Booking.com

Using content strategy to shape product direction

Project Overview

Defined high-level content principles

Role: UX Writer / Content Strategist

Tools: Figma, user research, content audit, competitor research, experiment documentation, roadmap planning docs

Collaborated with: Product, Design, Research, Engineering, Data, and senior leadership

Industry: Travel Tech

Problem / Goals:

Recommendation experiences across homescreen and trip-planning surfaces had become fragmented. Different surfaces used different logic, vocabulary, and content patterns, so the journey did not always feel connected, relevant, or trustworthy.

Users could see travel products and add-ons, but they were not always given enough context to understand why those options fit their trip, intent, or stage of planning.

The goal was to define a content strategy that could improve the live experience while shaping longer-term product and platform direction.

How I helped:

I led the content strategy work by synthesising research, auditing the existing experience, mapping user problems across the journey, and translating content opportunities into product requirements.
I also presented the strategy to senior Product and Engineering stakeholders, helping move the conversation from isolated copy optimisation to a shared roadmap for better recommendations.

My Process

Strategy

The strategy focused on four user problems across recommendation surfaces:

Relevance: Why this for me now?
Transparency: Why am I seeing this?
Integration: Does the story continue?
Autonomy: Can I choose or change this?

I used these problems to define a content vision for recommendations: travellers should know why something is shown, feel that it fits their trip or intent, and stay in control.

To make this practical, I created a reusable content spine:

Intent -> Signal -> Reason -> Value -> Action -> Control

This gave teams a shared way to structure recommendation content across surfaces, while still allowing each part of the journey to do a different job.

Resuable content spine

What I Did

Built the strategy around users' needs

I mapped the end-to-end recommendation journey from homescreen discovery to later planning moments, showing how user questions change over time:

Homescreen: What is worth exploring?
Pre-book: Why is this useful here?
Post-book: Can I trust this recommendation and act on it?

This helped the team connect individual messages to the wider traveller journey.

Created two execution swimlanes

I split the work into two swimlanes so teams could act quickly while building toward a stronger system.

Content-led initiatives focused on reusable patterns, a shared content model, and recommendations that could become more trip-, traveller-, and stage-aware over time.

Copy experimentation focused on fast learning: validating hypotheses, reducing friction on live surfaces, and contributing to short-term KPIs without waiting for major technical changes.

Turned content strategy into product direction

For larger opportunities, I clarified:

The user problem being solved.
The trip stage or surface where it appeared.
The signals needed to make the recommendation relevant.
The content pattern needed to explain the recommendation.
The product, ML, or platform behaviour required to support it.

This helped Product and Engineering see content as a roadmap input, not just a delivery layer.

Content objectives were built around identified user problems

Execution was phased into two swimlanes

Results

Created a shared content strategy for recommendation surfaces across homescreen and trip-planning experiences (end-to-end).
Reframed the work around four user problems: relevance, transparency, integration, and autonomy.
Built a two-swimlane operating model that balanced content-led initiatives with fast copy experimentation.
Helped align Product, Design, Engineering, Data, and ML around the capabilities needed to make recommendations more explainable and trip-aware.
Demonstrated early impact through content-led experiments, driving ~60% of track's KPI.
Drove the development of new product and ML features.

What I learned

Content strategy is most powerful when it helps teams make better product decisions.

In this project, the work was not only about improving words on existing surfaces. It was also about defining the logic behind better recommendations: what the product should know, what the user needs to understand, and what systems need to enable so content can stay relevant at scale.