AI Guest Experience Analytics Training Course

Participants can define the data structures and controls required for reliable guest analytics.

• Customer data platform architecture: profiles, identities, events, consent and activation
• Property-management, CRM, POS and reputation-platform data mapped to common entities
• Identity resolution rules for duplicate guests, households, corporate accounts and agents
• Data dictionaries documenting definitions, grain, refresh frequency, lineage and stewardship
• Missing-value treatment selected by variable type, mechanism and decision risk
• Outlier checks distinguish exceptional stays from integration and entry errors

Participants can apply privacy, fairness and accountability controls to guest analytics use cases.

• GDPR lawful bases assessed for guest profiling, marketing and service personalisation
• Consent records linked to purpose, channel, retention period and withdrawal status
• Data minimisation applied to model features and analyst access permissions
• PCI DSS boundaries identified when payment data intersects guest analytics
• Bias assessments test differential recommendations across language, age and booking channel
• Model cards record intended use, limitations, monitoring and human escalation

Participants can turn surveys and unstructured reviews into validated experience insights.

• Net Promoter Score, CSAT and CES matched to journey-stage decisions
• Survey sampling plans control invitation timing, response bias and representativeness
• Review text prepared through tokenisation, language detection and duplicate removal
• Aspect-based sentiment analysis separates comments about rooms, staff, food and cleanliness
• Topic modelling compares recurring issues across properties, segments and seasons
• Qualitative coding validates automated themes against sampled guest comments

Participants can build experience reporting that supports commercial and operational decisions.

• Descriptive dashboards distinguish volume, rate, distribution and cohort comparison
• Control charts identify statistically unusual complaint rates and service delays
• Driver analysis combines correlation, segmentation and operational plausibility checks
• Regression models estimate associations between service attributes, ratings and spend
• Confidence intervals prevent overreaction to small samples and volatile scores
• Executive scorecards connect experience measures to occupancy, ADR and RevPAR

Participants can specify and assess predictive models for high-value guest decisions.

• Supervised learning problem statements define target, horizon, unit and intervention
• Feature engineering converts stay history, preferences and incidents into predictors
• Churn propensity models identify guests at risk before loyalty disengagement
• Upsell propensity scores rank relevant offers without excessive guest contact
• Train-validation-test splits prevent temporal leakage in booking and stay data
• Precision, recall, lift and calibration guide model selection thresholds

Participants can create usable guest segments and personalisation rules with appropriate safeguards.

• Clustering segments guests using behavioural, value, preference and channel variables
• RFM analysis distinguishes recency, frequency and monetary value for targeting
• Persona cards translate segment patterns into service design and communication rules
• Next-best-action logic combines eligibility, propensity, capacity and guest preferences
• Recommendation approaches compared: rules, content-based filtering and collaborative filtering
• Personalisation guardrails limit frequency, respect consent and preserve service authenticity

Participants can identify AI analytics applications that improve daily hospitality operations.

• Demand forecasts combine bookings, cancellations, events, weather and lead-time patterns
• Queueing metrics estimate staffing requirements from arrivals, service times and variability
• Service recovery alerts prioritise unresolved issues by severity, value and departure time
• Anomaly detection flags unusual housekeeping delays, maintenance requests and complaints
• Experiment design tests amenity, message and check-in changes using control groups
• Uplift measurement separates campaign response from incremental experience improvement

Participants can evaluate AI model quality and establish operating controls for deployment.

• CRISP-DM phases adapted for guest-experience analytics products and operating decisions
• Model validation checks accuracy, fairness, drift, privacy and business fit
• SHAP explanations communicate influential factors without presenting causation claims
• Human-in-the-loop workflows assign override authority, escalation paths and audit trails
• Monitoring thresholds trigger retraining, investigation or model retirement decisions
• Vendor due diligence tests data ownership, integration, security and exit provisions

Participants can prioritise an AI guest analytics initiative and define its first implementation steps.

• Use-case canvases specify decision owner, guest benefit, metric, data and risk
• Value cases quantify revenue, cost, retention and service-quality assumptions
• Prioritisation matrices score feasibility, impact, data readiness and change effort
• Implementation roadmaps sequence pilots, integrations, governance controls and adoption activities
• Operating cadences assign KPI reviews, model monitoring and service-improvement actions
• Individual application plans define first use case, stakeholders and 90-day milestones