Welcome

Project Overview

Welcome started with the idea that travel should be personalized and in the moment. Through research and experimentation we envisioned a platform that learns users' preferences, considers real-time context (weather, time of day, and distance), and delivers best-in-class recommendations tailored to each individual's unique needs.

Role

Product Design Lead

Client

Welcome

Project Overview

Role

Product Design Lead

Client

Welcome

Intro video animated in After Effects

Personas

Consumer

Mark Webb just moved to Austin and is looking for a great place to for dinner with friends.

Creator

Jenni Sparks is the author of a book on tacos. She posts videos on tiktok, but she’s looking to expand her network and increase the shelf life of her content.

Onboarding

Intro

Intro to Welcome Score / Invitation to Personalize

Interest Selection

Preferences Selection

Connect Contacts

Loading Welcome Score

Discover Feed

Video Share

Friend Recomendation

Dynamic Article

Map

The Map is an active discovery experience that brings together all the user preferences and makes recommendations for the moment you’re ready to go. The swipe-able cards are intelligently chosen recommendations – each with a rich media preview. Essential details are in the forefront; which friend has recommended, publication articles and the Welcome Score.

Map / For You recomendations

Transition to Place View

Place View

Profile Lists

Lists are automatically generated and organized from Likes and Saves make through the experience. They are sharable and explorable on the map for easier use.

Profile

Saves / Likes Lists

List viewed on Map

Product Strategy

I use a variety of visual diagrams to help explain some of the more abstract under-the-hood workings of the product. Here are a few examples I did for Welcome features.

Realtime

"Realtime" recommendation engine preferences influenced by time of day.

Complementary Pairings

Complementary Pairings were sets of recommendations that are shared with users as 2 or 3 activities that go well together.

Feed Algorithm

Complementary Pairings were sets of recommendations that are shared with users as 2 or 3 activities that go well together.

Marketing

Lists are automatically generated and organized from Likes and Saves make through the experience. They are sharable and explorable on the map for easier use.