Booking.com is one of the largest e-commerce organizations and the World’s leading online accommodation service. Its products support more than 40 languages and is used by millions of users daily.
Hotel Page, Tablet Web, Consumer Psychology
3 years 2 months
Working closely with developers, product owners, copywriters, data-scientists and user researchers, I’ve been part of the full product development cycle. This includes user research and user behavioral data analysis, construction of hypotheses, design and prototyping, implementation as an A/B test, deploying code on production, reviewing the data and planning follow-up steps.
On top of my regular design responsibilities, I’ve been part of the recruitment team conducting interviews on a weekly basis. My main task was to assess technical, creative and communication skills for candidates applying for roles of UX designer, mobile app designer, and copywriter. I’ve also helped in on-boarding new designers, getting them up to speed during their first weeks in the organization and the team.
I've been responsible for the design efforts of the Consumer Psychology team. The team’s core focus was to incentivize users to book with the platform, by drawing inspiration from Academic research on Consumer Psychology. Being a cross-platform team, I had the opportunity to work on Desktop, Tablet and Mobile.
I've been the designer of the Tablet Web version of the platform. The team’s core focus was on the lower funnel of the product and in particular the Hotel Page and the Booking Process.
I've been responsible for the design efforts of the desktop Hotel Page, the most visited page of Booking.com.
Way of working
The development teams at Booking.com work in a fast-paced agile environment, driven by data and experimentation. The long-term vision is broken up in small, measurable hypotheses, which get validated based on defined metrics of success. At Booking.com customers are the ones who decide on the improvements and features proposed by the development teams.
Ideas for improvements and new features come from various channels. That could be customer feedback, findings from user research, learnings from previous experiments, heuristic evaluation, business strategy, and other. After prioritizing the ideas based on potential impact and effort, a hypothesis is constructed. The hypothesis needs to frame the problem, propose a solution and define success on specific metrics. The solution is then designed and implemented as an A/B (or multi-variant) test.
Depending on the traffic, the experiment needs to run for the necessary time to get significant results. If the experiment significantly impacts positively the defined success metrics, it would go live to all customers. If not, which was usually the case, in collaboration with the Product Owner and a Data Scientist (if needed) we would analyze the data, trying to understand the reasons of failure and iterate based on the learnings.