I cross-analyzed properties across the portfolio to map how each property's unique offerings were surfaced on their own websites versus their profiles on Expedia. That review surfaced content gaps, inconsistent feature descriptions, and presentation differences that could affect conversion.

From there I designed a practical backend structure to hold the scraped partner data. I defined canonical fields, normalization and enrichment rules, metadata labels, and straightforward ingestion steps so messy records have a consistent place to live. The system keeps flexibility for varied inputs while adding enough order to support reporting, content reuse, and product improvements.

I partnered with a designer to build a simple partner-facing editor that surfaces the scraped property data and lets partners make corrections or add detail, while clearly communicating that traveler-facing copy may be reformatted or selectively pulled into EG’s pages. The editor includes field-level guidance and validation so partners understand how their inputs get normalized and which pieces are most likely to be extracted for display.

To make editing practical at scale, I mapped a chatbot assistant with detailed IF–THEN flows: (e.g., IF a partner updates an amenity list THEN normalize naming and match to canonical tags IF they paste a long description THEN extract highlights and suggest headline copy; IF conflicting data is detected THEN prompt for clarification and log the decision).

Defined where the new partner data surfaces—search, listing cards, detail pages, comparisons, and booking flow—and redesigned the content hierarchy so fields slot predictably by touchpoint. I set simple priority rules (confidence, relevance, freshness) so high-confidence USPs become headlines/badges, long-form becomes highlights, and amenities map to normalized icons, with fallbacks for low-confidence inputs. Everything includes progressive disclosure and instrumentation for A/B testing impact on discovery and conversion.