2. On most flights, hundreds of passengers share the same cabin. Some
are travelling to the unknown; others know every street and
commerce at the destination, and are flying back home. More and
more travelling alone, seeking human interactions …
.. the former are looking for the hidden gems no online outlet talks
about, the latter would love to share their favourite spots …
… yet no one talks to each other
2
Pain Point: inflight time is the least social link in the
travel chain
3. “flocals aims to make travel more enjoyable by making
more social.”
flocals app facilitates interactions between travelers on
the same flight by enabling insiders to recommend their
favorite locations to first time visitors.
3
Our Solution:
4. Why we believe it will work:
4
We asked ~100 travelers
Male
Female
from 20 countries
What they look for…
Hidden gems
Touristic attractions
… and how they research
Online only
Both online
word of
mouth
Word of mouth only
Unknown
5. Why we believe it will work:
5
… we confirmed that talking to people beats online research !!!
If only travelers could find locals to talk to …
Word of
mouth
Online
research
Would like to
ask a local for
advice
Would
not ask
Maybe
average
average
Satisfaction. [1-10]
Density
6. The tech
6
Postgres
Firebase
To store and
retrieve
recommendations
For real-time chat
To store
information
locally and
retrieve it
offline in-
destination
For navigation and
serve/translate
recommendations
Python/Flask
React
iOS / android
Google APIs:
translate, places, sign-in
Desktop / IFE browsers
In development In development
Note: We believe that an app is better suited than a web-app for chat (e.g. notifications, etc ) and to store information to be retrieved offline after the
flight, so flocals is an app-first experience
7. 7
Roadmap: What we need to build/test
• Authentication app-2-app or app-within-app component protocol
• Available / Not Available flag + User notifications
• Security
• Users analytics (mixpanel or other)
• Scalability
• Browser version (with reactjs) or IFE
• Spam and other abuse detection / prevention
• Alternative API partners in non-Google areas (Baidu, Yandex, Naver, etc …)
• Airline interface for broadcasting / interacting
• Local Storage to retrieve recommendations offline and avoid roaming
• “Your Flight Digest” (summary of recommendations sent to email)
• Open questions (e.g. what’s the best way to get to the city center?)
• Match making based on recommendation and interest history
• Personalization input for ONE ORDER
• Gamification and rewards to contributors
▲ MVP (days) ▲
▲ Mid term (weeks) ▲
8. 8
brought to you by:
Faical Allou
Product / Development / Sales
Sabato Leo
Data Science / Machine Learning / Development
Start-upStart-up airline
