COURSE FORMAT & DELIVERY DETAILS Self-Paced, On-Demand, and Built for Maximum Flexibility and Career Impact
Enrol now and gain full access to a rigorously structured, expert-developed curriculum designed to deliver measurable career ROI. This course is crafted for professionals who demand clarity, credibility, and tangible outcomes-without sacrificing flexibility or control over their schedule. Immediate Online Access with Zero Time Pressure
The course is 100% self-paced and available on-demand. You decide when, where, and how fast you engage. There are no fixed start dates, no deadlines, and no mandatory live sessions. You can begin today, complete it over several weeks, or revisit modules years from now-your learning, your timeline. Typical Completion Time and Realistic Results Timeline
Most learners complete the course within 4 to 6 weeks, dedicating 6 to 8 hours per week. However, many report applying foundational strategies in their current roles within the first 72 hours. Real results-such as improved campaign performance, higher conversion rates, and data-driven decision frameworks-emerge early and compound as you progress. Lifetime Access, Continuous Updates, No Extra Cost
You receive lifetime access to all course materials, including every future update. As AI tools evolve and data analytics platforms advance, new content, frameworks, and strategies are added at no additional charge. This is not a one-time resource-it's a long-term growth partner that grows with you. 24/7 Global Access, Fully Mobile-Friendly
Access your course anytime from any device. Whether you’re on a desktop, tablet, or smartphone, the experience is seamless and intuitive. Study during your commute, review strategies between meetings, or implement insights in real time-it's all designed for real-world practicality. Direct Instructor Support and Expert Guidance
You are not learning in isolation. Throughout the course, you’ll receive direct guidance through curated response pathways, scenario-based troubleshooting, and expert-reviewed implementation frameworks. Each concept is reinforced with actionable checklists, support references, and expert annotations designed to reduce confusion and accelerate mastery. Receive a Globally Recognised Certificate of Completion
Upon finishing the course, you will earn a Certificate of Completion issued by The Art of Service. This credential is trusted by professionals in over 90 countries, recognised for its rigour, relevance, and real-world applicability. Adding this certification to your LinkedIn profile, resume, or portfolio signals to employers that you master advanced growth strategies at the intersection of AI, data analytics, and marketing execution. Transparent Pricing, No Hidden Fees
The price you see is the price you pay-no subscription traps, no surprise charges, no recurring fees. One straightforward payment grants you full, lifetime access. You’ll know exactly what you’re investing in, with nothing held back or gated behind upsells. Secure Payment via Visa, Mastercard, and PayPal
We accept all major payment methods including Visa, Mastercard, and PayPal. Transactions are processed securely with bank-level encryption, ensuring your financial information remains protected at all times. 100% Money-Back Guarantee – Satisfied or Refunded
Your success is our priority. That’s why we stand behind this course with an unconditional money-back guarantee. If you follow the material, apply the strategies, and don’t feel it delivered clarity, confidence, and career value, simply request a full refund. There are no hoops to jump through-your investment is risk-free. Confirmation and Access Delivery Process
After enrolment, you’ll immediately receive a confirmation email. Shortly after, a separate access email will be sent containing your login details and instructions for entering the course platform. Please allow for standard processing time-your materials are prepared with precision to ensure a seamless start. Will This Work for Me? Addressing the #1 Objection Directly
Absolutely. This course is specifically engineered to work regardless of your current role, company size, or technical background. Whether you're a digital marketer, growth lead, startup founder, product manager, or transitioning into analytics-driven marketing, the frameworks are modular, role-adaptable, and designed for immediate implementation. - If you're a marketing manager at a mid-sized SaaS company, you'll learn how to build AI-powered campaign forecasting models that reduce spend waste by up to 37%.
- If you're a solopreneur or agency owner, you’ll gain plug-and-play customer segmentation templates that increase lifetime value through hyper-personalised messaging.
- If you're an analyst moving into growth strategy, you’ll master the exact data storytelling techniques used by top-tier tech firms to influence executive decisions.
This works even if: you’ve never used AI tools before, your current data access is limited, you're short on time, or you've tried other courses that didn't deliver practical results. The content strips away fluff and focuses only on battle-tested strategies with documented ROI across diverse industries. Your Risk is Fully Reversed-You Gain Everything, Lose Nothing
This course eliminates risk through guaranteed access, guaranteed support, and guaranteed results-or your money back. You’re not just buying information. You're investing in a proven system that delivers clarity, confidence, and a competitive edge. With lifetime updates, mobile access, certification, and full payment security, you’re positioned to win-no matter your starting point.
EXTENSIVE & DETAILED COURSE CURRICULUM
Module 1: Foundations of AI-Driven Growth Marketing - The evolution of growth marketing in the AI era
- Core principles of data-informed decision making
- Differentiating AI, machine learning, and predictive analytics
- Understanding the growth marketer’s role in algorithmic environments
- Mapping the customer journey with AI-enhanced touchpoints
- Defining key metrics that matter in modern growth strategies
- Building a growth mindset: experimentation, iteration, and learning
- Common myths and misconceptions about AI in marketing
- Ethical considerations and responsible AI use in customer acquisition
- Case study: How a bootstrapped startup scaled to $2M ARR using AI segmentation
Module 2: Strategic Frameworks for AI-Powered Growth - The Predictive Growth Loop: Identify, Predict, Act, Learn
- Implementing the AARRR framework with AI enhancements
- Building dynamic growth hypotheses using historical data
- Designing growth experiments with minimal viable data sets
- Integrating AI into early-stage funnel optimisation
- Using clustering algorithms to define micro-segments
- Developing trigger-based marketing sequences powered by behavioural data
- The Flywheel Effect: aligning AI, content, and automation
- Building agile growth roadmaps with adaptive AI inputs
- Analysing successful AI-driven campaigns across B2B and DTC sectors
Module 3: Data Infrastructure and Analytics Readiness - Assessing your organisation’s data maturity level
- Essential data sources for AI-driven marketing: CRM, web analytics, email, ads
- Setting up clean, centralised data pipelines for real-time insights
- Choosing between data warehouses and data lakes for marketing use cases
- Best practices for data governance and privacy compliance
- Automating data collection with no-code and low-code tools
- Normalising and enriching customer data for AI processing
- Creating unified customer profiles across online and offline channels
- Validating data quality to prevent AI hallucination and bias
- Building a marketing analytics dashboard that feeds predictive models
Module 4: AI Tools for Intelligent Marketing Automation - Overview of leading AI tools for marketing automation
- Selecting AI platforms based on scalability and integration needs
- Configuring AI workflows for lead scoring and prioritisation
- Building autonomous drip campaigns that adapt to user behaviour
- Automating A/B testing with AI-driven variant generation
- Scheduling content delivery using predictive engagement models
- Reducing manual workload with AI-powered response templates
- Trigger-based re-engagement campaigns for dormant users
- Using AI to detect churn risk and initiate retention sequences
- Integrating AI automation with existing martech stacks
Module 5: Predictive Analytics for Customer Acquisition - Introduction to predictive modelling in marketing
- Identifying high-propensity leads using logistic regression basics
- Implementing lookalike audience modelling with machine learning
- Forecasting conversion likelihood across acquisition channels
- Using time-series analysis to predict seasonal demand patterns
- Optimising ad spend allocation with predictive ROI models
- Analysing multichannel attribution using algorithmic approaches
- Reducing customer acquisition cost through AI-driven lead filtering
- Building real-time acquisition dashboards with predictive overlays
- Case study: How an e-commerce brand reduced CPA by 41% using predictive targeting
Module 6: Customer Lifetime Value Optimisation with AI - Understanding the components of CLV in subscription and transactional models
- Building CLV prediction models using cohort analysis and decay curves
- Incorporating product usage data into CLV calculations
- Using AI to segment users by predicted lifetime value tiers
- Aligning marketing spend with high-CLV customer acquisition
- Designing retention campaigns based on CLV thresholds
- Linking upsell and cross-sell strategies to CLV drivers
- Creating dynamic retention budgets using CLV forecasts
- Integrating CLV insights into pricing and packaging decisions
- Measuring the impact of CLV optimisation on company valuation
Module 7: AI-Powered Content Strategy and Personalisation - The role of AI in content ideation and topic selection
- Using natural language processing to extract customer intent
- Generating content variations for A/B testing at scale
- Building dynamic content blocks that personalise in real time
- Adapting tone, length, and format based on audience segments
- Automating blog post outlines using semantic analysis
- Enhancing SEO strategy with AI-driven keyword clustering
- Creating hyper-personalised email content using behavioural triggers
- Testing content performance with AI-based simulation tools
- Scaling content production without sacrificing quality or brand voice
Module 8: AI in Paid Media and Programmatic Advertising - How AI transforms programmatic ad buying processes
- Understanding smart bidding algorithms in Google and Meta platforms
- Setting up conversion-based bidding strategies with machine learning
- Automating audience expansion using AI-powered affinity detection
- Dynamic creative optimisation for ad copy and visuals
- Real-time bid adjustments based on predicted conversion probability
- Using AI to detect ad fatigue and refresh creatives automatically
- Identifying incremental lift with holdout group testing
- Building closed-loop attribution models for paid campaigns
- Case study: AI-optimised ad campaign that achieved 5.8x ROAS
Module 9: Web and UX Optimisation Using Behavioural AI - Using session recording and heatmaps enhanced by AI analysis
- Identifying friction points in user journeys through pattern recognition
- Predicting drop-off risks on landing pages with behavioural scoring
- Automating hypothesis generation for CRO tests
- Personalising website layouts based on visitor attributes and intent
- Implementing AI-driven A/B testing prioritisation
- Using predictive form analytics to reduce abandonment
- Optimising navigation flow with pathing algorithms
- Testing microcopy variations with NLP-powered recommendations
- Measuring emotional engagement through facial recognition (where applicable)
Module 10: Advanced Segmentation and Micro-Targeting - Evolution from demographic to behavioural and predictive segmentation
- Implementing k-means clustering for customer grouping
- Using decision trees to define segmentation logic
- Building RFM models enhanced with AI features
- Creating real-time segment activation rules
- Targeting high-value micro-segments across email and ads
- Using AI to detect emerging segments from raw behavioural data
- Developing persona refresh systems that auto-update over time
- Aligning product messaging to segment-specific pain points
- Measuring segmentation effectiveness through lift analysis
Module 11: AI for Email and Lifecycle Marketing - Optimising send time prediction using individual engagement history
- Subject line optimisation with AI-powered sentiment analysis
- Automating email sequence branching based on user actions
- Using churn prediction models to trigger win-back campaigns
- Building dynamic email content that adapts to lifecycle stage
- Analysing email fatigue indicators and adjusting frequency
- Scoring subscriber engagement to prioritise list segments
- Integrating transactional data into lifecycle messaging
- Creating hyper-relevant re-engagement campaigns
- Measuring long-term email performance with cohort retention curves
Module 12: Building Predictive Sales and Marketing Alignment - Creating shared definitions of MQLs and SQLs using data
- Using AI to predict sales readiness from marketing interactions
- Automating lead handoff processes with conditional logic
- Forecasting pipeline contribution by campaign and channel
- Mapping marketing touchpoints to sales cycle stages
- Building joint dashboards for marketing and sales visibility
- Reducing lead response time with AI-triggered alerts
- Scoring leads based on fit, behaviour, and timing
- Using AI to identify cross-sell opportunities during onboarding
- Aligning content strategy with sales enablement needs
Module 13: AI in Product-Led Growth Strategies - Understanding the intersection of product usage and growth marketing
- Defining activation, adoption, and expansion metrics
- Using AI to detect early indicators of product friction
- Identifying power feature combinations that drive retention
- Triggering in-app messages based on user behaviour patterns
- Automating onboarding personalisation using user traits
- Building predictive health scores for customer accounts
- Using AI to recommend next actions for trial users
- Scaling self-serve growth through intelligent guidance systems
- Case study: How a PLG SaaS reduced time-to-value by 62%
Module 14: Attribution Modelling and Marketing Mix Optimisation - Limitations of last-click and first-click attribution
- Implementing multi-touch attribution with algorithmic weighting
- Using Shapley values to assign channel credit fairly
- Building marketing mix models with regression analysis
- Incorporating seasonality, trends, and external factors
- Simulating budget reallocation scenarios for maximum impact
- Integrating offline marketing data into digital attribution
- Using AI to detect cannibalisation between channels
- Communicating attribution insights to stakeholders
- Automating monthly mix reports with predictive recommendations
Module 15: AI for Competitive Intelligence and Market Positioning - Monitoring competitor campaigns with AI-powered web scraping
- Analysing pricing changes and promotional moves in real time
- Using NLP to assess competitor messaging and differentiation
- Tracking feature launches and product updates automatically
- Identifying whitespace opportunities using gap analysis
- Mapping competitive positioning through sentiment mining
- Benchmarking performance against industry AI adoption
- Using AI to predict competitor moves based on historical patterns
- Developing counter-strategies for emerging threats
- Building real-time competitor dashboards for agile response
Module 16: Ethical AI and Bias Mitigation in Growth Marketing - Understanding sources of bias in training data
- Detecting discriminatory patterns in audience targeting
- Ensuring fairness in AI-driven personalisation
- Conducting AI model audits for transparency
- Implementing human oversight in automated decisions
- Designing opt-out and control mechanisms for users
- Aligning AI practices with GDPR, CCPA, and other regulations
- Communicating AI use to customers with clarity and trust
- Building ethical guidelines for your growth team
- Case study: Recovering brand trust after an AI bias incident
Module 17: Implementing AI Projects in Real Organisations - Running internal stakeholder alignment workshops
- Building a business case for AI marketing initiatives
- Securing budget and executive buy-in
- Defining success metrics and KPIs for AI projects
- Managing cross-functional collaboration between teams
- Phasing AI implementation: pilot, scale, embed
- Documenting processes and creating playbooks
- Training teams on new AI-enhanced workflows
- Establishing feedback loops for continuous improvement
- Measuring ROI and presenting results to leadership
Module 18: Future-Proofing Your Growth Career with AI Mastery - Tracking emerging AI trends in marketing technology
- Building a personal learning roadmap for continuous upskilling
- Curating AI tools and resources for ongoing development
- Positioning yourself as a strategic growth leader
- Creating a portfolio of AI-driven campaign results
- Leveraging the Certificate of Completion for career advancement
- Updating your LinkedIn profile with AI and data analytics keywords
- Negotiating higher compensation based on advanced skill set
- Transitioning into senior roles: Growth Manager, Director, CMO
- Contributing to the future of intelligent marketing
Module 19: Capstone Project – Building a Full AI-Driven Growth Strategy - Selecting a real or fictional business for your project
- Conducting a diagnostic assessment of current marketing maturity
- Defining strategic growth goals and KPIs
- Mapping the customer journey with AI enhancement opportunities
- Designing predictive acquisition and retention models
- Building a 90-day implementation plan
- Creating a budget and resource allocation framework
- Developing dashboards for monitoring AI performance
- Writing an executive summary for stakeholder presentation
- Receiving expert feedback and validation on your final strategy
Module 20: Certification, Next Steps, and Ongoing Support - Final review of all key concepts and frameworks
- Accessing your Certificate of Completion from The Art of Service
- Sharing your certification on professional networks
- Joining the alumni community for continued learning
- Receiving updates on new AI tools and case studies
- Accessing downloadable templates, checklists, and frameworks
- Using progress tracking to monitor mastery of each module
- Participating in gamified knowledge assessments
- Unlocking advanced bonus materials with continued engagement
- Planning your next career move with confidence and clarity
Module 1: Foundations of AI-Driven Growth Marketing - The evolution of growth marketing in the AI era
- Core principles of data-informed decision making
- Differentiating AI, machine learning, and predictive analytics
- Understanding the growth marketer’s role in algorithmic environments
- Mapping the customer journey with AI-enhanced touchpoints
- Defining key metrics that matter in modern growth strategies
- Building a growth mindset: experimentation, iteration, and learning
- Common myths and misconceptions about AI in marketing
- Ethical considerations and responsible AI use in customer acquisition
- Case study: How a bootstrapped startup scaled to $2M ARR using AI segmentation
Module 2: Strategic Frameworks for AI-Powered Growth - The Predictive Growth Loop: Identify, Predict, Act, Learn
- Implementing the AARRR framework with AI enhancements
- Building dynamic growth hypotheses using historical data
- Designing growth experiments with minimal viable data sets
- Integrating AI into early-stage funnel optimisation
- Using clustering algorithms to define micro-segments
- Developing trigger-based marketing sequences powered by behavioural data
- The Flywheel Effect: aligning AI, content, and automation
- Building agile growth roadmaps with adaptive AI inputs
- Analysing successful AI-driven campaigns across B2B and DTC sectors
Module 3: Data Infrastructure and Analytics Readiness - Assessing your organisation’s data maturity level
- Essential data sources for AI-driven marketing: CRM, web analytics, email, ads
- Setting up clean, centralised data pipelines for real-time insights
- Choosing between data warehouses and data lakes for marketing use cases
- Best practices for data governance and privacy compliance
- Automating data collection with no-code and low-code tools
- Normalising and enriching customer data for AI processing
- Creating unified customer profiles across online and offline channels
- Validating data quality to prevent AI hallucination and bias
- Building a marketing analytics dashboard that feeds predictive models
Module 4: AI Tools for Intelligent Marketing Automation - Overview of leading AI tools for marketing automation
- Selecting AI platforms based on scalability and integration needs
- Configuring AI workflows for lead scoring and prioritisation
- Building autonomous drip campaigns that adapt to user behaviour
- Automating A/B testing with AI-driven variant generation
- Scheduling content delivery using predictive engagement models
- Reducing manual workload with AI-powered response templates
- Trigger-based re-engagement campaigns for dormant users
- Using AI to detect churn risk and initiate retention sequences
- Integrating AI automation with existing martech stacks
Module 5: Predictive Analytics for Customer Acquisition - Introduction to predictive modelling in marketing
- Identifying high-propensity leads using logistic regression basics
- Implementing lookalike audience modelling with machine learning
- Forecasting conversion likelihood across acquisition channels
- Using time-series analysis to predict seasonal demand patterns
- Optimising ad spend allocation with predictive ROI models
- Analysing multichannel attribution using algorithmic approaches
- Reducing customer acquisition cost through AI-driven lead filtering
- Building real-time acquisition dashboards with predictive overlays
- Case study: How an e-commerce brand reduced CPA by 41% using predictive targeting
Module 6: Customer Lifetime Value Optimisation with AI - Understanding the components of CLV in subscription and transactional models
- Building CLV prediction models using cohort analysis and decay curves
- Incorporating product usage data into CLV calculations
- Using AI to segment users by predicted lifetime value tiers
- Aligning marketing spend with high-CLV customer acquisition
- Designing retention campaigns based on CLV thresholds
- Linking upsell and cross-sell strategies to CLV drivers
- Creating dynamic retention budgets using CLV forecasts
- Integrating CLV insights into pricing and packaging decisions
- Measuring the impact of CLV optimisation on company valuation
Module 7: AI-Powered Content Strategy and Personalisation - The role of AI in content ideation and topic selection
- Using natural language processing to extract customer intent
- Generating content variations for A/B testing at scale
- Building dynamic content blocks that personalise in real time
- Adapting tone, length, and format based on audience segments
- Automating blog post outlines using semantic analysis
- Enhancing SEO strategy with AI-driven keyword clustering
- Creating hyper-personalised email content using behavioural triggers
- Testing content performance with AI-based simulation tools
- Scaling content production without sacrificing quality or brand voice
Module 8: AI in Paid Media and Programmatic Advertising - How AI transforms programmatic ad buying processes
- Understanding smart bidding algorithms in Google and Meta platforms
- Setting up conversion-based bidding strategies with machine learning
- Automating audience expansion using AI-powered affinity detection
- Dynamic creative optimisation for ad copy and visuals
- Real-time bid adjustments based on predicted conversion probability
- Using AI to detect ad fatigue and refresh creatives automatically
- Identifying incremental lift with holdout group testing
- Building closed-loop attribution models for paid campaigns
- Case study: AI-optimised ad campaign that achieved 5.8x ROAS
Module 9: Web and UX Optimisation Using Behavioural AI - Using session recording and heatmaps enhanced by AI analysis
- Identifying friction points in user journeys through pattern recognition
- Predicting drop-off risks on landing pages with behavioural scoring
- Automating hypothesis generation for CRO tests
- Personalising website layouts based on visitor attributes and intent
- Implementing AI-driven A/B testing prioritisation
- Using predictive form analytics to reduce abandonment
- Optimising navigation flow with pathing algorithms
- Testing microcopy variations with NLP-powered recommendations
- Measuring emotional engagement through facial recognition (where applicable)
Module 10: Advanced Segmentation and Micro-Targeting - Evolution from demographic to behavioural and predictive segmentation
- Implementing k-means clustering for customer grouping
- Using decision trees to define segmentation logic
- Building RFM models enhanced with AI features
- Creating real-time segment activation rules
- Targeting high-value micro-segments across email and ads
- Using AI to detect emerging segments from raw behavioural data
- Developing persona refresh systems that auto-update over time
- Aligning product messaging to segment-specific pain points
- Measuring segmentation effectiveness through lift analysis
Module 11: AI for Email and Lifecycle Marketing - Optimising send time prediction using individual engagement history
- Subject line optimisation with AI-powered sentiment analysis
- Automating email sequence branching based on user actions
- Using churn prediction models to trigger win-back campaigns
- Building dynamic email content that adapts to lifecycle stage
- Analysing email fatigue indicators and adjusting frequency
- Scoring subscriber engagement to prioritise list segments
- Integrating transactional data into lifecycle messaging
- Creating hyper-relevant re-engagement campaigns
- Measuring long-term email performance with cohort retention curves
Module 12: Building Predictive Sales and Marketing Alignment - Creating shared definitions of MQLs and SQLs using data
- Using AI to predict sales readiness from marketing interactions
- Automating lead handoff processes with conditional logic
- Forecasting pipeline contribution by campaign and channel
- Mapping marketing touchpoints to sales cycle stages
- Building joint dashboards for marketing and sales visibility
- Reducing lead response time with AI-triggered alerts
- Scoring leads based on fit, behaviour, and timing
- Using AI to identify cross-sell opportunities during onboarding
- Aligning content strategy with sales enablement needs
Module 13: AI in Product-Led Growth Strategies - Understanding the intersection of product usage and growth marketing
- Defining activation, adoption, and expansion metrics
- Using AI to detect early indicators of product friction
- Identifying power feature combinations that drive retention
- Triggering in-app messages based on user behaviour patterns
- Automating onboarding personalisation using user traits
- Building predictive health scores for customer accounts
- Using AI to recommend next actions for trial users
- Scaling self-serve growth through intelligent guidance systems
- Case study: How a PLG SaaS reduced time-to-value by 62%
Module 14: Attribution Modelling and Marketing Mix Optimisation - Limitations of last-click and first-click attribution
- Implementing multi-touch attribution with algorithmic weighting
- Using Shapley values to assign channel credit fairly
- Building marketing mix models with regression analysis
- Incorporating seasonality, trends, and external factors
- Simulating budget reallocation scenarios for maximum impact
- Integrating offline marketing data into digital attribution
- Using AI to detect cannibalisation between channels
- Communicating attribution insights to stakeholders
- Automating monthly mix reports with predictive recommendations
Module 15: AI for Competitive Intelligence and Market Positioning - Monitoring competitor campaigns with AI-powered web scraping
- Analysing pricing changes and promotional moves in real time
- Using NLP to assess competitor messaging and differentiation
- Tracking feature launches and product updates automatically
- Identifying whitespace opportunities using gap analysis
- Mapping competitive positioning through sentiment mining
- Benchmarking performance against industry AI adoption
- Using AI to predict competitor moves based on historical patterns
- Developing counter-strategies for emerging threats
- Building real-time competitor dashboards for agile response
Module 16: Ethical AI and Bias Mitigation in Growth Marketing - Understanding sources of bias in training data
- Detecting discriminatory patterns in audience targeting
- Ensuring fairness in AI-driven personalisation
- Conducting AI model audits for transparency
- Implementing human oversight in automated decisions
- Designing opt-out and control mechanisms for users
- Aligning AI practices with GDPR, CCPA, and other regulations
- Communicating AI use to customers with clarity and trust
- Building ethical guidelines for your growth team
- Case study: Recovering brand trust after an AI bias incident
Module 17: Implementing AI Projects in Real Organisations - Running internal stakeholder alignment workshops
- Building a business case for AI marketing initiatives
- Securing budget and executive buy-in
- Defining success metrics and KPIs for AI projects
- Managing cross-functional collaboration between teams
- Phasing AI implementation: pilot, scale, embed
- Documenting processes and creating playbooks
- Training teams on new AI-enhanced workflows
- Establishing feedback loops for continuous improvement
- Measuring ROI and presenting results to leadership
Module 18: Future-Proofing Your Growth Career with AI Mastery - Tracking emerging AI trends in marketing technology
- Building a personal learning roadmap for continuous upskilling
- Curating AI tools and resources for ongoing development
- Positioning yourself as a strategic growth leader
- Creating a portfolio of AI-driven campaign results
- Leveraging the Certificate of Completion for career advancement
- Updating your LinkedIn profile with AI and data analytics keywords
- Negotiating higher compensation based on advanced skill set
- Transitioning into senior roles: Growth Manager, Director, CMO
- Contributing to the future of intelligent marketing
Module 19: Capstone Project – Building a Full AI-Driven Growth Strategy - Selecting a real or fictional business for your project
- Conducting a diagnostic assessment of current marketing maturity
- Defining strategic growth goals and KPIs
- Mapping the customer journey with AI enhancement opportunities
- Designing predictive acquisition and retention models
- Building a 90-day implementation plan
- Creating a budget and resource allocation framework
- Developing dashboards for monitoring AI performance
- Writing an executive summary for stakeholder presentation
- Receiving expert feedback and validation on your final strategy
Module 20: Certification, Next Steps, and Ongoing Support - Final review of all key concepts and frameworks
- Accessing your Certificate of Completion from The Art of Service
- Sharing your certification on professional networks
- Joining the alumni community for continued learning
- Receiving updates on new AI tools and case studies
- Accessing downloadable templates, checklists, and frameworks
- Using progress tracking to monitor mastery of each module
- Participating in gamified knowledge assessments
- Unlocking advanced bonus materials with continued engagement
- Planning your next career move with confidence and clarity
- The Predictive Growth Loop: Identify, Predict, Act, Learn
- Implementing the AARRR framework with AI enhancements
- Building dynamic growth hypotheses using historical data
- Designing growth experiments with minimal viable data sets
- Integrating AI into early-stage funnel optimisation
- Using clustering algorithms to define micro-segments
- Developing trigger-based marketing sequences powered by behavioural data
- The Flywheel Effect: aligning AI, content, and automation
- Building agile growth roadmaps with adaptive AI inputs
- Analysing successful AI-driven campaigns across B2B and DTC sectors
Module 3: Data Infrastructure and Analytics Readiness - Assessing your organisation’s data maturity level
- Essential data sources for AI-driven marketing: CRM, web analytics, email, ads
- Setting up clean, centralised data pipelines for real-time insights
- Choosing between data warehouses and data lakes for marketing use cases
- Best practices for data governance and privacy compliance
- Automating data collection with no-code and low-code tools
- Normalising and enriching customer data for AI processing
- Creating unified customer profiles across online and offline channels
- Validating data quality to prevent AI hallucination and bias
- Building a marketing analytics dashboard that feeds predictive models
Module 4: AI Tools for Intelligent Marketing Automation - Overview of leading AI tools for marketing automation
- Selecting AI platforms based on scalability and integration needs
- Configuring AI workflows for lead scoring and prioritisation
- Building autonomous drip campaigns that adapt to user behaviour
- Automating A/B testing with AI-driven variant generation
- Scheduling content delivery using predictive engagement models
- Reducing manual workload with AI-powered response templates
- Trigger-based re-engagement campaigns for dormant users
- Using AI to detect churn risk and initiate retention sequences
- Integrating AI automation with existing martech stacks
Module 5: Predictive Analytics for Customer Acquisition - Introduction to predictive modelling in marketing
- Identifying high-propensity leads using logistic regression basics
- Implementing lookalike audience modelling with machine learning
- Forecasting conversion likelihood across acquisition channels
- Using time-series analysis to predict seasonal demand patterns
- Optimising ad spend allocation with predictive ROI models
- Analysing multichannel attribution using algorithmic approaches
- Reducing customer acquisition cost through AI-driven lead filtering
- Building real-time acquisition dashboards with predictive overlays
- Case study: How an e-commerce brand reduced CPA by 41% using predictive targeting
Module 6: Customer Lifetime Value Optimisation with AI - Understanding the components of CLV in subscription and transactional models
- Building CLV prediction models using cohort analysis and decay curves
- Incorporating product usage data into CLV calculations
- Using AI to segment users by predicted lifetime value tiers
- Aligning marketing spend with high-CLV customer acquisition
- Designing retention campaigns based on CLV thresholds
- Linking upsell and cross-sell strategies to CLV drivers
- Creating dynamic retention budgets using CLV forecasts
- Integrating CLV insights into pricing and packaging decisions
- Measuring the impact of CLV optimisation on company valuation
Module 7: AI-Powered Content Strategy and Personalisation - The role of AI in content ideation and topic selection
- Using natural language processing to extract customer intent
- Generating content variations for A/B testing at scale
- Building dynamic content blocks that personalise in real time
- Adapting tone, length, and format based on audience segments
- Automating blog post outlines using semantic analysis
- Enhancing SEO strategy with AI-driven keyword clustering
- Creating hyper-personalised email content using behavioural triggers
- Testing content performance with AI-based simulation tools
- Scaling content production without sacrificing quality or brand voice
Module 8: AI in Paid Media and Programmatic Advertising - How AI transforms programmatic ad buying processes
- Understanding smart bidding algorithms in Google and Meta platforms
- Setting up conversion-based bidding strategies with machine learning
- Automating audience expansion using AI-powered affinity detection
- Dynamic creative optimisation for ad copy and visuals
- Real-time bid adjustments based on predicted conversion probability
- Using AI to detect ad fatigue and refresh creatives automatically
- Identifying incremental lift with holdout group testing
- Building closed-loop attribution models for paid campaigns
- Case study: AI-optimised ad campaign that achieved 5.8x ROAS
Module 9: Web and UX Optimisation Using Behavioural AI - Using session recording and heatmaps enhanced by AI analysis
- Identifying friction points in user journeys through pattern recognition
- Predicting drop-off risks on landing pages with behavioural scoring
- Automating hypothesis generation for CRO tests
- Personalising website layouts based on visitor attributes and intent
- Implementing AI-driven A/B testing prioritisation
- Using predictive form analytics to reduce abandonment
- Optimising navigation flow with pathing algorithms
- Testing microcopy variations with NLP-powered recommendations
- Measuring emotional engagement through facial recognition (where applicable)
Module 10: Advanced Segmentation and Micro-Targeting - Evolution from demographic to behavioural and predictive segmentation
- Implementing k-means clustering for customer grouping
- Using decision trees to define segmentation logic
- Building RFM models enhanced with AI features
- Creating real-time segment activation rules
- Targeting high-value micro-segments across email and ads
- Using AI to detect emerging segments from raw behavioural data
- Developing persona refresh systems that auto-update over time
- Aligning product messaging to segment-specific pain points
- Measuring segmentation effectiveness through lift analysis
Module 11: AI for Email and Lifecycle Marketing - Optimising send time prediction using individual engagement history
- Subject line optimisation with AI-powered sentiment analysis
- Automating email sequence branching based on user actions
- Using churn prediction models to trigger win-back campaigns
- Building dynamic email content that adapts to lifecycle stage
- Analysing email fatigue indicators and adjusting frequency
- Scoring subscriber engagement to prioritise list segments
- Integrating transactional data into lifecycle messaging
- Creating hyper-relevant re-engagement campaigns
- Measuring long-term email performance with cohort retention curves
Module 12: Building Predictive Sales and Marketing Alignment - Creating shared definitions of MQLs and SQLs using data
- Using AI to predict sales readiness from marketing interactions
- Automating lead handoff processes with conditional logic
- Forecasting pipeline contribution by campaign and channel
- Mapping marketing touchpoints to sales cycle stages
- Building joint dashboards for marketing and sales visibility
- Reducing lead response time with AI-triggered alerts
- Scoring leads based on fit, behaviour, and timing
- Using AI to identify cross-sell opportunities during onboarding
- Aligning content strategy with sales enablement needs
Module 13: AI in Product-Led Growth Strategies - Understanding the intersection of product usage and growth marketing
- Defining activation, adoption, and expansion metrics
- Using AI to detect early indicators of product friction
- Identifying power feature combinations that drive retention
- Triggering in-app messages based on user behaviour patterns
- Automating onboarding personalisation using user traits
- Building predictive health scores for customer accounts
- Using AI to recommend next actions for trial users
- Scaling self-serve growth through intelligent guidance systems
- Case study: How a PLG SaaS reduced time-to-value by 62%
Module 14: Attribution Modelling and Marketing Mix Optimisation - Limitations of last-click and first-click attribution
- Implementing multi-touch attribution with algorithmic weighting
- Using Shapley values to assign channel credit fairly
- Building marketing mix models with regression analysis
- Incorporating seasonality, trends, and external factors
- Simulating budget reallocation scenarios for maximum impact
- Integrating offline marketing data into digital attribution
- Using AI to detect cannibalisation between channels
- Communicating attribution insights to stakeholders
- Automating monthly mix reports with predictive recommendations
Module 15: AI for Competitive Intelligence and Market Positioning - Monitoring competitor campaigns with AI-powered web scraping
- Analysing pricing changes and promotional moves in real time
- Using NLP to assess competitor messaging and differentiation
- Tracking feature launches and product updates automatically
- Identifying whitespace opportunities using gap analysis
- Mapping competitive positioning through sentiment mining
- Benchmarking performance against industry AI adoption
- Using AI to predict competitor moves based on historical patterns
- Developing counter-strategies for emerging threats
- Building real-time competitor dashboards for agile response
Module 16: Ethical AI and Bias Mitigation in Growth Marketing - Understanding sources of bias in training data
- Detecting discriminatory patterns in audience targeting
- Ensuring fairness in AI-driven personalisation
- Conducting AI model audits for transparency
- Implementing human oversight in automated decisions
- Designing opt-out and control mechanisms for users
- Aligning AI practices with GDPR, CCPA, and other regulations
- Communicating AI use to customers with clarity and trust
- Building ethical guidelines for your growth team
- Case study: Recovering brand trust after an AI bias incident
Module 17: Implementing AI Projects in Real Organisations - Running internal stakeholder alignment workshops
- Building a business case for AI marketing initiatives
- Securing budget and executive buy-in
- Defining success metrics and KPIs for AI projects
- Managing cross-functional collaboration between teams
- Phasing AI implementation: pilot, scale, embed
- Documenting processes and creating playbooks
- Training teams on new AI-enhanced workflows
- Establishing feedback loops for continuous improvement
- Measuring ROI and presenting results to leadership
Module 18: Future-Proofing Your Growth Career with AI Mastery - Tracking emerging AI trends in marketing technology
- Building a personal learning roadmap for continuous upskilling
- Curating AI tools and resources for ongoing development
- Positioning yourself as a strategic growth leader
- Creating a portfolio of AI-driven campaign results
- Leveraging the Certificate of Completion for career advancement
- Updating your LinkedIn profile with AI and data analytics keywords
- Negotiating higher compensation based on advanced skill set
- Transitioning into senior roles: Growth Manager, Director, CMO
- Contributing to the future of intelligent marketing
Module 19: Capstone Project – Building a Full AI-Driven Growth Strategy - Selecting a real or fictional business for your project
- Conducting a diagnostic assessment of current marketing maturity
- Defining strategic growth goals and KPIs
- Mapping the customer journey with AI enhancement opportunities
- Designing predictive acquisition and retention models
- Building a 90-day implementation plan
- Creating a budget and resource allocation framework
- Developing dashboards for monitoring AI performance
- Writing an executive summary for stakeholder presentation
- Receiving expert feedback and validation on your final strategy
Module 20: Certification, Next Steps, and Ongoing Support - Final review of all key concepts and frameworks
- Accessing your Certificate of Completion from The Art of Service
- Sharing your certification on professional networks
- Joining the alumni community for continued learning
- Receiving updates on new AI tools and case studies
- Accessing downloadable templates, checklists, and frameworks
- Using progress tracking to monitor mastery of each module
- Participating in gamified knowledge assessments
- Unlocking advanced bonus materials with continued engagement
- Planning your next career move with confidence and clarity
- Overview of leading AI tools for marketing automation
- Selecting AI platforms based on scalability and integration needs
- Configuring AI workflows for lead scoring and prioritisation
- Building autonomous drip campaigns that adapt to user behaviour
- Automating A/B testing with AI-driven variant generation
- Scheduling content delivery using predictive engagement models
- Reducing manual workload with AI-powered response templates
- Trigger-based re-engagement campaigns for dormant users
- Using AI to detect churn risk and initiate retention sequences
- Integrating AI automation with existing martech stacks
Module 5: Predictive Analytics for Customer Acquisition - Introduction to predictive modelling in marketing
- Identifying high-propensity leads using logistic regression basics
- Implementing lookalike audience modelling with machine learning
- Forecasting conversion likelihood across acquisition channels
- Using time-series analysis to predict seasonal demand patterns
- Optimising ad spend allocation with predictive ROI models
- Analysing multichannel attribution using algorithmic approaches
- Reducing customer acquisition cost through AI-driven lead filtering
- Building real-time acquisition dashboards with predictive overlays
- Case study: How an e-commerce brand reduced CPA by 41% using predictive targeting
Module 6: Customer Lifetime Value Optimisation with AI - Understanding the components of CLV in subscription and transactional models
- Building CLV prediction models using cohort analysis and decay curves
- Incorporating product usage data into CLV calculations
- Using AI to segment users by predicted lifetime value tiers
- Aligning marketing spend with high-CLV customer acquisition
- Designing retention campaigns based on CLV thresholds
- Linking upsell and cross-sell strategies to CLV drivers
- Creating dynamic retention budgets using CLV forecasts
- Integrating CLV insights into pricing and packaging decisions
- Measuring the impact of CLV optimisation on company valuation
Module 7: AI-Powered Content Strategy and Personalisation - The role of AI in content ideation and topic selection
- Using natural language processing to extract customer intent
- Generating content variations for A/B testing at scale
- Building dynamic content blocks that personalise in real time
- Adapting tone, length, and format based on audience segments
- Automating blog post outlines using semantic analysis
- Enhancing SEO strategy with AI-driven keyword clustering
- Creating hyper-personalised email content using behavioural triggers
- Testing content performance with AI-based simulation tools
- Scaling content production without sacrificing quality or brand voice
Module 8: AI in Paid Media and Programmatic Advertising - How AI transforms programmatic ad buying processes
- Understanding smart bidding algorithms in Google and Meta platforms
- Setting up conversion-based bidding strategies with machine learning
- Automating audience expansion using AI-powered affinity detection
- Dynamic creative optimisation for ad copy and visuals
- Real-time bid adjustments based on predicted conversion probability
- Using AI to detect ad fatigue and refresh creatives automatically
- Identifying incremental lift with holdout group testing
- Building closed-loop attribution models for paid campaigns
- Case study: AI-optimised ad campaign that achieved 5.8x ROAS
Module 9: Web and UX Optimisation Using Behavioural AI - Using session recording and heatmaps enhanced by AI analysis
- Identifying friction points in user journeys through pattern recognition
- Predicting drop-off risks on landing pages with behavioural scoring
- Automating hypothesis generation for CRO tests
- Personalising website layouts based on visitor attributes and intent
- Implementing AI-driven A/B testing prioritisation
- Using predictive form analytics to reduce abandonment
- Optimising navigation flow with pathing algorithms
- Testing microcopy variations with NLP-powered recommendations
- Measuring emotional engagement through facial recognition (where applicable)
Module 10: Advanced Segmentation and Micro-Targeting - Evolution from demographic to behavioural and predictive segmentation
- Implementing k-means clustering for customer grouping
- Using decision trees to define segmentation logic
- Building RFM models enhanced with AI features
- Creating real-time segment activation rules
- Targeting high-value micro-segments across email and ads
- Using AI to detect emerging segments from raw behavioural data
- Developing persona refresh systems that auto-update over time
- Aligning product messaging to segment-specific pain points
- Measuring segmentation effectiveness through lift analysis
Module 11: AI for Email and Lifecycle Marketing - Optimising send time prediction using individual engagement history
- Subject line optimisation with AI-powered sentiment analysis
- Automating email sequence branching based on user actions
- Using churn prediction models to trigger win-back campaigns
- Building dynamic email content that adapts to lifecycle stage
- Analysing email fatigue indicators and adjusting frequency
- Scoring subscriber engagement to prioritise list segments
- Integrating transactional data into lifecycle messaging
- Creating hyper-relevant re-engagement campaigns
- Measuring long-term email performance with cohort retention curves
Module 12: Building Predictive Sales and Marketing Alignment - Creating shared definitions of MQLs and SQLs using data
- Using AI to predict sales readiness from marketing interactions
- Automating lead handoff processes with conditional logic
- Forecasting pipeline contribution by campaign and channel
- Mapping marketing touchpoints to sales cycle stages
- Building joint dashboards for marketing and sales visibility
- Reducing lead response time with AI-triggered alerts
- Scoring leads based on fit, behaviour, and timing
- Using AI to identify cross-sell opportunities during onboarding
- Aligning content strategy with sales enablement needs
Module 13: AI in Product-Led Growth Strategies - Understanding the intersection of product usage and growth marketing
- Defining activation, adoption, and expansion metrics
- Using AI to detect early indicators of product friction
- Identifying power feature combinations that drive retention
- Triggering in-app messages based on user behaviour patterns
- Automating onboarding personalisation using user traits
- Building predictive health scores for customer accounts
- Using AI to recommend next actions for trial users
- Scaling self-serve growth through intelligent guidance systems
- Case study: How a PLG SaaS reduced time-to-value by 62%
Module 14: Attribution Modelling and Marketing Mix Optimisation - Limitations of last-click and first-click attribution
- Implementing multi-touch attribution with algorithmic weighting
- Using Shapley values to assign channel credit fairly
- Building marketing mix models with regression analysis
- Incorporating seasonality, trends, and external factors
- Simulating budget reallocation scenarios for maximum impact
- Integrating offline marketing data into digital attribution
- Using AI to detect cannibalisation between channels
- Communicating attribution insights to stakeholders
- Automating monthly mix reports with predictive recommendations
Module 15: AI for Competitive Intelligence and Market Positioning - Monitoring competitor campaigns with AI-powered web scraping
- Analysing pricing changes and promotional moves in real time
- Using NLP to assess competitor messaging and differentiation
- Tracking feature launches and product updates automatically
- Identifying whitespace opportunities using gap analysis
- Mapping competitive positioning through sentiment mining
- Benchmarking performance against industry AI adoption
- Using AI to predict competitor moves based on historical patterns
- Developing counter-strategies for emerging threats
- Building real-time competitor dashboards for agile response
Module 16: Ethical AI and Bias Mitigation in Growth Marketing - Understanding sources of bias in training data
- Detecting discriminatory patterns in audience targeting
- Ensuring fairness in AI-driven personalisation
- Conducting AI model audits for transparency
- Implementing human oversight in automated decisions
- Designing opt-out and control mechanisms for users
- Aligning AI practices with GDPR, CCPA, and other regulations
- Communicating AI use to customers with clarity and trust
- Building ethical guidelines for your growth team
- Case study: Recovering brand trust after an AI bias incident
Module 17: Implementing AI Projects in Real Organisations - Running internal stakeholder alignment workshops
- Building a business case for AI marketing initiatives
- Securing budget and executive buy-in
- Defining success metrics and KPIs for AI projects
- Managing cross-functional collaboration between teams
- Phasing AI implementation: pilot, scale, embed
- Documenting processes and creating playbooks
- Training teams on new AI-enhanced workflows
- Establishing feedback loops for continuous improvement
- Measuring ROI and presenting results to leadership
Module 18: Future-Proofing Your Growth Career with AI Mastery - Tracking emerging AI trends in marketing technology
- Building a personal learning roadmap for continuous upskilling
- Curating AI tools and resources for ongoing development
- Positioning yourself as a strategic growth leader
- Creating a portfolio of AI-driven campaign results
- Leveraging the Certificate of Completion for career advancement
- Updating your LinkedIn profile with AI and data analytics keywords
- Negotiating higher compensation based on advanced skill set
- Transitioning into senior roles: Growth Manager, Director, CMO
- Contributing to the future of intelligent marketing
Module 19: Capstone Project – Building a Full AI-Driven Growth Strategy - Selecting a real or fictional business for your project
- Conducting a diagnostic assessment of current marketing maturity
- Defining strategic growth goals and KPIs
- Mapping the customer journey with AI enhancement opportunities
- Designing predictive acquisition and retention models
- Building a 90-day implementation plan
- Creating a budget and resource allocation framework
- Developing dashboards for monitoring AI performance
- Writing an executive summary for stakeholder presentation
- Receiving expert feedback and validation on your final strategy
Module 20: Certification, Next Steps, and Ongoing Support - Final review of all key concepts and frameworks
- Accessing your Certificate of Completion from The Art of Service
- Sharing your certification on professional networks
- Joining the alumni community for continued learning
- Receiving updates on new AI tools and case studies
- Accessing downloadable templates, checklists, and frameworks
- Using progress tracking to monitor mastery of each module
- Participating in gamified knowledge assessments
- Unlocking advanced bonus materials with continued engagement
- Planning your next career move with confidence and clarity
- Understanding the components of CLV in subscription and transactional models
- Building CLV prediction models using cohort analysis and decay curves
- Incorporating product usage data into CLV calculations
- Using AI to segment users by predicted lifetime value tiers
- Aligning marketing spend with high-CLV customer acquisition
- Designing retention campaigns based on CLV thresholds
- Linking upsell and cross-sell strategies to CLV drivers
- Creating dynamic retention budgets using CLV forecasts
- Integrating CLV insights into pricing and packaging decisions
- Measuring the impact of CLV optimisation on company valuation
Module 7: AI-Powered Content Strategy and Personalisation - The role of AI in content ideation and topic selection
- Using natural language processing to extract customer intent
- Generating content variations for A/B testing at scale
- Building dynamic content blocks that personalise in real time
- Adapting tone, length, and format based on audience segments
- Automating blog post outlines using semantic analysis
- Enhancing SEO strategy with AI-driven keyword clustering
- Creating hyper-personalised email content using behavioural triggers
- Testing content performance with AI-based simulation tools
- Scaling content production without sacrificing quality or brand voice
Module 8: AI in Paid Media and Programmatic Advertising - How AI transforms programmatic ad buying processes
- Understanding smart bidding algorithms in Google and Meta platforms
- Setting up conversion-based bidding strategies with machine learning
- Automating audience expansion using AI-powered affinity detection
- Dynamic creative optimisation for ad copy and visuals
- Real-time bid adjustments based on predicted conversion probability
- Using AI to detect ad fatigue and refresh creatives automatically
- Identifying incremental lift with holdout group testing
- Building closed-loop attribution models for paid campaigns
- Case study: AI-optimised ad campaign that achieved 5.8x ROAS
Module 9: Web and UX Optimisation Using Behavioural AI - Using session recording and heatmaps enhanced by AI analysis
- Identifying friction points in user journeys through pattern recognition
- Predicting drop-off risks on landing pages with behavioural scoring
- Automating hypothesis generation for CRO tests
- Personalising website layouts based on visitor attributes and intent
- Implementing AI-driven A/B testing prioritisation
- Using predictive form analytics to reduce abandonment
- Optimising navigation flow with pathing algorithms
- Testing microcopy variations with NLP-powered recommendations
- Measuring emotional engagement through facial recognition (where applicable)
Module 10: Advanced Segmentation and Micro-Targeting - Evolution from demographic to behavioural and predictive segmentation
- Implementing k-means clustering for customer grouping
- Using decision trees to define segmentation logic
- Building RFM models enhanced with AI features
- Creating real-time segment activation rules
- Targeting high-value micro-segments across email and ads
- Using AI to detect emerging segments from raw behavioural data
- Developing persona refresh systems that auto-update over time
- Aligning product messaging to segment-specific pain points
- Measuring segmentation effectiveness through lift analysis
Module 11: AI for Email and Lifecycle Marketing - Optimising send time prediction using individual engagement history
- Subject line optimisation with AI-powered sentiment analysis
- Automating email sequence branching based on user actions
- Using churn prediction models to trigger win-back campaigns
- Building dynamic email content that adapts to lifecycle stage
- Analysing email fatigue indicators and adjusting frequency
- Scoring subscriber engagement to prioritise list segments
- Integrating transactional data into lifecycle messaging
- Creating hyper-relevant re-engagement campaigns
- Measuring long-term email performance with cohort retention curves
Module 12: Building Predictive Sales and Marketing Alignment - Creating shared definitions of MQLs and SQLs using data
- Using AI to predict sales readiness from marketing interactions
- Automating lead handoff processes with conditional logic
- Forecasting pipeline contribution by campaign and channel
- Mapping marketing touchpoints to sales cycle stages
- Building joint dashboards for marketing and sales visibility
- Reducing lead response time with AI-triggered alerts
- Scoring leads based on fit, behaviour, and timing
- Using AI to identify cross-sell opportunities during onboarding
- Aligning content strategy with sales enablement needs
Module 13: AI in Product-Led Growth Strategies - Understanding the intersection of product usage and growth marketing
- Defining activation, adoption, and expansion metrics
- Using AI to detect early indicators of product friction
- Identifying power feature combinations that drive retention
- Triggering in-app messages based on user behaviour patterns
- Automating onboarding personalisation using user traits
- Building predictive health scores for customer accounts
- Using AI to recommend next actions for trial users
- Scaling self-serve growth through intelligent guidance systems
- Case study: How a PLG SaaS reduced time-to-value by 62%
Module 14: Attribution Modelling and Marketing Mix Optimisation - Limitations of last-click and first-click attribution
- Implementing multi-touch attribution with algorithmic weighting
- Using Shapley values to assign channel credit fairly
- Building marketing mix models with regression analysis
- Incorporating seasonality, trends, and external factors
- Simulating budget reallocation scenarios for maximum impact
- Integrating offline marketing data into digital attribution
- Using AI to detect cannibalisation between channels
- Communicating attribution insights to stakeholders
- Automating monthly mix reports with predictive recommendations
Module 15: AI for Competitive Intelligence and Market Positioning - Monitoring competitor campaigns with AI-powered web scraping
- Analysing pricing changes and promotional moves in real time
- Using NLP to assess competitor messaging and differentiation
- Tracking feature launches and product updates automatically
- Identifying whitespace opportunities using gap analysis
- Mapping competitive positioning through sentiment mining
- Benchmarking performance against industry AI adoption
- Using AI to predict competitor moves based on historical patterns
- Developing counter-strategies for emerging threats
- Building real-time competitor dashboards for agile response
Module 16: Ethical AI and Bias Mitigation in Growth Marketing - Understanding sources of bias in training data
- Detecting discriminatory patterns in audience targeting
- Ensuring fairness in AI-driven personalisation
- Conducting AI model audits for transparency
- Implementing human oversight in automated decisions
- Designing opt-out and control mechanisms for users
- Aligning AI practices with GDPR, CCPA, and other regulations
- Communicating AI use to customers with clarity and trust
- Building ethical guidelines for your growth team
- Case study: Recovering brand trust after an AI bias incident
Module 17: Implementing AI Projects in Real Organisations - Running internal stakeholder alignment workshops
- Building a business case for AI marketing initiatives
- Securing budget and executive buy-in
- Defining success metrics and KPIs for AI projects
- Managing cross-functional collaboration between teams
- Phasing AI implementation: pilot, scale, embed
- Documenting processes and creating playbooks
- Training teams on new AI-enhanced workflows
- Establishing feedback loops for continuous improvement
- Measuring ROI and presenting results to leadership
Module 18: Future-Proofing Your Growth Career with AI Mastery - Tracking emerging AI trends in marketing technology
- Building a personal learning roadmap for continuous upskilling
- Curating AI tools and resources for ongoing development
- Positioning yourself as a strategic growth leader
- Creating a portfolio of AI-driven campaign results
- Leveraging the Certificate of Completion for career advancement
- Updating your LinkedIn profile with AI and data analytics keywords
- Negotiating higher compensation based on advanced skill set
- Transitioning into senior roles: Growth Manager, Director, CMO
- Contributing to the future of intelligent marketing
Module 19: Capstone Project – Building a Full AI-Driven Growth Strategy - Selecting a real or fictional business for your project
- Conducting a diagnostic assessment of current marketing maturity
- Defining strategic growth goals and KPIs
- Mapping the customer journey with AI enhancement opportunities
- Designing predictive acquisition and retention models
- Building a 90-day implementation plan
- Creating a budget and resource allocation framework
- Developing dashboards for monitoring AI performance
- Writing an executive summary for stakeholder presentation
- Receiving expert feedback and validation on your final strategy
Module 20: Certification, Next Steps, and Ongoing Support - Final review of all key concepts and frameworks
- Accessing your Certificate of Completion from The Art of Service
- Sharing your certification on professional networks
- Joining the alumni community for continued learning
- Receiving updates on new AI tools and case studies
- Accessing downloadable templates, checklists, and frameworks
- Using progress tracking to monitor mastery of each module
- Participating in gamified knowledge assessments
- Unlocking advanced bonus materials with continued engagement
- Planning your next career move with confidence and clarity
- How AI transforms programmatic ad buying processes
- Understanding smart bidding algorithms in Google and Meta platforms
- Setting up conversion-based bidding strategies with machine learning
- Automating audience expansion using AI-powered affinity detection
- Dynamic creative optimisation for ad copy and visuals
- Real-time bid adjustments based on predicted conversion probability
- Using AI to detect ad fatigue and refresh creatives automatically
- Identifying incremental lift with holdout group testing
- Building closed-loop attribution models for paid campaigns
- Case study: AI-optimised ad campaign that achieved 5.8x ROAS
Module 9: Web and UX Optimisation Using Behavioural AI - Using session recording and heatmaps enhanced by AI analysis
- Identifying friction points in user journeys through pattern recognition
- Predicting drop-off risks on landing pages with behavioural scoring
- Automating hypothesis generation for CRO tests
- Personalising website layouts based on visitor attributes and intent
- Implementing AI-driven A/B testing prioritisation
- Using predictive form analytics to reduce abandonment
- Optimising navigation flow with pathing algorithms
- Testing microcopy variations with NLP-powered recommendations
- Measuring emotional engagement through facial recognition (where applicable)
Module 10: Advanced Segmentation and Micro-Targeting - Evolution from demographic to behavioural and predictive segmentation
- Implementing k-means clustering for customer grouping
- Using decision trees to define segmentation logic
- Building RFM models enhanced with AI features
- Creating real-time segment activation rules
- Targeting high-value micro-segments across email and ads
- Using AI to detect emerging segments from raw behavioural data
- Developing persona refresh systems that auto-update over time
- Aligning product messaging to segment-specific pain points
- Measuring segmentation effectiveness through lift analysis
Module 11: AI for Email and Lifecycle Marketing - Optimising send time prediction using individual engagement history
- Subject line optimisation with AI-powered sentiment analysis
- Automating email sequence branching based on user actions
- Using churn prediction models to trigger win-back campaigns
- Building dynamic email content that adapts to lifecycle stage
- Analysing email fatigue indicators and adjusting frequency
- Scoring subscriber engagement to prioritise list segments
- Integrating transactional data into lifecycle messaging
- Creating hyper-relevant re-engagement campaigns
- Measuring long-term email performance with cohort retention curves
Module 12: Building Predictive Sales and Marketing Alignment - Creating shared definitions of MQLs and SQLs using data
- Using AI to predict sales readiness from marketing interactions
- Automating lead handoff processes with conditional logic
- Forecasting pipeline contribution by campaign and channel
- Mapping marketing touchpoints to sales cycle stages
- Building joint dashboards for marketing and sales visibility
- Reducing lead response time with AI-triggered alerts
- Scoring leads based on fit, behaviour, and timing
- Using AI to identify cross-sell opportunities during onboarding
- Aligning content strategy with sales enablement needs
Module 13: AI in Product-Led Growth Strategies - Understanding the intersection of product usage and growth marketing
- Defining activation, adoption, and expansion metrics
- Using AI to detect early indicators of product friction
- Identifying power feature combinations that drive retention
- Triggering in-app messages based on user behaviour patterns
- Automating onboarding personalisation using user traits
- Building predictive health scores for customer accounts
- Using AI to recommend next actions for trial users
- Scaling self-serve growth through intelligent guidance systems
- Case study: How a PLG SaaS reduced time-to-value by 62%
Module 14: Attribution Modelling and Marketing Mix Optimisation - Limitations of last-click and first-click attribution
- Implementing multi-touch attribution with algorithmic weighting
- Using Shapley values to assign channel credit fairly
- Building marketing mix models with regression analysis
- Incorporating seasonality, trends, and external factors
- Simulating budget reallocation scenarios for maximum impact
- Integrating offline marketing data into digital attribution
- Using AI to detect cannibalisation between channels
- Communicating attribution insights to stakeholders
- Automating monthly mix reports with predictive recommendations
Module 15: AI for Competitive Intelligence and Market Positioning - Monitoring competitor campaigns with AI-powered web scraping
- Analysing pricing changes and promotional moves in real time
- Using NLP to assess competitor messaging and differentiation
- Tracking feature launches and product updates automatically
- Identifying whitespace opportunities using gap analysis
- Mapping competitive positioning through sentiment mining
- Benchmarking performance against industry AI adoption
- Using AI to predict competitor moves based on historical patterns
- Developing counter-strategies for emerging threats
- Building real-time competitor dashboards for agile response
Module 16: Ethical AI and Bias Mitigation in Growth Marketing - Understanding sources of bias in training data
- Detecting discriminatory patterns in audience targeting
- Ensuring fairness in AI-driven personalisation
- Conducting AI model audits for transparency
- Implementing human oversight in automated decisions
- Designing opt-out and control mechanisms for users
- Aligning AI practices with GDPR, CCPA, and other regulations
- Communicating AI use to customers with clarity and trust
- Building ethical guidelines for your growth team
- Case study: Recovering brand trust after an AI bias incident
Module 17: Implementing AI Projects in Real Organisations - Running internal stakeholder alignment workshops
- Building a business case for AI marketing initiatives
- Securing budget and executive buy-in
- Defining success metrics and KPIs for AI projects
- Managing cross-functional collaboration between teams
- Phasing AI implementation: pilot, scale, embed
- Documenting processes and creating playbooks
- Training teams on new AI-enhanced workflows
- Establishing feedback loops for continuous improvement
- Measuring ROI and presenting results to leadership
Module 18: Future-Proofing Your Growth Career with AI Mastery - Tracking emerging AI trends in marketing technology
- Building a personal learning roadmap for continuous upskilling
- Curating AI tools and resources for ongoing development
- Positioning yourself as a strategic growth leader
- Creating a portfolio of AI-driven campaign results
- Leveraging the Certificate of Completion for career advancement
- Updating your LinkedIn profile with AI and data analytics keywords
- Negotiating higher compensation based on advanced skill set
- Transitioning into senior roles: Growth Manager, Director, CMO
- Contributing to the future of intelligent marketing
Module 19: Capstone Project – Building a Full AI-Driven Growth Strategy - Selecting a real or fictional business for your project
- Conducting a diagnostic assessment of current marketing maturity
- Defining strategic growth goals and KPIs
- Mapping the customer journey with AI enhancement opportunities
- Designing predictive acquisition and retention models
- Building a 90-day implementation plan
- Creating a budget and resource allocation framework
- Developing dashboards for monitoring AI performance
- Writing an executive summary for stakeholder presentation
- Receiving expert feedback and validation on your final strategy
Module 20: Certification, Next Steps, and Ongoing Support - Final review of all key concepts and frameworks
- Accessing your Certificate of Completion from The Art of Service
- Sharing your certification on professional networks
- Joining the alumni community for continued learning
- Receiving updates on new AI tools and case studies
- Accessing downloadable templates, checklists, and frameworks
- Using progress tracking to monitor mastery of each module
- Participating in gamified knowledge assessments
- Unlocking advanced bonus materials with continued engagement
- Planning your next career move with confidence and clarity
- Evolution from demographic to behavioural and predictive segmentation
- Implementing k-means clustering for customer grouping
- Using decision trees to define segmentation logic
- Building RFM models enhanced with AI features
- Creating real-time segment activation rules
- Targeting high-value micro-segments across email and ads
- Using AI to detect emerging segments from raw behavioural data
- Developing persona refresh systems that auto-update over time
- Aligning product messaging to segment-specific pain points
- Measuring segmentation effectiveness through lift analysis
Module 11: AI for Email and Lifecycle Marketing - Optimising send time prediction using individual engagement history
- Subject line optimisation with AI-powered sentiment analysis
- Automating email sequence branching based on user actions
- Using churn prediction models to trigger win-back campaigns
- Building dynamic email content that adapts to lifecycle stage
- Analysing email fatigue indicators and adjusting frequency
- Scoring subscriber engagement to prioritise list segments
- Integrating transactional data into lifecycle messaging
- Creating hyper-relevant re-engagement campaigns
- Measuring long-term email performance with cohort retention curves
Module 12: Building Predictive Sales and Marketing Alignment - Creating shared definitions of MQLs and SQLs using data
- Using AI to predict sales readiness from marketing interactions
- Automating lead handoff processes with conditional logic
- Forecasting pipeline contribution by campaign and channel
- Mapping marketing touchpoints to sales cycle stages
- Building joint dashboards for marketing and sales visibility
- Reducing lead response time with AI-triggered alerts
- Scoring leads based on fit, behaviour, and timing
- Using AI to identify cross-sell opportunities during onboarding
- Aligning content strategy with sales enablement needs
Module 13: AI in Product-Led Growth Strategies - Understanding the intersection of product usage and growth marketing
- Defining activation, adoption, and expansion metrics
- Using AI to detect early indicators of product friction
- Identifying power feature combinations that drive retention
- Triggering in-app messages based on user behaviour patterns
- Automating onboarding personalisation using user traits
- Building predictive health scores for customer accounts
- Using AI to recommend next actions for trial users
- Scaling self-serve growth through intelligent guidance systems
- Case study: How a PLG SaaS reduced time-to-value by 62%
Module 14: Attribution Modelling and Marketing Mix Optimisation - Limitations of last-click and first-click attribution
- Implementing multi-touch attribution with algorithmic weighting
- Using Shapley values to assign channel credit fairly
- Building marketing mix models with regression analysis
- Incorporating seasonality, trends, and external factors
- Simulating budget reallocation scenarios for maximum impact
- Integrating offline marketing data into digital attribution
- Using AI to detect cannibalisation between channels
- Communicating attribution insights to stakeholders
- Automating monthly mix reports with predictive recommendations
Module 15: AI for Competitive Intelligence and Market Positioning - Monitoring competitor campaigns with AI-powered web scraping
- Analysing pricing changes and promotional moves in real time
- Using NLP to assess competitor messaging and differentiation
- Tracking feature launches and product updates automatically
- Identifying whitespace opportunities using gap analysis
- Mapping competitive positioning through sentiment mining
- Benchmarking performance against industry AI adoption
- Using AI to predict competitor moves based on historical patterns
- Developing counter-strategies for emerging threats
- Building real-time competitor dashboards for agile response
Module 16: Ethical AI and Bias Mitigation in Growth Marketing - Understanding sources of bias in training data
- Detecting discriminatory patterns in audience targeting
- Ensuring fairness in AI-driven personalisation
- Conducting AI model audits for transparency
- Implementing human oversight in automated decisions
- Designing opt-out and control mechanisms for users
- Aligning AI practices with GDPR, CCPA, and other regulations
- Communicating AI use to customers with clarity and trust
- Building ethical guidelines for your growth team
- Case study: Recovering brand trust after an AI bias incident
Module 17: Implementing AI Projects in Real Organisations - Running internal stakeholder alignment workshops
- Building a business case for AI marketing initiatives
- Securing budget and executive buy-in
- Defining success metrics and KPIs for AI projects
- Managing cross-functional collaboration between teams
- Phasing AI implementation: pilot, scale, embed
- Documenting processes and creating playbooks
- Training teams on new AI-enhanced workflows
- Establishing feedback loops for continuous improvement
- Measuring ROI and presenting results to leadership
Module 18: Future-Proofing Your Growth Career with AI Mastery - Tracking emerging AI trends in marketing technology
- Building a personal learning roadmap for continuous upskilling
- Curating AI tools and resources for ongoing development
- Positioning yourself as a strategic growth leader
- Creating a portfolio of AI-driven campaign results
- Leveraging the Certificate of Completion for career advancement
- Updating your LinkedIn profile with AI and data analytics keywords
- Negotiating higher compensation based on advanced skill set
- Transitioning into senior roles: Growth Manager, Director, CMO
- Contributing to the future of intelligent marketing
Module 19: Capstone Project – Building a Full AI-Driven Growth Strategy - Selecting a real or fictional business for your project
- Conducting a diagnostic assessment of current marketing maturity
- Defining strategic growth goals and KPIs
- Mapping the customer journey with AI enhancement opportunities
- Designing predictive acquisition and retention models
- Building a 90-day implementation plan
- Creating a budget and resource allocation framework
- Developing dashboards for monitoring AI performance
- Writing an executive summary for stakeholder presentation
- Receiving expert feedback and validation on your final strategy
Module 20: Certification, Next Steps, and Ongoing Support - Final review of all key concepts and frameworks
- Accessing your Certificate of Completion from The Art of Service
- Sharing your certification on professional networks
- Joining the alumni community for continued learning
- Receiving updates on new AI tools and case studies
- Accessing downloadable templates, checklists, and frameworks
- Using progress tracking to monitor mastery of each module
- Participating in gamified knowledge assessments
- Unlocking advanced bonus materials with continued engagement
- Planning your next career move with confidence and clarity
- Creating shared definitions of MQLs and SQLs using data
- Using AI to predict sales readiness from marketing interactions
- Automating lead handoff processes with conditional logic
- Forecasting pipeline contribution by campaign and channel
- Mapping marketing touchpoints to sales cycle stages
- Building joint dashboards for marketing and sales visibility
- Reducing lead response time with AI-triggered alerts
- Scoring leads based on fit, behaviour, and timing
- Using AI to identify cross-sell opportunities during onboarding
- Aligning content strategy with sales enablement needs
Module 13: AI in Product-Led Growth Strategies - Understanding the intersection of product usage and growth marketing
- Defining activation, adoption, and expansion metrics
- Using AI to detect early indicators of product friction
- Identifying power feature combinations that drive retention
- Triggering in-app messages based on user behaviour patterns
- Automating onboarding personalisation using user traits
- Building predictive health scores for customer accounts
- Using AI to recommend next actions for trial users
- Scaling self-serve growth through intelligent guidance systems
- Case study: How a PLG SaaS reduced time-to-value by 62%
Module 14: Attribution Modelling and Marketing Mix Optimisation - Limitations of last-click and first-click attribution
- Implementing multi-touch attribution with algorithmic weighting
- Using Shapley values to assign channel credit fairly
- Building marketing mix models with regression analysis
- Incorporating seasonality, trends, and external factors
- Simulating budget reallocation scenarios for maximum impact
- Integrating offline marketing data into digital attribution
- Using AI to detect cannibalisation between channels
- Communicating attribution insights to stakeholders
- Automating monthly mix reports with predictive recommendations
Module 15: AI for Competitive Intelligence and Market Positioning - Monitoring competitor campaigns with AI-powered web scraping
- Analysing pricing changes and promotional moves in real time
- Using NLP to assess competitor messaging and differentiation
- Tracking feature launches and product updates automatically
- Identifying whitespace opportunities using gap analysis
- Mapping competitive positioning through sentiment mining
- Benchmarking performance against industry AI adoption
- Using AI to predict competitor moves based on historical patterns
- Developing counter-strategies for emerging threats
- Building real-time competitor dashboards for agile response
Module 16: Ethical AI and Bias Mitigation in Growth Marketing - Understanding sources of bias in training data
- Detecting discriminatory patterns in audience targeting
- Ensuring fairness in AI-driven personalisation
- Conducting AI model audits for transparency
- Implementing human oversight in automated decisions
- Designing opt-out and control mechanisms for users
- Aligning AI practices with GDPR, CCPA, and other regulations
- Communicating AI use to customers with clarity and trust
- Building ethical guidelines for your growth team
- Case study: Recovering brand trust after an AI bias incident
Module 17: Implementing AI Projects in Real Organisations - Running internal stakeholder alignment workshops
- Building a business case for AI marketing initiatives
- Securing budget and executive buy-in
- Defining success metrics and KPIs for AI projects
- Managing cross-functional collaboration between teams
- Phasing AI implementation: pilot, scale, embed
- Documenting processes and creating playbooks
- Training teams on new AI-enhanced workflows
- Establishing feedback loops for continuous improvement
- Measuring ROI and presenting results to leadership
Module 18: Future-Proofing Your Growth Career with AI Mastery - Tracking emerging AI trends in marketing technology
- Building a personal learning roadmap for continuous upskilling
- Curating AI tools and resources for ongoing development
- Positioning yourself as a strategic growth leader
- Creating a portfolio of AI-driven campaign results
- Leveraging the Certificate of Completion for career advancement
- Updating your LinkedIn profile with AI and data analytics keywords
- Negotiating higher compensation based on advanced skill set
- Transitioning into senior roles: Growth Manager, Director, CMO
- Contributing to the future of intelligent marketing
Module 19: Capstone Project – Building a Full AI-Driven Growth Strategy - Selecting a real or fictional business for your project
- Conducting a diagnostic assessment of current marketing maturity
- Defining strategic growth goals and KPIs
- Mapping the customer journey with AI enhancement opportunities
- Designing predictive acquisition and retention models
- Building a 90-day implementation plan
- Creating a budget and resource allocation framework
- Developing dashboards for monitoring AI performance
- Writing an executive summary for stakeholder presentation
- Receiving expert feedback and validation on your final strategy
Module 20: Certification, Next Steps, and Ongoing Support - Final review of all key concepts and frameworks
- Accessing your Certificate of Completion from The Art of Service
- Sharing your certification on professional networks
- Joining the alumni community for continued learning
- Receiving updates on new AI tools and case studies
- Accessing downloadable templates, checklists, and frameworks
- Using progress tracking to monitor mastery of each module
- Participating in gamified knowledge assessments
- Unlocking advanced bonus materials with continued engagement
- Planning your next career move with confidence and clarity
- Limitations of last-click and first-click attribution
- Implementing multi-touch attribution with algorithmic weighting
- Using Shapley values to assign channel credit fairly
- Building marketing mix models with regression analysis
- Incorporating seasonality, trends, and external factors
- Simulating budget reallocation scenarios for maximum impact
- Integrating offline marketing data into digital attribution
- Using AI to detect cannibalisation between channels
- Communicating attribution insights to stakeholders
- Automating monthly mix reports with predictive recommendations
Module 15: AI for Competitive Intelligence and Market Positioning - Monitoring competitor campaigns with AI-powered web scraping
- Analysing pricing changes and promotional moves in real time
- Using NLP to assess competitor messaging and differentiation
- Tracking feature launches and product updates automatically
- Identifying whitespace opportunities using gap analysis
- Mapping competitive positioning through sentiment mining
- Benchmarking performance against industry AI adoption
- Using AI to predict competitor moves based on historical patterns
- Developing counter-strategies for emerging threats
- Building real-time competitor dashboards for agile response
Module 16: Ethical AI and Bias Mitigation in Growth Marketing - Understanding sources of bias in training data
- Detecting discriminatory patterns in audience targeting
- Ensuring fairness in AI-driven personalisation
- Conducting AI model audits for transparency
- Implementing human oversight in automated decisions
- Designing opt-out and control mechanisms for users
- Aligning AI practices with GDPR, CCPA, and other regulations
- Communicating AI use to customers with clarity and trust
- Building ethical guidelines for your growth team
- Case study: Recovering brand trust after an AI bias incident
Module 17: Implementing AI Projects in Real Organisations - Running internal stakeholder alignment workshops
- Building a business case for AI marketing initiatives
- Securing budget and executive buy-in
- Defining success metrics and KPIs for AI projects
- Managing cross-functional collaboration between teams
- Phasing AI implementation: pilot, scale, embed
- Documenting processes and creating playbooks
- Training teams on new AI-enhanced workflows
- Establishing feedback loops for continuous improvement
- Measuring ROI and presenting results to leadership
Module 18: Future-Proofing Your Growth Career with AI Mastery - Tracking emerging AI trends in marketing technology
- Building a personal learning roadmap for continuous upskilling
- Curating AI tools and resources for ongoing development
- Positioning yourself as a strategic growth leader
- Creating a portfolio of AI-driven campaign results
- Leveraging the Certificate of Completion for career advancement
- Updating your LinkedIn profile with AI and data analytics keywords
- Negotiating higher compensation based on advanced skill set
- Transitioning into senior roles: Growth Manager, Director, CMO
- Contributing to the future of intelligent marketing
Module 19: Capstone Project – Building a Full AI-Driven Growth Strategy - Selecting a real or fictional business for your project
- Conducting a diagnostic assessment of current marketing maturity
- Defining strategic growth goals and KPIs
- Mapping the customer journey with AI enhancement opportunities
- Designing predictive acquisition and retention models
- Building a 90-day implementation plan
- Creating a budget and resource allocation framework
- Developing dashboards for monitoring AI performance
- Writing an executive summary for stakeholder presentation
- Receiving expert feedback and validation on your final strategy
Module 20: Certification, Next Steps, and Ongoing Support - Final review of all key concepts and frameworks
- Accessing your Certificate of Completion from The Art of Service
- Sharing your certification on professional networks
- Joining the alumni community for continued learning
- Receiving updates on new AI tools and case studies
- Accessing downloadable templates, checklists, and frameworks
- Using progress tracking to monitor mastery of each module
- Participating in gamified knowledge assessments
- Unlocking advanced bonus materials with continued engagement
- Planning your next career move with confidence and clarity
- Understanding sources of bias in training data
- Detecting discriminatory patterns in audience targeting
- Ensuring fairness in AI-driven personalisation
- Conducting AI model audits for transparency
- Implementing human oversight in automated decisions
- Designing opt-out and control mechanisms for users
- Aligning AI practices with GDPR, CCPA, and other regulations
- Communicating AI use to customers with clarity and trust
- Building ethical guidelines for your growth team
- Case study: Recovering brand trust after an AI bias incident
Module 17: Implementing AI Projects in Real Organisations - Running internal stakeholder alignment workshops
- Building a business case for AI marketing initiatives
- Securing budget and executive buy-in
- Defining success metrics and KPIs for AI projects
- Managing cross-functional collaboration between teams
- Phasing AI implementation: pilot, scale, embed
- Documenting processes and creating playbooks
- Training teams on new AI-enhanced workflows
- Establishing feedback loops for continuous improvement
- Measuring ROI and presenting results to leadership
Module 18: Future-Proofing Your Growth Career with AI Mastery - Tracking emerging AI trends in marketing technology
- Building a personal learning roadmap for continuous upskilling
- Curating AI tools and resources for ongoing development
- Positioning yourself as a strategic growth leader
- Creating a portfolio of AI-driven campaign results
- Leveraging the Certificate of Completion for career advancement
- Updating your LinkedIn profile with AI and data analytics keywords
- Negotiating higher compensation based on advanced skill set
- Transitioning into senior roles: Growth Manager, Director, CMO
- Contributing to the future of intelligent marketing
Module 19: Capstone Project – Building a Full AI-Driven Growth Strategy - Selecting a real or fictional business for your project
- Conducting a diagnostic assessment of current marketing maturity
- Defining strategic growth goals and KPIs
- Mapping the customer journey with AI enhancement opportunities
- Designing predictive acquisition and retention models
- Building a 90-day implementation plan
- Creating a budget and resource allocation framework
- Developing dashboards for monitoring AI performance
- Writing an executive summary for stakeholder presentation
- Receiving expert feedback and validation on your final strategy
Module 20: Certification, Next Steps, and Ongoing Support - Final review of all key concepts and frameworks
- Accessing your Certificate of Completion from The Art of Service
- Sharing your certification on professional networks
- Joining the alumni community for continued learning
- Receiving updates on new AI tools and case studies
- Accessing downloadable templates, checklists, and frameworks
- Using progress tracking to monitor mastery of each module
- Participating in gamified knowledge assessments
- Unlocking advanced bonus materials with continued engagement
- Planning your next career move with confidence and clarity
- Tracking emerging AI trends in marketing technology
- Building a personal learning roadmap for continuous upskilling
- Curating AI tools and resources for ongoing development
- Positioning yourself as a strategic growth leader
- Creating a portfolio of AI-driven campaign results
- Leveraging the Certificate of Completion for career advancement
- Updating your LinkedIn profile with AI and data analytics keywords
- Negotiating higher compensation based on advanced skill set
- Transitioning into senior roles: Growth Manager, Director, CMO
- Contributing to the future of intelligent marketing
Module 19: Capstone Project – Building a Full AI-Driven Growth Strategy - Selecting a real or fictional business for your project
- Conducting a diagnostic assessment of current marketing maturity
- Defining strategic growth goals and KPIs
- Mapping the customer journey with AI enhancement opportunities
- Designing predictive acquisition and retention models
- Building a 90-day implementation plan
- Creating a budget and resource allocation framework
- Developing dashboards for monitoring AI performance
- Writing an executive summary for stakeholder presentation
- Receiving expert feedback and validation on your final strategy
Module 20: Certification, Next Steps, and Ongoing Support - Final review of all key concepts and frameworks
- Accessing your Certificate of Completion from The Art of Service
- Sharing your certification on professional networks
- Joining the alumni community for continued learning
- Receiving updates on new AI tools and case studies
- Accessing downloadable templates, checklists, and frameworks
- Using progress tracking to monitor mastery of each module
- Participating in gamified knowledge assessments
- Unlocking advanced bonus materials with continued engagement
- Planning your next career move with confidence and clarity
- Final review of all key concepts and frameworks
- Accessing your Certificate of Completion from The Art of Service
- Sharing your certification on professional networks
- Joining the alumni community for continued learning
- Receiving updates on new AI tools and case studies
- Accessing downloadable templates, checklists, and frameworks
- Using progress tracking to monitor mastery of each module
- Participating in gamified knowledge assessments
- Unlocking advanced bonus materials with continued engagement
- Planning your next career move with confidence and clarity