Mastering AI-Driven Customer Journey Analytics for Strategic Decision-Making
You’re under pressure. Stakeholders want proof that every customer interaction drives value. Executives demand insights, not just data. But your analytics still feel reactive, siloed, and too slow to act on - caught between outdated attribution models and fragmented journey mapping. What if you could move from guessing at touchpoints to precisely engineering customer outcomes? This is where Mastering AI-Driven Customer Journey Analytics for Strategic Decision-Making transforms your career trajectory. It’s the missing system that turns noise into clarity, hesitation into influence, and complexity into boardroom-ready strategy. One course participant, Sarah Lin (Director of CX Analytics, B2B SaaS, 1,200 employees), used the framework inside this program to identify a recurring abandonment bottleneck in her company’s trial-to-paid funnel. Within 18 days, she built an AI-driven journey model that isolated the exact decision threshold, leading to a 37% increase in conversion - and a standing ovation in her next executive review. This course doesn’t just teach analytics. It arms you with an end-to-end, AI-powered decision architecture that turns customer behavior into predictable, optimizable paths. You’ll graduate with a fully developed use case, complete with strategic narrative, predictive logic, and implementation plan - ready for funding or immediate deployment. No more waiting. No more patchwork tools. Just a high-precision, ROI-generating system that positions you as the strategic architect of customer growth. Here’s how this course is structured to help you get there.Course Format & Delivery Details This program is designed for real-world professionals who need fast, frictionless, and credible upskilling without disruption to their workflow. Every detail is engineered to maximise trust, minimise risk, and accelerate your path to influence. Flexible, Self-Paced Learning with Immediate Online Access
This is a 100% self-paced course. As soon as you enroll, you gain secure online access to the full curriculum, structured in bite-sized, outcome-focused modules. There are no fixed start dates, no weekly schedules, and no time zones to worry about. You progress at your own speed, on your own terms - whether that means 3 hours a week or full immersion over a weekend. Most learners complete the core journey in 21 to 28 days. However, many apply the frameworks to live projects and see actionable results in as little as 7 days. The system is designed for immediate real-world application - not just theoretical understanding. Lifetime Access, Future Updates Included
You’re not buying a single course. You’re gaining permanent access to a living, evolving methodology. All future content updates, refinements, and expanded frameworks are delivered at no additional cost. As AI, privacy regulations, and journey modelling techniques evolve, your knowledge base evolves with them - automatically. Mobile-Friendly, 24/7 Global Access
Access your materials anytime, from any device. Whether you’re on a commuter train, in a late-night strategy session, or working remotely from another country, the entire system is optimised for responsiveness, fast loading, and seamless navigation. Designed with global learners in mind, it performs flawlessly across regions, networks, and screen sizes. Expert-Led Support and Guidance
While the course is self-guided, you’re never alone. Enrollees receive structured instructor support through curated feedback checkpoints, detailed implementation templates, and access to an exclusive peer reference network. You’ll also receive prioritised responses to content-specific questions via a dedicated support channel - ensuring clarity throughout your journey. Certificate of Completion from The Art of Service
Upon successful completion, you’ll earn a Certificate of Completion issued by The Art of Service - a globally recognised credential trusted by professionals in over 140 countries. This isn’t a participation badge. It’s a verified endorsement of your ability to design, interpret, and act on AI-driven customer journey analytics at a strategic level. Share it on LinkedIn, include it in your performance reviews, and use it as evidence of advanced analytical leadership. No Hidden Fees. Transparent Pricing. Full Confidence.
Pricing is straightforward, one-time, and includes everything. There are no subscription traps, no add-ons, and no surprise charges. You pay once, get everything, forever. We accept all major payment methods, including Visa, Mastercard, and PayPal - processed securely through encrypted gateways to protect your information. 100% Satisfied or Refunded - Zero Risk Enrollment
This isn’t just a course. It’s a professional investment - and we stand behind it completely. If you complete the first two modules and don’t believe the content delivers exceptional value, you can request a full refund. No questions, no hurdles, no risk. Secure Enrollment Process & Access Confirmation
After enrollment, you’ll receive a confirmation email outlining your next steps. Your access credentials and learning dashboard details will be sent in a separate communication once your course materials are fully prepared and your account is active. This ensures a smooth, error-free start to your experience. Will This Work For Me? We’ve Designed for Maximum Transferability
This course works even if you’re not a data scientist. It works even if your organisation uses legacy CRM systems. It works even if you’ve never built a predictive model before. Designed by former chief analytics officers and customer strategy leaders, the methodology is role-agnostic and platform-flexible. Whether you’re a marketing manager, product owner, CX lead, or data analyst, the frameworks adapt to your context. Learners from regulated industries, B2B, B2C, e-commerce, fintech, and healthcare have all applied these techniques with measurable success. One learner, a mid-level digital analyst at a European bank, used the journey segmentation module to restructure their customer onboarding narrative. Without coding, using only Excel and existing BI tools, they achieved a 29% reduction in drop-off - earning a promotion within three months. We remove the friction between knowledge and implementation. This isn’t abstract theory. It’s repeatable, documented, and battle-tested in real enterprise environments. Your success isn’t left to chance - it’s engineered into the design.
Module 1: Foundations of AI-Driven Customer Journey Analytics - Understanding the limitations of traditional customer journey mapping
- Why conventional analytics fail to predict customer behavior
- Introduction to AI-driven journey modelling: key principles and definitions
- The role of intention, context, and sequence in predictive analytics
- From data collection to decision-ready insights: the end-to-end pipeline
- Defining strategic decision-making in customer experience
- Common organisational roadblocks and how to navigate them
- Building cross-functional alignment for journey analytics projects
- Establishing a customer-centric data governance policy
- Introduction to journey-based KPIs and success metrics
Module 2: Data Architecture for Journey Analytics - Identifying and integrating first, second, and third-party data sources
- Designing unified customer identifiers across touchpoints
- Handling data latency, missingness, and cross-device tracking
- Building a clean, structured journey data pipeline
- Event tagging standards for consistency and scalability
- Sanitising and normalising data for AI readiness
- Designing for privacy compliance (GDPR, CCPA, etc.) from day one
- Setting up data lineage and audit trails
- Creating data dictionaries specific to journey analysis
- Leveraging cloud-based data warehouses for scalability
Module 3: AI and Machine Learning Fundamentals for Journeys - Demystifying AI: no-code approaches to journey analytics
- Understanding supervised vs unsupervised learning in customer contexts
- Introduction to clustering: identifying hidden customer segments
- Classification models for predicting conversion and churn
- Sequence analysis: detecting critical decision paths
- Time-to-event models for forecasting drop-off points
- Feature engineering for journey data: what to include and exclude
- Model interpretability: making AI insights explainable to stakeholders
- Evaluating model performance with journey-specific metrics
- Validating models against real-world outcomes
Module 4: Journey Mapping 2.0 - Dynamic, Predictive, and Actionable - From static maps to dynamic, evolving customer journey models
- Incorporating real-time data into journey visualisations
- Mapping emotional states and intent shifts across touchpoints
- Identifying micro-moments that drive macro-outcomes
- Creating journey heatmaps to surface friction zones
- Using probabilistic path analysis to forecast future behaviors
- Integrating journey data with service design blueprints
- Building journey maps that support root cause analysis
- Creating multiple journey variants for different segments
- Linking journey stages to business outcomes and financial impact
Module 5: Attribution Reimagined - AI-Powered Models - The flaws of last-click and linear attribution
- Shapley value and game theory in multi-touch attribution
- Using Markov chains to model customer path influence
- Algorithmic attribution: how to assign credit accurately
- Time decay models and recency weighting
- Modelling offline and online channel convergence
- Balancing marketing spend based on AI-driven attribution
- Communicating attribution results to non-technical teams
- Using attribution to identify underperforming channels
- Validating attribution models with controlled experiments
Module 6: Predictive Journey Analytics Frameworks - Designing AI models to predict next-best actions
- Creating propensity models for conversion, upsell, and retention
- Forecasting customer lifetime value using journey patterns
- Early warning systems for churn and disengagement
- Identifying high-impact intervention points in real time
- Modelling customer resilience to friction and delays
- Using historical journeys to simulate future scenarios
- Building decision trees based on predicted outcomes
- Scenario planning with AI-driven journey outcomes
- Integrating predictive insights into automated workflows
Module 7: Implementing AI Models with Low-Code Tools - Selecting the right tools for non-technical users
- Using Excel, Google Sheets, and BI platforms for predictive insights
- Setting up AI-powered journey models in Power BI and Tableau
- Integrating with CRM systems like Salesforce and HubSpot
- Using no-code AI platforms: MonkeyLearn, Akkio, Lobe
- Exporting and applying model outputs to marketing automation
- Creating dashboards that align journey insights with business goals
- Automating reports for executive stakeholders
- Setting up alerts for deviation from predicted journey paths
- Linking journey analytics to campaign optimisation
Module 8: Real-World Project - Build Your AI-Driven Journey Model - Selecting a high-impact use case from your organisation
- Defining the business objective and success criteria
- Mapping current-state customer journey
- Identifying data gaps and sourcing requirements
- Designing a predictive model framework
- Building a segment-specific journey hypothesis
- Creating a data collection and processing plan
- Applying AI techniques to historical interaction data
- Generating a predictive journey map
- Drafting an executive summary and visual presentation
Module 9: From Insight to Strategic Influence - Framing journey insights as business risks and opportunities
- Building a compelling narrative for executive buy-in
- Translating technical findings into strategic recommendations
- Aligning journey outcomes with revenue and cost objectives
- Presenting AI-driven insights with confidence and clarity
- Using journey data to prioritise product and service investments
- Integrating journey analytics into quarterly planning cycles
- Securing budget and resources for future projects
- Influencing C-suite decisions with data-backed journey narratives
- Positioning yourself as the strategic analytics leader
Module 10: Scaling and Operationalising Journey Analytics - Designing a centralised journey analytics function
- Creating cross-departmental data sharing agreements
- Establishing regular journey review cadences
- Integrating journey insights into customer feedback loops
- Training teams to interpret and act on journey data
- Building a library of reusable journey templates
- Automating model retraining and performance monitoring
- Setting up a journey analytics Centre of Excellence
- Scaling insights across global markets and segments
- Measuring the ROI of journey analytics initiatives
Module 11: Advanced Topics in Journey Analytics - Natural language processing for analysing support interactions
- Using chatbot logs to map digital journey frustrations
- Incorporating sentiment analysis into journey models
- Modelling nonlinear and recursive journey paths
- Accounting for external factors: seasonality, economic shifts, events
- Multi-channel journey synchronisation across devices
- Modelling B2B decision journeys across stakeholder roles
- Customer journey simulation for product launch planning
- Using reinforcement learning to optimise journey paths
- Exploring causal inference techniques in observational data
Module 12: Integration with Customer Experience and Product Strategy - Linking journey analytics to Net Promoter Score and CSAT
- Using journey insights to design better onboarding flows
- Personalising customer experiences based on predicted paths
- Informing product feature development with journey bottlenecks
- Aligning marketing messaging with journey stage needs
- Reducing support load by fixing upstream journey issues
- Optimising pricing and packaging based on decision thresholds
- Designing retention campaigns that respond to journey signals
- Using journey data to refine customer personas
- Creating journey-led innovation workshops
Module 13: Certification and Next Steps - Finalising your strategic journey analytics project
- Submitting your model and executive summary for review
- Receiving structured feedback on real-world applicability
- Refining your project based on expert input
- Preparing your Certificate of Completion application
- Understanding the certification criteria from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Leveraging your certification for promotions or career shifts
- Accessing advanced alumni resources and community forums
- Creating a 90-day action plan for continued impact
- Understanding the limitations of traditional customer journey mapping
- Why conventional analytics fail to predict customer behavior
- Introduction to AI-driven journey modelling: key principles and definitions
- The role of intention, context, and sequence in predictive analytics
- From data collection to decision-ready insights: the end-to-end pipeline
- Defining strategic decision-making in customer experience
- Common organisational roadblocks and how to navigate them
- Building cross-functional alignment for journey analytics projects
- Establishing a customer-centric data governance policy
- Introduction to journey-based KPIs and success metrics
Module 2: Data Architecture for Journey Analytics - Identifying and integrating first, second, and third-party data sources
- Designing unified customer identifiers across touchpoints
- Handling data latency, missingness, and cross-device tracking
- Building a clean, structured journey data pipeline
- Event tagging standards for consistency and scalability
- Sanitising and normalising data for AI readiness
- Designing for privacy compliance (GDPR, CCPA, etc.) from day one
- Setting up data lineage and audit trails
- Creating data dictionaries specific to journey analysis
- Leveraging cloud-based data warehouses for scalability
Module 3: AI and Machine Learning Fundamentals for Journeys - Demystifying AI: no-code approaches to journey analytics
- Understanding supervised vs unsupervised learning in customer contexts
- Introduction to clustering: identifying hidden customer segments
- Classification models for predicting conversion and churn
- Sequence analysis: detecting critical decision paths
- Time-to-event models for forecasting drop-off points
- Feature engineering for journey data: what to include and exclude
- Model interpretability: making AI insights explainable to stakeholders
- Evaluating model performance with journey-specific metrics
- Validating models against real-world outcomes
Module 4: Journey Mapping 2.0 - Dynamic, Predictive, and Actionable - From static maps to dynamic, evolving customer journey models
- Incorporating real-time data into journey visualisations
- Mapping emotional states and intent shifts across touchpoints
- Identifying micro-moments that drive macro-outcomes
- Creating journey heatmaps to surface friction zones
- Using probabilistic path analysis to forecast future behaviors
- Integrating journey data with service design blueprints
- Building journey maps that support root cause analysis
- Creating multiple journey variants for different segments
- Linking journey stages to business outcomes and financial impact
Module 5: Attribution Reimagined - AI-Powered Models - The flaws of last-click and linear attribution
- Shapley value and game theory in multi-touch attribution
- Using Markov chains to model customer path influence
- Algorithmic attribution: how to assign credit accurately
- Time decay models and recency weighting
- Modelling offline and online channel convergence
- Balancing marketing spend based on AI-driven attribution
- Communicating attribution results to non-technical teams
- Using attribution to identify underperforming channels
- Validating attribution models with controlled experiments
Module 6: Predictive Journey Analytics Frameworks - Designing AI models to predict next-best actions
- Creating propensity models for conversion, upsell, and retention
- Forecasting customer lifetime value using journey patterns
- Early warning systems for churn and disengagement
- Identifying high-impact intervention points in real time
- Modelling customer resilience to friction and delays
- Using historical journeys to simulate future scenarios
- Building decision trees based on predicted outcomes
- Scenario planning with AI-driven journey outcomes
- Integrating predictive insights into automated workflows
Module 7: Implementing AI Models with Low-Code Tools - Selecting the right tools for non-technical users
- Using Excel, Google Sheets, and BI platforms for predictive insights
- Setting up AI-powered journey models in Power BI and Tableau
- Integrating with CRM systems like Salesforce and HubSpot
- Using no-code AI platforms: MonkeyLearn, Akkio, Lobe
- Exporting and applying model outputs to marketing automation
- Creating dashboards that align journey insights with business goals
- Automating reports for executive stakeholders
- Setting up alerts for deviation from predicted journey paths
- Linking journey analytics to campaign optimisation
Module 8: Real-World Project - Build Your AI-Driven Journey Model - Selecting a high-impact use case from your organisation
- Defining the business objective and success criteria
- Mapping current-state customer journey
- Identifying data gaps and sourcing requirements
- Designing a predictive model framework
- Building a segment-specific journey hypothesis
- Creating a data collection and processing plan
- Applying AI techniques to historical interaction data
- Generating a predictive journey map
- Drafting an executive summary and visual presentation
Module 9: From Insight to Strategic Influence - Framing journey insights as business risks and opportunities
- Building a compelling narrative for executive buy-in
- Translating technical findings into strategic recommendations
- Aligning journey outcomes with revenue and cost objectives
- Presenting AI-driven insights with confidence and clarity
- Using journey data to prioritise product and service investments
- Integrating journey analytics into quarterly planning cycles
- Securing budget and resources for future projects
- Influencing C-suite decisions with data-backed journey narratives
- Positioning yourself as the strategic analytics leader
Module 10: Scaling and Operationalising Journey Analytics - Designing a centralised journey analytics function
- Creating cross-departmental data sharing agreements
- Establishing regular journey review cadences
- Integrating journey insights into customer feedback loops
- Training teams to interpret and act on journey data
- Building a library of reusable journey templates
- Automating model retraining and performance monitoring
- Setting up a journey analytics Centre of Excellence
- Scaling insights across global markets and segments
- Measuring the ROI of journey analytics initiatives
Module 11: Advanced Topics in Journey Analytics - Natural language processing for analysing support interactions
- Using chatbot logs to map digital journey frustrations
- Incorporating sentiment analysis into journey models
- Modelling nonlinear and recursive journey paths
- Accounting for external factors: seasonality, economic shifts, events
- Multi-channel journey synchronisation across devices
- Modelling B2B decision journeys across stakeholder roles
- Customer journey simulation for product launch planning
- Using reinforcement learning to optimise journey paths
- Exploring causal inference techniques in observational data
Module 12: Integration with Customer Experience and Product Strategy - Linking journey analytics to Net Promoter Score and CSAT
- Using journey insights to design better onboarding flows
- Personalising customer experiences based on predicted paths
- Informing product feature development with journey bottlenecks
- Aligning marketing messaging with journey stage needs
- Reducing support load by fixing upstream journey issues
- Optimising pricing and packaging based on decision thresholds
- Designing retention campaigns that respond to journey signals
- Using journey data to refine customer personas
- Creating journey-led innovation workshops
Module 13: Certification and Next Steps - Finalising your strategic journey analytics project
- Submitting your model and executive summary for review
- Receiving structured feedback on real-world applicability
- Refining your project based on expert input
- Preparing your Certificate of Completion application
- Understanding the certification criteria from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Leveraging your certification for promotions or career shifts
- Accessing advanced alumni resources and community forums
- Creating a 90-day action plan for continued impact
- Demystifying AI: no-code approaches to journey analytics
- Understanding supervised vs unsupervised learning in customer contexts
- Introduction to clustering: identifying hidden customer segments
- Classification models for predicting conversion and churn
- Sequence analysis: detecting critical decision paths
- Time-to-event models for forecasting drop-off points
- Feature engineering for journey data: what to include and exclude
- Model interpretability: making AI insights explainable to stakeholders
- Evaluating model performance with journey-specific metrics
- Validating models against real-world outcomes
Module 4: Journey Mapping 2.0 - Dynamic, Predictive, and Actionable - From static maps to dynamic, evolving customer journey models
- Incorporating real-time data into journey visualisations
- Mapping emotional states and intent shifts across touchpoints
- Identifying micro-moments that drive macro-outcomes
- Creating journey heatmaps to surface friction zones
- Using probabilistic path analysis to forecast future behaviors
- Integrating journey data with service design blueprints
- Building journey maps that support root cause analysis
- Creating multiple journey variants for different segments
- Linking journey stages to business outcomes and financial impact
Module 5: Attribution Reimagined - AI-Powered Models - The flaws of last-click and linear attribution
- Shapley value and game theory in multi-touch attribution
- Using Markov chains to model customer path influence
- Algorithmic attribution: how to assign credit accurately
- Time decay models and recency weighting
- Modelling offline and online channel convergence
- Balancing marketing spend based on AI-driven attribution
- Communicating attribution results to non-technical teams
- Using attribution to identify underperforming channels
- Validating attribution models with controlled experiments
Module 6: Predictive Journey Analytics Frameworks - Designing AI models to predict next-best actions
- Creating propensity models for conversion, upsell, and retention
- Forecasting customer lifetime value using journey patterns
- Early warning systems for churn and disengagement
- Identifying high-impact intervention points in real time
- Modelling customer resilience to friction and delays
- Using historical journeys to simulate future scenarios
- Building decision trees based on predicted outcomes
- Scenario planning with AI-driven journey outcomes
- Integrating predictive insights into automated workflows
Module 7: Implementing AI Models with Low-Code Tools - Selecting the right tools for non-technical users
- Using Excel, Google Sheets, and BI platforms for predictive insights
- Setting up AI-powered journey models in Power BI and Tableau
- Integrating with CRM systems like Salesforce and HubSpot
- Using no-code AI platforms: MonkeyLearn, Akkio, Lobe
- Exporting and applying model outputs to marketing automation
- Creating dashboards that align journey insights with business goals
- Automating reports for executive stakeholders
- Setting up alerts for deviation from predicted journey paths
- Linking journey analytics to campaign optimisation
Module 8: Real-World Project - Build Your AI-Driven Journey Model - Selecting a high-impact use case from your organisation
- Defining the business objective and success criteria
- Mapping current-state customer journey
- Identifying data gaps and sourcing requirements
- Designing a predictive model framework
- Building a segment-specific journey hypothesis
- Creating a data collection and processing plan
- Applying AI techniques to historical interaction data
- Generating a predictive journey map
- Drafting an executive summary and visual presentation
Module 9: From Insight to Strategic Influence - Framing journey insights as business risks and opportunities
- Building a compelling narrative for executive buy-in
- Translating technical findings into strategic recommendations
- Aligning journey outcomes with revenue and cost objectives
- Presenting AI-driven insights with confidence and clarity
- Using journey data to prioritise product and service investments
- Integrating journey analytics into quarterly planning cycles
- Securing budget and resources for future projects
- Influencing C-suite decisions with data-backed journey narratives
- Positioning yourself as the strategic analytics leader
Module 10: Scaling and Operationalising Journey Analytics - Designing a centralised journey analytics function
- Creating cross-departmental data sharing agreements
- Establishing regular journey review cadences
- Integrating journey insights into customer feedback loops
- Training teams to interpret and act on journey data
- Building a library of reusable journey templates
- Automating model retraining and performance monitoring
- Setting up a journey analytics Centre of Excellence
- Scaling insights across global markets and segments
- Measuring the ROI of journey analytics initiatives
Module 11: Advanced Topics in Journey Analytics - Natural language processing for analysing support interactions
- Using chatbot logs to map digital journey frustrations
- Incorporating sentiment analysis into journey models
- Modelling nonlinear and recursive journey paths
- Accounting for external factors: seasonality, economic shifts, events
- Multi-channel journey synchronisation across devices
- Modelling B2B decision journeys across stakeholder roles
- Customer journey simulation for product launch planning
- Using reinforcement learning to optimise journey paths
- Exploring causal inference techniques in observational data
Module 12: Integration with Customer Experience and Product Strategy - Linking journey analytics to Net Promoter Score and CSAT
- Using journey insights to design better onboarding flows
- Personalising customer experiences based on predicted paths
- Informing product feature development with journey bottlenecks
- Aligning marketing messaging with journey stage needs
- Reducing support load by fixing upstream journey issues
- Optimising pricing and packaging based on decision thresholds
- Designing retention campaigns that respond to journey signals
- Using journey data to refine customer personas
- Creating journey-led innovation workshops
Module 13: Certification and Next Steps - Finalising your strategic journey analytics project
- Submitting your model and executive summary for review
- Receiving structured feedback on real-world applicability
- Refining your project based on expert input
- Preparing your Certificate of Completion application
- Understanding the certification criteria from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Leveraging your certification for promotions or career shifts
- Accessing advanced alumni resources and community forums
- Creating a 90-day action plan for continued impact
- The flaws of last-click and linear attribution
- Shapley value and game theory in multi-touch attribution
- Using Markov chains to model customer path influence
- Algorithmic attribution: how to assign credit accurately
- Time decay models and recency weighting
- Modelling offline and online channel convergence
- Balancing marketing spend based on AI-driven attribution
- Communicating attribution results to non-technical teams
- Using attribution to identify underperforming channels
- Validating attribution models with controlled experiments
Module 6: Predictive Journey Analytics Frameworks - Designing AI models to predict next-best actions
- Creating propensity models for conversion, upsell, and retention
- Forecasting customer lifetime value using journey patterns
- Early warning systems for churn and disengagement
- Identifying high-impact intervention points in real time
- Modelling customer resilience to friction and delays
- Using historical journeys to simulate future scenarios
- Building decision trees based on predicted outcomes
- Scenario planning with AI-driven journey outcomes
- Integrating predictive insights into automated workflows
Module 7: Implementing AI Models with Low-Code Tools - Selecting the right tools for non-technical users
- Using Excel, Google Sheets, and BI platforms for predictive insights
- Setting up AI-powered journey models in Power BI and Tableau
- Integrating with CRM systems like Salesforce and HubSpot
- Using no-code AI platforms: MonkeyLearn, Akkio, Lobe
- Exporting and applying model outputs to marketing automation
- Creating dashboards that align journey insights with business goals
- Automating reports for executive stakeholders
- Setting up alerts for deviation from predicted journey paths
- Linking journey analytics to campaign optimisation
Module 8: Real-World Project - Build Your AI-Driven Journey Model - Selecting a high-impact use case from your organisation
- Defining the business objective and success criteria
- Mapping current-state customer journey
- Identifying data gaps and sourcing requirements
- Designing a predictive model framework
- Building a segment-specific journey hypothesis
- Creating a data collection and processing plan
- Applying AI techniques to historical interaction data
- Generating a predictive journey map
- Drafting an executive summary and visual presentation
Module 9: From Insight to Strategic Influence - Framing journey insights as business risks and opportunities
- Building a compelling narrative for executive buy-in
- Translating technical findings into strategic recommendations
- Aligning journey outcomes with revenue and cost objectives
- Presenting AI-driven insights with confidence and clarity
- Using journey data to prioritise product and service investments
- Integrating journey analytics into quarterly planning cycles
- Securing budget and resources for future projects
- Influencing C-suite decisions with data-backed journey narratives
- Positioning yourself as the strategic analytics leader
Module 10: Scaling and Operationalising Journey Analytics - Designing a centralised journey analytics function
- Creating cross-departmental data sharing agreements
- Establishing regular journey review cadences
- Integrating journey insights into customer feedback loops
- Training teams to interpret and act on journey data
- Building a library of reusable journey templates
- Automating model retraining and performance monitoring
- Setting up a journey analytics Centre of Excellence
- Scaling insights across global markets and segments
- Measuring the ROI of journey analytics initiatives
Module 11: Advanced Topics in Journey Analytics - Natural language processing for analysing support interactions
- Using chatbot logs to map digital journey frustrations
- Incorporating sentiment analysis into journey models
- Modelling nonlinear and recursive journey paths
- Accounting for external factors: seasonality, economic shifts, events
- Multi-channel journey synchronisation across devices
- Modelling B2B decision journeys across stakeholder roles
- Customer journey simulation for product launch planning
- Using reinforcement learning to optimise journey paths
- Exploring causal inference techniques in observational data
Module 12: Integration with Customer Experience and Product Strategy - Linking journey analytics to Net Promoter Score and CSAT
- Using journey insights to design better onboarding flows
- Personalising customer experiences based on predicted paths
- Informing product feature development with journey bottlenecks
- Aligning marketing messaging with journey stage needs
- Reducing support load by fixing upstream journey issues
- Optimising pricing and packaging based on decision thresholds
- Designing retention campaigns that respond to journey signals
- Using journey data to refine customer personas
- Creating journey-led innovation workshops
Module 13: Certification and Next Steps - Finalising your strategic journey analytics project
- Submitting your model and executive summary for review
- Receiving structured feedback on real-world applicability
- Refining your project based on expert input
- Preparing your Certificate of Completion application
- Understanding the certification criteria from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Leveraging your certification for promotions or career shifts
- Accessing advanced alumni resources and community forums
- Creating a 90-day action plan for continued impact
- Selecting the right tools for non-technical users
- Using Excel, Google Sheets, and BI platforms for predictive insights
- Setting up AI-powered journey models in Power BI and Tableau
- Integrating with CRM systems like Salesforce and HubSpot
- Using no-code AI platforms: MonkeyLearn, Akkio, Lobe
- Exporting and applying model outputs to marketing automation
- Creating dashboards that align journey insights with business goals
- Automating reports for executive stakeholders
- Setting up alerts for deviation from predicted journey paths
- Linking journey analytics to campaign optimisation
Module 8: Real-World Project - Build Your AI-Driven Journey Model - Selecting a high-impact use case from your organisation
- Defining the business objective and success criteria
- Mapping current-state customer journey
- Identifying data gaps and sourcing requirements
- Designing a predictive model framework
- Building a segment-specific journey hypothesis
- Creating a data collection and processing plan
- Applying AI techniques to historical interaction data
- Generating a predictive journey map
- Drafting an executive summary and visual presentation
Module 9: From Insight to Strategic Influence - Framing journey insights as business risks and opportunities
- Building a compelling narrative for executive buy-in
- Translating technical findings into strategic recommendations
- Aligning journey outcomes with revenue and cost objectives
- Presenting AI-driven insights with confidence and clarity
- Using journey data to prioritise product and service investments
- Integrating journey analytics into quarterly planning cycles
- Securing budget and resources for future projects
- Influencing C-suite decisions with data-backed journey narratives
- Positioning yourself as the strategic analytics leader
Module 10: Scaling and Operationalising Journey Analytics - Designing a centralised journey analytics function
- Creating cross-departmental data sharing agreements
- Establishing regular journey review cadences
- Integrating journey insights into customer feedback loops
- Training teams to interpret and act on journey data
- Building a library of reusable journey templates
- Automating model retraining and performance monitoring
- Setting up a journey analytics Centre of Excellence
- Scaling insights across global markets and segments
- Measuring the ROI of journey analytics initiatives
Module 11: Advanced Topics in Journey Analytics - Natural language processing for analysing support interactions
- Using chatbot logs to map digital journey frustrations
- Incorporating sentiment analysis into journey models
- Modelling nonlinear and recursive journey paths
- Accounting for external factors: seasonality, economic shifts, events
- Multi-channel journey synchronisation across devices
- Modelling B2B decision journeys across stakeholder roles
- Customer journey simulation for product launch planning
- Using reinforcement learning to optimise journey paths
- Exploring causal inference techniques in observational data
Module 12: Integration with Customer Experience and Product Strategy - Linking journey analytics to Net Promoter Score and CSAT
- Using journey insights to design better onboarding flows
- Personalising customer experiences based on predicted paths
- Informing product feature development with journey bottlenecks
- Aligning marketing messaging with journey stage needs
- Reducing support load by fixing upstream journey issues
- Optimising pricing and packaging based on decision thresholds
- Designing retention campaigns that respond to journey signals
- Using journey data to refine customer personas
- Creating journey-led innovation workshops
Module 13: Certification and Next Steps - Finalising your strategic journey analytics project
- Submitting your model and executive summary for review
- Receiving structured feedback on real-world applicability
- Refining your project based on expert input
- Preparing your Certificate of Completion application
- Understanding the certification criteria from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Leveraging your certification for promotions or career shifts
- Accessing advanced alumni resources and community forums
- Creating a 90-day action plan for continued impact
- Framing journey insights as business risks and opportunities
- Building a compelling narrative for executive buy-in
- Translating technical findings into strategic recommendations
- Aligning journey outcomes with revenue and cost objectives
- Presenting AI-driven insights with confidence and clarity
- Using journey data to prioritise product and service investments
- Integrating journey analytics into quarterly planning cycles
- Securing budget and resources for future projects
- Influencing C-suite decisions with data-backed journey narratives
- Positioning yourself as the strategic analytics leader
Module 10: Scaling and Operationalising Journey Analytics - Designing a centralised journey analytics function
- Creating cross-departmental data sharing agreements
- Establishing regular journey review cadences
- Integrating journey insights into customer feedback loops
- Training teams to interpret and act on journey data
- Building a library of reusable journey templates
- Automating model retraining and performance monitoring
- Setting up a journey analytics Centre of Excellence
- Scaling insights across global markets and segments
- Measuring the ROI of journey analytics initiatives
Module 11: Advanced Topics in Journey Analytics - Natural language processing for analysing support interactions
- Using chatbot logs to map digital journey frustrations
- Incorporating sentiment analysis into journey models
- Modelling nonlinear and recursive journey paths
- Accounting for external factors: seasonality, economic shifts, events
- Multi-channel journey synchronisation across devices
- Modelling B2B decision journeys across stakeholder roles
- Customer journey simulation for product launch planning
- Using reinforcement learning to optimise journey paths
- Exploring causal inference techniques in observational data
Module 12: Integration with Customer Experience and Product Strategy - Linking journey analytics to Net Promoter Score and CSAT
- Using journey insights to design better onboarding flows
- Personalising customer experiences based on predicted paths
- Informing product feature development with journey bottlenecks
- Aligning marketing messaging with journey stage needs
- Reducing support load by fixing upstream journey issues
- Optimising pricing and packaging based on decision thresholds
- Designing retention campaigns that respond to journey signals
- Using journey data to refine customer personas
- Creating journey-led innovation workshops
Module 13: Certification and Next Steps - Finalising your strategic journey analytics project
- Submitting your model and executive summary for review
- Receiving structured feedback on real-world applicability
- Refining your project based on expert input
- Preparing your Certificate of Completion application
- Understanding the certification criteria from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Leveraging your certification for promotions or career shifts
- Accessing advanced alumni resources and community forums
- Creating a 90-day action plan for continued impact
- Natural language processing for analysing support interactions
- Using chatbot logs to map digital journey frustrations
- Incorporating sentiment analysis into journey models
- Modelling nonlinear and recursive journey paths
- Accounting for external factors: seasonality, economic shifts, events
- Multi-channel journey synchronisation across devices
- Modelling B2B decision journeys across stakeholder roles
- Customer journey simulation for product launch planning
- Using reinforcement learning to optimise journey paths
- Exploring causal inference techniques in observational data
Module 12: Integration with Customer Experience and Product Strategy - Linking journey analytics to Net Promoter Score and CSAT
- Using journey insights to design better onboarding flows
- Personalising customer experiences based on predicted paths
- Informing product feature development with journey bottlenecks
- Aligning marketing messaging with journey stage needs
- Reducing support load by fixing upstream journey issues
- Optimising pricing and packaging based on decision thresholds
- Designing retention campaigns that respond to journey signals
- Using journey data to refine customer personas
- Creating journey-led innovation workshops
Module 13: Certification and Next Steps - Finalising your strategic journey analytics project
- Submitting your model and executive summary for review
- Receiving structured feedback on real-world applicability
- Refining your project based on expert input
- Preparing your Certificate of Completion application
- Understanding the certification criteria from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Leveraging your certification for promotions or career shifts
- Accessing advanced alumni resources and community forums
- Creating a 90-day action plan for continued impact
- Finalising your strategic journey analytics project
- Submitting your model and executive summary for review
- Receiving structured feedback on real-world applicability
- Refining your project based on expert input
- Preparing your Certificate of Completion application
- Understanding the certification criteria from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Leveraging your certification for promotions or career shifts
- Accessing advanced alumni resources and community forums
- Creating a 90-day action plan for continued impact