Rob Winters - Travelbird

Building a Personalised Offering

8 November 2016

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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

3We 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

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

5TravelBird’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


6Fed into collaborative filtering (like Netflix)

Based on all customers and all products ever, rank online* offers from best to worst for each recipient


7Problem: Offers will be quite similar

Denmark

Germany

Long-haul trips:

(Cuba, Nepal, USA, Iceland,

Morocco)


8So customers will get thisBuilding a real personalised offer

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, …


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ONE MESSAGE AT THE RIGHT TIME BEATS MANY MESSAGES

And we target timing and dateBuilding a real personalised offer

11In the end: What we builtBuilding a real personalised offer

EventsMonitoring every platform for

user interaction, each day’s events are fed back into our databases for inclusion in the

next day’s selections

ModelsIn Apache Spark we use a variety

of models to come up with scores for product recommendations

DiversificationThese scores are the enriched with weather, seasonality, and

other data to build an optimised planning calendar for each

recipient

CommunicationCommunication is automatically

scheduled to deliver this optimised content at the right time and frequency for each

customer

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Next, unlock value with continuous improvement

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Ops MeetingsWeekly sessions are held with all country teams to

identify opportunities

Model AnalyticsConversion results are

analysed to identify which customer groups under/overperform the

average.

Business AnalyticsOverall company trends are assessed to identify which macro activities are not captured in the

model.

External ResearchBlogs, white papers, etc are explored to identify

potential tests

Surfacing opportunitiesBuilding a real personalised offer

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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

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Ops-driven developmentRelease 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 partnersIn 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 presentationsChanges in personalisation and impacts are shared each month in a company-wide presentation and weekly with company leadership

Maintaining alignmentBuilding a real personalised offer

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13

25

38

50

Open Rate CTOR Conversion Profit

ControlTest Marketing Definition

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Open RatesOpen rates increased 8% due to more relevant products in the subjectt line, improving deliverability

Profit per SendAs a result of the higher conversion and better targeting of high profit products, profit more than doubled per send

Click Through from Open RateCTOR increased 30-50% per market, driving a 60% growth in email traffic

Conversion from SendAs 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.

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Questions?

For further questions, please feel free to contact:

Rob Winters: [email protected]

Rob Winters - Travelbird

Data & Analytics

Transcript of Rob Winters - Travelbird