Competitive Analysis

Conducted a cognitive walkthrough of the TripAdvisor Trip Planner mobile app evaluates how well its AI-powered itinerary builder supports a first-time, less tech-savvy traveler.

Findings

strong, confidence-building AI setup flow but a confusing and brittle post-generation editing experience, where key actions like saving, customizing, and collaborating are hard to discover, inconsistent, or even destructive.

Major usability gaps—especially around mental models for “saves” vs. “itinerary,” user control, and navigation—and distills them into concrete design recommendations for building a more intuitive, non-destructive itinerary planner.

Read the full report here:

Concept Testing

Conducted concept testing of a proposed AI-powered itinerary builder plus in-depth interviews focused on the end-to-end HGV owner trip planning journey

Participants

11 owners + 3 agents.

Problem

Booking is stressful + planning is fragmented; owners leave HGV post-booking.

Evidence

Workarounds (midnight booking, walking reservations), multi-tool planning, points confusion, low trust in search results, slow performance.

Opportunity

Build an integrated trip hub (AI itinerary builder) + alerts + integrations + points intelligence.

Goal

Owners can book and never need to leave HGV to plan, coordinate, or manage their trip.

MVP

A lightweight, chat-bot-driven itinerary builder to let members create, edit, save, and visualize trip plans (itineraries) and segments (activities, dining, reservations) with minimal risk exposure.

Scope

Chatbot integration only (access via chatbot); create/read/update/delete itineraries and segments; aspirational (recommendation) items primarily — not full booking integrations; basic UI to view itinerary by day and map items (latitude/longitude demo data); guardrails and feature flag for controlled rollout.

Analytics

Implement comprehensive logging for MVP1: capture user actions, intents, session context, login behavior, token usage, and chatbot interactions.

Next Phase

Use pilot data to prioritize deeper integrations (booking, concrete reservations), advanced agents, and personalization scoring.