles
Module 16: Pricing Governance and Risk Management - Establishing pricing oversight committees
- Defining AI pricing authority levels
- Automated compliance checks in pricing workflows
- Maximum price change tolerance thresholds
- Blackout periods for price changes
- Handling regulatory audits of pricing algorithms
- Insurance considerations for AI pricing risk
- Vendor risk assessment for third-party pricing tools
- Contract clauses for AI pricing service providers
- Incident response plan for pricing errors
Module 17: Hands-On Implementation Projects - Project 1: Build a price elasticity model from sample dataset
- Project 2: Design a competitive response algorithm
- Project 3: Create a dynamic pricing rule set for e-commerce
- Project 4: Integrate pricing recommendations into a CRM
- Project 5: Simulate a pricing strategy shift for market entry
- Project 6: Audit an existing pricing model for bias and risk
- Project 7: Develop a board-ready pricing proposal with ROI
- Project 8: Build a pricing health monitoring dashboard
- Project 9: Optimise a promotional campaign using AI
- Project 10: Draft a pricing governance policy for AI use
Module 18: Certification Preparation and Career Advancement - Review of key AI pricing concepts and frameworks
- Practice exercises for certification assessment
- Case study analysis: real-world pricing failures and wins
- Building a portfolio of pricing projects for LinkedIn
- Using your certification in performance reviews
- Networking strategies with AI and pricing professionals
- Positioning yourself as a pricing innovator internally
- Salary negotiation leverage with certification
- Preparing for advanced roles: pricing director, revenue officer
- Next steps: continuous learning and specialisation paths
- Automated competitor price monitoring systems
- Web scraping ethics and legal considerations
- Natural language processing for promotional text analysis
- Image recognition for in-store price validation
- Real-time competitor response modelling
- Predicting competitor reactions using game theory AI
- Building a competitive pricing heatmap
- Identifying pricing leaders and followers in your category
- Detecting predatory pricing patterns with anomaly detection
- Automating competitive price matching rules
Module 6: Dynamic and Real-Time Pricing Systems - Defining dynamic pricing in AI contexts
- Event-triggered pricing adjustments
- Demand forecasting integration with pricing engines
- Inventory-level sensitivity in pricing decisions
- Time-based pricing optimisation: hourly, daily, seasonal
- Geolocation-based dynamic pricing models
- Personalised pricing without violating privacy
- Balancing personalisation and fairness in AI pricing
- Real-time pricing in e-commerce and subscription models
- Latency requirements for live pricing engines
Module 7: Ethical AI and Regulatory Compliance in Pricing - Understanding algorithmic bias in pricing models
- Avoiding discriminatory pricing by protected class
- Transparency requirements under GDPR and CCPA
- Explainable AI methods for pricing decisions
- Building audit trails for AI price changes
- Price fairness assessment frameworks
- Documentation standards for regulatory review
- Handling customer complaints about AI pricing
- Designing opt-out mechanisms for personalised pricing
- Aligning AI pricing with corporate social responsibility
Module 8: Integration with ERP, CRM, and Pricing Software - Common enterprise systems used in pricing operations
- API integration strategies for AI pricing modules
- Syncing AI outputs with SAP, Oracle, and NetSuite
- CRM integration for customer-tier-based pricing
- Feeding AI recommendations into Salesforce CPQ
- Middleware solutions for legacy system compatibility
- Data flow architecture for bidirectional pricing updates
- Version control for pricing model deployment
- Integration testing protocols for pricing systems
- Monitoring live integrations for data drift or failure
Module 9: AI Pricing in B2B vs B2C Environments - Key differences in B2B pricing dynamics
- Contractual pricing and AI override mechanisms
- Handling negotiated discounts in AI pricing
- Volume-tiered pricing model optimisation
- AI in distributor pricing governance
- B2C challenges: flash sales, bundling, and promotions
- Subscription pricing with churn prediction
- Freemium conversion optimisation using AI
- Cross-industry pricing patterns: retail, SaaS, telecom
- Customising AI models by customer acquisition cost
Module 10: Implementation Roadmap and Change Management - Assessing organisational readiness for AI pricing
- Identifying key stakeholders and decision gatekeepers
- Building a business case with quantified ROI projections
- Phased rollout strategy: pilot, expand, scale
- Change management communication templates
- Training sales and finance teams on AI pricing outputs
- Handling resistance from legacy pricing managers
- Creating feedback loops between field teams and AI
- Establishing governance for AI pricing approvals
- Developing escalation protocols for model anomalies
Module 11: Model Validation and Performance Monitoring - Defining success metrics for AI pricing models
- Accuracy benchmarks for price prediction
- Backtesting pricing models against historical data
- Holdout sample validation techniques
- Monitoring model drift over time
- Alert systems for statistical degradation
- Profitability tracking post-price change
- Attribution analysis: isolating AI impact from other factors
- Confidence intervals for AI-generated price recommendations
- Monthly model health dashboard template
Module 12: Advanced AI Pricing Techniques - Predictive cannibalisation modelling across product lines
- Basket-based pricing optimisation
- AI-driven price bundling strategies
- Loss leader identification and optimisation
- Psychological pricing thresholds and AI
- Sentiment analysis for pricing perception tracking
- Generative AI for pricing scenario simulation
- Multi-objective optimisation: margin, volume, share
- Fuzzy logic for edge-case pricing decisions
- Monte Carlo simulation for pricing risk assessment
Module 13: Pricing AI in Subscription and SaaS Models - Churn prediction as a pricing input variable
- Customer lifetime value optimisation
- Tiered pricing model elasticity testing
- Add-on and upsell pricing with AI
- Trial-to-paid conversion price sensitivity
- Downgrade prevention pricing strategies
- Usage-based pricing optimisation
- Regional pricing in global SaaS deployments
- Competitive response in freemium markets
- Renewal discount optimisation with AI
Module 14: AI for Promotions, Discounts, and Markdowns - Optimal timing for promotional price changes
- Promotion lift prediction models
- AI-powered A/B testing for discount levels
- Preventing discount dependency in customer behaviour
- Markdown optimisation for excess inventory
- Network effects in bundled promotions
- Dynamic coupon generation based on customer value
- Post-promotion dip forecasting
- Balancing short-term gains with long-term margin
- Automating approval workflows for discounts
Module 15: Pricing Strategy Simulation and Scenario Planning - Building digital twins for pricing environments
- Simulating competitor price wars
- Demand shock modelling with AI
- What-if analysis for new market entry
- Scenario planning for supply chain disruptions
- Stress-testing pricing models under volatility
- Generating executive briefing reports from simulations
- Interactive dashboards for leadership review
- Exporting simulation results for board presentations
- Aligning simulations with strategic planning cycles
Module 16: Pricing Governance and Risk Management - Establishing pricing oversight committees
- Defining AI pricing authority levels
- Automated compliance checks in pricing workflows
- Maximum price change tolerance thresholds
- Blackout periods for price changes
- Handling regulatory audits of pricing algorithms
- Insurance considerations for AI pricing risk
- Vendor risk assessment for third-party pricing tools
- Contract clauses for AI pricing service providers
- Incident response plan for pricing errors
Module 17: Hands-On Implementation Projects - Project 1: Build a price elasticity model from sample dataset
- Project 2: Design a competitive response algorithm
- Project 3: Create a dynamic pricing rule set for e-commerce
- Project 4: Integrate pricing recommendations into a CRM
- Project 5: Simulate a pricing strategy shift for market entry
- Project 6: Audit an existing pricing model for bias and risk
- Project 7: Develop a board-ready pricing proposal with ROI
- Project 8: Build a pricing health monitoring dashboard
- Project 9: Optimise a promotional campaign using AI
- Project 10: Draft a pricing governance policy for AI use
Module 18: Certification Preparation and Career Advancement - Review of key AI pricing concepts and frameworks
- Practice exercises for certification assessment
- Case study analysis: real-world pricing failures and wins
- Building a portfolio of pricing projects for LinkedIn
- Using your certification in performance reviews
- Networking strategies with AI and pricing professionals
- Positioning yourself as a pricing innovator internally
- Salary negotiation leverage with certification
- Preparing for advanced roles: pricing director, revenue officer
- Next steps: continuous learning and specialisation paths
- Understanding algorithmic bias in pricing models
- Avoiding discriminatory pricing by protected class
- Transparency requirements under GDPR and CCPA
- Explainable AI methods for pricing decisions
- Building audit trails for AI price changes
- Price fairness assessment frameworks
- Documentation standards for regulatory review
- Handling customer complaints about AI pricing
- Designing opt-out mechanisms for personalised pricing
- Aligning AI pricing with corporate social responsibility
Module 8: Integration with ERP, CRM, and Pricing Software - Common enterprise systems used in pricing operations
- API integration strategies for AI pricing modules
- Syncing AI outputs with SAP, Oracle, and NetSuite
- CRM integration for customer-tier-based pricing
- Feeding AI recommendations into Salesforce CPQ
- Middleware solutions for legacy system compatibility
- Data flow architecture for bidirectional pricing updates
- Version control for pricing model deployment
- Integration testing protocols for pricing systems
- Monitoring live integrations for data drift or failure
Module 9: AI Pricing in B2B vs B2C Environments - Key differences in B2B pricing dynamics
- Contractual pricing and AI override mechanisms
- Handling negotiated discounts in AI pricing
- Volume-tiered pricing model optimisation
- AI in distributor pricing governance
- B2C challenges: flash sales, bundling, and promotions
- Subscription pricing with churn prediction
- Freemium conversion optimisation using AI
- Cross-industry pricing patterns: retail, SaaS, telecom
- Customising AI models by customer acquisition cost
Module 10: Implementation Roadmap and Change Management - Assessing organisational readiness for AI pricing
- Identifying key stakeholders and decision gatekeepers
- Building a business case with quantified ROI projections
- Phased rollout strategy: pilot, expand, scale
- Change management communication templates
- Training sales and finance teams on AI pricing outputs
- Handling resistance from legacy pricing managers
- Creating feedback loops between field teams and AI
- Establishing governance for AI pricing approvals
- Developing escalation protocols for model anomalies
Module 11: Model Validation and Performance Monitoring - Defining success metrics for AI pricing models
- Accuracy benchmarks for price prediction
- Backtesting pricing models against historical data
- Holdout sample validation techniques
- Monitoring model drift over time
- Alert systems for statistical degradation
- Profitability tracking post-price change
- Attribution analysis: isolating AI impact from other factors
- Confidence intervals for AI-generated price recommendations
- Monthly model health dashboard template
Module 12: Advanced AI Pricing Techniques - Predictive cannibalisation modelling across product lines
- Basket-based pricing optimisation
- AI-driven price bundling strategies
- Loss leader identification and optimisation
- Psychological pricing thresholds and AI
- Sentiment analysis for pricing perception tracking
- Generative AI for pricing scenario simulation
- Multi-objective optimisation: margin, volume, share
- Fuzzy logic for edge-case pricing decisions
- Monte Carlo simulation for pricing risk assessment
Module 13: Pricing AI in Subscription and SaaS Models - Churn prediction as a pricing input variable
- Customer lifetime value optimisation
- Tiered pricing model elasticity testing
- Add-on and upsell pricing with AI
- Trial-to-paid conversion price sensitivity
- Downgrade prevention pricing strategies
- Usage-based pricing optimisation
- Regional pricing in global SaaS deployments
- Competitive response in freemium markets
- Renewal discount optimisation with AI
Module 14: AI for Promotions, Discounts, and Markdowns - Optimal timing for promotional price changes
- Promotion lift prediction models
- AI-powered A/B testing for discount levels
- Preventing discount dependency in customer behaviour
- Markdown optimisation for excess inventory
- Network effects in bundled promotions
- Dynamic coupon generation based on customer value
- Post-promotion dip forecasting
- Balancing short-term gains with long-term margin
- Automating approval workflows for discounts
Module 15: Pricing Strategy Simulation and Scenario Planning - Building digital twins for pricing environments
- Simulating competitor price wars
- Demand shock modelling with AI
- What-if analysis for new market entry
- Scenario planning for supply chain disruptions
- Stress-testing pricing models under volatility
- Generating executive briefing reports from simulations
- Interactive dashboards for leadership review
- Exporting simulation results for board presentations
- Aligning simulations with strategic planning cycles
Module 16: Pricing Governance and Risk Management - Establishing pricing oversight committees
- Defining AI pricing authority levels
- Automated compliance checks in pricing workflows
- Maximum price change tolerance thresholds
- Blackout periods for price changes
- Handling regulatory audits of pricing algorithms
- Insurance considerations for AI pricing risk
- Vendor risk assessment for third-party pricing tools
- Contract clauses for AI pricing service providers
- Incident response plan for pricing errors
Module 17: Hands-On Implementation Projects - Project 1: Build a price elasticity model from sample dataset
- Project 2: Design a competitive response algorithm
- Project 3: Create a dynamic pricing rule set for e-commerce
- Project 4: Integrate pricing recommendations into a CRM
- Project 5: Simulate a pricing strategy shift for market entry
- Project 6: Audit an existing pricing model for bias and risk
- Project 7: Develop a board-ready pricing proposal with ROI
- Project 8: Build a pricing health monitoring dashboard
- Project 9: Optimise a promotional campaign using AI
- Project 10: Draft a pricing governance policy for AI use
Module 18: Certification Preparation and Career Advancement - Review of key AI pricing concepts and frameworks
- Practice exercises for certification assessment
- Case study analysis: real-world pricing failures and wins
- Building a portfolio of pricing projects for LinkedIn
- Using your certification in performance reviews
- Networking strategies with AI and pricing professionals
- Positioning yourself as a pricing innovator internally
- Salary negotiation leverage with certification
- Preparing for advanced roles: pricing director, revenue officer
- Next steps: continuous learning and specialisation paths
- Key differences in B2B pricing dynamics
- Contractual pricing and AI override mechanisms
- Handling negotiated discounts in AI pricing
- Volume-tiered pricing model optimisation
- AI in distributor pricing governance
- B2C challenges: flash sales, bundling, and promotions
- Subscription pricing with churn prediction
- Freemium conversion optimisation using AI
- Cross-industry pricing patterns: retail, SaaS, telecom
- Customising AI models by customer acquisition cost
Module 10: Implementation Roadmap and Change Management - Assessing organisational readiness for AI pricing
- Identifying key stakeholders and decision gatekeepers
- Building a business case with quantified ROI projections
- Phased rollout strategy: pilot, expand, scale
- Change management communication templates
- Training sales and finance teams on AI pricing outputs
- Handling resistance from legacy pricing managers
- Creating feedback loops between field teams and AI
- Establishing governance for AI pricing approvals
- Developing escalation protocols for model anomalies
Module 11: Model Validation and Performance Monitoring - Defining success metrics for AI pricing models
- Accuracy benchmarks for price prediction
- Backtesting pricing models against historical data
- Holdout sample validation techniques
- Monitoring model drift over time
- Alert systems for statistical degradation
- Profitability tracking post-price change
- Attribution analysis: isolating AI impact from other factors
- Confidence intervals for AI-generated price recommendations
- Monthly model health dashboard template
Module 12: Advanced AI Pricing Techniques - Predictive cannibalisation modelling across product lines
- Basket-based pricing optimisation
- AI-driven price bundling strategies
- Loss leader identification and optimisation
- Psychological pricing thresholds and AI
- Sentiment analysis for pricing perception tracking
- Generative AI for pricing scenario simulation
- Multi-objective optimisation: margin, volume, share
- Fuzzy logic for edge-case pricing decisions
- Monte Carlo simulation for pricing risk assessment
Module 13: Pricing AI in Subscription and SaaS Models - Churn prediction as a pricing input variable
- Customer lifetime value optimisation
- Tiered pricing model elasticity testing
- Add-on and upsell pricing with AI
- Trial-to-paid conversion price sensitivity
- Downgrade prevention pricing strategies
- Usage-based pricing optimisation
- Regional pricing in global SaaS deployments
- Competitive response in freemium markets
- Renewal discount optimisation with AI
Module 14: AI for Promotions, Discounts, and Markdowns - Optimal timing for promotional price changes
- Promotion lift prediction models
- AI-powered A/B testing for discount levels
- Preventing discount dependency in customer behaviour
- Markdown optimisation for excess inventory
- Network effects in bundled promotions
- Dynamic coupon generation based on customer value
- Post-promotion dip forecasting
- Balancing short-term gains with long-term margin
- Automating approval workflows for discounts
Module 15: Pricing Strategy Simulation and Scenario Planning - Building digital twins for pricing environments
- Simulating competitor price wars
- Demand shock modelling with AI
- What-if analysis for new market entry
- Scenario planning for supply chain disruptions
- Stress-testing pricing models under volatility
- Generating executive briefing reports from simulations
- Interactive dashboards for leadership review
- Exporting simulation results for board presentations
- Aligning simulations with strategic planning cycles
Module 16: Pricing Governance and Risk Management - Establishing pricing oversight committees
- Defining AI pricing authority levels
- Automated compliance checks in pricing workflows
- Maximum price change tolerance thresholds
- Blackout periods for price changes
- Handling regulatory audits of pricing algorithms
- Insurance considerations for AI pricing risk
- Vendor risk assessment for third-party pricing tools
- Contract clauses for AI pricing service providers
- Incident response plan for pricing errors
Module 17: Hands-On Implementation Projects - Project 1: Build a price elasticity model from sample dataset
- Project 2: Design a competitive response algorithm
- Project 3: Create a dynamic pricing rule set for e-commerce
- Project 4: Integrate pricing recommendations into a CRM
- Project 5: Simulate a pricing strategy shift for market entry
- Project 6: Audit an existing pricing model for bias and risk
- Project 7: Develop a board-ready pricing proposal with ROI
- Project 8: Build a pricing health monitoring dashboard
- Project 9: Optimise a promotional campaign using AI
- Project 10: Draft a pricing governance policy for AI use
Module 18: Certification Preparation and Career Advancement - Review of key AI pricing concepts and frameworks
- Practice exercises for certification assessment
- Case study analysis: real-world pricing failures and wins
- Building a portfolio of pricing projects for LinkedIn
- Using your certification in performance reviews
- Networking strategies with AI and pricing professionals
- Positioning yourself as a pricing innovator internally
- Salary negotiation leverage with certification
- Preparing for advanced roles: pricing director, revenue officer
- Next steps: continuous learning and specialisation paths
- Defining success metrics for AI pricing models
- Accuracy benchmarks for price prediction
- Backtesting pricing models against historical data
- Holdout sample validation techniques
- Monitoring model drift over time
- Alert systems for statistical degradation
- Profitability tracking post-price change
- Attribution analysis: isolating AI impact from other factors
- Confidence intervals for AI-generated price recommendations
- Monthly model health dashboard template
Module 12: Advanced AI Pricing Techniques - Predictive cannibalisation modelling across product lines
- Basket-based pricing optimisation
- AI-driven price bundling strategies
- Loss leader identification and optimisation
- Psychological pricing thresholds and AI
- Sentiment analysis for pricing perception tracking
- Generative AI for pricing scenario simulation
- Multi-objective optimisation: margin, volume, share
- Fuzzy logic for edge-case pricing decisions
- Monte Carlo simulation for pricing risk assessment
Module 13: Pricing AI in Subscription and SaaS Models - Churn prediction as a pricing input variable
- Customer lifetime value optimisation
- Tiered pricing model elasticity testing
- Add-on and upsell pricing with AI
- Trial-to-paid conversion price sensitivity
- Downgrade prevention pricing strategies
- Usage-based pricing optimisation
- Regional pricing in global SaaS deployments
- Competitive response in freemium markets
- Renewal discount optimisation with AI
Module 14: AI for Promotions, Discounts, and Markdowns - Optimal timing for promotional price changes
- Promotion lift prediction models
- AI-powered A/B testing for discount levels
- Preventing discount dependency in customer behaviour
- Markdown optimisation for excess inventory
- Network effects in bundled promotions
- Dynamic coupon generation based on customer value
- Post-promotion dip forecasting
- Balancing short-term gains with long-term margin
- Automating approval workflows for discounts
Module 15: Pricing Strategy Simulation and Scenario Planning - Building digital twins for pricing environments
- Simulating competitor price wars
- Demand shock modelling with AI
- What-if analysis for new market entry
- Scenario planning for supply chain disruptions
- Stress-testing pricing models under volatility
- Generating executive briefing reports from simulations
- Interactive dashboards for leadership review
- Exporting simulation results for board presentations
- Aligning simulations with strategic planning cycles
Module 16: Pricing Governance and Risk Management - Establishing pricing oversight committees
- Defining AI pricing authority levels
- Automated compliance checks in pricing workflows
- Maximum price change tolerance thresholds
- Blackout periods for price changes
- Handling regulatory audits of pricing algorithms
- Insurance considerations for AI pricing risk
- Vendor risk assessment for third-party pricing tools
- Contract clauses for AI pricing service providers
- Incident response plan for pricing errors
Module 17: Hands-On Implementation Projects - Project 1: Build a price elasticity model from sample dataset
- Project 2: Design a competitive response algorithm
- Project 3: Create a dynamic pricing rule set for e-commerce
- Project 4: Integrate pricing recommendations into a CRM
- Project 5: Simulate a pricing strategy shift for market entry
- Project 6: Audit an existing pricing model for bias and risk
- Project 7: Develop a board-ready pricing proposal with ROI
- Project 8: Build a pricing health monitoring dashboard
- Project 9: Optimise a promotional campaign using AI
- Project 10: Draft a pricing governance policy for AI use
Module 18: Certification Preparation and Career Advancement - Review of key AI pricing concepts and frameworks
- Practice exercises for certification assessment
- Case study analysis: real-world pricing failures and wins
- Building a portfolio of pricing projects for LinkedIn
- Using your certification in performance reviews
- Networking strategies with AI and pricing professionals
- Positioning yourself as a pricing innovator internally
- Salary negotiation leverage with certification
- Preparing for advanced roles: pricing director, revenue officer
- Next steps: continuous learning and specialisation paths
- Churn prediction as a pricing input variable
- Customer lifetime value optimisation
- Tiered pricing model elasticity testing
- Add-on and upsell pricing with AI
- Trial-to-paid conversion price sensitivity
- Downgrade prevention pricing strategies
- Usage-based pricing optimisation
- Regional pricing in global SaaS deployments
- Competitive response in freemium markets
- Renewal discount optimisation with AI
Module 14: AI for Promotions, Discounts, and Markdowns - Optimal timing for promotional price changes
- Promotion lift prediction models
- AI-powered A/B testing for discount levels
- Preventing discount dependency in customer behaviour
- Markdown optimisation for excess inventory
- Network effects in bundled promotions
- Dynamic coupon generation based on customer value
- Post-promotion dip forecasting
- Balancing short-term gains with long-term margin
- Automating approval workflows for discounts
Module 15: Pricing Strategy Simulation and Scenario Planning - Building digital twins for pricing environments
- Simulating competitor price wars
- Demand shock modelling with AI
- What-if analysis for new market entry
- Scenario planning for supply chain disruptions
- Stress-testing pricing models under volatility
- Generating executive briefing reports from simulations
- Interactive dashboards for leadership review
- Exporting simulation results for board presentations
- Aligning simulations with strategic planning cycles
Module 16: Pricing Governance and Risk Management - Establishing pricing oversight committees
- Defining AI pricing authority levels
- Automated compliance checks in pricing workflows
- Maximum price change tolerance thresholds
- Blackout periods for price changes
- Handling regulatory audits of pricing algorithms
- Insurance considerations for AI pricing risk
- Vendor risk assessment for third-party pricing tools
- Contract clauses for AI pricing service providers
- Incident response plan for pricing errors
Module 17: Hands-On Implementation Projects - Project 1: Build a price elasticity model from sample dataset
- Project 2: Design a competitive response algorithm
- Project 3: Create a dynamic pricing rule set for e-commerce
- Project 4: Integrate pricing recommendations into a CRM
- Project 5: Simulate a pricing strategy shift for market entry
- Project 6: Audit an existing pricing model for bias and risk
- Project 7: Develop a board-ready pricing proposal with ROI
- Project 8: Build a pricing health monitoring dashboard
- Project 9: Optimise a promotional campaign using AI
- Project 10: Draft a pricing governance policy for AI use
Module 18: Certification Preparation and Career Advancement - Review of key AI pricing concepts and frameworks
- Practice exercises for certification assessment
- Case study analysis: real-world pricing failures and wins
- Building a portfolio of pricing projects for LinkedIn
- Using your certification in performance reviews
- Networking strategies with AI and pricing professionals
- Positioning yourself as a pricing innovator internally
- Salary negotiation leverage with certification
- Preparing for advanced roles: pricing director, revenue officer
- Next steps: continuous learning and specialisation paths
- Building digital twins for pricing environments
- Simulating competitor price wars
- Demand shock modelling with AI
- What-if analysis for new market entry
- Scenario planning for supply chain disruptions
- Stress-testing pricing models under volatility
- Generating executive briefing reports from simulations
- Interactive dashboards for leadership review
- Exporting simulation results for board presentations
- Aligning simulations with strategic planning cycles
Module 16: Pricing Governance and Risk Management - Establishing pricing oversight committees
- Defining AI pricing authority levels
- Automated compliance checks in pricing workflows
- Maximum price change tolerance thresholds
- Blackout periods for price changes
- Handling regulatory audits of pricing algorithms
- Insurance considerations for AI pricing risk
- Vendor risk assessment for third-party pricing tools
- Contract clauses for AI pricing service providers
- Incident response plan for pricing errors
Module 17: Hands-On Implementation Projects - Project 1: Build a price elasticity model from sample dataset
- Project 2: Design a competitive response algorithm
- Project 3: Create a dynamic pricing rule set for e-commerce
- Project 4: Integrate pricing recommendations into a CRM
- Project 5: Simulate a pricing strategy shift for market entry
- Project 6: Audit an existing pricing model for bias and risk
- Project 7: Develop a board-ready pricing proposal with ROI
- Project 8: Build a pricing health monitoring dashboard
- Project 9: Optimise a promotional campaign using AI
- Project 10: Draft a pricing governance policy for AI use
Module 18: Certification Preparation and Career Advancement - Review of key AI pricing concepts and frameworks
- Practice exercises for certification assessment
- Case study analysis: real-world pricing failures and wins
- Building a portfolio of pricing projects for LinkedIn
- Using your certification in performance reviews
- Networking strategies with AI and pricing professionals
- Positioning yourself as a pricing innovator internally
- Salary negotiation leverage with certification
- Preparing for advanced roles: pricing director, revenue officer
- Next steps: continuous learning and specialisation paths
- Project 1: Build a price elasticity model from sample dataset
- Project 2: Design a competitive response algorithm
- Project 3: Create a dynamic pricing rule set for e-commerce
- Project 4: Integrate pricing recommendations into a CRM
- Project 5: Simulate a pricing strategy shift for market entry
- Project 6: Audit an existing pricing model for bias and risk
- Project 7: Develop a board-ready pricing proposal with ROI
- Project 8: Build a pricing health monitoring dashboard
- Project 9: Optimise a promotional campaign using AI
- Project 10: Draft a pricing governance policy for AI use