Presentation Travelbird

1. Building a Personalised
Offering
8 November 2016

2. 2
800k emails sent to NL
recipients (2,6M across
EU)
Every person in each
market gets the same 6
offers
Goal: 100%
personalisation
TravelBird: Six daily dealsBuilding a real personalised offer

3. 3
We had three personalisation goals
Deliver what would someone be interested in
Ensure the right amount of diversity and “freshness”
Send the selection at the most relevant time
Building a real personalised offer

4. So we built a personalisation platform!
Building a real personalised offer

5. 5
TravelBird’s indicators of interest
Pageviews
Email opens
Sales flow interactions
Favorites
Searches
Image clicks
….
Customer Interactions
>500M events over 2,5 years
(but now >15M/day!)
Other Attributes
Similar customers
Time since last activity
User seasonal preferences
“Normal” behaviour
All of this is used to create a score per
customer per offer interaction
Building a real personalised offer

6. 6
Fed into collaborative filtering (like Netflix)
Based on all customers and all products ever, rank online* offers from best to worst for each recipient
Building a real personalised offer

7. 7
Problem: Offers will be quite similar
Denmark
Germany
Long-haul trips:
(Cuba, Nepal, USA, Iceland,
Morocco)
Building a real personalised offer

8. 8
So customers will get thisBuilding a real personalised offer

9. Region Similarity: 80%
distance 397 km
Package Similarity: 100%
both incl. flight & hotel,
2/3/4 nights available
Price Similarity: 96%
10 Euro difference
In addition: Text description, image, clicks
Overal Similarity: 96%
Solution: Diversify using similarity
Distance metrics: Canberra, Cosine, Great-
circle, …
Building a real personalised offer

10. 10
ONE MESSAGE AT THE RIGHT TIME BEATS MANY MESSAGES
And we target timing and dateBuilding a real personalised offer

11. 11
In the end: What we builtBuilding a real personalised offer
Events
Monitoring every platform for
user interaction, each day’s
events are fed back into our
databases for inclusion in the
next day’s selections
Models
In Apache Spark we use a variety
of models to come up with
scores for product
recommendations
Diversification
These scores are the enriched
with weather, seasonality, and
other data to build an optimised
planning calendar for each
recipient
Communication
Communication is automatically
scheduled to deliver this
optimised content at the right
time and frequency for each
customer

12. 12
Building a real personalised offer
Next, unlock value with continuous improvement

13. 13
Ops Meetings
Weekly sessions are held
with all country teams to
identify opportunities
Model Analytics
Conversion results are
analysed to identify
which customer groups
under/overperform the
average.
Business Analytics
Overall company trends
are assessed to identify
which macro activities
are not captured in the
model.
External Research
Blogs, white papers, etc
are explored to identify
potential tests
Surfacing opportunitiesBuilding a real personalised offer

14. 14
Additional market
chosen and
original scaled to
50%
Roll out to all
markets
Test market
chosen and
25% tested
Test and micro
conversion
defined
Our testing cycleBuilding a real personalised offer
Result: More than ten tests and 50 code releases completed per week

15. 15
Ops-driven development
Release notes are publicly available, suggestions are
continuously captured via Slack and email, the
suggestions log is adjusted weekly with two groups:
-Product planners: operational improvements
-Regional managers: overall program direction
Assigned partners
In each country team, operational partners are
assigned from each team to conduct business
analytics, audit the product portfolio, and
coordinate learnings within their discipline
Company presentations
Changes in personalisation and impacts are shared
each month in a company-wide presentation and
weekly with company leadership
Maintaining alignmentBuilding a real personalised offer

16. 0
13
25
38
50
Open Rate CTOR Conversion Profit
Control
Test
Marketing Definition
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nec sem congue convallis. Pellentesque vel
Open Rates
Open rates increased 8% due to more relevant products in
the subjectt line, improving deliverability
Profit per Send
As a result of the higher conversion and better targeting of
high profit products, profit more than doubled per send
Click Through from Open Rate
CTOR increased 30-50% per market, driving a 60% growth
in email traffic
Conversion from Send
As a result of the significant traffic increase and higher
interest level to products, conversion from send doubled
Our ResultsBuilding a real personalised offer
Performance improvements were observed in all metrics relative to
the status quo due to the effect of personalization, with the highest
gains coming in engagement. Unsubscription rates dropped >25% in
the test group.

17. 17
Questions?
For further questions, please feel free to contact:
Rob Winters: rob@travelbird.nl