AI-Driven SaaS Growth: Scalable Strategies for Customer Retention and Expansion
You're under pressure. Investors are asking for proof of retention, not just acquisition. Your churn rate is creeping up, expansion revenue is flatlining, and the board wants answers. You know AI holds the key, but you’re tired of vague theories, generic frameworks, and solutions that don’t scale with your product. Real growth isn’t about throwing data into black-box models. It’s about implementing structured, repeatable systems that turn customer insights into revenue. The difference between stagnation and hypergrowth lies in your ability to move fast, act with precision, and align AI directly with business outcomes. The AI-Driven SaaS Growth: Scalable Strategies for Customer Retention and Expansion course is your definitive blueprint for doing exactly that. No fluff, no filler. Just a proven, step-by-step methodology to go from fragmented data signals to automated, revenue-generating retention and expansion engines-within 30 days. One Product Lead at a Series B cybersecurity SaaS used this course to redesign their health scoring model. Within two weeks, they identified 37 high-risk accounts and activated targeted intervention workflows. Churn dropped by 28% in one quarter. Expansion revenue from upsell campaigns increased by 41%. This wasn’t luck. It was system execution. If you’re ready to stop reacting and start leading with intelligence-if you want to be the strategist who delivers real, measurable margin improvement-this is your turning point. This course equips you with the exact frameworks used by top-performing SaaS growth teams to scale retention and multiply expansion revenue. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced. On-Demand. Lifetime Access. Access the full course immediately upon enrollment. No fixed start dates, no deadlines, no forced schedules. You control the pace. Whether you're a senior executive reviewing one module a week or a hands-on operator diving deep for three days straight, the structure supports your rhythm. Fast Results, Lasting Value
Most learners implement their first high-impact retention strategy within 7 days. Complete the full course in 4–6 weeks with just 2–3 hours per week. The curriculum is sequenced for rapid application-every lesson builds toward a deliverable you can use immediately. Future-Proof Access
You receive lifetime access to all course materials. Every update, every new case study, every framework refinement is included at no extra cost. AI evolves fast. Your access keeps pace. Learn Anytime, Anywhere
Full mobile-friendly design means you can engage during transit, between meetings, or from any global location. Whether on iOS, Android, or desktop, your progress syncs seamlessly. 24/7 global access ensures flexibility without compromise. Instructor Support & Expert Guidance
Every module is authored and reviewed by senior SaaS growth architects with 10+ years of experience at scale-ups and public tech companies. You’re not left guessing. Each framework includes annotated decision trees, real-world implementation checklists, and expert commentary to guide execution. Certificate of Completion: A Career-Advancing Credential
Upon completion, you earn a Certificate of Completion issued by The Art of Service-a globally recognised credential used by professionals in over 120 countries. This isn’t a participation badge. It’s verified proof of mastery in AI-powered retention and expansion strategy. LinkedIn-optimised and employer-trusted. Transparent, One-Time Pricing. No Hidden Fees.
You pay a single, upfront fee with no recurring charges, no upsells, no add-ons. The price includes everything: curriculum, tools, templates, updates, and certification. No fine print. Accepted Payment Methods
Secure checkout supports Visa, Mastercard, and PayPal. Enterprise billing and invoice requests are supported for team purchases. 100% Satisfaction Guarantee: Try It Risk-Free
Enroll with complete confidence. If you’re not convinced the course delivers immediate, actionable value within the first two modules, request a full refund. No questions, no hassle. Your investment is fully protected. Affirmation: This Works Even If…
This course works even if you’re not a data scientist. Even if your business lacks a mature AI stack. Even if past retention initiatives failed. The frameworks are role-specific, outcome-oriented, and built for real-world constraints. Social Proof: Real Roles, Real Results
- Director of Growth, B2B SaaS: “I applied the churn prediction scoring method in Module 4. We blocked $230K in at-risk ARR within 10 days.”
- Customer Success Lead, Fintech Startup: “The expansion segmentation model helped us increase upsell conversion by 3.2x. Our team now uses it monthly.”
- Founder, AI Analytics Platform: “I thought I understood AI retention. I was wrong. This course gave me the structure to scale what works-and stop wasting engineering hours.”
Your Next Step Is Protected, Clear, and Immediate
After enrollment, you’ll receive a confirmation email. Your access details and onboarding guide will be sent shortly afterward. You’re supported at every stage. This isn’t just learning. It’s transformation-built for clarity, speed, and measurable business impact.
Module 1: Foundations of AI-Driven SaaS Growth - Defining customer retention and expansion in the AI era
- The evolution of SaaS growth: From acquisition to intelligence-led engagement
- Why traditional retention models fail in dynamic markets
- Core principles of AI-enabled growth systems
- Mapping business outcomes to AI-driven actions
- Understanding the feedback loop between data, insight, and action
- Identifying high-leverage moments in the customer lifecycle
- Aligning AI initiatives with product, sales, and success teams
- Common pitfalls and how to avoid them
- Setting measurable KPIs for retention and expansion success
Module 2: Data Architecture for Intelligent Retention - Essential customer data types for AI modeling
- Building a unified customer data layer from siloed sources
- Key data pipelines for real-time retention signals
- Event tracking: What to capture, what to ignore
- Product usage data: Extracting behavioural insights
- CRM and support ticket integration for churn signals
- Data quality assessment and cleansing protocols
- Setting up automated data validation checks
- Data governance and compliance in AI applications
- Preparing datasets for predictive modeling
Module 3: AI-Powered Customer Health Scoring - Designing a multi-dimensional health score
- Choosing weights for usage, engagement, support, and growth signals
- Creating dynamic scoring thresholds
- Implementing health score automation with rule-based logic
- Introducing ML models for adaptive health scoring
- Validating health score accuracy against actual churn
- Calibrating scores for different customer segments
- Visualising health scores in dashboards
- Triggering interventions based on health deterioration
- Integrating health scores into CRM and CSM workflows
Module 4: Predictive Churn Modeling - Understanding survival analysis in SaaS contexts
- Selecting features for churn prediction
- Preparing training and test datasets for churn models
- Building logistic regression models for early warning
- Implementing decision trees for interpretability
- Using random forest for higher accuracy predictions
- Validating model performance with precision and recall
- Setting probability thresholds for intervention
- Generating predicted churn risk reports
- Deploying models in low-code environments
Module 5: Proactive Retention Workflows - Designing tiered intervention pathways
- Automating alerts to customer success teams
- Creating escalation protocols for high-risk accounts
- Developing AI-triggered email nurture sequences
- Personalising outbound messaging using behavioural triggers
- Building in-app nudges for at-risk users
- Routing accounts to specialised intervention teams
- Measuring the impact of retention actions
- Optimising workflows with A/B testing
- Integrating retention workflows with existing tech stacks
Module 6: Expansion Opportunity Identification - Defining expansion signals across usage, engagement, and sentiment
- Analysing feature adoption patterns for upsell potential
- Using time-in-product and milestone tracking for expansion cues
- Scoring customers for expansion readiness
- Mapping product usage to licensing tiers
- Identifying whitespace within existing accounts
- Using cross-functional data to spot expansion triggers
- Creating expansion heatmaps by team or department
- Monitoring usage saturation as a signal for upgrades
- Building automated expansion scorecards
Module 7: AI-Driven Upsell & Cross-Sell Campaigns - Segmenting customers for targeted expansion offers
- Matching AI-identified opportunities with product features
- Designing data-informed sales playbooks
- Automating expansion outreach for sales teams
- Generating personalised expansion proposals
- Using AI to draft bespoke email templates
- Scheduling expansion touchpoints based on usage peaks
- Integrating expansion workflows into sales CRMs
- Tracking expansion campaign performance
- Scaling expansion through self-serve product paths
Module 8: Churn Intervention Effectiveness Analysis - Measuring success of retention actions
- Calculating intervention ROI
- Attributing churn reduction to specific workflows
- Identifying which interventions fail and why
- Analysing response rates to automated nudges
- Using cohort analysis to validate strategy impact
- Adjusting models based on intervention outcomes
- Creating feedback loops for continuous improvement
- Reporting retention success to leadership
- Building a closed-loop retention system
Module 9: Expansion Revenue Forecasting - Projecting expansion revenue using AI signals
- Building forecast models with historical adoption data
- Estimating time-to-upsell based on usage patterns
- Incorporating seasonality and macro trends
- Validating forecast accuracy over time
- Communicating expansion projections to finance teams
- Using forecasts for headcount and capacity planning
- Scenario planning for different growth assumptions
- Adjusting forecasts based on real-time signals
- Integrating expansion forecasts into board reporting
Module 10: AI Integration in Customer Success Platforms - Evaluating AI capabilities in Gainsight, Totango, and Salesforce
- Configuring AI alerts in ChurnZero
- Using Pendo and Mixpanel for retention insights
- Embedding AI models into CSM workflows
- Automating health score updates in real time
- Creating custom AI-powered dashboards
- Setting up automated task creation for CSMs
- Synchronising AI insights across tools
- Reducing manual workload with intelligent automation
- Scaling customer success with AI augmentation
Module 11: Building Autonomous Retention Systems - Designing self-correcting retention models
- Implementing feedback loops for model retraining
- Automating data refresh and feature engineering
- Scheduling model performance reviews
- Alerting on data drift and concept drift
- Version controlling AI models and logic
- Establishing ownership and monitoring protocols
- Creating audit trails for AI decisions
- Building resilience into retention systems
- Scaling systems for thousands of accounts
Module 12: Organisational Alignment & Change Management - Onboarding sales, success, and product teams to AI insights
- Creating shared definitions of health and risk
- Conducting cross-functional AI strategy workshops
- Developing communication plans for new workflows
- Overcoming resistance to AI-driven decisions
- Establishing data literacy programs
- Training teams on interpreting AI outputs
- Integrating AI recommendations into operational rhythms
- Measuring team adoption of new systems
- Creating feedback channels for system improvement
Module 13: Real-World Implementation Projects - Project 1: Build a customer health score from real data
- Project 2: Design a churn intervention workflow
- Project 3: Identify expansion opportunities in a sample account
- Project 4: Create an AI-powered email nurture sequence
- Project 5: Develop a board-ready retention progress report
- Project 6: Forecast expansion revenue for next quarter
- Project 7: Audit your current tech stack for AI readiness
- Project 8: Map AI use cases to business KPIs
- Project 9: Develop a model validation checklist
- Project 10: Create a rollout plan for AI adoption across teams
Module 14: Certification & Career Advancement - Reviewing key frameworks and outcomes
- Submitting your final capstone project
- Evaluation criteria for certification
- Preparing your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn and resumes
- Using certification to lead internal AI initiatives
- Positioning yourself for promotion or new roles
- Joining the global community of certified professionals
- Accessing post-course templates and resources
- Next steps: Advanced AI specialisations and leadership pathways
- Defining customer retention and expansion in the AI era
- The evolution of SaaS growth: From acquisition to intelligence-led engagement
- Why traditional retention models fail in dynamic markets
- Core principles of AI-enabled growth systems
- Mapping business outcomes to AI-driven actions
- Understanding the feedback loop between data, insight, and action
- Identifying high-leverage moments in the customer lifecycle
- Aligning AI initiatives with product, sales, and success teams
- Common pitfalls and how to avoid them
- Setting measurable KPIs for retention and expansion success
Module 2: Data Architecture for Intelligent Retention - Essential customer data types for AI modeling
- Building a unified customer data layer from siloed sources
- Key data pipelines for real-time retention signals
- Event tracking: What to capture, what to ignore
- Product usage data: Extracting behavioural insights
- CRM and support ticket integration for churn signals
- Data quality assessment and cleansing protocols
- Setting up automated data validation checks
- Data governance and compliance in AI applications
- Preparing datasets for predictive modeling
Module 3: AI-Powered Customer Health Scoring - Designing a multi-dimensional health score
- Choosing weights for usage, engagement, support, and growth signals
- Creating dynamic scoring thresholds
- Implementing health score automation with rule-based logic
- Introducing ML models for adaptive health scoring
- Validating health score accuracy against actual churn
- Calibrating scores for different customer segments
- Visualising health scores in dashboards
- Triggering interventions based on health deterioration
- Integrating health scores into CRM and CSM workflows
Module 4: Predictive Churn Modeling - Understanding survival analysis in SaaS contexts
- Selecting features for churn prediction
- Preparing training and test datasets for churn models
- Building logistic regression models for early warning
- Implementing decision trees for interpretability
- Using random forest for higher accuracy predictions
- Validating model performance with precision and recall
- Setting probability thresholds for intervention
- Generating predicted churn risk reports
- Deploying models in low-code environments
Module 5: Proactive Retention Workflows - Designing tiered intervention pathways
- Automating alerts to customer success teams
- Creating escalation protocols for high-risk accounts
- Developing AI-triggered email nurture sequences
- Personalising outbound messaging using behavioural triggers
- Building in-app nudges for at-risk users
- Routing accounts to specialised intervention teams
- Measuring the impact of retention actions
- Optimising workflows with A/B testing
- Integrating retention workflows with existing tech stacks
Module 6: Expansion Opportunity Identification - Defining expansion signals across usage, engagement, and sentiment
- Analysing feature adoption patterns for upsell potential
- Using time-in-product and milestone tracking for expansion cues
- Scoring customers for expansion readiness
- Mapping product usage to licensing tiers
- Identifying whitespace within existing accounts
- Using cross-functional data to spot expansion triggers
- Creating expansion heatmaps by team or department
- Monitoring usage saturation as a signal for upgrades
- Building automated expansion scorecards
Module 7: AI-Driven Upsell & Cross-Sell Campaigns - Segmenting customers for targeted expansion offers
- Matching AI-identified opportunities with product features
- Designing data-informed sales playbooks
- Automating expansion outreach for sales teams
- Generating personalised expansion proposals
- Using AI to draft bespoke email templates
- Scheduling expansion touchpoints based on usage peaks
- Integrating expansion workflows into sales CRMs
- Tracking expansion campaign performance
- Scaling expansion through self-serve product paths
Module 8: Churn Intervention Effectiveness Analysis - Measuring success of retention actions
- Calculating intervention ROI
- Attributing churn reduction to specific workflows
- Identifying which interventions fail and why
- Analysing response rates to automated nudges
- Using cohort analysis to validate strategy impact
- Adjusting models based on intervention outcomes
- Creating feedback loops for continuous improvement
- Reporting retention success to leadership
- Building a closed-loop retention system
Module 9: Expansion Revenue Forecasting - Projecting expansion revenue using AI signals
- Building forecast models with historical adoption data
- Estimating time-to-upsell based on usage patterns
- Incorporating seasonality and macro trends
- Validating forecast accuracy over time
- Communicating expansion projections to finance teams
- Using forecasts for headcount and capacity planning
- Scenario planning for different growth assumptions
- Adjusting forecasts based on real-time signals
- Integrating expansion forecasts into board reporting
Module 10: AI Integration in Customer Success Platforms - Evaluating AI capabilities in Gainsight, Totango, and Salesforce
- Configuring AI alerts in ChurnZero
- Using Pendo and Mixpanel for retention insights
- Embedding AI models into CSM workflows
- Automating health score updates in real time
- Creating custom AI-powered dashboards
- Setting up automated task creation for CSMs
- Synchronising AI insights across tools
- Reducing manual workload with intelligent automation
- Scaling customer success with AI augmentation
Module 11: Building Autonomous Retention Systems - Designing self-correcting retention models
- Implementing feedback loops for model retraining
- Automating data refresh and feature engineering
- Scheduling model performance reviews
- Alerting on data drift and concept drift
- Version controlling AI models and logic
- Establishing ownership and monitoring protocols
- Creating audit trails for AI decisions
- Building resilience into retention systems
- Scaling systems for thousands of accounts
Module 12: Organisational Alignment & Change Management - Onboarding sales, success, and product teams to AI insights
- Creating shared definitions of health and risk
- Conducting cross-functional AI strategy workshops
- Developing communication plans for new workflows
- Overcoming resistance to AI-driven decisions
- Establishing data literacy programs
- Training teams on interpreting AI outputs
- Integrating AI recommendations into operational rhythms
- Measuring team adoption of new systems
- Creating feedback channels for system improvement
Module 13: Real-World Implementation Projects - Project 1: Build a customer health score from real data
- Project 2: Design a churn intervention workflow
- Project 3: Identify expansion opportunities in a sample account
- Project 4: Create an AI-powered email nurture sequence
- Project 5: Develop a board-ready retention progress report
- Project 6: Forecast expansion revenue for next quarter
- Project 7: Audit your current tech stack for AI readiness
- Project 8: Map AI use cases to business KPIs
- Project 9: Develop a model validation checklist
- Project 10: Create a rollout plan for AI adoption across teams
Module 14: Certification & Career Advancement - Reviewing key frameworks and outcomes
- Submitting your final capstone project
- Evaluation criteria for certification
- Preparing your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn and resumes
- Using certification to lead internal AI initiatives
- Positioning yourself for promotion or new roles
- Joining the global community of certified professionals
- Accessing post-course templates and resources
- Next steps: Advanced AI specialisations and leadership pathways
- Designing a multi-dimensional health score
- Choosing weights for usage, engagement, support, and growth signals
- Creating dynamic scoring thresholds
- Implementing health score automation with rule-based logic
- Introducing ML models for adaptive health scoring
- Validating health score accuracy against actual churn
- Calibrating scores for different customer segments
- Visualising health scores in dashboards
- Triggering interventions based on health deterioration
- Integrating health scores into CRM and CSM workflows
Module 4: Predictive Churn Modeling - Understanding survival analysis in SaaS contexts
- Selecting features for churn prediction
- Preparing training and test datasets for churn models
- Building logistic regression models for early warning
- Implementing decision trees for interpretability
- Using random forest for higher accuracy predictions
- Validating model performance with precision and recall
- Setting probability thresholds for intervention
- Generating predicted churn risk reports
- Deploying models in low-code environments
Module 5: Proactive Retention Workflows - Designing tiered intervention pathways
- Automating alerts to customer success teams
- Creating escalation protocols for high-risk accounts
- Developing AI-triggered email nurture sequences
- Personalising outbound messaging using behavioural triggers
- Building in-app nudges for at-risk users
- Routing accounts to specialised intervention teams
- Measuring the impact of retention actions
- Optimising workflows with A/B testing
- Integrating retention workflows with existing tech stacks
Module 6: Expansion Opportunity Identification - Defining expansion signals across usage, engagement, and sentiment
- Analysing feature adoption patterns for upsell potential
- Using time-in-product and milestone tracking for expansion cues
- Scoring customers for expansion readiness
- Mapping product usage to licensing tiers
- Identifying whitespace within existing accounts
- Using cross-functional data to spot expansion triggers
- Creating expansion heatmaps by team or department
- Monitoring usage saturation as a signal for upgrades
- Building automated expansion scorecards
Module 7: AI-Driven Upsell & Cross-Sell Campaigns - Segmenting customers for targeted expansion offers
- Matching AI-identified opportunities with product features
- Designing data-informed sales playbooks
- Automating expansion outreach for sales teams
- Generating personalised expansion proposals
- Using AI to draft bespoke email templates
- Scheduling expansion touchpoints based on usage peaks
- Integrating expansion workflows into sales CRMs
- Tracking expansion campaign performance
- Scaling expansion through self-serve product paths
Module 8: Churn Intervention Effectiveness Analysis - Measuring success of retention actions
- Calculating intervention ROI
- Attributing churn reduction to specific workflows
- Identifying which interventions fail and why
- Analysing response rates to automated nudges
- Using cohort analysis to validate strategy impact
- Adjusting models based on intervention outcomes
- Creating feedback loops for continuous improvement
- Reporting retention success to leadership
- Building a closed-loop retention system
Module 9: Expansion Revenue Forecasting - Projecting expansion revenue using AI signals
- Building forecast models with historical adoption data
- Estimating time-to-upsell based on usage patterns
- Incorporating seasonality and macro trends
- Validating forecast accuracy over time
- Communicating expansion projections to finance teams
- Using forecasts for headcount and capacity planning
- Scenario planning for different growth assumptions
- Adjusting forecasts based on real-time signals
- Integrating expansion forecasts into board reporting
Module 10: AI Integration in Customer Success Platforms - Evaluating AI capabilities in Gainsight, Totango, and Salesforce
- Configuring AI alerts in ChurnZero
- Using Pendo and Mixpanel for retention insights
- Embedding AI models into CSM workflows
- Automating health score updates in real time
- Creating custom AI-powered dashboards
- Setting up automated task creation for CSMs
- Synchronising AI insights across tools
- Reducing manual workload with intelligent automation
- Scaling customer success with AI augmentation
Module 11: Building Autonomous Retention Systems - Designing self-correcting retention models
- Implementing feedback loops for model retraining
- Automating data refresh and feature engineering
- Scheduling model performance reviews
- Alerting on data drift and concept drift
- Version controlling AI models and logic
- Establishing ownership and monitoring protocols
- Creating audit trails for AI decisions
- Building resilience into retention systems
- Scaling systems for thousands of accounts
Module 12: Organisational Alignment & Change Management - Onboarding sales, success, and product teams to AI insights
- Creating shared definitions of health and risk
- Conducting cross-functional AI strategy workshops
- Developing communication plans for new workflows
- Overcoming resistance to AI-driven decisions
- Establishing data literacy programs
- Training teams on interpreting AI outputs
- Integrating AI recommendations into operational rhythms
- Measuring team adoption of new systems
- Creating feedback channels for system improvement
Module 13: Real-World Implementation Projects - Project 1: Build a customer health score from real data
- Project 2: Design a churn intervention workflow
- Project 3: Identify expansion opportunities in a sample account
- Project 4: Create an AI-powered email nurture sequence
- Project 5: Develop a board-ready retention progress report
- Project 6: Forecast expansion revenue for next quarter
- Project 7: Audit your current tech stack for AI readiness
- Project 8: Map AI use cases to business KPIs
- Project 9: Develop a model validation checklist
- Project 10: Create a rollout plan for AI adoption across teams
Module 14: Certification & Career Advancement - Reviewing key frameworks and outcomes
- Submitting your final capstone project
- Evaluation criteria for certification
- Preparing your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn and resumes
- Using certification to lead internal AI initiatives
- Positioning yourself for promotion or new roles
- Joining the global community of certified professionals
- Accessing post-course templates and resources
- Next steps: Advanced AI specialisations and leadership pathways
- Designing tiered intervention pathways
- Automating alerts to customer success teams
- Creating escalation protocols for high-risk accounts
- Developing AI-triggered email nurture sequences
- Personalising outbound messaging using behavioural triggers
- Building in-app nudges for at-risk users
- Routing accounts to specialised intervention teams
- Measuring the impact of retention actions
- Optimising workflows with A/B testing
- Integrating retention workflows with existing tech stacks
Module 6: Expansion Opportunity Identification - Defining expansion signals across usage, engagement, and sentiment
- Analysing feature adoption patterns for upsell potential
- Using time-in-product and milestone tracking for expansion cues
- Scoring customers for expansion readiness
- Mapping product usage to licensing tiers
- Identifying whitespace within existing accounts
- Using cross-functional data to spot expansion triggers
- Creating expansion heatmaps by team or department
- Monitoring usage saturation as a signal for upgrades
- Building automated expansion scorecards
Module 7: AI-Driven Upsell & Cross-Sell Campaigns - Segmenting customers for targeted expansion offers
- Matching AI-identified opportunities with product features
- Designing data-informed sales playbooks
- Automating expansion outreach for sales teams
- Generating personalised expansion proposals
- Using AI to draft bespoke email templates
- Scheduling expansion touchpoints based on usage peaks
- Integrating expansion workflows into sales CRMs
- Tracking expansion campaign performance
- Scaling expansion through self-serve product paths
Module 8: Churn Intervention Effectiveness Analysis - Measuring success of retention actions
- Calculating intervention ROI
- Attributing churn reduction to specific workflows
- Identifying which interventions fail and why
- Analysing response rates to automated nudges
- Using cohort analysis to validate strategy impact
- Adjusting models based on intervention outcomes
- Creating feedback loops for continuous improvement
- Reporting retention success to leadership
- Building a closed-loop retention system
Module 9: Expansion Revenue Forecasting - Projecting expansion revenue using AI signals
- Building forecast models with historical adoption data
- Estimating time-to-upsell based on usage patterns
- Incorporating seasonality and macro trends
- Validating forecast accuracy over time
- Communicating expansion projections to finance teams
- Using forecasts for headcount and capacity planning
- Scenario planning for different growth assumptions
- Adjusting forecasts based on real-time signals
- Integrating expansion forecasts into board reporting
Module 10: AI Integration in Customer Success Platforms - Evaluating AI capabilities in Gainsight, Totango, and Salesforce
- Configuring AI alerts in ChurnZero
- Using Pendo and Mixpanel for retention insights
- Embedding AI models into CSM workflows
- Automating health score updates in real time
- Creating custom AI-powered dashboards
- Setting up automated task creation for CSMs
- Synchronising AI insights across tools
- Reducing manual workload with intelligent automation
- Scaling customer success with AI augmentation
Module 11: Building Autonomous Retention Systems - Designing self-correcting retention models
- Implementing feedback loops for model retraining
- Automating data refresh and feature engineering
- Scheduling model performance reviews
- Alerting on data drift and concept drift
- Version controlling AI models and logic
- Establishing ownership and monitoring protocols
- Creating audit trails for AI decisions
- Building resilience into retention systems
- Scaling systems for thousands of accounts
Module 12: Organisational Alignment & Change Management - Onboarding sales, success, and product teams to AI insights
- Creating shared definitions of health and risk
- Conducting cross-functional AI strategy workshops
- Developing communication plans for new workflows
- Overcoming resistance to AI-driven decisions
- Establishing data literacy programs
- Training teams on interpreting AI outputs
- Integrating AI recommendations into operational rhythms
- Measuring team adoption of new systems
- Creating feedback channels for system improvement
Module 13: Real-World Implementation Projects - Project 1: Build a customer health score from real data
- Project 2: Design a churn intervention workflow
- Project 3: Identify expansion opportunities in a sample account
- Project 4: Create an AI-powered email nurture sequence
- Project 5: Develop a board-ready retention progress report
- Project 6: Forecast expansion revenue for next quarter
- Project 7: Audit your current tech stack for AI readiness
- Project 8: Map AI use cases to business KPIs
- Project 9: Develop a model validation checklist
- Project 10: Create a rollout plan for AI adoption across teams
Module 14: Certification & Career Advancement - Reviewing key frameworks and outcomes
- Submitting your final capstone project
- Evaluation criteria for certification
- Preparing your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn and resumes
- Using certification to lead internal AI initiatives
- Positioning yourself for promotion or new roles
- Joining the global community of certified professionals
- Accessing post-course templates and resources
- Next steps: Advanced AI specialisations and leadership pathways
- Segmenting customers for targeted expansion offers
- Matching AI-identified opportunities with product features
- Designing data-informed sales playbooks
- Automating expansion outreach for sales teams
- Generating personalised expansion proposals
- Using AI to draft bespoke email templates
- Scheduling expansion touchpoints based on usage peaks
- Integrating expansion workflows into sales CRMs
- Tracking expansion campaign performance
- Scaling expansion through self-serve product paths
Module 8: Churn Intervention Effectiveness Analysis - Measuring success of retention actions
- Calculating intervention ROI
- Attributing churn reduction to specific workflows
- Identifying which interventions fail and why
- Analysing response rates to automated nudges
- Using cohort analysis to validate strategy impact
- Adjusting models based on intervention outcomes
- Creating feedback loops for continuous improvement
- Reporting retention success to leadership
- Building a closed-loop retention system
Module 9: Expansion Revenue Forecasting - Projecting expansion revenue using AI signals
- Building forecast models with historical adoption data
- Estimating time-to-upsell based on usage patterns
- Incorporating seasonality and macro trends
- Validating forecast accuracy over time
- Communicating expansion projections to finance teams
- Using forecasts for headcount and capacity planning
- Scenario planning for different growth assumptions
- Adjusting forecasts based on real-time signals
- Integrating expansion forecasts into board reporting
Module 10: AI Integration in Customer Success Platforms - Evaluating AI capabilities in Gainsight, Totango, and Salesforce
- Configuring AI alerts in ChurnZero
- Using Pendo and Mixpanel for retention insights
- Embedding AI models into CSM workflows
- Automating health score updates in real time
- Creating custom AI-powered dashboards
- Setting up automated task creation for CSMs
- Synchronising AI insights across tools
- Reducing manual workload with intelligent automation
- Scaling customer success with AI augmentation
Module 11: Building Autonomous Retention Systems - Designing self-correcting retention models
- Implementing feedback loops for model retraining
- Automating data refresh and feature engineering
- Scheduling model performance reviews
- Alerting on data drift and concept drift
- Version controlling AI models and logic
- Establishing ownership and monitoring protocols
- Creating audit trails for AI decisions
- Building resilience into retention systems
- Scaling systems for thousands of accounts
Module 12: Organisational Alignment & Change Management - Onboarding sales, success, and product teams to AI insights
- Creating shared definitions of health and risk
- Conducting cross-functional AI strategy workshops
- Developing communication plans for new workflows
- Overcoming resistance to AI-driven decisions
- Establishing data literacy programs
- Training teams on interpreting AI outputs
- Integrating AI recommendations into operational rhythms
- Measuring team adoption of new systems
- Creating feedback channels for system improvement
Module 13: Real-World Implementation Projects - Project 1: Build a customer health score from real data
- Project 2: Design a churn intervention workflow
- Project 3: Identify expansion opportunities in a sample account
- Project 4: Create an AI-powered email nurture sequence
- Project 5: Develop a board-ready retention progress report
- Project 6: Forecast expansion revenue for next quarter
- Project 7: Audit your current tech stack for AI readiness
- Project 8: Map AI use cases to business KPIs
- Project 9: Develop a model validation checklist
- Project 10: Create a rollout plan for AI adoption across teams
Module 14: Certification & Career Advancement - Reviewing key frameworks and outcomes
- Submitting your final capstone project
- Evaluation criteria for certification
- Preparing your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn and resumes
- Using certification to lead internal AI initiatives
- Positioning yourself for promotion or new roles
- Joining the global community of certified professionals
- Accessing post-course templates and resources
- Next steps: Advanced AI specialisations and leadership pathways
- Projecting expansion revenue using AI signals
- Building forecast models with historical adoption data
- Estimating time-to-upsell based on usage patterns
- Incorporating seasonality and macro trends
- Validating forecast accuracy over time
- Communicating expansion projections to finance teams
- Using forecasts for headcount and capacity planning
- Scenario planning for different growth assumptions
- Adjusting forecasts based on real-time signals
- Integrating expansion forecasts into board reporting
Module 10: AI Integration in Customer Success Platforms - Evaluating AI capabilities in Gainsight, Totango, and Salesforce
- Configuring AI alerts in ChurnZero
- Using Pendo and Mixpanel for retention insights
- Embedding AI models into CSM workflows
- Automating health score updates in real time
- Creating custom AI-powered dashboards
- Setting up automated task creation for CSMs
- Synchronising AI insights across tools
- Reducing manual workload with intelligent automation
- Scaling customer success with AI augmentation
Module 11: Building Autonomous Retention Systems - Designing self-correcting retention models
- Implementing feedback loops for model retraining
- Automating data refresh and feature engineering
- Scheduling model performance reviews
- Alerting on data drift and concept drift
- Version controlling AI models and logic
- Establishing ownership and monitoring protocols
- Creating audit trails for AI decisions
- Building resilience into retention systems
- Scaling systems for thousands of accounts
Module 12: Organisational Alignment & Change Management - Onboarding sales, success, and product teams to AI insights
- Creating shared definitions of health and risk
- Conducting cross-functional AI strategy workshops
- Developing communication plans for new workflows
- Overcoming resistance to AI-driven decisions
- Establishing data literacy programs
- Training teams on interpreting AI outputs
- Integrating AI recommendations into operational rhythms
- Measuring team adoption of new systems
- Creating feedback channels for system improvement
Module 13: Real-World Implementation Projects - Project 1: Build a customer health score from real data
- Project 2: Design a churn intervention workflow
- Project 3: Identify expansion opportunities in a sample account
- Project 4: Create an AI-powered email nurture sequence
- Project 5: Develop a board-ready retention progress report
- Project 6: Forecast expansion revenue for next quarter
- Project 7: Audit your current tech stack for AI readiness
- Project 8: Map AI use cases to business KPIs
- Project 9: Develop a model validation checklist
- Project 10: Create a rollout plan for AI adoption across teams
Module 14: Certification & Career Advancement - Reviewing key frameworks and outcomes
- Submitting your final capstone project
- Evaluation criteria for certification
- Preparing your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn and resumes
- Using certification to lead internal AI initiatives
- Positioning yourself for promotion or new roles
- Joining the global community of certified professionals
- Accessing post-course templates and resources
- Next steps: Advanced AI specialisations and leadership pathways
- Designing self-correcting retention models
- Implementing feedback loops for model retraining
- Automating data refresh and feature engineering
- Scheduling model performance reviews
- Alerting on data drift and concept drift
- Version controlling AI models and logic
- Establishing ownership and monitoring protocols
- Creating audit trails for AI decisions
- Building resilience into retention systems
- Scaling systems for thousands of accounts
Module 12: Organisational Alignment & Change Management - Onboarding sales, success, and product teams to AI insights
- Creating shared definitions of health and risk
- Conducting cross-functional AI strategy workshops
- Developing communication plans for new workflows
- Overcoming resistance to AI-driven decisions
- Establishing data literacy programs
- Training teams on interpreting AI outputs
- Integrating AI recommendations into operational rhythms
- Measuring team adoption of new systems
- Creating feedback channels for system improvement
Module 13: Real-World Implementation Projects - Project 1: Build a customer health score from real data
- Project 2: Design a churn intervention workflow
- Project 3: Identify expansion opportunities in a sample account
- Project 4: Create an AI-powered email nurture sequence
- Project 5: Develop a board-ready retention progress report
- Project 6: Forecast expansion revenue for next quarter
- Project 7: Audit your current tech stack for AI readiness
- Project 8: Map AI use cases to business KPIs
- Project 9: Develop a model validation checklist
- Project 10: Create a rollout plan for AI adoption across teams
Module 14: Certification & Career Advancement - Reviewing key frameworks and outcomes
- Submitting your final capstone project
- Evaluation criteria for certification
- Preparing your Certificate of Completion from The Art of Service
- Adding the credential to LinkedIn and resumes
- Using certification to lead internal AI initiatives
- Positioning yourself for promotion or new roles
- Joining the global community of certified professionals
- Accessing post-course templates and resources
- Next steps: Advanced AI specialisations and leadership pathways
- Project 1: Build a customer health score from real data
- Project 2: Design a churn intervention workflow
- Project 3: Identify expansion opportunities in a sample account
- Project 4: Create an AI-powered email nurture sequence
- Project 5: Develop a board-ready retention progress report
- Project 6: Forecast expansion revenue for next quarter
- Project 7: Audit your current tech stack for AI readiness
- Project 8: Map AI use cases to business KPIs
- Project 9: Develop a model validation checklist
- Project 10: Create a rollout plan for AI adoption across teams