Building a Personalized Offer Using Machine Learning
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How TravelBird moved from generic to fully personalized emails in a few months using open source technologies.
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Building a Personalized Offer Using Machine Learning
- 1. Building a Personalised Offering 21 September 2016
- 2. 2 George Herber Project Manager, Analytics at TopBI Rob Winters Head of BI, TravelBird Today’s presentersBuilding a real personalised offer Together we bring 50 year’s experience across BI, analytics, and data science
- 3. 3 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
- 4. 4 Three 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
- 5. 5 First challenge: What data do you need? Building a real personalised offer
- 6. 6 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
- 7. 7 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
- 8. 8 Recommendations will be quite similar Denmark Germany Long-haul trips: (Cuba, Nepal, USA, Iceland, Morocco) Building a real personalised offer
- 9. 9 So customers will get thisBuilding a real personalised offer
- 10. 10 Building a real personalised offer And don’t models and data influence each other?
- 11. 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
- 12. What’s more critical, accurate or interpretable? Building a real personalised offer
- 13. 13 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 Delivering +12% opens, +45% clicks, and +22% profit
- 14. 14 Questions? For further questions, please feel free to contact: George: george.herber@topbi.nl Rob: wintersrd@gmail.com