An overview of how MyLittleAdventure use Artificial Intelligence technologies to help travellers to choose their best memories : Smart inspiration engine, easy comparison of things to do in destination and many more

1. B2B Travel technology

2. MyLittleAdventure
A personalization technology
provider that inspires travellers with
things to do at destination
2
« Tomorrow’s travelers will want to travel the
world in just one way – their own way. »
A personalized travel future - Amadeus IT Group

3. 3
A highly fragmented market

4. 4
Hundreds of websites

5. MyLittleAdventure
Solution & Tech insights
5

6. Intelligent engine inside
6
First class components and intelligent Technology
X
The right product to
the right person at
the right moment
!
Data Collection
Bookable & informative
"
Segmentation
Machine Learning
#
Personalization
Collaborative Filtering
X
31 2

7. Architecture overview
7
#
Personalization
Collaborative Filtering
!
Data Collection
Bookable & informative
$ % &
' ( )
Social Feeds
"
Segmentation
Machine Learning
*
Reviews
⋆
Ratings
,
Product Feeds
|
Statistics
SupplierLayer
BusinessLayer
.
Travel data
/
Web Content
0
Booking
1
Profile context
2
Accounting
core
Product channels
3 4Widgets
5API / Webservices
6
IoT Feeds

Big data
First class components and intelligent Technology
… …
1 2 3
8Ads & Banners

8. Clustering
1. Detect similar products
Unsupervised problem
Not possible for a human being
Clustering strategy
Too many products (270k) & updates (everyday)
8
2. ML Algorithms & tools
Mean shift, K-Means, Spectral Co-Clustering, Hierarchical
clustering
SVD (LSA), Embeddings
NLTK, Scikit learn

9. Clustering
9
25 similar
products

10. Features scoring
1. Detect & score product features
Classification : not reliable and not uniform across suppliers
Score each products for each available categories
Generic technology
New kind of search engine : Preferences first, not just filters
10
2. ML Algorithms & tools
Random forest, Gradient Boosting, Neural networks, Ridge
regression, Lasso, Naive Bayes
SVD (LSA), Embeddings
NLTK, Scikit learn, Dataiku

11. Features scoring
11

12. Hyper-Personalization
1. With some Traveler information
Benefit from content and collaborative filtering
12
Foursquare Swarm TripAdvisor Places Facebook Yelp! Twitter
3. Without any travel context
Tap billions of signals from social network content
Intelligent engine to calculate traveler trends around the world
2. With some Travel context
Use of implicit features : duration, customer segment, weather,…

13. And some others…
1. Language detection
Ridge regression, Lasso, Naive Bayes
13
2. Social places merge
Random forest
3. Product image similarity
Neural networks (CNN)

14. Traveler advantages
14
+ Gain time
+ Personalization
+ New activity ideas
+ More happiness
+ More confidence - less risk
+ Easy comparison (Price - Activities - Options)

15. + Increase customer experience and loyalty
+ New source of margin (Cross-Sell)
+ Enrich customer global services
+ One sole agreement
+ Essential for Concierge
+ No cost of development nor integration
Travel Stakeholder advantages
15

16. 16
Business case
Customer
satisfaction
More loyalty
More revenue
Smart
Recommandations
Travel
stakeholders

17. Thank you
CONTACT
Mr Valéry BERNARD - CEO & Founder
valery.bernard@mylittleadventure.com
06.17.21.52.81

18. MyLittleAdventure
Integration examples
18

19. API / Webservices
19
Json / Xml API verbs

20. Widgets - Web
20

21. Widgets - Mobile
21
A clear, simple and seamless customer experience

22. MyLittleAdventure
Catalog
22

23. 23
MyLittleAdventure catalog
270 000 Products - 175 Countries - 1617 Cities
15 Languages

24. 24
European products

25. Technical stack
https://stackshare.io/mylittleadventure/mylittleadventure
25