building a personalized offer using machine learning
TRANSCRIPT
Building a Personalised Offering
21 September 2016
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George HerberProject Manager, Analytics at TopBI
Rob WintersHead of BI, TravelBird
Today’s presentersBuilding a real personalised offer
Together we bring 50 year’s experience across
BI, analytics, and data science
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700K emails sent to NL recipients (3,5M
across EU)
Every person gets the same 6 offers
Goal: 100% personalisation
TravelBird: Six daily dealsBuilding a real personalised offer
4Three objectives for personalisation
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
5First challenge: What data do you need?Building a real personalised offer
6TravelBird’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
7Fed 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
8Recommendations will be quite similar
Denmark
Germany
Long-haul trips:
(Cuba, Nepal, USA, Iceland,
Morocco)
Building a real personalised offer
9So customers will get thisBuilding a real personalised offer
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Building a real personalised offer
And don’t models and data influence each other?
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%
So we diversify: Offer similarity
Distance metrics: Canberra, Cosine, Great-
circle, …
Building a real personalised offer
What’s more critical, accurate or interpretable?Building a real personalised offer
13In 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
Delivering +12% opens, +45% clicks, and +22% profit
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Questions?
For further questions, please feel free to contact:
George: [email protected]
Rob: [email protected]