AI-Powered Growth Hacking: Future-Proof Your Marketing Career in 2024
You're under pressure. The tools you mastered just two years ago are now obsolete. AI is rewriting the rules of customer acquisition, and if you’re not leading that change, you risk being replaced by someone who is. The gap between the marketers who adapt and those who don’t is widening fast. Every week without a strategic AI advantage means lost opportunities, slower growth, and falling behind peers who are already leveraging intelligent automation. But what if you could master the exact systems top-performing growth teams use to generate scalable, predictable results - without needing a data science degree? AI-Powered Growth Hacking: Future-Proof Your Marketing Career in 2024 is your proven roadmap from uncertainty to authority. This isn’t a theory-heavy course. It’s a battle-tested, action-oriented framework that guides you from idea to a fully developed AI-growth strategy in 30 days - complete with a board-ready proposal, integrated KPIs, and implementation roadmap. Consider Sara Chen, Senior Marketing Manager at a mid-market SaaS company. After completing this program, she designed an AI-driven lead scoring model that increased qualified demo bookings by 62% in one quarter. Her initiative was fast-tracked to company-wide rollout - and she was promoted to Director of Growth within six months. You don’t need more content. You need clarity, confidence, and a repeatable system that delivers measurable ROI. This course eliminates the guesswork, noise, and outdated tactics holding you back. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced, On-Demand, and Designed for Real Careers
This course is 100% self-paced, with on-demand access available the moment you enroll. There are no fixed dates, live sessions, or rigid timelines. You decide when and where you learn - fitting critical upskilling into your schedule, not the other way around. Most learners complete the core content in 4–6 weeks while applying concepts directly to their current role. Many report seeing measurable improvements in campaign efficiency or strategy impact within the first 10 days. Lifetime Access with Continuous Updates
You’re not buying a one-time resource - you’re gaining permanent access to a living curriculum. As AI trends evolve, new tools emerge, and best practices shift, the course content is updated regularly at no extra cost. Your investment remains future-relevant for years. Access is available 24/7 from any device - desktop, tablet, or mobile - with a seamless, responsive experience. Navigate modules, track progress, and revisit frameworks anytime, anywhere in the world. Expert-Led Guidance & Support
Throughout the course, you’ll receive direct feedback and clarification through structured instructor review channels. This isn’t passive learning. You’re supported by growth strategists with proven experience scaling AI-powered campaigns across B2B, DTC, and enterprise environments. Your work culminates in a final project review. Upon successful completion, you’ll earn a Certificate of Completion issued by The Art of Service - a globally recognised credential trusted by professionals in over 140 countries. This certification validates your mastery of AI-driven growth systems and strengthens your credibility in a competitive job market. Transparent Pricing & Zero Risk
The enrollment fee includes full access to all materials, tools, templates, and support. There are no hidden fees, upsells, or recurring charges. You pay once, own forever. Payment is accepted via Visa, Mastercard, and PayPal - all processed securely through encrypted gateways. We stand behind the course with a 30-day satisfaction guarantee. If you complete the first three modules and don’t feel confident in your ability to apply AI to real marketing challenges, you can request a full refund - no questions asked. This Works Even If…
You’re not technical. You’ve never written a line of code. You work in a traditional industry. Your company hasn’t adopted AI yet. You’re unsure where to start. - This course is built for marketers, not engineers. Every concept is taught through real business applications and pragmatic decision frameworks.
- You’ll follow step-by-step workflows used by growth leaders at companies like HubSpot, Notion, and Shopify - adapted for immediate use in any organisation.
- Recent graduates, agency strategists, and corporate marketing leads have all achieved breakthrough results using the same system.
After enrollment, you’ll receive a confirmation email. Your access details and onboarding instructions will be sent separately once your course materials are prepared - ensuring a smooth, secure, and personalised start.
Module 1: Foundations of AI-Driven Growth - Understanding the AI revolution in marketing and customer acquisition
- How AI is disrupting traditional growth strategies
- Defining AI-powered growth hacking vs. conventional marketing
- Core principles of machine learning in customer behaviour analysis
- The ethical boundaries of AI in marketing: compliance and transparency
- Mapping the modern buyer journey in an AI-automated world
- Identifying low-hanging AI opportunities in your current role
- Assessing organisational AI readiness
- Recognising the gap between AI hype and real marketing ROI
- Establishing your personal AI fluency score and learning path
Module 2: Strategic Frameworks for AI Integration - The Growth Funnel AI Integration Matrix
- Building your AI Opportunity Audit template
- Prioritising AI use cases by impact and feasibility
- Designing AI-enhanced customer segmentation models
- The Feedback Loop Principle: how to let AI optimise itself
- Integrating AI into your existing growth stack
- Creating a cross-functional AI adoption roadmap
- Aligning AI initiatives with business KPIs and OKRs
- Developing your personal AI strategy canvas
- Mapping AI tools to specific funnel stages: awareness to advocacy
Module 3: Intelligent Customer Acquisition Systems - Automated audience targeting with predictive analytics
- Dynamic ad copy generation using language models
- AI-powered A/B testing at scale
- Smart bidding and budget allocation algorithms
- Programmatic audience expansion using lookalike modelling
- Natural language processing for intent-based targeting
- Social listening and real-time trend detection with AI
- Building self-optimising ad campaigns
- Augmenting paid media with AI-driven audience insights
- Reducing acquisition costs through intelligent bid control
- Integrating AI insights into multi-channel campaign planning
- Using AI to predict campaign fatigue and refresh timing
Module 4: AI for Conversion Rate Optimisation - Predictive heatmaps and user behaviour modelling
- Automated landing page personalisation
- Dynamic call-to-action selection based on visitor profile
- AI-driven form optimisation and friction detection
- Real-time session analysis for conversion bottlenecks
- Churn prediction and pre-emptive retention messaging
- Automated checkout flow recommendations
- Intelligent pop-up and offer timing triggers
- Testing thousands of variation combinations with machine learning
- Using AI to interpret qualitative feedback at scale
- Building conversion dashboards with predictive alerts
- Deploying real-time personalisation engines without coding
Module 5: Hyper-Personalisation at Scale - Building next-best-action engines for marketing workflows
- Creating individualised email sequences using predictive content
- Dynamic content generation for newsletters and nurture flows
- Personalisation scoring and relevance calibration
- Leveraging past engagement to predict future preferences
- Automated subject line optimisation with sentiment analysis
- AI-generated behavioural triggers for email campaigns
- Segment-of-one marketing: principles and practical limits
- Using AI to personalise website experiences in real time
- Integrating CRM data with real-time personalisation tools
- Building closed-loop feedback from personalisation performance
- Achieving scalability without sacrificing relevance
Module 6: Predictive Analytics & Forecasting - Customer lifetime value prediction models
- Lead scoring automation with machine learning
- Churn risk identification and intervention planning
- Predictive budget forecasting for growth initiatives
- AI-powered revenue attribution modelling
- Simulating campaign outcomes before launch
- Building dynamic reporting dashboards with AI insights
- Scenario planning using predictive scenario engines
- Early warning systems for performance decline
- Automating monthly performance summaries with AI
- Using forecasting to justify growth investments to leadership
- Validating model accuracy with real-world data sets
Module 7: AI Content Strategy & Automation - Developing your AI content augmentation framework
- Generating high-converting blog outlines and briefs
- Automated SEO optimisation and keyword clustering
- Scaling content production with AI-assisted drafting
- Repurposing long-form content into micro-assets
- Creating AI-augmented video scripts without video production
- Topic clustering for authority-building content strategies
- Automated content gap analysis against competitors
- Using AI to maintain brand voice consistency at scale
- Scheduling and publishing workflows with AI timing insights
- Measuring content resonance with sentiment and engagement AI
- Automating content performance retrospectives
Module 8: AI for Social Media & Community Growth - Smart posting scheduling based on predictive engagement
- Automated community moderation with sentiment filtering
- Identifying viral content patterns using historical data
- Generating social copy variations for A/B testing
- AI-driven influencer identification and outreach
- Real-time brand sentiment tracking across platforms
- Automated community engagement suggestion engine
- Content recommendation engines for user feeds
- Using AI to detect emerging community subcultures
- Predicting platform algorithm changes based on signals
- Building AI-augmented user-generated content campaigns
- Scaling community management without hiring
Module 9: Chatbots, Conversational AI & Lead Engagement - Building no-code conversational workflows for lead qualification
- Training AI assistants on your product and service knowledge
- Integrating chatbots with CRM and email systems
- Designing emotional intelligence into AI conversations
- Natural language understanding for prospect intent detection
- Handoff protocols from AI to human agents
- Automating FAQ resolution and support triage
- Using chatbot data to improve product messaging
- Building persistent conversational memory for returning users
- Measuring chatbot contribution to conversion paths
- Scaling personalised onboarding experiences
- Analysing conversation transcripts for insight extraction
Module 10: Data Infrastructure for AI Marketing - Building a centralised behavioural data lake
- Integrating first-party data with AI tools
- Tagging and tracking best practices for AI readiness
- Clean room strategies for privacy-compliant AI
- Setting up data pipelines for automated AI ingestion
- Validating data accuracy for AI-driven decisions
- Using APIs to connect AI tools with your tech stack
- Ensuring GDPR, CCPA, and privacy law compliance
- Setting up data governance for marketing AI
- Automated data anomaly detection and alerts
- Preparing legacy data for AI interpretation
- Establishing data quality benchmarks
Module 11: AI Experimentation & Hypothesis Testing - Formulating testable AI-driven growth hypotheses
- Designing controlled experiments with machine learning
- Sample size calculation for AI-powered tests
- Automated result interpretation and insight generation
- Using Bayesian models for faster decision making
- Multi-armed bandit testing for continuous optimisation
- Running AI experiments without statistical PhDs
- Documenting test outcomes for organisational learning
- Scaling experimentation across teams and regions
- Building a culture of AI-informed risk taking
- Using AI to suggest new test ideas based on past results
- Creating an AI experimentation backlog
Module 12: AI Tools & Platform Selection - Comparing AI platforms by use case and integration needs
- Vendor evaluation framework: cost, scalability, support
- Piloting AI tools with minimal risk
- Understanding AI pricing models and hidden costs
- Budgeting for AI tools in marketing operations
- Negotiating AI tool contracts with legal and IT
- Assessing tool reliability and uptime SLAs
- Choosing between open-source and proprietary AI systems
- Integrating new AI tools without disrupting workflows
- Ensuring vendor alignment with data privacy standards
- Setting up sandbox environments for testing
- Transition planning from legacy systems to AI platforms
Module 13: AI in Product-Led Growth (PLG) - Using AI to identify product-led growth triggers
- Automated onboarding path personalisation
- Predicting feature adoption and driving activation
- AI-powered in-app messaging for behavioural nudges
- Identifying friction points in the user journey
- Scaling self-serve growth without support load
- Using AI to detect power user patterns
- Building virality loops with intelligent sharing prompts
- Automated trial-to-paid conversion pathways
- Measuring and optimising PLG motion with AI
- Connecting product usage data to marketing outcomes
- Leveraging AI for expansion revenue in SaaS
Module 14: AI for Retention & Revenue Expansion - Predictive renewal risk scoring models
- Automated upsell and cross-sell recommendation engines
- AI-driven customer success outreach sequencing
- Identifying expansion opportunities from usage data
- Churn mitigation workflows with AI triggers
- Personalised renewal offers using historical behaviour
- Automating customer health scoring dashboards
- Scaling account management across portfolios
- Using AI to detect downsell or downgrade signals
- Dynamic pricing models based on customer value
- Automated reference and testimonial collection
- Linking retention efforts directly to margin impact
Module 15: Ethical AI & Responsible Marketing - Avoiding bias in AI-driven customer targeting
- Ensuring transparency in automated decision making
- Designing for accessibility and inclusivity in AI tools
- Maintaining human oversight in AI workflows
- Disclosing AI use to customers where appropriate
- Preventing manipulative or exploitative AI applications
- Setting ethical boundaries for personalisation
- Conducting AI impact assessments
- Building trust with customers in algorithmic relationships
- Navigating the fine line between relevance and creepiness
- Establishing an AI ethics review process
- Communicating responsible AI use in brand messaging
Module 16: AI Governance & Change Management - Creating AI adoption playbooks for marketing teams
- Running AI literacy workshops for non-technical staff
- Managing resistance to AI-driven change
- Documenting AI workflows for audit and training
- Setting up AI performance monitoring and review cycles
- Establishing AI usage policies and approval processes
- Ensuring accountability in AI-assisted decisions
- Creating feedback loops from users to AI systems
- Scaling AI use without losing strategic control
- Integrating AI decisions into leadership reviews
- Preparing your team for continuous AI evolution
- Measuring the ROI of AI adoption efforts
Module 17: Building Your Personal AI Advantage - Creating your unique AI-powered marketing signature
- Positioning yourself as an AI-savvy leader internally
- Documenting and showcasing your AI projects
- Updating your LinkedIn and professional profile
- Speaking with confidence about AI in interviews
- Networking with AI-focused marketing communities
- Building a personal knowledge repository
- Staying ahead of emerging AI trends
- Continuing education pathways after course completion
- Contributing to industry discussions with authority
- Monetising your AI expertise through consulting
- Developing your own AI growth frameworks
Module 18: Capstone Project & Certification Path - Choosing your real-world AI growth challenge
- Defining project scope and success metrics
- Conducting stakeholder alignment for your initiative
- Building your AI strategy one-pager
- Developing a detailed implementation plan
- Creating a presentation for executive review
- Incorporating feedback into final proposal
- Submitting for instructor evaluation
- Revising based on expert guidance
- Demonstrating practical application of all course modules
- Proving your ability to deliver AI-driven ROI
- Earning your Certificate of Completion issued by The Art of Service
- Adding your credential to LinkedIn and professional portfolios
- Gaining access to advanced alumni resources
- Joining a global network of AI-powered marketers
- Understanding the AI revolution in marketing and customer acquisition
- How AI is disrupting traditional growth strategies
- Defining AI-powered growth hacking vs. conventional marketing
- Core principles of machine learning in customer behaviour analysis
- The ethical boundaries of AI in marketing: compliance and transparency
- Mapping the modern buyer journey in an AI-automated world
- Identifying low-hanging AI opportunities in your current role
- Assessing organisational AI readiness
- Recognising the gap between AI hype and real marketing ROI
- Establishing your personal AI fluency score and learning path
Module 2: Strategic Frameworks for AI Integration - The Growth Funnel AI Integration Matrix
- Building your AI Opportunity Audit template
- Prioritising AI use cases by impact and feasibility
- Designing AI-enhanced customer segmentation models
- The Feedback Loop Principle: how to let AI optimise itself
- Integrating AI into your existing growth stack
- Creating a cross-functional AI adoption roadmap
- Aligning AI initiatives with business KPIs and OKRs
- Developing your personal AI strategy canvas
- Mapping AI tools to specific funnel stages: awareness to advocacy
Module 3: Intelligent Customer Acquisition Systems - Automated audience targeting with predictive analytics
- Dynamic ad copy generation using language models
- AI-powered A/B testing at scale
- Smart bidding and budget allocation algorithms
- Programmatic audience expansion using lookalike modelling
- Natural language processing for intent-based targeting
- Social listening and real-time trend detection with AI
- Building self-optimising ad campaigns
- Augmenting paid media with AI-driven audience insights
- Reducing acquisition costs through intelligent bid control
- Integrating AI insights into multi-channel campaign planning
- Using AI to predict campaign fatigue and refresh timing
Module 4: AI for Conversion Rate Optimisation - Predictive heatmaps and user behaviour modelling
- Automated landing page personalisation
- Dynamic call-to-action selection based on visitor profile
- AI-driven form optimisation and friction detection
- Real-time session analysis for conversion bottlenecks
- Churn prediction and pre-emptive retention messaging
- Automated checkout flow recommendations
- Intelligent pop-up and offer timing triggers
- Testing thousands of variation combinations with machine learning
- Using AI to interpret qualitative feedback at scale
- Building conversion dashboards with predictive alerts
- Deploying real-time personalisation engines without coding
Module 5: Hyper-Personalisation at Scale - Building next-best-action engines for marketing workflows
- Creating individualised email sequences using predictive content
- Dynamic content generation for newsletters and nurture flows
- Personalisation scoring and relevance calibration
- Leveraging past engagement to predict future preferences
- Automated subject line optimisation with sentiment analysis
- AI-generated behavioural triggers for email campaigns
- Segment-of-one marketing: principles and practical limits
- Using AI to personalise website experiences in real time
- Integrating CRM data with real-time personalisation tools
- Building closed-loop feedback from personalisation performance
- Achieving scalability without sacrificing relevance
Module 6: Predictive Analytics & Forecasting - Customer lifetime value prediction models
- Lead scoring automation with machine learning
- Churn risk identification and intervention planning
- Predictive budget forecasting for growth initiatives
- AI-powered revenue attribution modelling
- Simulating campaign outcomes before launch
- Building dynamic reporting dashboards with AI insights
- Scenario planning using predictive scenario engines
- Early warning systems for performance decline
- Automating monthly performance summaries with AI
- Using forecasting to justify growth investments to leadership
- Validating model accuracy with real-world data sets
Module 7: AI Content Strategy & Automation - Developing your AI content augmentation framework
- Generating high-converting blog outlines and briefs
- Automated SEO optimisation and keyword clustering
- Scaling content production with AI-assisted drafting
- Repurposing long-form content into micro-assets
- Creating AI-augmented video scripts without video production
- Topic clustering for authority-building content strategies
- Automated content gap analysis against competitors
- Using AI to maintain brand voice consistency at scale
- Scheduling and publishing workflows with AI timing insights
- Measuring content resonance with sentiment and engagement AI
- Automating content performance retrospectives
Module 8: AI for Social Media & Community Growth - Smart posting scheduling based on predictive engagement
- Automated community moderation with sentiment filtering
- Identifying viral content patterns using historical data
- Generating social copy variations for A/B testing
- AI-driven influencer identification and outreach
- Real-time brand sentiment tracking across platforms
- Automated community engagement suggestion engine
- Content recommendation engines for user feeds
- Using AI to detect emerging community subcultures
- Predicting platform algorithm changes based on signals
- Building AI-augmented user-generated content campaigns
- Scaling community management without hiring
Module 9: Chatbots, Conversational AI & Lead Engagement - Building no-code conversational workflows for lead qualification
- Training AI assistants on your product and service knowledge
- Integrating chatbots with CRM and email systems
- Designing emotional intelligence into AI conversations
- Natural language understanding for prospect intent detection
- Handoff protocols from AI to human agents
- Automating FAQ resolution and support triage
- Using chatbot data to improve product messaging
- Building persistent conversational memory for returning users
- Measuring chatbot contribution to conversion paths
- Scaling personalised onboarding experiences
- Analysing conversation transcripts for insight extraction
Module 10: Data Infrastructure for AI Marketing - Building a centralised behavioural data lake
- Integrating first-party data with AI tools
- Tagging and tracking best practices for AI readiness
- Clean room strategies for privacy-compliant AI
- Setting up data pipelines for automated AI ingestion
- Validating data accuracy for AI-driven decisions
- Using APIs to connect AI tools with your tech stack
- Ensuring GDPR, CCPA, and privacy law compliance
- Setting up data governance for marketing AI
- Automated data anomaly detection and alerts
- Preparing legacy data for AI interpretation
- Establishing data quality benchmarks
Module 11: AI Experimentation & Hypothesis Testing - Formulating testable AI-driven growth hypotheses
- Designing controlled experiments with machine learning
- Sample size calculation for AI-powered tests
- Automated result interpretation and insight generation
- Using Bayesian models for faster decision making
- Multi-armed bandit testing for continuous optimisation
- Running AI experiments without statistical PhDs
- Documenting test outcomes for organisational learning
- Scaling experimentation across teams and regions
- Building a culture of AI-informed risk taking
- Using AI to suggest new test ideas based on past results
- Creating an AI experimentation backlog
Module 12: AI Tools & Platform Selection - Comparing AI platforms by use case and integration needs
- Vendor evaluation framework: cost, scalability, support
- Piloting AI tools with minimal risk
- Understanding AI pricing models and hidden costs
- Budgeting for AI tools in marketing operations
- Negotiating AI tool contracts with legal and IT
- Assessing tool reliability and uptime SLAs
- Choosing between open-source and proprietary AI systems
- Integrating new AI tools without disrupting workflows
- Ensuring vendor alignment with data privacy standards
- Setting up sandbox environments for testing
- Transition planning from legacy systems to AI platforms
Module 13: AI in Product-Led Growth (PLG) - Using AI to identify product-led growth triggers
- Automated onboarding path personalisation
- Predicting feature adoption and driving activation
- AI-powered in-app messaging for behavioural nudges
- Identifying friction points in the user journey
- Scaling self-serve growth without support load
- Using AI to detect power user patterns
- Building virality loops with intelligent sharing prompts
- Automated trial-to-paid conversion pathways
- Measuring and optimising PLG motion with AI
- Connecting product usage data to marketing outcomes
- Leveraging AI for expansion revenue in SaaS
Module 14: AI for Retention & Revenue Expansion - Predictive renewal risk scoring models
- Automated upsell and cross-sell recommendation engines
- AI-driven customer success outreach sequencing
- Identifying expansion opportunities from usage data
- Churn mitigation workflows with AI triggers
- Personalised renewal offers using historical behaviour
- Automating customer health scoring dashboards
- Scaling account management across portfolios
- Using AI to detect downsell or downgrade signals
- Dynamic pricing models based on customer value
- Automated reference and testimonial collection
- Linking retention efforts directly to margin impact
Module 15: Ethical AI & Responsible Marketing - Avoiding bias in AI-driven customer targeting
- Ensuring transparency in automated decision making
- Designing for accessibility and inclusivity in AI tools
- Maintaining human oversight in AI workflows
- Disclosing AI use to customers where appropriate
- Preventing manipulative or exploitative AI applications
- Setting ethical boundaries for personalisation
- Conducting AI impact assessments
- Building trust with customers in algorithmic relationships
- Navigating the fine line between relevance and creepiness
- Establishing an AI ethics review process
- Communicating responsible AI use in brand messaging
Module 16: AI Governance & Change Management - Creating AI adoption playbooks for marketing teams
- Running AI literacy workshops for non-technical staff
- Managing resistance to AI-driven change
- Documenting AI workflows for audit and training
- Setting up AI performance monitoring and review cycles
- Establishing AI usage policies and approval processes
- Ensuring accountability in AI-assisted decisions
- Creating feedback loops from users to AI systems
- Scaling AI use without losing strategic control
- Integrating AI decisions into leadership reviews
- Preparing your team for continuous AI evolution
- Measuring the ROI of AI adoption efforts
Module 17: Building Your Personal AI Advantage - Creating your unique AI-powered marketing signature
- Positioning yourself as an AI-savvy leader internally
- Documenting and showcasing your AI projects
- Updating your LinkedIn and professional profile
- Speaking with confidence about AI in interviews
- Networking with AI-focused marketing communities
- Building a personal knowledge repository
- Staying ahead of emerging AI trends
- Continuing education pathways after course completion
- Contributing to industry discussions with authority
- Monetising your AI expertise through consulting
- Developing your own AI growth frameworks
Module 18: Capstone Project & Certification Path - Choosing your real-world AI growth challenge
- Defining project scope and success metrics
- Conducting stakeholder alignment for your initiative
- Building your AI strategy one-pager
- Developing a detailed implementation plan
- Creating a presentation for executive review
- Incorporating feedback into final proposal
- Submitting for instructor evaluation
- Revising based on expert guidance
- Demonstrating practical application of all course modules
- Proving your ability to deliver AI-driven ROI
- Earning your Certificate of Completion issued by The Art of Service
- Adding your credential to LinkedIn and professional portfolios
- Gaining access to advanced alumni resources
- Joining a global network of AI-powered marketers
- Automated audience targeting with predictive analytics
- Dynamic ad copy generation using language models
- AI-powered A/B testing at scale
- Smart bidding and budget allocation algorithms
- Programmatic audience expansion using lookalike modelling
- Natural language processing for intent-based targeting
- Social listening and real-time trend detection with AI
- Building self-optimising ad campaigns
- Augmenting paid media with AI-driven audience insights
- Reducing acquisition costs through intelligent bid control
- Integrating AI insights into multi-channel campaign planning
- Using AI to predict campaign fatigue and refresh timing
Module 4: AI for Conversion Rate Optimisation - Predictive heatmaps and user behaviour modelling
- Automated landing page personalisation
- Dynamic call-to-action selection based on visitor profile
- AI-driven form optimisation and friction detection
- Real-time session analysis for conversion bottlenecks
- Churn prediction and pre-emptive retention messaging
- Automated checkout flow recommendations
- Intelligent pop-up and offer timing triggers
- Testing thousands of variation combinations with machine learning
- Using AI to interpret qualitative feedback at scale
- Building conversion dashboards with predictive alerts
- Deploying real-time personalisation engines without coding
Module 5: Hyper-Personalisation at Scale - Building next-best-action engines for marketing workflows
- Creating individualised email sequences using predictive content
- Dynamic content generation for newsletters and nurture flows
- Personalisation scoring and relevance calibration
- Leveraging past engagement to predict future preferences
- Automated subject line optimisation with sentiment analysis
- AI-generated behavioural triggers for email campaigns
- Segment-of-one marketing: principles and practical limits
- Using AI to personalise website experiences in real time
- Integrating CRM data with real-time personalisation tools
- Building closed-loop feedback from personalisation performance
- Achieving scalability without sacrificing relevance
Module 6: Predictive Analytics & Forecasting - Customer lifetime value prediction models
- Lead scoring automation with machine learning
- Churn risk identification and intervention planning
- Predictive budget forecasting for growth initiatives
- AI-powered revenue attribution modelling
- Simulating campaign outcomes before launch
- Building dynamic reporting dashboards with AI insights
- Scenario planning using predictive scenario engines
- Early warning systems for performance decline
- Automating monthly performance summaries with AI
- Using forecasting to justify growth investments to leadership
- Validating model accuracy with real-world data sets
Module 7: AI Content Strategy & Automation - Developing your AI content augmentation framework
- Generating high-converting blog outlines and briefs
- Automated SEO optimisation and keyword clustering
- Scaling content production with AI-assisted drafting
- Repurposing long-form content into micro-assets
- Creating AI-augmented video scripts without video production
- Topic clustering for authority-building content strategies
- Automated content gap analysis against competitors
- Using AI to maintain brand voice consistency at scale
- Scheduling and publishing workflows with AI timing insights
- Measuring content resonance with sentiment and engagement AI
- Automating content performance retrospectives
Module 8: AI for Social Media & Community Growth - Smart posting scheduling based on predictive engagement
- Automated community moderation with sentiment filtering
- Identifying viral content patterns using historical data
- Generating social copy variations for A/B testing
- AI-driven influencer identification and outreach
- Real-time brand sentiment tracking across platforms
- Automated community engagement suggestion engine
- Content recommendation engines for user feeds
- Using AI to detect emerging community subcultures
- Predicting platform algorithm changes based on signals
- Building AI-augmented user-generated content campaigns
- Scaling community management without hiring
Module 9: Chatbots, Conversational AI & Lead Engagement - Building no-code conversational workflows for lead qualification
- Training AI assistants on your product and service knowledge
- Integrating chatbots with CRM and email systems
- Designing emotional intelligence into AI conversations
- Natural language understanding for prospect intent detection
- Handoff protocols from AI to human agents
- Automating FAQ resolution and support triage
- Using chatbot data to improve product messaging
- Building persistent conversational memory for returning users
- Measuring chatbot contribution to conversion paths
- Scaling personalised onboarding experiences
- Analysing conversation transcripts for insight extraction
Module 10: Data Infrastructure for AI Marketing - Building a centralised behavioural data lake
- Integrating first-party data with AI tools
- Tagging and tracking best practices for AI readiness
- Clean room strategies for privacy-compliant AI
- Setting up data pipelines for automated AI ingestion
- Validating data accuracy for AI-driven decisions
- Using APIs to connect AI tools with your tech stack
- Ensuring GDPR, CCPA, and privacy law compliance
- Setting up data governance for marketing AI
- Automated data anomaly detection and alerts
- Preparing legacy data for AI interpretation
- Establishing data quality benchmarks
Module 11: AI Experimentation & Hypothesis Testing - Formulating testable AI-driven growth hypotheses
- Designing controlled experiments with machine learning
- Sample size calculation for AI-powered tests
- Automated result interpretation and insight generation
- Using Bayesian models for faster decision making
- Multi-armed bandit testing for continuous optimisation
- Running AI experiments without statistical PhDs
- Documenting test outcomes for organisational learning
- Scaling experimentation across teams and regions
- Building a culture of AI-informed risk taking
- Using AI to suggest new test ideas based on past results
- Creating an AI experimentation backlog
Module 12: AI Tools & Platform Selection - Comparing AI platforms by use case and integration needs
- Vendor evaluation framework: cost, scalability, support
- Piloting AI tools with minimal risk
- Understanding AI pricing models and hidden costs
- Budgeting for AI tools in marketing operations
- Negotiating AI tool contracts with legal and IT
- Assessing tool reliability and uptime SLAs
- Choosing between open-source and proprietary AI systems
- Integrating new AI tools without disrupting workflows
- Ensuring vendor alignment with data privacy standards
- Setting up sandbox environments for testing
- Transition planning from legacy systems to AI platforms
Module 13: AI in Product-Led Growth (PLG) - Using AI to identify product-led growth triggers
- Automated onboarding path personalisation
- Predicting feature adoption and driving activation
- AI-powered in-app messaging for behavioural nudges
- Identifying friction points in the user journey
- Scaling self-serve growth without support load
- Using AI to detect power user patterns
- Building virality loops with intelligent sharing prompts
- Automated trial-to-paid conversion pathways
- Measuring and optimising PLG motion with AI
- Connecting product usage data to marketing outcomes
- Leveraging AI for expansion revenue in SaaS
Module 14: AI for Retention & Revenue Expansion - Predictive renewal risk scoring models
- Automated upsell and cross-sell recommendation engines
- AI-driven customer success outreach sequencing
- Identifying expansion opportunities from usage data
- Churn mitigation workflows with AI triggers
- Personalised renewal offers using historical behaviour
- Automating customer health scoring dashboards
- Scaling account management across portfolios
- Using AI to detect downsell or downgrade signals
- Dynamic pricing models based on customer value
- Automated reference and testimonial collection
- Linking retention efforts directly to margin impact
Module 15: Ethical AI & Responsible Marketing - Avoiding bias in AI-driven customer targeting
- Ensuring transparency in automated decision making
- Designing for accessibility and inclusivity in AI tools
- Maintaining human oversight in AI workflows
- Disclosing AI use to customers where appropriate
- Preventing manipulative or exploitative AI applications
- Setting ethical boundaries for personalisation
- Conducting AI impact assessments
- Building trust with customers in algorithmic relationships
- Navigating the fine line between relevance and creepiness
- Establishing an AI ethics review process
- Communicating responsible AI use in brand messaging
Module 16: AI Governance & Change Management - Creating AI adoption playbooks for marketing teams
- Running AI literacy workshops for non-technical staff
- Managing resistance to AI-driven change
- Documenting AI workflows for audit and training
- Setting up AI performance monitoring and review cycles
- Establishing AI usage policies and approval processes
- Ensuring accountability in AI-assisted decisions
- Creating feedback loops from users to AI systems
- Scaling AI use without losing strategic control
- Integrating AI decisions into leadership reviews
- Preparing your team for continuous AI evolution
- Measuring the ROI of AI adoption efforts
Module 17: Building Your Personal AI Advantage - Creating your unique AI-powered marketing signature
- Positioning yourself as an AI-savvy leader internally
- Documenting and showcasing your AI projects
- Updating your LinkedIn and professional profile
- Speaking with confidence about AI in interviews
- Networking with AI-focused marketing communities
- Building a personal knowledge repository
- Staying ahead of emerging AI trends
- Continuing education pathways after course completion
- Contributing to industry discussions with authority
- Monetising your AI expertise through consulting
- Developing your own AI growth frameworks
Module 18: Capstone Project & Certification Path - Choosing your real-world AI growth challenge
- Defining project scope and success metrics
- Conducting stakeholder alignment for your initiative
- Building your AI strategy one-pager
- Developing a detailed implementation plan
- Creating a presentation for executive review
- Incorporating feedback into final proposal
- Submitting for instructor evaluation
- Revising based on expert guidance
- Demonstrating practical application of all course modules
- Proving your ability to deliver AI-driven ROI
- Earning your Certificate of Completion issued by The Art of Service
- Adding your credential to LinkedIn and professional portfolios
- Gaining access to advanced alumni resources
- Joining a global network of AI-powered marketers
- Building next-best-action engines for marketing workflows
- Creating individualised email sequences using predictive content
- Dynamic content generation for newsletters and nurture flows
- Personalisation scoring and relevance calibration
- Leveraging past engagement to predict future preferences
- Automated subject line optimisation with sentiment analysis
- AI-generated behavioural triggers for email campaigns
- Segment-of-one marketing: principles and practical limits
- Using AI to personalise website experiences in real time
- Integrating CRM data with real-time personalisation tools
- Building closed-loop feedback from personalisation performance
- Achieving scalability without sacrificing relevance
Module 6: Predictive Analytics & Forecasting - Customer lifetime value prediction models
- Lead scoring automation with machine learning
- Churn risk identification and intervention planning
- Predictive budget forecasting for growth initiatives
- AI-powered revenue attribution modelling
- Simulating campaign outcomes before launch
- Building dynamic reporting dashboards with AI insights
- Scenario planning using predictive scenario engines
- Early warning systems for performance decline
- Automating monthly performance summaries with AI
- Using forecasting to justify growth investments to leadership
- Validating model accuracy with real-world data sets
Module 7: AI Content Strategy & Automation - Developing your AI content augmentation framework
- Generating high-converting blog outlines and briefs
- Automated SEO optimisation and keyword clustering
- Scaling content production with AI-assisted drafting
- Repurposing long-form content into micro-assets
- Creating AI-augmented video scripts without video production
- Topic clustering for authority-building content strategies
- Automated content gap analysis against competitors
- Using AI to maintain brand voice consistency at scale
- Scheduling and publishing workflows with AI timing insights
- Measuring content resonance with sentiment and engagement AI
- Automating content performance retrospectives
Module 8: AI for Social Media & Community Growth - Smart posting scheduling based on predictive engagement
- Automated community moderation with sentiment filtering
- Identifying viral content patterns using historical data
- Generating social copy variations for A/B testing
- AI-driven influencer identification and outreach
- Real-time brand sentiment tracking across platforms
- Automated community engagement suggestion engine
- Content recommendation engines for user feeds
- Using AI to detect emerging community subcultures
- Predicting platform algorithm changes based on signals
- Building AI-augmented user-generated content campaigns
- Scaling community management without hiring
Module 9: Chatbots, Conversational AI & Lead Engagement - Building no-code conversational workflows for lead qualification
- Training AI assistants on your product and service knowledge
- Integrating chatbots with CRM and email systems
- Designing emotional intelligence into AI conversations
- Natural language understanding for prospect intent detection
- Handoff protocols from AI to human agents
- Automating FAQ resolution and support triage
- Using chatbot data to improve product messaging
- Building persistent conversational memory for returning users
- Measuring chatbot contribution to conversion paths
- Scaling personalised onboarding experiences
- Analysing conversation transcripts for insight extraction
Module 10: Data Infrastructure for AI Marketing - Building a centralised behavioural data lake
- Integrating first-party data with AI tools
- Tagging and tracking best practices for AI readiness
- Clean room strategies for privacy-compliant AI
- Setting up data pipelines for automated AI ingestion
- Validating data accuracy for AI-driven decisions
- Using APIs to connect AI tools with your tech stack
- Ensuring GDPR, CCPA, and privacy law compliance
- Setting up data governance for marketing AI
- Automated data anomaly detection and alerts
- Preparing legacy data for AI interpretation
- Establishing data quality benchmarks
Module 11: AI Experimentation & Hypothesis Testing - Formulating testable AI-driven growth hypotheses
- Designing controlled experiments with machine learning
- Sample size calculation for AI-powered tests
- Automated result interpretation and insight generation
- Using Bayesian models for faster decision making
- Multi-armed bandit testing for continuous optimisation
- Running AI experiments without statistical PhDs
- Documenting test outcomes for organisational learning
- Scaling experimentation across teams and regions
- Building a culture of AI-informed risk taking
- Using AI to suggest new test ideas based on past results
- Creating an AI experimentation backlog
Module 12: AI Tools & Platform Selection - Comparing AI platforms by use case and integration needs
- Vendor evaluation framework: cost, scalability, support
- Piloting AI tools with minimal risk
- Understanding AI pricing models and hidden costs
- Budgeting for AI tools in marketing operations
- Negotiating AI tool contracts with legal and IT
- Assessing tool reliability and uptime SLAs
- Choosing between open-source and proprietary AI systems
- Integrating new AI tools without disrupting workflows
- Ensuring vendor alignment with data privacy standards
- Setting up sandbox environments for testing
- Transition planning from legacy systems to AI platforms
Module 13: AI in Product-Led Growth (PLG) - Using AI to identify product-led growth triggers
- Automated onboarding path personalisation
- Predicting feature adoption and driving activation
- AI-powered in-app messaging for behavioural nudges
- Identifying friction points in the user journey
- Scaling self-serve growth without support load
- Using AI to detect power user patterns
- Building virality loops with intelligent sharing prompts
- Automated trial-to-paid conversion pathways
- Measuring and optimising PLG motion with AI
- Connecting product usage data to marketing outcomes
- Leveraging AI for expansion revenue in SaaS
Module 14: AI for Retention & Revenue Expansion - Predictive renewal risk scoring models
- Automated upsell and cross-sell recommendation engines
- AI-driven customer success outreach sequencing
- Identifying expansion opportunities from usage data
- Churn mitigation workflows with AI triggers
- Personalised renewal offers using historical behaviour
- Automating customer health scoring dashboards
- Scaling account management across portfolios
- Using AI to detect downsell or downgrade signals
- Dynamic pricing models based on customer value
- Automated reference and testimonial collection
- Linking retention efforts directly to margin impact
Module 15: Ethical AI & Responsible Marketing - Avoiding bias in AI-driven customer targeting
- Ensuring transparency in automated decision making
- Designing for accessibility and inclusivity in AI tools
- Maintaining human oversight in AI workflows
- Disclosing AI use to customers where appropriate
- Preventing manipulative or exploitative AI applications
- Setting ethical boundaries for personalisation
- Conducting AI impact assessments
- Building trust with customers in algorithmic relationships
- Navigating the fine line between relevance and creepiness
- Establishing an AI ethics review process
- Communicating responsible AI use in brand messaging
Module 16: AI Governance & Change Management - Creating AI adoption playbooks for marketing teams
- Running AI literacy workshops for non-technical staff
- Managing resistance to AI-driven change
- Documenting AI workflows for audit and training
- Setting up AI performance monitoring and review cycles
- Establishing AI usage policies and approval processes
- Ensuring accountability in AI-assisted decisions
- Creating feedback loops from users to AI systems
- Scaling AI use without losing strategic control
- Integrating AI decisions into leadership reviews
- Preparing your team for continuous AI evolution
- Measuring the ROI of AI adoption efforts
Module 17: Building Your Personal AI Advantage - Creating your unique AI-powered marketing signature
- Positioning yourself as an AI-savvy leader internally
- Documenting and showcasing your AI projects
- Updating your LinkedIn and professional profile
- Speaking with confidence about AI in interviews
- Networking with AI-focused marketing communities
- Building a personal knowledge repository
- Staying ahead of emerging AI trends
- Continuing education pathways after course completion
- Contributing to industry discussions with authority
- Monetising your AI expertise through consulting
- Developing your own AI growth frameworks
Module 18: Capstone Project & Certification Path - Choosing your real-world AI growth challenge
- Defining project scope and success metrics
- Conducting stakeholder alignment for your initiative
- Building your AI strategy one-pager
- Developing a detailed implementation plan
- Creating a presentation for executive review
- Incorporating feedback into final proposal
- Submitting for instructor evaluation
- Revising based on expert guidance
- Demonstrating practical application of all course modules
- Proving your ability to deliver AI-driven ROI
- Earning your Certificate of Completion issued by The Art of Service
- Adding your credential to LinkedIn and professional portfolios
- Gaining access to advanced alumni resources
- Joining a global network of AI-powered marketers
- Developing your AI content augmentation framework
- Generating high-converting blog outlines and briefs
- Automated SEO optimisation and keyword clustering
- Scaling content production with AI-assisted drafting
- Repurposing long-form content into micro-assets
- Creating AI-augmented video scripts without video production
- Topic clustering for authority-building content strategies
- Automated content gap analysis against competitors
- Using AI to maintain brand voice consistency at scale
- Scheduling and publishing workflows with AI timing insights
- Measuring content resonance with sentiment and engagement AI
- Automating content performance retrospectives
Module 8: AI for Social Media & Community Growth - Smart posting scheduling based on predictive engagement
- Automated community moderation with sentiment filtering
- Identifying viral content patterns using historical data
- Generating social copy variations for A/B testing
- AI-driven influencer identification and outreach
- Real-time brand sentiment tracking across platforms
- Automated community engagement suggestion engine
- Content recommendation engines for user feeds
- Using AI to detect emerging community subcultures
- Predicting platform algorithm changes based on signals
- Building AI-augmented user-generated content campaigns
- Scaling community management without hiring
Module 9: Chatbots, Conversational AI & Lead Engagement - Building no-code conversational workflows for lead qualification
- Training AI assistants on your product and service knowledge
- Integrating chatbots with CRM and email systems
- Designing emotional intelligence into AI conversations
- Natural language understanding for prospect intent detection
- Handoff protocols from AI to human agents
- Automating FAQ resolution and support triage
- Using chatbot data to improve product messaging
- Building persistent conversational memory for returning users
- Measuring chatbot contribution to conversion paths
- Scaling personalised onboarding experiences
- Analysing conversation transcripts for insight extraction
Module 10: Data Infrastructure for AI Marketing - Building a centralised behavioural data lake
- Integrating first-party data with AI tools
- Tagging and tracking best practices for AI readiness
- Clean room strategies for privacy-compliant AI
- Setting up data pipelines for automated AI ingestion
- Validating data accuracy for AI-driven decisions
- Using APIs to connect AI tools with your tech stack
- Ensuring GDPR, CCPA, and privacy law compliance
- Setting up data governance for marketing AI
- Automated data anomaly detection and alerts
- Preparing legacy data for AI interpretation
- Establishing data quality benchmarks
Module 11: AI Experimentation & Hypothesis Testing - Formulating testable AI-driven growth hypotheses
- Designing controlled experiments with machine learning
- Sample size calculation for AI-powered tests
- Automated result interpretation and insight generation
- Using Bayesian models for faster decision making
- Multi-armed bandit testing for continuous optimisation
- Running AI experiments without statistical PhDs
- Documenting test outcomes for organisational learning
- Scaling experimentation across teams and regions
- Building a culture of AI-informed risk taking
- Using AI to suggest new test ideas based on past results
- Creating an AI experimentation backlog
Module 12: AI Tools & Platform Selection - Comparing AI platforms by use case and integration needs
- Vendor evaluation framework: cost, scalability, support
- Piloting AI tools with minimal risk
- Understanding AI pricing models and hidden costs
- Budgeting for AI tools in marketing operations
- Negotiating AI tool contracts with legal and IT
- Assessing tool reliability and uptime SLAs
- Choosing between open-source and proprietary AI systems
- Integrating new AI tools without disrupting workflows
- Ensuring vendor alignment with data privacy standards
- Setting up sandbox environments for testing
- Transition planning from legacy systems to AI platforms
Module 13: AI in Product-Led Growth (PLG) - Using AI to identify product-led growth triggers
- Automated onboarding path personalisation
- Predicting feature adoption and driving activation
- AI-powered in-app messaging for behavioural nudges
- Identifying friction points in the user journey
- Scaling self-serve growth without support load
- Using AI to detect power user patterns
- Building virality loops with intelligent sharing prompts
- Automated trial-to-paid conversion pathways
- Measuring and optimising PLG motion with AI
- Connecting product usage data to marketing outcomes
- Leveraging AI for expansion revenue in SaaS
Module 14: AI for Retention & Revenue Expansion - Predictive renewal risk scoring models
- Automated upsell and cross-sell recommendation engines
- AI-driven customer success outreach sequencing
- Identifying expansion opportunities from usage data
- Churn mitigation workflows with AI triggers
- Personalised renewal offers using historical behaviour
- Automating customer health scoring dashboards
- Scaling account management across portfolios
- Using AI to detect downsell or downgrade signals
- Dynamic pricing models based on customer value
- Automated reference and testimonial collection
- Linking retention efforts directly to margin impact
Module 15: Ethical AI & Responsible Marketing - Avoiding bias in AI-driven customer targeting
- Ensuring transparency in automated decision making
- Designing for accessibility and inclusivity in AI tools
- Maintaining human oversight in AI workflows
- Disclosing AI use to customers where appropriate
- Preventing manipulative or exploitative AI applications
- Setting ethical boundaries for personalisation
- Conducting AI impact assessments
- Building trust with customers in algorithmic relationships
- Navigating the fine line between relevance and creepiness
- Establishing an AI ethics review process
- Communicating responsible AI use in brand messaging
Module 16: AI Governance & Change Management - Creating AI adoption playbooks for marketing teams
- Running AI literacy workshops for non-technical staff
- Managing resistance to AI-driven change
- Documenting AI workflows for audit and training
- Setting up AI performance monitoring and review cycles
- Establishing AI usage policies and approval processes
- Ensuring accountability in AI-assisted decisions
- Creating feedback loops from users to AI systems
- Scaling AI use without losing strategic control
- Integrating AI decisions into leadership reviews
- Preparing your team for continuous AI evolution
- Measuring the ROI of AI adoption efforts
Module 17: Building Your Personal AI Advantage - Creating your unique AI-powered marketing signature
- Positioning yourself as an AI-savvy leader internally
- Documenting and showcasing your AI projects
- Updating your LinkedIn and professional profile
- Speaking with confidence about AI in interviews
- Networking with AI-focused marketing communities
- Building a personal knowledge repository
- Staying ahead of emerging AI trends
- Continuing education pathways after course completion
- Contributing to industry discussions with authority
- Monetising your AI expertise through consulting
- Developing your own AI growth frameworks
Module 18: Capstone Project & Certification Path - Choosing your real-world AI growth challenge
- Defining project scope and success metrics
- Conducting stakeholder alignment for your initiative
- Building your AI strategy one-pager
- Developing a detailed implementation plan
- Creating a presentation for executive review
- Incorporating feedback into final proposal
- Submitting for instructor evaluation
- Revising based on expert guidance
- Demonstrating practical application of all course modules
- Proving your ability to deliver AI-driven ROI
- Earning your Certificate of Completion issued by The Art of Service
- Adding your credential to LinkedIn and professional portfolios
- Gaining access to advanced alumni resources
- Joining a global network of AI-powered marketers
- Building no-code conversational workflows for lead qualification
- Training AI assistants on your product and service knowledge
- Integrating chatbots with CRM and email systems
- Designing emotional intelligence into AI conversations
- Natural language understanding for prospect intent detection
- Handoff protocols from AI to human agents
- Automating FAQ resolution and support triage
- Using chatbot data to improve product messaging
- Building persistent conversational memory for returning users
- Measuring chatbot contribution to conversion paths
- Scaling personalised onboarding experiences
- Analysing conversation transcripts for insight extraction
Module 10: Data Infrastructure for AI Marketing - Building a centralised behavioural data lake
- Integrating first-party data with AI tools
- Tagging and tracking best practices for AI readiness
- Clean room strategies for privacy-compliant AI
- Setting up data pipelines for automated AI ingestion
- Validating data accuracy for AI-driven decisions
- Using APIs to connect AI tools with your tech stack
- Ensuring GDPR, CCPA, and privacy law compliance
- Setting up data governance for marketing AI
- Automated data anomaly detection and alerts
- Preparing legacy data for AI interpretation
- Establishing data quality benchmarks
Module 11: AI Experimentation & Hypothesis Testing - Formulating testable AI-driven growth hypotheses
- Designing controlled experiments with machine learning
- Sample size calculation for AI-powered tests
- Automated result interpretation and insight generation
- Using Bayesian models for faster decision making
- Multi-armed bandit testing for continuous optimisation
- Running AI experiments without statistical PhDs
- Documenting test outcomes for organisational learning
- Scaling experimentation across teams and regions
- Building a culture of AI-informed risk taking
- Using AI to suggest new test ideas based on past results
- Creating an AI experimentation backlog
Module 12: AI Tools & Platform Selection - Comparing AI platforms by use case and integration needs
- Vendor evaluation framework: cost, scalability, support
- Piloting AI tools with minimal risk
- Understanding AI pricing models and hidden costs
- Budgeting for AI tools in marketing operations
- Negotiating AI tool contracts with legal and IT
- Assessing tool reliability and uptime SLAs
- Choosing between open-source and proprietary AI systems
- Integrating new AI tools without disrupting workflows
- Ensuring vendor alignment with data privacy standards
- Setting up sandbox environments for testing
- Transition planning from legacy systems to AI platforms
Module 13: AI in Product-Led Growth (PLG) - Using AI to identify product-led growth triggers
- Automated onboarding path personalisation
- Predicting feature adoption and driving activation
- AI-powered in-app messaging for behavioural nudges
- Identifying friction points in the user journey
- Scaling self-serve growth without support load
- Using AI to detect power user patterns
- Building virality loops with intelligent sharing prompts
- Automated trial-to-paid conversion pathways
- Measuring and optimising PLG motion with AI
- Connecting product usage data to marketing outcomes
- Leveraging AI for expansion revenue in SaaS
Module 14: AI for Retention & Revenue Expansion - Predictive renewal risk scoring models
- Automated upsell and cross-sell recommendation engines
- AI-driven customer success outreach sequencing
- Identifying expansion opportunities from usage data
- Churn mitigation workflows with AI triggers
- Personalised renewal offers using historical behaviour
- Automating customer health scoring dashboards
- Scaling account management across portfolios
- Using AI to detect downsell or downgrade signals
- Dynamic pricing models based on customer value
- Automated reference and testimonial collection
- Linking retention efforts directly to margin impact
Module 15: Ethical AI & Responsible Marketing - Avoiding bias in AI-driven customer targeting
- Ensuring transparency in automated decision making
- Designing for accessibility and inclusivity in AI tools
- Maintaining human oversight in AI workflows
- Disclosing AI use to customers where appropriate
- Preventing manipulative or exploitative AI applications
- Setting ethical boundaries for personalisation
- Conducting AI impact assessments
- Building trust with customers in algorithmic relationships
- Navigating the fine line between relevance and creepiness
- Establishing an AI ethics review process
- Communicating responsible AI use in brand messaging
Module 16: AI Governance & Change Management - Creating AI adoption playbooks for marketing teams
- Running AI literacy workshops for non-technical staff
- Managing resistance to AI-driven change
- Documenting AI workflows for audit and training
- Setting up AI performance monitoring and review cycles
- Establishing AI usage policies and approval processes
- Ensuring accountability in AI-assisted decisions
- Creating feedback loops from users to AI systems
- Scaling AI use without losing strategic control
- Integrating AI decisions into leadership reviews
- Preparing your team for continuous AI evolution
- Measuring the ROI of AI adoption efforts
Module 17: Building Your Personal AI Advantage - Creating your unique AI-powered marketing signature
- Positioning yourself as an AI-savvy leader internally
- Documenting and showcasing your AI projects
- Updating your LinkedIn and professional profile
- Speaking with confidence about AI in interviews
- Networking with AI-focused marketing communities
- Building a personal knowledge repository
- Staying ahead of emerging AI trends
- Continuing education pathways after course completion
- Contributing to industry discussions with authority
- Monetising your AI expertise through consulting
- Developing your own AI growth frameworks
Module 18: Capstone Project & Certification Path - Choosing your real-world AI growth challenge
- Defining project scope and success metrics
- Conducting stakeholder alignment for your initiative
- Building your AI strategy one-pager
- Developing a detailed implementation plan
- Creating a presentation for executive review
- Incorporating feedback into final proposal
- Submitting for instructor evaluation
- Revising based on expert guidance
- Demonstrating practical application of all course modules
- Proving your ability to deliver AI-driven ROI
- Earning your Certificate of Completion issued by The Art of Service
- Adding your credential to LinkedIn and professional portfolios
- Gaining access to advanced alumni resources
- Joining a global network of AI-powered marketers
- Formulating testable AI-driven growth hypotheses
- Designing controlled experiments with machine learning
- Sample size calculation for AI-powered tests
- Automated result interpretation and insight generation
- Using Bayesian models for faster decision making
- Multi-armed bandit testing for continuous optimisation
- Running AI experiments without statistical PhDs
- Documenting test outcomes for organisational learning
- Scaling experimentation across teams and regions
- Building a culture of AI-informed risk taking
- Using AI to suggest new test ideas based on past results
- Creating an AI experimentation backlog
Module 12: AI Tools & Platform Selection - Comparing AI platforms by use case and integration needs
- Vendor evaluation framework: cost, scalability, support
- Piloting AI tools with minimal risk
- Understanding AI pricing models and hidden costs
- Budgeting for AI tools in marketing operations
- Negotiating AI tool contracts with legal and IT
- Assessing tool reliability and uptime SLAs
- Choosing between open-source and proprietary AI systems
- Integrating new AI tools without disrupting workflows
- Ensuring vendor alignment with data privacy standards
- Setting up sandbox environments for testing
- Transition planning from legacy systems to AI platforms
Module 13: AI in Product-Led Growth (PLG) - Using AI to identify product-led growth triggers
- Automated onboarding path personalisation
- Predicting feature adoption and driving activation
- AI-powered in-app messaging for behavioural nudges
- Identifying friction points in the user journey
- Scaling self-serve growth without support load
- Using AI to detect power user patterns
- Building virality loops with intelligent sharing prompts
- Automated trial-to-paid conversion pathways
- Measuring and optimising PLG motion with AI
- Connecting product usage data to marketing outcomes
- Leveraging AI for expansion revenue in SaaS
Module 14: AI for Retention & Revenue Expansion - Predictive renewal risk scoring models
- Automated upsell and cross-sell recommendation engines
- AI-driven customer success outreach sequencing
- Identifying expansion opportunities from usage data
- Churn mitigation workflows with AI triggers
- Personalised renewal offers using historical behaviour
- Automating customer health scoring dashboards
- Scaling account management across portfolios
- Using AI to detect downsell or downgrade signals
- Dynamic pricing models based on customer value
- Automated reference and testimonial collection
- Linking retention efforts directly to margin impact
Module 15: Ethical AI & Responsible Marketing - Avoiding bias in AI-driven customer targeting
- Ensuring transparency in automated decision making
- Designing for accessibility and inclusivity in AI tools
- Maintaining human oversight in AI workflows
- Disclosing AI use to customers where appropriate
- Preventing manipulative or exploitative AI applications
- Setting ethical boundaries for personalisation
- Conducting AI impact assessments
- Building trust with customers in algorithmic relationships
- Navigating the fine line between relevance and creepiness
- Establishing an AI ethics review process
- Communicating responsible AI use in brand messaging
Module 16: AI Governance & Change Management - Creating AI adoption playbooks for marketing teams
- Running AI literacy workshops for non-technical staff
- Managing resistance to AI-driven change
- Documenting AI workflows for audit and training
- Setting up AI performance monitoring and review cycles
- Establishing AI usage policies and approval processes
- Ensuring accountability in AI-assisted decisions
- Creating feedback loops from users to AI systems
- Scaling AI use without losing strategic control
- Integrating AI decisions into leadership reviews
- Preparing your team for continuous AI evolution
- Measuring the ROI of AI adoption efforts
Module 17: Building Your Personal AI Advantage - Creating your unique AI-powered marketing signature
- Positioning yourself as an AI-savvy leader internally
- Documenting and showcasing your AI projects
- Updating your LinkedIn and professional profile
- Speaking with confidence about AI in interviews
- Networking with AI-focused marketing communities
- Building a personal knowledge repository
- Staying ahead of emerging AI trends
- Continuing education pathways after course completion
- Contributing to industry discussions with authority
- Monetising your AI expertise through consulting
- Developing your own AI growth frameworks
Module 18: Capstone Project & Certification Path - Choosing your real-world AI growth challenge
- Defining project scope and success metrics
- Conducting stakeholder alignment for your initiative
- Building your AI strategy one-pager
- Developing a detailed implementation plan
- Creating a presentation for executive review
- Incorporating feedback into final proposal
- Submitting for instructor evaluation
- Revising based on expert guidance
- Demonstrating practical application of all course modules
- Proving your ability to deliver AI-driven ROI
- Earning your Certificate of Completion issued by The Art of Service
- Adding your credential to LinkedIn and professional portfolios
- Gaining access to advanced alumni resources
- Joining a global network of AI-powered marketers
- Using AI to identify product-led growth triggers
- Automated onboarding path personalisation
- Predicting feature adoption and driving activation
- AI-powered in-app messaging for behavioural nudges
- Identifying friction points in the user journey
- Scaling self-serve growth without support load
- Using AI to detect power user patterns
- Building virality loops with intelligent sharing prompts
- Automated trial-to-paid conversion pathways
- Measuring and optimising PLG motion with AI
- Connecting product usage data to marketing outcomes
- Leveraging AI for expansion revenue in SaaS
Module 14: AI for Retention & Revenue Expansion - Predictive renewal risk scoring models
- Automated upsell and cross-sell recommendation engines
- AI-driven customer success outreach sequencing
- Identifying expansion opportunities from usage data
- Churn mitigation workflows with AI triggers
- Personalised renewal offers using historical behaviour
- Automating customer health scoring dashboards
- Scaling account management across portfolios
- Using AI to detect downsell or downgrade signals
- Dynamic pricing models based on customer value
- Automated reference and testimonial collection
- Linking retention efforts directly to margin impact
Module 15: Ethical AI & Responsible Marketing - Avoiding bias in AI-driven customer targeting
- Ensuring transparency in automated decision making
- Designing for accessibility and inclusivity in AI tools
- Maintaining human oversight in AI workflows
- Disclosing AI use to customers where appropriate
- Preventing manipulative or exploitative AI applications
- Setting ethical boundaries for personalisation
- Conducting AI impact assessments
- Building trust with customers in algorithmic relationships
- Navigating the fine line between relevance and creepiness
- Establishing an AI ethics review process
- Communicating responsible AI use in brand messaging
Module 16: AI Governance & Change Management - Creating AI adoption playbooks for marketing teams
- Running AI literacy workshops for non-technical staff
- Managing resistance to AI-driven change
- Documenting AI workflows for audit and training
- Setting up AI performance monitoring and review cycles
- Establishing AI usage policies and approval processes
- Ensuring accountability in AI-assisted decisions
- Creating feedback loops from users to AI systems
- Scaling AI use without losing strategic control
- Integrating AI decisions into leadership reviews
- Preparing your team for continuous AI evolution
- Measuring the ROI of AI adoption efforts
Module 17: Building Your Personal AI Advantage - Creating your unique AI-powered marketing signature
- Positioning yourself as an AI-savvy leader internally
- Documenting and showcasing your AI projects
- Updating your LinkedIn and professional profile
- Speaking with confidence about AI in interviews
- Networking with AI-focused marketing communities
- Building a personal knowledge repository
- Staying ahead of emerging AI trends
- Continuing education pathways after course completion
- Contributing to industry discussions with authority
- Monetising your AI expertise through consulting
- Developing your own AI growth frameworks
Module 18: Capstone Project & Certification Path - Choosing your real-world AI growth challenge
- Defining project scope and success metrics
- Conducting stakeholder alignment for your initiative
- Building your AI strategy one-pager
- Developing a detailed implementation plan
- Creating a presentation for executive review
- Incorporating feedback into final proposal
- Submitting for instructor evaluation
- Revising based on expert guidance
- Demonstrating practical application of all course modules
- Proving your ability to deliver AI-driven ROI
- Earning your Certificate of Completion issued by The Art of Service
- Adding your credential to LinkedIn and professional portfolios
- Gaining access to advanced alumni resources
- Joining a global network of AI-powered marketers
- Avoiding bias in AI-driven customer targeting
- Ensuring transparency in automated decision making
- Designing for accessibility and inclusivity in AI tools
- Maintaining human oversight in AI workflows
- Disclosing AI use to customers where appropriate
- Preventing manipulative or exploitative AI applications
- Setting ethical boundaries for personalisation
- Conducting AI impact assessments
- Building trust with customers in algorithmic relationships
- Navigating the fine line between relevance and creepiness
- Establishing an AI ethics review process
- Communicating responsible AI use in brand messaging
Module 16: AI Governance & Change Management - Creating AI adoption playbooks for marketing teams
- Running AI literacy workshops for non-technical staff
- Managing resistance to AI-driven change
- Documenting AI workflows for audit and training
- Setting up AI performance monitoring and review cycles
- Establishing AI usage policies and approval processes
- Ensuring accountability in AI-assisted decisions
- Creating feedback loops from users to AI systems
- Scaling AI use without losing strategic control
- Integrating AI decisions into leadership reviews
- Preparing your team for continuous AI evolution
- Measuring the ROI of AI adoption efforts
Module 17: Building Your Personal AI Advantage - Creating your unique AI-powered marketing signature
- Positioning yourself as an AI-savvy leader internally
- Documenting and showcasing your AI projects
- Updating your LinkedIn and professional profile
- Speaking with confidence about AI in interviews
- Networking with AI-focused marketing communities
- Building a personal knowledge repository
- Staying ahead of emerging AI trends
- Continuing education pathways after course completion
- Contributing to industry discussions with authority
- Monetising your AI expertise through consulting
- Developing your own AI growth frameworks
Module 18: Capstone Project & Certification Path - Choosing your real-world AI growth challenge
- Defining project scope and success metrics
- Conducting stakeholder alignment for your initiative
- Building your AI strategy one-pager
- Developing a detailed implementation plan
- Creating a presentation for executive review
- Incorporating feedback into final proposal
- Submitting for instructor evaluation
- Revising based on expert guidance
- Demonstrating practical application of all course modules
- Proving your ability to deliver AI-driven ROI
- Earning your Certificate of Completion issued by The Art of Service
- Adding your credential to LinkedIn and professional portfolios
- Gaining access to advanced alumni resources
- Joining a global network of AI-powered marketers
- Creating your unique AI-powered marketing signature
- Positioning yourself as an AI-savvy leader internally
- Documenting and showcasing your AI projects
- Updating your LinkedIn and professional profile
- Speaking with confidence about AI in interviews
- Networking with AI-focused marketing communities
- Building a personal knowledge repository
- Staying ahead of emerging AI trends
- Continuing education pathways after course completion
- Contributing to industry discussions with authority
- Monetising your AI expertise through consulting
- Developing your own AI growth frameworks