COURSE FORMAT & DELIVERY DETAILS Your Learning, Your Schedule: Start Anytime, Learn at Your Pace
This course is designed for professionals who demand flexibility without sacrificing quality. You gain immediate online access upon enrollment, allowing you to begin learning the moment you're ready. There are no fixed schedules, deadlines, or live sessions to attend-this is an on-demand program built for real lives and real careers. Designed for Real Results, Fast
Most learners complete the course within 6 to 8 weeks when dedicating 3 to 5 hours per week. However, because the course is self-paced, you can accelerate your progress or take more time as needed. Many participants report applying core frameworks to their current role and seeing measurable improvements in campaign performance, lead quality, and customer conversion rates within the first 14 days. Lifetime Access, Future-Proof Knowledge
Once enrolled, you receive lifetime access to all course materials. This includes every framework, template, and strategy taught in the program, as well as all future updates at no additional cost. As AI evolves and new customer acquisition tools emerge, the content will be continuously refined and expanded-ensuring your knowledge stays cutting edge, forever. Learn Anytime, Anywhere, on Any Device
Access your coursework 24/7 from any device with an internet connection. The platform is fully mobile-friendly, meaning you can review strategies on your morning commute, practice AI prompt design during a lunch break, or refine your acquisition funnel from your tablet at home. Global accessibility ensures that wherever you are, your upskilling journey continues uninterrupted. Direct Instructor Support & Expert Guidance
You are not learning in isolation. Throughout the course, you’ll have access to guided support from experienced marketing strategists who specialise in AI-driven acquisition. Whether you're troubleshooting a segmentation model, refining your AI targeting logic, or optimising a predictive scoring system, expert insights are available to help you overcome obstacles and stay on track. Receive a Globally Recognised Certificate of Completion
Upon finishing all modules and applying the core practices, you will earn a Certificate of Completion issued by The Art of Service. This credential is recognised by professionals across 90+ countries and validates your mastery of AI-powered customer acquisition at an industry-accepted standard. It’s shareable on LinkedIn, ideal for résumé enhancement, and trusted by hiring managers globally. No Hidden Fees, No Surprises-Just Straightforward Value
The price you see is the price you pay. There are no hidden fees, recurring charges, or upsells. What you invest covers full access, lifetime updates, certificate issuance, and ongoing support. We believe transparency builds trust, and trust drives transformation. Secure Payment Through Trusted Channels
We accept all major payment methods including Visa, Mastercard, and PayPal. Transactions are processed through a secure, encrypted gateway, ensuring your financial information remains protected at every step. Zero-Risk Enrollment: Satisfied or Refunded
We stand behind this course with a strong satisfaction promise. If you complete the materials in good faith and do not find the strategies, systems, and tools to be transformative for your marketing performance, you may request a full refund. Our goal is your success, not your obligation. Clear Onboarding, Immediate Clarity
After enrollment, you’ll receive a confirmation email outlining your participation. Once the course materials are prepared, your access details will be sent separately. This ensures you receive fully tested, structured, and ready-to-use resources, not raw or incomplete content. This Works for You-Even If You’re Starting from Scratch
Whether you're a marketing manager overwhelmed by data, a founder trying to scale customer acquisition on a limited budget, or a digital strategist navigating AI tools for the first time, this course meets you where you are. It works even if you’ve never used AI in your campaigns, even if your current funnel is underperforming, and even if you're unsure where to start. Real Impact Across Roles and Industries
Our learners include growth marketers at tech startups who used the AI segmentation framework to reduce customer acquisition cost by 38%, e-commerce directors who increased conversion rates using predictive personalisation models, and agency leaders who rebuilt their client acquisition strategy around AI-optimised funnels. These results are repeatable, and the systems are transferable. - Marketing Managers gain precision targeting tools to reduce wasted spend and increase ROI per campaign.
- Founders and Start-up Leaders learn how to compete with limited budgets using intelligent, automated acquisition workflows.
- Agency Strategists discover how to position AI as a service differentiator and deliver client results that justify premium pricing.
- Customer Success Leads master AI-driven onboarding sequences that convert trial users at scale.
- E-commerce Executives deploy AI recommendation engines that boost AOV and LTV across digital channels.
Your Risk is Reversed-Our Confidence is Absolute
You are not betting on hype. You’re investing in a proven, structured, step-by-step methodology that de-risks AI adoption and makes customer acquisition predictable, measurable, and scalable. With lifetime access, expert support, a recognised certification, and a satisfaction guarantee, the only real risk is not taking action-and watching competitors move ahead with the very same systems you could be mastering today.
EXTENSIVE & DETAILED COURSE CURRICULUM
Module 1: Foundations of AI-Powered Marketing - Understanding the shift from traditional to AI-driven customer acquisition
- Core principles of machine learning in marketing contexts
- Demystifying AI, automation, and predictive analytics
- How AI transforms customer journey mapping
- Essential terminology every marketer must know
- Differentiating between AI tools, platforms, and algorithms
- Identifying common misconceptions and avoiding implementation pitfalls
- Assessing your organisation’s AI readiness and data maturity
- Building a cross-functional team for AI integration
- Creating an ethical AI use policy for customer data
Module 2: Strategic Frameworks for AI Adoption - The 5-Stage AI Integration Roadmap
- Aligning AI initiatives with business objectives
- Setting KPIs for AI-driven acquisition campaigns
- Balancing innovation with operational feasibility
- Developing an AI innovation sandbox for testing
- Creating a feedback loop for continuous model improvement
- Mapping AI tools to specific funnel stages
- Integrating AI into quarterly marketing planning
- Building stakeholder buy-in for AI transformation
- Overcoming internal resistance with data storytelling
Module 3: Data Infrastructure for Intelligent Acquisition - Essential data types for AI-powered marketing
- Designing a clean, centralised customer data repository
- Implementing data governance and quality controls
- Linking CRM, email, and web analytics systems
- Using data pipelines to feed real-time insights to AI models
- Managing customer data consent and compliance
- Preparing data for segmentation and prediction models
- Handling incomplete or missing data intelligently
- Using synthetic data to overcome data scarcity
- Ensuring data privacy across global markets
Module 4: AI-Driven Customer Segmentation - Limitations of manual segmentation vs AI clustering
- Using unsupervised learning to discover hidden customer groups
- Setting parameters for effective AI clustering
- Interpreting cluster profiles for marketing action
- Validating AI segments with real campaign results
- Creating dynamic segments that update in real time
- Integrating behavioural, demographic, and transactional signals
- Applying RFM analysis enhanced with AI pattern detection
- Using psychographic inference through natural language processing
- Building segments for retention, upsell, and reactivation
Module 5: Predictive Lead Scoring Models - Why traditional lead scoring fails at scale
- How predictive scoring increases conversion rates
- Selecting features for lead scoring algorithms
- Training models on historical conversion data
- Validating model accuracy with holdout datasets
- Calibrating lead score thresholds for sales readiness
- Integrating predictive scores into CRM workflows
- Adjusting scoring models based on seasonality
- Handling cold leads with propensity-to-convert models
- Making scoring models transparent and actionable for marketing teams
Module 6: AI-Optimised Acquisition Channels - Maximising paid social through AI audience expansion
- Using lookalike modelling to identify high-value prospects
- Automating ad creative testing with multivariate analysis
- Dynamic bidding strategies based on conversion probability
- AI-powered A/B testing for landing page optimisation
- Programmatic ad placement with predictive performance scoring
- Using AI to identify underperforming channels and redirect budgets
- Custom audience creation using intent signals
- Budget allocation optimisation across channels
- Attribution modelling powered by multi-touch AI
Module 7: Smart Content Personalisation - Dynamic website content based on visitor profiles
- AI-generated email subject lines that increase open rates
- Personalised product recommendations using collaborative filtering
- Content sequencing for automated nurture campaigns
- Sentiment analysis for tailoring messaging tone
- Using NLP to generate customer-specific copy variants
- Building content libraries for AI to draw from
- Real-time personalisation in live chat and chatbots
- A/B testing AI-generated content variants
- Maintaining brand voice within AI personalisation systems
Module 8: AI in Email & Lifecycle Marketing - Optimising send time using individual engagement patterns
- AI-driven email segmentation for lifecycle stages
- Predicting unsubscribe risk and triggering retention flows
- Content personalisation based on past interaction history
- Automated re-engagement campaigns for dormant leads
- Using churn prediction to prioritise high-risk customers
- Subject line generation using emotional trigger analysis
- Dynamic content blocks that adapt to recipient behaviour
- Email deliverability monitoring with anomaly detection
- Scaling email campaigns without losing personal relevance
Module 9: Conversational AI & Chatbot Strategy - Designing chatbots with customer acquisition intent
- Building decision trees enhanced with natural language understanding
- Using chatbots to qualify leads 24/7
- Integrating chatbot conversations into CRM
- Analysing chat transcripts for intent and friction points
- Training AI on brand-specific language and tone
- Routing high-intent leads to sales instantly
- Using chatbots for pre-qualification surveys
- Measuring conversion lift from chatbot interactions
- Escalating complex queries seamlessly to humans
Module 10: Search & SEO with AI Assistance - AI-powered keyword clustering by search intent
- Content gap analysis using competitor data scraping
- Automated meta description generation for higher CTR
- Predicting content performance before publication
- Using AI to structure articles for featured snippets
- Topic modelling for comprehensive content coverage
- Analysing user engagement signals for SERP ranking
- Automating internal linking strategies
- Monitoring algorithm changes and adjusting strategy
- Scaling SEO content creation across markets
Module 11: AI for Social Media Growth - Optimal posting times using engagement forecasting
- Content recommendation engines for social feeds
- Using AI to identify viral content patterns
- Real-time sentiment analysis of brand mentions
- Identifying brand advocates and potential influencers
- Automating comment responses with predefined logic
- Detecting crisis signals in social conversations
- Generating social post variations from core messages
- Tracking competitor campaign performance with AI
- Building community through AI-powered engagement triggers
Module 12: Predictive Customer Lifetime Value - Why CLV prediction is essential for acquisition ROI
- Selecting variables that influence lifetime value
- Using survival analysis to estimate customer longevity
- Combining transactional and behavioural data into models
- Calculating break-even acquisition costs with AI
- Segmenting acquisition efforts by predicted CLV tiers
- Adjusting strategies for high-CLV customer acquisition
- Validating model predictions with actual retention data
- Using CLV to justify premium acquisition spend
- Updating CLV models monthly for accuracy
Module 13: AI for Referral & Advocacy Programs - Identifying customers most likely to refer others
- Predicting referral conversion probability
- Automating personal invitation messages
- Tracking referral attribution across complex journeys
- Using AI to personalise incentive offers
- Scaling referral campaigns without manual oversight
- Analysing social sharing patterns for optimisation
- Integrating referral data into overall acquisition models
- Using network effect modelling to project growth
- Automating fraud detection in referral programs
Module 14: Voice & Visual Search Acquisition - Optimising for natural language queries
- Building FAQ structures that answer voice questions
- Using schema markup enhanced by AI suggestions
- Creating concise, direct responses for voice assistants
- Optimising product images for visual search recognition
- Using reverse image search to monitor brand usage
- Generating alt text automatically with image recognition
- Adapting for smart speaker and screen-based interactions
- Tracking voice search performance with intent categorisation
- Building zero-click strategies for discovery without clicks
Module 15: AI in Paid Search (PPC) Strategy - Automated keyword discovery using search query mining
- Dynamic ad copy generation based on search intent
- Bid adjustments based on conversion likelihood
- Using AI to detect click fraud and invalid traffic
- Ad schedule optimisation using conversion timing
- Forecasting budget impact of bid changes
- Identifying negative keywords using pattern recognition
- Optimising Quality Score through historical pattern analysis
- Testing hundreds of ad combinations efficiently
- Creating responsive search ads with AI-recommended assets
Module 16: Multi-Touch Attribution with AI - Problems with last-click and linear models
- How AI calculates true touchpoint influence
- Building custom attribution weights based on data
- Integrating offline and online interaction data
- Creating channel-specific contribution reports
- Using attribution to justify acquisition spend
- Forecasting ROI under different media mixes
- Automating budget reallocation based on contribution
- Explaining complex models to non-technical stakeholders
- Validating model accuracy with test campaigns
Module 17: AI for Event-Driven Marketing - Creating trigger-based campaigns from behavioural data
- Setting up real-time decision engines for personalisation
- Using AI to predict high-intent moments
- Automating follow-up sequences based on engagement
- Scaling triggered messaging without fatigue
- Measuring incremental lift from event campaigns
- Combining first-party data with real-time triggers
- Using weather, location, and time data as triggers
- Integrating with CDPs for unified event processing
- Building re-engagement flows for lapsed interactions
Module 18: AI Tools for Market Research - Using NLP to analyse customer reviews and feedback
- Automated competitor feature comparison
- Identifying emerging customer needs through trend analysis
- Clustering open-ended survey responses
- Real-time social listening with sentiment tracking
- Generating market opportunity reports using AI
- Monitoring pricing changes across competitors
- Predicting market shifts based on external signals
- Automating SWOT analysis from public data
- Generating buyer persona insights from qualitative data
Module 19: Building AI-Powered Landing Pages - Dynamic content based on visitor source and profile
- AI-generated headlines that maximise conversions
- Automated layout testing for user experience
- Personalised call-to-action recommendations
- Using heatmaps and session data to inform design
- Real-time form optimisation based on drop-off analysis
- Chatbot integration on high-intent pages
- Predicting conversion probability per visitor
- Routing traffic to customised experiences
- Scaling landing page variations across campaigns
Module 20: AI in Account-Based Marketing (ABM) - Identifying high-value target accounts using firmographic data
- Enriching account profiles with technographic signals
- Predicting account engagement likelihood
- Mapping decision-makers and influence networks
- Personalising outreach at scale using AI copy
- Tracking cross-channel engagement per account
- Scoring account progression through buyer journey
- Triggering sales alerts for high-intent signals
- Measuring ABM campaign ROI with precision
- Automating report generation for account teams
Module 21: Legal, Ethical & Compliance Considerations - GDPR, CCPA, and global data privacy regulations
- Avoiding discriminatory targeting in AI models
- Ensuring transparency in automated decision-making
- Handling biometric and inferred data responsibly
- Audit trails for AI-driven marketing actions
- Obtaining explicit consent for AI personalisation
- Managing data subject access requests
- Using AI without reinforcing bias in acquisition
- Building trust through ethical AI use
- Creating compliance checklists for AI campaigns
Module 22: Integration with Marketing Technology Stack - Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
Module 1: Foundations of AI-Powered Marketing - Understanding the shift from traditional to AI-driven customer acquisition
- Core principles of machine learning in marketing contexts
- Demystifying AI, automation, and predictive analytics
- How AI transforms customer journey mapping
- Essential terminology every marketer must know
- Differentiating between AI tools, platforms, and algorithms
- Identifying common misconceptions and avoiding implementation pitfalls
- Assessing your organisation’s AI readiness and data maturity
- Building a cross-functional team for AI integration
- Creating an ethical AI use policy for customer data
Module 2: Strategic Frameworks for AI Adoption - The 5-Stage AI Integration Roadmap
- Aligning AI initiatives with business objectives
- Setting KPIs for AI-driven acquisition campaigns
- Balancing innovation with operational feasibility
- Developing an AI innovation sandbox for testing
- Creating a feedback loop for continuous model improvement
- Mapping AI tools to specific funnel stages
- Integrating AI into quarterly marketing planning
- Building stakeholder buy-in for AI transformation
- Overcoming internal resistance with data storytelling
Module 3: Data Infrastructure for Intelligent Acquisition - Essential data types for AI-powered marketing
- Designing a clean, centralised customer data repository
- Implementing data governance and quality controls
- Linking CRM, email, and web analytics systems
- Using data pipelines to feed real-time insights to AI models
- Managing customer data consent and compliance
- Preparing data for segmentation and prediction models
- Handling incomplete or missing data intelligently
- Using synthetic data to overcome data scarcity
- Ensuring data privacy across global markets
Module 4: AI-Driven Customer Segmentation - Limitations of manual segmentation vs AI clustering
- Using unsupervised learning to discover hidden customer groups
- Setting parameters for effective AI clustering
- Interpreting cluster profiles for marketing action
- Validating AI segments with real campaign results
- Creating dynamic segments that update in real time
- Integrating behavioural, demographic, and transactional signals
- Applying RFM analysis enhanced with AI pattern detection
- Using psychographic inference through natural language processing
- Building segments for retention, upsell, and reactivation
Module 5: Predictive Lead Scoring Models - Why traditional lead scoring fails at scale
- How predictive scoring increases conversion rates
- Selecting features for lead scoring algorithms
- Training models on historical conversion data
- Validating model accuracy with holdout datasets
- Calibrating lead score thresholds for sales readiness
- Integrating predictive scores into CRM workflows
- Adjusting scoring models based on seasonality
- Handling cold leads with propensity-to-convert models
- Making scoring models transparent and actionable for marketing teams
Module 6: AI-Optimised Acquisition Channels - Maximising paid social through AI audience expansion
- Using lookalike modelling to identify high-value prospects
- Automating ad creative testing with multivariate analysis
- Dynamic bidding strategies based on conversion probability
- AI-powered A/B testing for landing page optimisation
- Programmatic ad placement with predictive performance scoring
- Using AI to identify underperforming channels and redirect budgets
- Custom audience creation using intent signals
- Budget allocation optimisation across channels
- Attribution modelling powered by multi-touch AI
Module 7: Smart Content Personalisation - Dynamic website content based on visitor profiles
- AI-generated email subject lines that increase open rates
- Personalised product recommendations using collaborative filtering
- Content sequencing for automated nurture campaigns
- Sentiment analysis for tailoring messaging tone
- Using NLP to generate customer-specific copy variants
- Building content libraries for AI to draw from
- Real-time personalisation in live chat and chatbots
- A/B testing AI-generated content variants
- Maintaining brand voice within AI personalisation systems
Module 8: AI in Email & Lifecycle Marketing - Optimising send time using individual engagement patterns
- AI-driven email segmentation for lifecycle stages
- Predicting unsubscribe risk and triggering retention flows
- Content personalisation based on past interaction history
- Automated re-engagement campaigns for dormant leads
- Using churn prediction to prioritise high-risk customers
- Subject line generation using emotional trigger analysis
- Dynamic content blocks that adapt to recipient behaviour
- Email deliverability monitoring with anomaly detection
- Scaling email campaigns without losing personal relevance
Module 9: Conversational AI & Chatbot Strategy - Designing chatbots with customer acquisition intent
- Building decision trees enhanced with natural language understanding
- Using chatbots to qualify leads 24/7
- Integrating chatbot conversations into CRM
- Analysing chat transcripts for intent and friction points
- Training AI on brand-specific language and tone
- Routing high-intent leads to sales instantly
- Using chatbots for pre-qualification surveys
- Measuring conversion lift from chatbot interactions
- Escalating complex queries seamlessly to humans
Module 10: Search & SEO with AI Assistance - AI-powered keyword clustering by search intent
- Content gap analysis using competitor data scraping
- Automated meta description generation for higher CTR
- Predicting content performance before publication
- Using AI to structure articles for featured snippets
- Topic modelling for comprehensive content coverage
- Analysing user engagement signals for SERP ranking
- Automating internal linking strategies
- Monitoring algorithm changes and adjusting strategy
- Scaling SEO content creation across markets
Module 11: AI for Social Media Growth - Optimal posting times using engagement forecasting
- Content recommendation engines for social feeds
- Using AI to identify viral content patterns
- Real-time sentiment analysis of brand mentions
- Identifying brand advocates and potential influencers
- Automating comment responses with predefined logic
- Detecting crisis signals in social conversations
- Generating social post variations from core messages
- Tracking competitor campaign performance with AI
- Building community through AI-powered engagement triggers
Module 12: Predictive Customer Lifetime Value - Why CLV prediction is essential for acquisition ROI
- Selecting variables that influence lifetime value
- Using survival analysis to estimate customer longevity
- Combining transactional and behavioural data into models
- Calculating break-even acquisition costs with AI
- Segmenting acquisition efforts by predicted CLV tiers
- Adjusting strategies for high-CLV customer acquisition
- Validating model predictions with actual retention data
- Using CLV to justify premium acquisition spend
- Updating CLV models monthly for accuracy
Module 13: AI for Referral & Advocacy Programs - Identifying customers most likely to refer others
- Predicting referral conversion probability
- Automating personal invitation messages
- Tracking referral attribution across complex journeys
- Using AI to personalise incentive offers
- Scaling referral campaigns without manual oversight
- Analysing social sharing patterns for optimisation
- Integrating referral data into overall acquisition models
- Using network effect modelling to project growth
- Automating fraud detection in referral programs
Module 14: Voice & Visual Search Acquisition - Optimising for natural language queries
- Building FAQ structures that answer voice questions
- Using schema markup enhanced by AI suggestions
- Creating concise, direct responses for voice assistants
- Optimising product images for visual search recognition
- Using reverse image search to monitor brand usage
- Generating alt text automatically with image recognition
- Adapting for smart speaker and screen-based interactions
- Tracking voice search performance with intent categorisation
- Building zero-click strategies for discovery without clicks
Module 15: AI in Paid Search (PPC) Strategy - Automated keyword discovery using search query mining
- Dynamic ad copy generation based on search intent
- Bid adjustments based on conversion likelihood
- Using AI to detect click fraud and invalid traffic
- Ad schedule optimisation using conversion timing
- Forecasting budget impact of bid changes
- Identifying negative keywords using pattern recognition
- Optimising Quality Score through historical pattern analysis
- Testing hundreds of ad combinations efficiently
- Creating responsive search ads with AI-recommended assets
Module 16: Multi-Touch Attribution with AI - Problems with last-click and linear models
- How AI calculates true touchpoint influence
- Building custom attribution weights based on data
- Integrating offline and online interaction data
- Creating channel-specific contribution reports
- Using attribution to justify acquisition spend
- Forecasting ROI under different media mixes
- Automating budget reallocation based on contribution
- Explaining complex models to non-technical stakeholders
- Validating model accuracy with test campaigns
Module 17: AI for Event-Driven Marketing - Creating trigger-based campaigns from behavioural data
- Setting up real-time decision engines for personalisation
- Using AI to predict high-intent moments
- Automating follow-up sequences based on engagement
- Scaling triggered messaging without fatigue
- Measuring incremental lift from event campaigns
- Combining first-party data with real-time triggers
- Using weather, location, and time data as triggers
- Integrating with CDPs for unified event processing
- Building re-engagement flows for lapsed interactions
Module 18: AI Tools for Market Research - Using NLP to analyse customer reviews and feedback
- Automated competitor feature comparison
- Identifying emerging customer needs through trend analysis
- Clustering open-ended survey responses
- Real-time social listening with sentiment tracking
- Generating market opportunity reports using AI
- Monitoring pricing changes across competitors
- Predicting market shifts based on external signals
- Automating SWOT analysis from public data
- Generating buyer persona insights from qualitative data
Module 19: Building AI-Powered Landing Pages - Dynamic content based on visitor source and profile
- AI-generated headlines that maximise conversions
- Automated layout testing for user experience
- Personalised call-to-action recommendations
- Using heatmaps and session data to inform design
- Real-time form optimisation based on drop-off analysis
- Chatbot integration on high-intent pages
- Predicting conversion probability per visitor
- Routing traffic to customised experiences
- Scaling landing page variations across campaigns
Module 20: AI in Account-Based Marketing (ABM) - Identifying high-value target accounts using firmographic data
- Enriching account profiles with technographic signals
- Predicting account engagement likelihood
- Mapping decision-makers and influence networks
- Personalising outreach at scale using AI copy
- Tracking cross-channel engagement per account
- Scoring account progression through buyer journey
- Triggering sales alerts for high-intent signals
- Measuring ABM campaign ROI with precision
- Automating report generation for account teams
Module 21: Legal, Ethical & Compliance Considerations - GDPR, CCPA, and global data privacy regulations
- Avoiding discriminatory targeting in AI models
- Ensuring transparency in automated decision-making
- Handling biometric and inferred data responsibly
- Audit trails for AI-driven marketing actions
- Obtaining explicit consent for AI personalisation
- Managing data subject access requests
- Using AI without reinforcing bias in acquisition
- Building trust through ethical AI use
- Creating compliance checklists for AI campaigns
Module 22: Integration with Marketing Technology Stack - Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- The 5-Stage AI Integration Roadmap
- Aligning AI initiatives with business objectives
- Setting KPIs for AI-driven acquisition campaigns
- Balancing innovation with operational feasibility
- Developing an AI innovation sandbox for testing
- Creating a feedback loop for continuous model improvement
- Mapping AI tools to specific funnel stages
- Integrating AI into quarterly marketing planning
- Building stakeholder buy-in for AI transformation
- Overcoming internal resistance with data storytelling
Module 3: Data Infrastructure for Intelligent Acquisition - Essential data types for AI-powered marketing
- Designing a clean, centralised customer data repository
- Implementing data governance and quality controls
- Linking CRM, email, and web analytics systems
- Using data pipelines to feed real-time insights to AI models
- Managing customer data consent and compliance
- Preparing data for segmentation and prediction models
- Handling incomplete or missing data intelligently
- Using synthetic data to overcome data scarcity
- Ensuring data privacy across global markets
Module 4: AI-Driven Customer Segmentation - Limitations of manual segmentation vs AI clustering
- Using unsupervised learning to discover hidden customer groups
- Setting parameters for effective AI clustering
- Interpreting cluster profiles for marketing action
- Validating AI segments with real campaign results
- Creating dynamic segments that update in real time
- Integrating behavioural, demographic, and transactional signals
- Applying RFM analysis enhanced with AI pattern detection
- Using psychographic inference through natural language processing
- Building segments for retention, upsell, and reactivation
Module 5: Predictive Lead Scoring Models - Why traditional lead scoring fails at scale
- How predictive scoring increases conversion rates
- Selecting features for lead scoring algorithms
- Training models on historical conversion data
- Validating model accuracy with holdout datasets
- Calibrating lead score thresholds for sales readiness
- Integrating predictive scores into CRM workflows
- Adjusting scoring models based on seasonality
- Handling cold leads with propensity-to-convert models
- Making scoring models transparent and actionable for marketing teams
Module 6: AI-Optimised Acquisition Channels - Maximising paid social through AI audience expansion
- Using lookalike modelling to identify high-value prospects
- Automating ad creative testing with multivariate analysis
- Dynamic bidding strategies based on conversion probability
- AI-powered A/B testing for landing page optimisation
- Programmatic ad placement with predictive performance scoring
- Using AI to identify underperforming channels and redirect budgets
- Custom audience creation using intent signals
- Budget allocation optimisation across channels
- Attribution modelling powered by multi-touch AI
Module 7: Smart Content Personalisation - Dynamic website content based on visitor profiles
- AI-generated email subject lines that increase open rates
- Personalised product recommendations using collaborative filtering
- Content sequencing for automated nurture campaigns
- Sentiment analysis for tailoring messaging tone
- Using NLP to generate customer-specific copy variants
- Building content libraries for AI to draw from
- Real-time personalisation in live chat and chatbots
- A/B testing AI-generated content variants
- Maintaining brand voice within AI personalisation systems
Module 8: AI in Email & Lifecycle Marketing - Optimising send time using individual engagement patterns
- AI-driven email segmentation for lifecycle stages
- Predicting unsubscribe risk and triggering retention flows
- Content personalisation based on past interaction history
- Automated re-engagement campaigns for dormant leads
- Using churn prediction to prioritise high-risk customers
- Subject line generation using emotional trigger analysis
- Dynamic content blocks that adapt to recipient behaviour
- Email deliverability monitoring with anomaly detection
- Scaling email campaigns without losing personal relevance
Module 9: Conversational AI & Chatbot Strategy - Designing chatbots with customer acquisition intent
- Building decision trees enhanced with natural language understanding
- Using chatbots to qualify leads 24/7
- Integrating chatbot conversations into CRM
- Analysing chat transcripts for intent and friction points
- Training AI on brand-specific language and tone
- Routing high-intent leads to sales instantly
- Using chatbots for pre-qualification surveys
- Measuring conversion lift from chatbot interactions
- Escalating complex queries seamlessly to humans
Module 10: Search & SEO with AI Assistance - AI-powered keyword clustering by search intent
- Content gap analysis using competitor data scraping
- Automated meta description generation for higher CTR
- Predicting content performance before publication
- Using AI to structure articles for featured snippets
- Topic modelling for comprehensive content coverage
- Analysing user engagement signals for SERP ranking
- Automating internal linking strategies
- Monitoring algorithm changes and adjusting strategy
- Scaling SEO content creation across markets
Module 11: AI for Social Media Growth - Optimal posting times using engagement forecasting
- Content recommendation engines for social feeds
- Using AI to identify viral content patterns
- Real-time sentiment analysis of brand mentions
- Identifying brand advocates and potential influencers
- Automating comment responses with predefined logic
- Detecting crisis signals in social conversations
- Generating social post variations from core messages
- Tracking competitor campaign performance with AI
- Building community through AI-powered engagement triggers
Module 12: Predictive Customer Lifetime Value - Why CLV prediction is essential for acquisition ROI
- Selecting variables that influence lifetime value
- Using survival analysis to estimate customer longevity
- Combining transactional and behavioural data into models
- Calculating break-even acquisition costs with AI
- Segmenting acquisition efforts by predicted CLV tiers
- Adjusting strategies for high-CLV customer acquisition
- Validating model predictions with actual retention data
- Using CLV to justify premium acquisition spend
- Updating CLV models monthly for accuracy
Module 13: AI for Referral & Advocacy Programs - Identifying customers most likely to refer others
- Predicting referral conversion probability
- Automating personal invitation messages
- Tracking referral attribution across complex journeys
- Using AI to personalise incentive offers
- Scaling referral campaigns without manual oversight
- Analysing social sharing patterns for optimisation
- Integrating referral data into overall acquisition models
- Using network effect modelling to project growth
- Automating fraud detection in referral programs
Module 14: Voice & Visual Search Acquisition - Optimising for natural language queries
- Building FAQ structures that answer voice questions
- Using schema markup enhanced by AI suggestions
- Creating concise, direct responses for voice assistants
- Optimising product images for visual search recognition
- Using reverse image search to monitor brand usage
- Generating alt text automatically with image recognition
- Adapting for smart speaker and screen-based interactions
- Tracking voice search performance with intent categorisation
- Building zero-click strategies for discovery without clicks
Module 15: AI in Paid Search (PPC) Strategy - Automated keyword discovery using search query mining
- Dynamic ad copy generation based on search intent
- Bid adjustments based on conversion likelihood
- Using AI to detect click fraud and invalid traffic
- Ad schedule optimisation using conversion timing
- Forecasting budget impact of bid changes
- Identifying negative keywords using pattern recognition
- Optimising Quality Score through historical pattern analysis
- Testing hundreds of ad combinations efficiently
- Creating responsive search ads with AI-recommended assets
Module 16: Multi-Touch Attribution with AI - Problems with last-click and linear models
- How AI calculates true touchpoint influence
- Building custom attribution weights based on data
- Integrating offline and online interaction data
- Creating channel-specific contribution reports
- Using attribution to justify acquisition spend
- Forecasting ROI under different media mixes
- Automating budget reallocation based on contribution
- Explaining complex models to non-technical stakeholders
- Validating model accuracy with test campaigns
Module 17: AI for Event-Driven Marketing - Creating trigger-based campaigns from behavioural data
- Setting up real-time decision engines for personalisation
- Using AI to predict high-intent moments
- Automating follow-up sequences based on engagement
- Scaling triggered messaging without fatigue
- Measuring incremental lift from event campaigns
- Combining first-party data with real-time triggers
- Using weather, location, and time data as triggers
- Integrating with CDPs for unified event processing
- Building re-engagement flows for lapsed interactions
Module 18: AI Tools for Market Research - Using NLP to analyse customer reviews and feedback
- Automated competitor feature comparison
- Identifying emerging customer needs through trend analysis
- Clustering open-ended survey responses
- Real-time social listening with sentiment tracking
- Generating market opportunity reports using AI
- Monitoring pricing changes across competitors
- Predicting market shifts based on external signals
- Automating SWOT analysis from public data
- Generating buyer persona insights from qualitative data
Module 19: Building AI-Powered Landing Pages - Dynamic content based on visitor source and profile
- AI-generated headlines that maximise conversions
- Automated layout testing for user experience
- Personalised call-to-action recommendations
- Using heatmaps and session data to inform design
- Real-time form optimisation based on drop-off analysis
- Chatbot integration on high-intent pages
- Predicting conversion probability per visitor
- Routing traffic to customised experiences
- Scaling landing page variations across campaigns
Module 20: AI in Account-Based Marketing (ABM) - Identifying high-value target accounts using firmographic data
- Enriching account profiles with technographic signals
- Predicting account engagement likelihood
- Mapping decision-makers and influence networks
- Personalising outreach at scale using AI copy
- Tracking cross-channel engagement per account
- Scoring account progression through buyer journey
- Triggering sales alerts for high-intent signals
- Measuring ABM campaign ROI with precision
- Automating report generation for account teams
Module 21: Legal, Ethical & Compliance Considerations - GDPR, CCPA, and global data privacy regulations
- Avoiding discriminatory targeting in AI models
- Ensuring transparency in automated decision-making
- Handling biometric and inferred data responsibly
- Audit trails for AI-driven marketing actions
- Obtaining explicit consent for AI personalisation
- Managing data subject access requests
- Using AI without reinforcing bias in acquisition
- Building trust through ethical AI use
- Creating compliance checklists for AI campaigns
Module 22: Integration with Marketing Technology Stack - Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- Limitations of manual segmentation vs AI clustering
- Using unsupervised learning to discover hidden customer groups
- Setting parameters for effective AI clustering
- Interpreting cluster profiles for marketing action
- Validating AI segments with real campaign results
- Creating dynamic segments that update in real time
- Integrating behavioural, demographic, and transactional signals
- Applying RFM analysis enhanced with AI pattern detection
- Using psychographic inference through natural language processing
- Building segments for retention, upsell, and reactivation
Module 5: Predictive Lead Scoring Models - Why traditional lead scoring fails at scale
- How predictive scoring increases conversion rates
- Selecting features for lead scoring algorithms
- Training models on historical conversion data
- Validating model accuracy with holdout datasets
- Calibrating lead score thresholds for sales readiness
- Integrating predictive scores into CRM workflows
- Adjusting scoring models based on seasonality
- Handling cold leads with propensity-to-convert models
- Making scoring models transparent and actionable for marketing teams
Module 6: AI-Optimised Acquisition Channels - Maximising paid social through AI audience expansion
- Using lookalike modelling to identify high-value prospects
- Automating ad creative testing with multivariate analysis
- Dynamic bidding strategies based on conversion probability
- AI-powered A/B testing for landing page optimisation
- Programmatic ad placement with predictive performance scoring
- Using AI to identify underperforming channels and redirect budgets
- Custom audience creation using intent signals
- Budget allocation optimisation across channels
- Attribution modelling powered by multi-touch AI
Module 7: Smart Content Personalisation - Dynamic website content based on visitor profiles
- AI-generated email subject lines that increase open rates
- Personalised product recommendations using collaborative filtering
- Content sequencing for automated nurture campaigns
- Sentiment analysis for tailoring messaging tone
- Using NLP to generate customer-specific copy variants
- Building content libraries for AI to draw from
- Real-time personalisation in live chat and chatbots
- A/B testing AI-generated content variants
- Maintaining brand voice within AI personalisation systems
Module 8: AI in Email & Lifecycle Marketing - Optimising send time using individual engagement patterns
- AI-driven email segmentation for lifecycle stages
- Predicting unsubscribe risk and triggering retention flows
- Content personalisation based on past interaction history
- Automated re-engagement campaigns for dormant leads
- Using churn prediction to prioritise high-risk customers
- Subject line generation using emotional trigger analysis
- Dynamic content blocks that adapt to recipient behaviour
- Email deliverability monitoring with anomaly detection
- Scaling email campaigns without losing personal relevance
Module 9: Conversational AI & Chatbot Strategy - Designing chatbots with customer acquisition intent
- Building decision trees enhanced with natural language understanding
- Using chatbots to qualify leads 24/7
- Integrating chatbot conversations into CRM
- Analysing chat transcripts for intent and friction points
- Training AI on brand-specific language and tone
- Routing high-intent leads to sales instantly
- Using chatbots for pre-qualification surveys
- Measuring conversion lift from chatbot interactions
- Escalating complex queries seamlessly to humans
Module 10: Search & SEO with AI Assistance - AI-powered keyword clustering by search intent
- Content gap analysis using competitor data scraping
- Automated meta description generation for higher CTR
- Predicting content performance before publication
- Using AI to structure articles for featured snippets
- Topic modelling for comprehensive content coverage
- Analysing user engagement signals for SERP ranking
- Automating internal linking strategies
- Monitoring algorithm changes and adjusting strategy
- Scaling SEO content creation across markets
Module 11: AI for Social Media Growth - Optimal posting times using engagement forecasting
- Content recommendation engines for social feeds
- Using AI to identify viral content patterns
- Real-time sentiment analysis of brand mentions
- Identifying brand advocates and potential influencers
- Automating comment responses with predefined logic
- Detecting crisis signals in social conversations
- Generating social post variations from core messages
- Tracking competitor campaign performance with AI
- Building community through AI-powered engagement triggers
Module 12: Predictive Customer Lifetime Value - Why CLV prediction is essential for acquisition ROI
- Selecting variables that influence lifetime value
- Using survival analysis to estimate customer longevity
- Combining transactional and behavioural data into models
- Calculating break-even acquisition costs with AI
- Segmenting acquisition efforts by predicted CLV tiers
- Adjusting strategies for high-CLV customer acquisition
- Validating model predictions with actual retention data
- Using CLV to justify premium acquisition spend
- Updating CLV models monthly for accuracy
Module 13: AI for Referral & Advocacy Programs - Identifying customers most likely to refer others
- Predicting referral conversion probability
- Automating personal invitation messages
- Tracking referral attribution across complex journeys
- Using AI to personalise incentive offers
- Scaling referral campaigns without manual oversight
- Analysing social sharing patterns for optimisation
- Integrating referral data into overall acquisition models
- Using network effect modelling to project growth
- Automating fraud detection in referral programs
Module 14: Voice & Visual Search Acquisition - Optimising for natural language queries
- Building FAQ structures that answer voice questions
- Using schema markup enhanced by AI suggestions
- Creating concise, direct responses for voice assistants
- Optimising product images for visual search recognition
- Using reverse image search to monitor brand usage
- Generating alt text automatically with image recognition
- Adapting for smart speaker and screen-based interactions
- Tracking voice search performance with intent categorisation
- Building zero-click strategies for discovery without clicks
Module 15: AI in Paid Search (PPC) Strategy - Automated keyword discovery using search query mining
- Dynamic ad copy generation based on search intent
- Bid adjustments based on conversion likelihood
- Using AI to detect click fraud and invalid traffic
- Ad schedule optimisation using conversion timing
- Forecasting budget impact of bid changes
- Identifying negative keywords using pattern recognition
- Optimising Quality Score through historical pattern analysis
- Testing hundreds of ad combinations efficiently
- Creating responsive search ads with AI-recommended assets
Module 16: Multi-Touch Attribution with AI - Problems with last-click and linear models
- How AI calculates true touchpoint influence
- Building custom attribution weights based on data
- Integrating offline and online interaction data
- Creating channel-specific contribution reports
- Using attribution to justify acquisition spend
- Forecasting ROI under different media mixes
- Automating budget reallocation based on contribution
- Explaining complex models to non-technical stakeholders
- Validating model accuracy with test campaigns
Module 17: AI for Event-Driven Marketing - Creating trigger-based campaigns from behavioural data
- Setting up real-time decision engines for personalisation
- Using AI to predict high-intent moments
- Automating follow-up sequences based on engagement
- Scaling triggered messaging without fatigue
- Measuring incremental lift from event campaigns
- Combining first-party data with real-time triggers
- Using weather, location, and time data as triggers
- Integrating with CDPs for unified event processing
- Building re-engagement flows for lapsed interactions
Module 18: AI Tools for Market Research - Using NLP to analyse customer reviews and feedback
- Automated competitor feature comparison
- Identifying emerging customer needs through trend analysis
- Clustering open-ended survey responses
- Real-time social listening with sentiment tracking
- Generating market opportunity reports using AI
- Monitoring pricing changes across competitors
- Predicting market shifts based on external signals
- Automating SWOT analysis from public data
- Generating buyer persona insights from qualitative data
Module 19: Building AI-Powered Landing Pages - Dynamic content based on visitor source and profile
- AI-generated headlines that maximise conversions
- Automated layout testing for user experience
- Personalised call-to-action recommendations
- Using heatmaps and session data to inform design
- Real-time form optimisation based on drop-off analysis
- Chatbot integration on high-intent pages
- Predicting conversion probability per visitor
- Routing traffic to customised experiences
- Scaling landing page variations across campaigns
Module 20: AI in Account-Based Marketing (ABM) - Identifying high-value target accounts using firmographic data
- Enriching account profiles with technographic signals
- Predicting account engagement likelihood
- Mapping decision-makers and influence networks
- Personalising outreach at scale using AI copy
- Tracking cross-channel engagement per account
- Scoring account progression through buyer journey
- Triggering sales alerts for high-intent signals
- Measuring ABM campaign ROI with precision
- Automating report generation for account teams
Module 21: Legal, Ethical & Compliance Considerations - GDPR, CCPA, and global data privacy regulations
- Avoiding discriminatory targeting in AI models
- Ensuring transparency in automated decision-making
- Handling biometric and inferred data responsibly
- Audit trails for AI-driven marketing actions
- Obtaining explicit consent for AI personalisation
- Managing data subject access requests
- Using AI without reinforcing bias in acquisition
- Building trust through ethical AI use
- Creating compliance checklists for AI campaigns
Module 22: Integration with Marketing Technology Stack - Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- Maximising paid social through AI audience expansion
- Using lookalike modelling to identify high-value prospects
- Automating ad creative testing with multivariate analysis
- Dynamic bidding strategies based on conversion probability
- AI-powered A/B testing for landing page optimisation
- Programmatic ad placement with predictive performance scoring
- Using AI to identify underperforming channels and redirect budgets
- Custom audience creation using intent signals
- Budget allocation optimisation across channels
- Attribution modelling powered by multi-touch AI
Module 7: Smart Content Personalisation - Dynamic website content based on visitor profiles
- AI-generated email subject lines that increase open rates
- Personalised product recommendations using collaborative filtering
- Content sequencing for automated nurture campaigns
- Sentiment analysis for tailoring messaging tone
- Using NLP to generate customer-specific copy variants
- Building content libraries for AI to draw from
- Real-time personalisation in live chat and chatbots
- A/B testing AI-generated content variants
- Maintaining brand voice within AI personalisation systems
Module 8: AI in Email & Lifecycle Marketing - Optimising send time using individual engagement patterns
- AI-driven email segmentation for lifecycle stages
- Predicting unsubscribe risk and triggering retention flows
- Content personalisation based on past interaction history
- Automated re-engagement campaigns for dormant leads
- Using churn prediction to prioritise high-risk customers
- Subject line generation using emotional trigger analysis
- Dynamic content blocks that adapt to recipient behaviour
- Email deliverability monitoring with anomaly detection
- Scaling email campaigns without losing personal relevance
Module 9: Conversational AI & Chatbot Strategy - Designing chatbots with customer acquisition intent
- Building decision trees enhanced with natural language understanding
- Using chatbots to qualify leads 24/7
- Integrating chatbot conversations into CRM
- Analysing chat transcripts for intent and friction points
- Training AI on brand-specific language and tone
- Routing high-intent leads to sales instantly
- Using chatbots for pre-qualification surveys
- Measuring conversion lift from chatbot interactions
- Escalating complex queries seamlessly to humans
Module 10: Search & SEO with AI Assistance - AI-powered keyword clustering by search intent
- Content gap analysis using competitor data scraping
- Automated meta description generation for higher CTR
- Predicting content performance before publication
- Using AI to structure articles for featured snippets
- Topic modelling for comprehensive content coverage
- Analysing user engagement signals for SERP ranking
- Automating internal linking strategies
- Monitoring algorithm changes and adjusting strategy
- Scaling SEO content creation across markets
Module 11: AI for Social Media Growth - Optimal posting times using engagement forecasting
- Content recommendation engines for social feeds
- Using AI to identify viral content patterns
- Real-time sentiment analysis of brand mentions
- Identifying brand advocates and potential influencers
- Automating comment responses with predefined logic
- Detecting crisis signals in social conversations
- Generating social post variations from core messages
- Tracking competitor campaign performance with AI
- Building community through AI-powered engagement triggers
Module 12: Predictive Customer Lifetime Value - Why CLV prediction is essential for acquisition ROI
- Selecting variables that influence lifetime value
- Using survival analysis to estimate customer longevity
- Combining transactional and behavioural data into models
- Calculating break-even acquisition costs with AI
- Segmenting acquisition efforts by predicted CLV tiers
- Adjusting strategies for high-CLV customer acquisition
- Validating model predictions with actual retention data
- Using CLV to justify premium acquisition spend
- Updating CLV models monthly for accuracy
Module 13: AI for Referral & Advocacy Programs - Identifying customers most likely to refer others
- Predicting referral conversion probability
- Automating personal invitation messages
- Tracking referral attribution across complex journeys
- Using AI to personalise incentive offers
- Scaling referral campaigns without manual oversight
- Analysing social sharing patterns for optimisation
- Integrating referral data into overall acquisition models
- Using network effect modelling to project growth
- Automating fraud detection in referral programs
Module 14: Voice & Visual Search Acquisition - Optimising for natural language queries
- Building FAQ structures that answer voice questions
- Using schema markup enhanced by AI suggestions
- Creating concise, direct responses for voice assistants
- Optimising product images for visual search recognition
- Using reverse image search to monitor brand usage
- Generating alt text automatically with image recognition
- Adapting for smart speaker and screen-based interactions
- Tracking voice search performance with intent categorisation
- Building zero-click strategies for discovery without clicks
Module 15: AI in Paid Search (PPC) Strategy - Automated keyword discovery using search query mining
- Dynamic ad copy generation based on search intent
- Bid adjustments based on conversion likelihood
- Using AI to detect click fraud and invalid traffic
- Ad schedule optimisation using conversion timing
- Forecasting budget impact of bid changes
- Identifying negative keywords using pattern recognition
- Optimising Quality Score through historical pattern analysis
- Testing hundreds of ad combinations efficiently
- Creating responsive search ads with AI-recommended assets
Module 16: Multi-Touch Attribution with AI - Problems with last-click and linear models
- How AI calculates true touchpoint influence
- Building custom attribution weights based on data
- Integrating offline and online interaction data
- Creating channel-specific contribution reports
- Using attribution to justify acquisition spend
- Forecasting ROI under different media mixes
- Automating budget reallocation based on contribution
- Explaining complex models to non-technical stakeholders
- Validating model accuracy with test campaigns
Module 17: AI for Event-Driven Marketing - Creating trigger-based campaigns from behavioural data
- Setting up real-time decision engines for personalisation
- Using AI to predict high-intent moments
- Automating follow-up sequences based on engagement
- Scaling triggered messaging without fatigue
- Measuring incremental lift from event campaigns
- Combining first-party data with real-time triggers
- Using weather, location, and time data as triggers
- Integrating with CDPs for unified event processing
- Building re-engagement flows for lapsed interactions
Module 18: AI Tools for Market Research - Using NLP to analyse customer reviews and feedback
- Automated competitor feature comparison
- Identifying emerging customer needs through trend analysis
- Clustering open-ended survey responses
- Real-time social listening with sentiment tracking
- Generating market opportunity reports using AI
- Monitoring pricing changes across competitors
- Predicting market shifts based on external signals
- Automating SWOT analysis from public data
- Generating buyer persona insights from qualitative data
Module 19: Building AI-Powered Landing Pages - Dynamic content based on visitor source and profile
- AI-generated headlines that maximise conversions
- Automated layout testing for user experience
- Personalised call-to-action recommendations
- Using heatmaps and session data to inform design
- Real-time form optimisation based on drop-off analysis
- Chatbot integration on high-intent pages
- Predicting conversion probability per visitor
- Routing traffic to customised experiences
- Scaling landing page variations across campaigns
Module 20: AI in Account-Based Marketing (ABM) - Identifying high-value target accounts using firmographic data
- Enriching account profiles with technographic signals
- Predicting account engagement likelihood
- Mapping decision-makers and influence networks
- Personalising outreach at scale using AI copy
- Tracking cross-channel engagement per account
- Scoring account progression through buyer journey
- Triggering sales alerts for high-intent signals
- Measuring ABM campaign ROI with precision
- Automating report generation for account teams
Module 21: Legal, Ethical & Compliance Considerations - GDPR, CCPA, and global data privacy regulations
- Avoiding discriminatory targeting in AI models
- Ensuring transparency in automated decision-making
- Handling biometric and inferred data responsibly
- Audit trails for AI-driven marketing actions
- Obtaining explicit consent for AI personalisation
- Managing data subject access requests
- Using AI without reinforcing bias in acquisition
- Building trust through ethical AI use
- Creating compliance checklists for AI campaigns
Module 22: Integration with Marketing Technology Stack - Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- Optimising send time using individual engagement patterns
- AI-driven email segmentation for lifecycle stages
- Predicting unsubscribe risk and triggering retention flows
- Content personalisation based on past interaction history
- Automated re-engagement campaigns for dormant leads
- Using churn prediction to prioritise high-risk customers
- Subject line generation using emotional trigger analysis
- Dynamic content blocks that adapt to recipient behaviour
- Email deliverability monitoring with anomaly detection
- Scaling email campaigns without losing personal relevance
Module 9: Conversational AI & Chatbot Strategy - Designing chatbots with customer acquisition intent
- Building decision trees enhanced with natural language understanding
- Using chatbots to qualify leads 24/7
- Integrating chatbot conversations into CRM
- Analysing chat transcripts for intent and friction points
- Training AI on brand-specific language and tone
- Routing high-intent leads to sales instantly
- Using chatbots for pre-qualification surveys
- Measuring conversion lift from chatbot interactions
- Escalating complex queries seamlessly to humans
Module 10: Search & SEO with AI Assistance - AI-powered keyword clustering by search intent
- Content gap analysis using competitor data scraping
- Automated meta description generation for higher CTR
- Predicting content performance before publication
- Using AI to structure articles for featured snippets
- Topic modelling for comprehensive content coverage
- Analysing user engagement signals for SERP ranking
- Automating internal linking strategies
- Monitoring algorithm changes and adjusting strategy
- Scaling SEO content creation across markets
Module 11: AI for Social Media Growth - Optimal posting times using engagement forecasting
- Content recommendation engines for social feeds
- Using AI to identify viral content patterns
- Real-time sentiment analysis of brand mentions
- Identifying brand advocates and potential influencers
- Automating comment responses with predefined logic
- Detecting crisis signals in social conversations
- Generating social post variations from core messages
- Tracking competitor campaign performance with AI
- Building community through AI-powered engagement triggers
Module 12: Predictive Customer Lifetime Value - Why CLV prediction is essential for acquisition ROI
- Selecting variables that influence lifetime value
- Using survival analysis to estimate customer longevity
- Combining transactional and behavioural data into models
- Calculating break-even acquisition costs with AI
- Segmenting acquisition efforts by predicted CLV tiers
- Adjusting strategies for high-CLV customer acquisition
- Validating model predictions with actual retention data
- Using CLV to justify premium acquisition spend
- Updating CLV models monthly for accuracy
Module 13: AI for Referral & Advocacy Programs - Identifying customers most likely to refer others
- Predicting referral conversion probability
- Automating personal invitation messages
- Tracking referral attribution across complex journeys
- Using AI to personalise incentive offers
- Scaling referral campaigns without manual oversight
- Analysing social sharing patterns for optimisation
- Integrating referral data into overall acquisition models
- Using network effect modelling to project growth
- Automating fraud detection in referral programs
Module 14: Voice & Visual Search Acquisition - Optimising for natural language queries
- Building FAQ structures that answer voice questions
- Using schema markup enhanced by AI suggestions
- Creating concise, direct responses for voice assistants
- Optimising product images for visual search recognition
- Using reverse image search to monitor brand usage
- Generating alt text automatically with image recognition
- Adapting for smart speaker and screen-based interactions
- Tracking voice search performance with intent categorisation
- Building zero-click strategies for discovery without clicks
Module 15: AI in Paid Search (PPC) Strategy - Automated keyword discovery using search query mining
- Dynamic ad copy generation based on search intent
- Bid adjustments based on conversion likelihood
- Using AI to detect click fraud and invalid traffic
- Ad schedule optimisation using conversion timing
- Forecasting budget impact of bid changes
- Identifying negative keywords using pattern recognition
- Optimising Quality Score through historical pattern analysis
- Testing hundreds of ad combinations efficiently
- Creating responsive search ads with AI-recommended assets
Module 16: Multi-Touch Attribution with AI - Problems with last-click and linear models
- How AI calculates true touchpoint influence
- Building custom attribution weights based on data
- Integrating offline and online interaction data
- Creating channel-specific contribution reports
- Using attribution to justify acquisition spend
- Forecasting ROI under different media mixes
- Automating budget reallocation based on contribution
- Explaining complex models to non-technical stakeholders
- Validating model accuracy with test campaigns
Module 17: AI for Event-Driven Marketing - Creating trigger-based campaigns from behavioural data
- Setting up real-time decision engines for personalisation
- Using AI to predict high-intent moments
- Automating follow-up sequences based on engagement
- Scaling triggered messaging without fatigue
- Measuring incremental lift from event campaigns
- Combining first-party data with real-time triggers
- Using weather, location, and time data as triggers
- Integrating with CDPs for unified event processing
- Building re-engagement flows for lapsed interactions
Module 18: AI Tools for Market Research - Using NLP to analyse customer reviews and feedback
- Automated competitor feature comparison
- Identifying emerging customer needs through trend analysis
- Clustering open-ended survey responses
- Real-time social listening with sentiment tracking
- Generating market opportunity reports using AI
- Monitoring pricing changes across competitors
- Predicting market shifts based on external signals
- Automating SWOT analysis from public data
- Generating buyer persona insights from qualitative data
Module 19: Building AI-Powered Landing Pages - Dynamic content based on visitor source and profile
- AI-generated headlines that maximise conversions
- Automated layout testing for user experience
- Personalised call-to-action recommendations
- Using heatmaps and session data to inform design
- Real-time form optimisation based on drop-off analysis
- Chatbot integration on high-intent pages
- Predicting conversion probability per visitor
- Routing traffic to customised experiences
- Scaling landing page variations across campaigns
Module 20: AI in Account-Based Marketing (ABM) - Identifying high-value target accounts using firmographic data
- Enriching account profiles with technographic signals
- Predicting account engagement likelihood
- Mapping decision-makers and influence networks
- Personalising outreach at scale using AI copy
- Tracking cross-channel engagement per account
- Scoring account progression through buyer journey
- Triggering sales alerts for high-intent signals
- Measuring ABM campaign ROI with precision
- Automating report generation for account teams
Module 21: Legal, Ethical & Compliance Considerations - GDPR, CCPA, and global data privacy regulations
- Avoiding discriminatory targeting in AI models
- Ensuring transparency in automated decision-making
- Handling biometric and inferred data responsibly
- Audit trails for AI-driven marketing actions
- Obtaining explicit consent for AI personalisation
- Managing data subject access requests
- Using AI without reinforcing bias in acquisition
- Building trust through ethical AI use
- Creating compliance checklists for AI campaigns
Module 22: Integration with Marketing Technology Stack - Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- AI-powered keyword clustering by search intent
- Content gap analysis using competitor data scraping
- Automated meta description generation for higher CTR
- Predicting content performance before publication
- Using AI to structure articles for featured snippets
- Topic modelling for comprehensive content coverage
- Analysing user engagement signals for SERP ranking
- Automating internal linking strategies
- Monitoring algorithm changes and adjusting strategy
- Scaling SEO content creation across markets
Module 11: AI for Social Media Growth - Optimal posting times using engagement forecasting
- Content recommendation engines for social feeds
- Using AI to identify viral content patterns
- Real-time sentiment analysis of brand mentions
- Identifying brand advocates and potential influencers
- Automating comment responses with predefined logic
- Detecting crisis signals in social conversations
- Generating social post variations from core messages
- Tracking competitor campaign performance with AI
- Building community through AI-powered engagement triggers
Module 12: Predictive Customer Lifetime Value - Why CLV prediction is essential for acquisition ROI
- Selecting variables that influence lifetime value
- Using survival analysis to estimate customer longevity
- Combining transactional and behavioural data into models
- Calculating break-even acquisition costs with AI
- Segmenting acquisition efforts by predicted CLV tiers
- Adjusting strategies for high-CLV customer acquisition
- Validating model predictions with actual retention data
- Using CLV to justify premium acquisition spend
- Updating CLV models monthly for accuracy
Module 13: AI for Referral & Advocacy Programs - Identifying customers most likely to refer others
- Predicting referral conversion probability
- Automating personal invitation messages
- Tracking referral attribution across complex journeys
- Using AI to personalise incentive offers
- Scaling referral campaigns without manual oversight
- Analysing social sharing patterns for optimisation
- Integrating referral data into overall acquisition models
- Using network effect modelling to project growth
- Automating fraud detection in referral programs
Module 14: Voice & Visual Search Acquisition - Optimising for natural language queries
- Building FAQ structures that answer voice questions
- Using schema markup enhanced by AI suggestions
- Creating concise, direct responses for voice assistants
- Optimising product images for visual search recognition
- Using reverse image search to monitor brand usage
- Generating alt text automatically with image recognition
- Adapting for smart speaker and screen-based interactions
- Tracking voice search performance with intent categorisation
- Building zero-click strategies for discovery without clicks
Module 15: AI in Paid Search (PPC) Strategy - Automated keyword discovery using search query mining
- Dynamic ad copy generation based on search intent
- Bid adjustments based on conversion likelihood
- Using AI to detect click fraud and invalid traffic
- Ad schedule optimisation using conversion timing
- Forecasting budget impact of bid changes
- Identifying negative keywords using pattern recognition
- Optimising Quality Score through historical pattern analysis
- Testing hundreds of ad combinations efficiently
- Creating responsive search ads with AI-recommended assets
Module 16: Multi-Touch Attribution with AI - Problems with last-click and linear models
- How AI calculates true touchpoint influence
- Building custom attribution weights based on data
- Integrating offline and online interaction data
- Creating channel-specific contribution reports
- Using attribution to justify acquisition spend
- Forecasting ROI under different media mixes
- Automating budget reallocation based on contribution
- Explaining complex models to non-technical stakeholders
- Validating model accuracy with test campaigns
Module 17: AI for Event-Driven Marketing - Creating trigger-based campaigns from behavioural data
- Setting up real-time decision engines for personalisation
- Using AI to predict high-intent moments
- Automating follow-up sequences based on engagement
- Scaling triggered messaging without fatigue
- Measuring incremental lift from event campaigns
- Combining first-party data with real-time triggers
- Using weather, location, and time data as triggers
- Integrating with CDPs for unified event processing
- Building re-engagement flows for lapsed interactions
Module 18: AI Tools for Market Research - Using NLP to analyse customer reviews and feedback
- Automated competitor feature comparison
- Identifying emerging customer needs through trend analysis
- Clustering open-ended survey responses
- Real-time social listening with sentiment tracking
- Generating market opportunity reports using AI
- Monitoring pricing changes across competitors
- Predicting market shifts based on external signals
- Automating SWOT analysis from public data
- Generating buyer persona insights from qualitative data
Module 19: Building AI-Powered Landing Pages - Dynamic content based on visitor source and profile
- AI-generated headlines that maximise conversions
- Automated layout testing for user experience
- Personalised call-to-action recommendations
- Using heatmaps and session data to inform design
- Real-time form optimisation based on drop-off analysis
- Chatbot integration on high-intent pages
- Predicting conversion probability per visitor
- Routing traffic to customised experiences
- Scaling landing page variations across campaigns
Module 20: AI in Account-Based Marketing (ABM) - Identifying high-value target accounts using firmographic data
- Enriching account profiles with technographic signals
- Predicting account engagement likelihood
- Mapping decision-makers and influence networks
- Personalising outreach at scale using AI copy
- Tracking cross-channel engagement per account
- Scoring account progression through buyer journey
- Triggering sales alerts for high-intent signals
- Measuring ABM campaign ROI with precision
- Automating report generation for account teams
Module 21: Legal, Ethical & Compliance Considerations - GDPR, CCPA, and global data privacy regulations
- Avoiding discriminatory targeting in AI models
- Ensuring transparency in automated decision-making
- Handling biometric and inferred data responsibly
- Audit trails for AI-driven marketing actions
- Obtaining explicit consent for AI personalisation
- Managing data subject access requests
- Using AI without reinforcing bias in acquisition
- Building trust through ethical AI use
- Creating compliance checklists for AI campaigns
Module 22: Integration with Marketing Technology Stack - Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- Why CLV prediction is essential for acquisition ROI
- Selecting variables that influence lifetime value
- Using survival analysis to estimate customer longevity
- Combining transactional and behavioural data into models
- Calculating break-even acquisition costs with AI
- Segmenting acquisition efforts by predicted CLV tiers
- Adjusting strategies for high-CLV customer acquisition
- Validating model predictions with actual retention data
- Using CLV to justify premium acquisition spend
- Updating CLV models monthly for accuracy
Module 13: AI for Referral & Advocacy Programs - Identifying customers most likely to refer others
- Predicting referral conversion probability
- Automating personal invitation messages
- Tracking referral attribution across complex journeys
- Using AI to personalise incentive offers
- Scaling referral campaigns without manual oversight
- Analysing social sharing patterns for optimisation
- Integrating referral data into overall acquisition models
- Using network effect modelling to project growth
- Automating fraud detection in referral programs
Module 14: Voice & Visual Search Acquisition - Optimising for natural language queries
- Building FAQ structures that answer voice questions
- Using schema markup enhanced by AI suggestions
- Creating concise, direct responses for voice assistants
- Optimising product images for visual search recognition
- Using reverse image search to monitor brand usage
- Generating alt text automatically with image recognition
- Adapting for smart speaker and screen-based interactions
- Tracking voice search performance with intent categorisation
- Building zero-click strategies for discovery without clicks
Module 15: AI in Paid Search (PPC) Strategy - Automated keyword discovery using search query mining
- Dynamic ad copy generation based on search intent
- Bid adjustments based on conversion likelihood
- Using AI to detect click fraud and invalid traffic
- Ad schedule optimisation using conversion timing
- Forecasting budget impact of bid changes
- Identifying negative keywords using pattern recognition
- Optimising Quality Score through historical pattern analysis
- Testing hundreds of ad combinations efficiently
- Creating responsive search ads with AI-recommended assets
Module 16: Multi-Touch Attribution with AI - Problems with last-click and linear models
- How AI calculates true touchpoint influence
- Building custom attribution weights based on data
- Integrating offline and online interaction data
- Creating channel-specific contribution reports
- Using attribution to justify acquisition spend
- Forecasting ROI under different media mixes
- Automating budget reallocation based on contribution
- Explaining complex models to non-technical stakeholders
- Validating model accuracy with test campaigns
Module 17: AI for Event-Driven Marketing - Creating trigger-based campaigns from behavioural data
- Setting up real-time decision engines for personalisation
- Using AI to predict high-intent moments
- Automating follow-up sequences based on engagement
- Scaling triggered messaging without fatigue
- Measuring incremental lift from event campaigns
- Combining first-party data with real-time triggers
- Using weather, location, and time data as triggers
- Integrating with CDPs for unified event processing
- Building re-engagement flows for lapsed interactions
Module 18: AI Tools for Market Research - Using NLP to analyse customer reviews and feedback
- Automated competitor feature comparison
- Identifying emerging customer needs through trend analysis
- Clustering open-ended survey responses
- Real-time social listening with sentiment tracking
- Generating market opportunity reports using AI
- Monitoring pricing changes across competitors
- Predicting market shifts based on external signals
- Automating SWOT analysis from public data
- Generating buyer persona insights from qualitative data
Module 19: Building AI-Powered Landing Pages - Dynamic content based on visitor source and profile
- AI-generated headlines that maximise conversions
- Automated layout testing for user experience
- Personalised call-to-action recommendations
- Using heatmaps and session data to inform design
- Real-time form optimisation based on drop-off analysis
- Chatbot integration on high-intent pages
- Predicting conversion probability per visitor
- Routing traffic to customised experiences
- Scaling landing page variations across campaigns
Module 20: AI in Account-Based Marketing (ABM) - Identifying high-value target accounts using firmographic data
- Enriching account profiles with technographic signals
- Predicting account engagement likelihood
- Mapping decision-makers and influence networks
- Personalising outreach at scale using AI copy
- Tracking cross-channel engagement per account
- Scoring account progression through buyer journey
- Triggering sales alerts for high-intent signals
- Measuring ABM campaign ROI with precision
- Automating report generation for account teams
Module 21: Legal, Ethical & Compliance Considerations - GDPR, CCPA, and global data privacy regulations
- Avoiding discriminatory targeting in AI models
- Ensuring transparency in automated decision-making
- Handling biometric and inferred data responsibly
- Audit trails for AI-driven marketing actions
- Obtaining explicit consent for AI personalisation
- Managing data subject access requests
- Using AI without reinforcing bias in acquisition
- Building trust through ethical AI use
- Creating compliance checklists for AI campaigns
Module 22: Integration with Marketing Technology Stack - Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- Optimising for natural language queries
- Building FAQ structures that answer voice questions
- Using schema markup enhanced by AI suggestions
- Creating concise, direct responses for voice assistants
- Optimising product images for visual search recognition
- Using reverse image search to monitor brand usage
- Generating alt text automatically with image recognition
- Adapting for smart speaker and screen-based interactions
- Tracking voice search performance with intent categorisation
- Building zero-click strategies for discovery without clicks
Module 15: AI in Paid Search (PPC) Strategy - Automated keyword discovery using search query mining
- Dynamic ad copy generation based on search intent
- Bid adjustments based on conversion likelihood
- Using AI to detect click fraud and invalid traffic
- Ad schedule optimisation using conversion timing
- Forecasting budget impact of bid changes
- Identifying negative keywords using pattern recognition
- Optimising Quality Score through historical pattern analysis
- Testing hundreds of ad combinations efficiently
- Creating responsive search ads with AI-recommended assets
Module 16: Multi-Touch Attribution with AI - Problems with last-click and linear models
- How AI calculates true touchpoint influence
- Building custom attribution weights based on data
- Integrating offline and online interaction data
- Creating channel-specific contribution reports
- Using attribution to justify acquisition spend
- Forecasting ROI under different media mixes
- Automating budget reallocation based on contribution
- Explaining complex models to non-technical stakeholders
- Validating model accuracy with test campaigns
Module 17: AI for Event-Driven Marketing - Creating trigger-based campaigns from behavioural data
- Setting up real-time decision engines for personalisation
- Using AI to predict high-intent moments
- Automating follow-up sequences based on engagement
- Scaling triggered messaging without fatigue
- Measuring incremental lift from event campaigns
- Combining first-party data with real-time triggers
- Using weather, location, and time data as triggers
- Integrating with CDPs for unified event processing
- Building re-engagement flows for lapsed interactions
Module 18: AI Tools for Market Research - Using NLP to analyse customer reviews and feedback
- Automated competitor feature comparison
- Identifying emerging customer needs through trend analysis
- Clustering open-ended survey responses
- Real-time social listening with sentiment tracking
- Generating market opportunity reports using AI
- Monitoring pricing changes across competitors
- Predicting market shifts based on external signals
- Automating SWOT analysis from public data
- Generating buyer persona insights from qualitative data
Module 19: Building AI-Powered Landing Pages - Dynamic content based on visitor source and profile
- AI-generated headlines that maximise conversions
- Automated layout testing for user experience
- Personalised call-to-action recommendations
- Using heatmaps and session data to inform design
- Real-time form optimisation based on drop-off analysis
- Chatbot integration on high-intent pages
- Predicting conversion probability per visitor
- Routing traffic to customised experiences
- Scaling landing page variations across campaigns
Module 20: AI in Account-Based Marketing (ABM) - Identifying high-value target accounts using firmographic data
- Enriching account profiles with technographic signals
- Predicting account engagement likelihood
- Mapping decision-makers and influence networks
- Personalising outreach at scale using AI copy
- Tracking cross-channel engagement per account
- Scoring account progression through buyer journey
- Triggering sales alerts for high-intent signals
- Measuring ABM campaign ROI with precision
- Automating report generation for account teams
Module 21: Legal, Ethical & Compliance Considerations - GDPR, CCPA, and global data privacy regulations
- Avoiding discriminatory targeting in AI models
- Ensuring transparency in automated decision-making
- Handling biometric and inferred data responsibly
- Audit trails for AI-driven marketing actions
- Obtaining explicit consent for AI personalisation
- Managing data subject access requests
- Using AI without reinforcing bias in acquisition
- Building trust through ethical AI use
- Creating compliance checklists for AI campaigns
Module 22: Integration with Marketing Technology Stack - Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- Problems with last-click and linear models
- How AI calculates true touchpoint influence
- Building custom attribution weights based on data
- Integrating offline and online interaction data
- Creating channel-specific contribution reports
- Using attribution to justify acquisition spend
- Forecasting ROI under different media mixes
- Automating budget reallocation based on contribution
- Explaining complex models to non-technical stakeholders
- Validating model accuracy with test campaigns
Module 17: AI for Event-Driven Marketing - Creating trigger-based campaigns from behavioural data
- Setting up real-time decision engines for personalisation
- Using AI to predict high-intent moments
- Automating follow-up sequences based on engagement
- Scaling triggered messaging without fatigue
- Measuring incremental lift from event campaigns
- Combining first-party data with real-time triggers
- Using weather, location, and time data as triggers
- Integrating with CDPs for unified event processing
- Building re-engagement flows for lapsed interactions
Module 18: AI Tools for Market Research - Using NLP to analyse customer reviews and feedback
- Automated competitor feature comparison
- Identifying emerging customer needs through trend analysis
- Clustering open-ended survey responses
- Real-time social listening with sentiment tracking
- Generating market opportunity reports using AI
- Monitoring pricing changes across competitors
- Predicting market shifts based on external signals
- Automating SWOT analysis from public data
- Generating buyer persona insights from qualitative data
Module 19: Building AI-Powered Landing Pages - Dynamic content based on visitor source and profile
- AI-generated headlines that maximise conversions
- Automated layout testing for user experience
- Personalised call-to-action recommendations
- Using heatmaps and session data to inform design
- Real-time form optimisation based on drop-off analysis
- Chatbot integration on high-intent pages
- Predicting conversion probability per visitor
- Routing traffic to customised experiences
- Scaling landing page variations across campaigns
Module 20: AI in Account-Based Marketing (ABM) - Identifying high-value target accounts using firmographic data
- Enriching account profiles with technographic signals
- Predicting account engagement likelihood
- Mapping decision-makers and influence networks
- Personalising outreach at scale using AI copy
- Tracking cross-channel engagement per account
- Scoring account progression through buyer journey
- Triggering sales alerts for high-intent signals
- Measuring ABM campaign ROI with precision
- Automating report generation for account teams
Module 21: Legal, Ethical & Compliance Considerations - GDPR, CCPA, and global data privacy regulations
- Avoiding discriminatory targeting in AI models
- Ensuring transparency in automated decision-making
- Handling biometric and inferred data responsibly
- Audit trails for AI-driven marketing actions
- Obtaining explicit consent for AI personalisation
- Managing data subject access requests
- Using AI without reinforcing bias in acquisition
- Building trust through ethical AI use
- Creating compliance checklists for AI campaigns
Module 22: Integration with Marketing Technology Stack - Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- Using NLP to analyse customer reviews and feedback
- Automated competitor feature comparison
- Identifying emerging customer needs through trend analysis
- Clustering open-ended survey responses
- Real-time social listening with sentiment tracking
- Generating market opportunity reports using AI
- Monitoring pricing changes across competitors
- Predicting market shifts based on external signals
- Automating SWOT analysis from public data
- Generating buyer persona insights from qualitative data
Module 19: Building AI-Powered Landing Pages - Dynamic content based on visitor source and profile
- AI-generated headlines that maximise conversions
- Automated layout testing for user experience
- Personalised call-to-action recommendations
- Using heatmaps and session data to inform design
- Real-time form optimisation based on drop-off analysis
- Chatbot integration on high-intent pages
- Predicting conversion probability per visitor
- Routing traffic to customised experiences
- Scaling landing page variations across campaigns
Module 20: AI in Account-Based Marketing (ABM) - Identifying high-value target accounts using firmographic data
- Enriching account profiles with technographic signals
- Predicting account engagement likelihood
- Mapping decision-makers and influence networks
- Personalising outreach at scale using AI copy
- Tracking cross-channel engagement per account
- Scoring account progression through buyer journey
- Triggering sales alerts for high-intent signals
- Measuring ABM campaign ROI with precision
- Automating report generation for account teams
Module 21: Legal, Ethical & Compliance Considerations - GDPR, CCPA, and global data privacy regulations
- Avoiding discriminatory targeting in AI models
- Ensuring transparency in automated decision-making
- Handling biometric and inferred data responsibly
- Audit trails for AI-driven marketing actions
- Obtaining explicit consent for AI personalisation
- Managing data subject access requests
- Using AI without reinforcing bias in acquisition
- Building trust through ethical AI use
- Creating compliance checklists for AI campaigns
Module 22: Integration with Marketing Technology Stack - Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- Identifying high-value target accounts using firmographic data
- Enriching account profiles with technographic signals
- Predicting account engagement likelihood
- Mapping decision-makers and influence networks
- Personalising outreach at scale using AI copy
- Tracking cross-channel engagement per account
- Scoring account progression through buyer journey
- Triggering sales alerts for high-intent signals
- Measuring ABM campaign ROI with precision
- Automating report generation for account teams
Module 21: Legal, Ethical & Compliance Considerations - GDPR, CCPA, and global data privacy regulations
- Avoiding discriminatory targeting in AI models
- Ensuring transparency in automated decision-making
- Handling biometric and inferred data responsibly
- Audit trails for AI-driven marketing actions
- Obtaining explicit consent for AI personalisation
- Managing data subject access requests
- Using AI without reinforcing bias in acquisition
- Building trust through ethical AI use
- Creating compliance checklists for AI campaigns
Module 22: Integration with Marketing Technology Stack - Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- Connecting AI tools to CRM platforms
- Using APIs to feed data to and from AI systems
- Ensuring real-time data synchronisation
- Building automated workflows between tools
- Reducing manual data export and import
- Using middleware like Zapier or Make for integration
- Setting up error monitoring and alerts
- Documenting integration architecture
- Training teams on integrated system usage
- Maintaining system compatibility over time
Module 23: Measuring AI Campaign Performance - Defining KPIs for AI acquisition initiatives
- Setting up dashboards for real-time monitoring
- Tracking incremental conversion lift from AI
- Calculating ROI for AI tool investments
- Comparing AI vs non-AI campaign results
- Using statistical significance testing
- Creating automated performance reports
- Identifying anomalies in campaign data
- Setting up alerts for performance deviations
- Presenting AI results to executives and stakeholders
Module 24: Scaling AI AcROSS Teams & Markets - Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- Training marketing teams on AI workflows
- Documenting standard operating procedures
- Creating AI playbooks for consistent execution
- Onboarding new team members efficiently
- Localising AI models for regional markets
- Managing language, culture, and regulatory differences
- Scaling personalisation across geographies
- Consolidating insights from global campaigns
- Building a centre of excellence for AI marketing
- Measuring efficiency gains from automation
Module 25: The Future of AI in Customer Acquisition - Emerging trends in generative AI for marketing
- Autonomous agents and self-optimising campaigns
- AI-driven market simulation and forecasting
- The role of quantum computing in future models
- Building AI literacy as a core marketing competency
- Preparing for AI regulation and policy changes
- The evolving role of the marketer in an AI world
- Continuous learning strategies for AI marketers
- Building a personal AI acquisition roadmap
- Finalising your implementation plan with confidence
Module 26: Practical Implementation & Certification - Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service
- Completing the AI Acquisition Readiness Audit
- Selecting your first AI use case for deployment
- Setting up a pilot campaign with full tracking
- Drafting an AI integration project plan
- Presenting your strategy to key stakeholders
- Building a progress tracking dashboard
- Submitting your final implementation outline
- Receiving expert feedback on your plan
- Reflecting on key learnings and growth
- Earning your Certificate of Completion from The Art of Service