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Arrival experience

AI-powered insight generation: Identifying what drives repeat bookings

Hospitality | Behavioral insight and AI-enabled analysis

High-volume service environments often struggle to connect voice-of-customer feedback to real booking behavior. This project demonstrated how AI-supported analysis and behavioral clustering can reveal patterns that traditional methods overlook.

citizenM needed a clearer understanding of which guest experiences most influenced satisfaction and repeat stays. Using AI-supported clustering and behavioral analysis,

 

I linked voice-of-customer feedback to booking patterns, revealing sleep quality as the strongest driver of loyalty and informing strategic operational changes.

CLIENT

citizenM hotels

01. Situation

Feedback was fragmented across tools and teams. Insights were often anecdotal (shared via Slack), and there was no connection between what guests said and how they behaved (e.g., repeat bookings, conversions).

02. Task

Design and lead a cross-functional proof of concept that:

  • Consolidated qualitative feedback with behavioral data

  • Used AI to surface guest themes and sentiment

  • Connected insights to real-world business levers

  • Built stakeholder confidence in AI-driven decision making

03. Action

  • Formed a cross-functional team with UX, Tech, Marketing, and an external ML partner

  • Consolidated 12 months of guest feedback + booking data

  • Applied NLP techniques:

    • Topic modeling for theme identification

    • Sentiment analysis to map tone

    • Clustering to reveal guest behavior patterns

  • Co-developed a feedback taxonomy aligned to journey stages (check-in, sleep, staff, etc.)

  • Audited classification logic to improve accuracy (e.g., resolving misclassified themes)

  • Delivered results through a dashboard and a series of cross-functional workshops

04. Result

  • Identified sleep quality as the top driver of satisfaction and repeat bookings

  • Enabled strategic resource reallocation across departments

  • Delivered a 10 percent lift in NPS through targeted operational improvements

  • Introduced an early model of AI-enabled behavioral clustering now reflected in other industries

Illustrating the process

This section outlines the architecture of our insight generation workflow. We applied NLP techniques—topic modeling, sentiment analysis, and clustering—to 12 months of guest feedback, mapped results to a custom journey-based taxonomy, and linked them to behavioral metrics such as repeat bookings and conversion. The resulting pipeline enabled scalable, interpretable insight delivery across UX, data, and strategy functions.

Project kickoff session

Aligning on goals, taxonomy, and collaboration structure

citizenM.png

This early-stage workshop brought together UX, data, and marketing stakeholders to define project goals and co-develop the initial feedback taxonomy. It set the foundation for shared language and cross-functional alignment throughout the AI insight pilot.

VOC to action: Insight pipeline

Mapping unstructured feedback to behavioral and business outcomes

flow diagram

A visual flow diagram showing the pipeline from raw guest feedback to executive insights. Steps include data consolidation, NLP processing (topic modeling, sentiment analysis, clustering), taxonomy alignment to guest journey stages, and behavioral correlation—culminating in an executive dashboard.

Feedback taxonomy

Structuring guest feedback by journey stage

Taxonomy

The guest experience taxonomy was developed in collaboration with internal teams. Aligned to key journey stages—'Moments of truth'—it allowed for consistent classification of qualitative input.

Sentiment & behavior dashboard

Identifying high-impact themes through AI-generated insight

Relation_edited.jpg

This outcome shows how guest sentiment across feedback themes aligned with booking behavior. By surfacing key patterns—such as the strength of sleep as a loyalty driver and the limited impact of room controls—it helped teams prioritize improvements where they mattered most. The visualization translated abstract AI outputs into actionable insights for business and service design.

Why it worked

  • Combined qualitative and behavioral data for more accurate insight

  • High alignment with existing operational decision-making

  • Clear, organization-wide actionability

Navigator methods & frameworks used

 

  • Feedback taxonomy creation

  • NLP + Behavioral Correlation Model

  • Human-in-the-loop model review

  • AI literacy workshop

I was skeptical at first—we were already looking at guest feedback on a day-to-day basis. But seeing longer-term patterns emerge from the data was a real win. It helped us understand what truly matters to guests.

– PM, citizenM hotels

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