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Greenhouse Group presentation for Snowplow Amsterdam Meetup #3

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Ruben Mak, Team Lead Data Science at Greenhouse Group showed a live demo of a light-weight recommendation algorithm for banner ads, built using Snowplow, which measures viewable time and optimizes dynamic content. The demo is aimed at challenging people to think in a different way about retargeting and making offers more personally relevant.
He also mentioned Eneco, one of the Netherlands’ largest energy providers, as an example of how they optimise their marketing communications across the customer journey. Presented on 5 April 2017.

Published in: Data & Analytics
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Greenhouse Group presentation for Snowplow Amsterdam Meetup #3

  1. 1. DATA HUB MARKETING AGENCIES TECH HUB CREATIVE HUB
  2. 2. DATA HUB MARKETING AGENCIES TECH HUB CREATIVE HUB
  3. 3. DEMO bit.ly/foonhouse
  4. 4. COLLECT EXTRACT ACT Data What can we know about the consumer? Insights What does the consumer wants to achieve? Accordingly How can we help the consumer achieve this?
  5. 5. COLLECT EXTRACT ACT 2 1 3
  6. 6. EXTRACT1
  7. 7. INNOVATION & PRIVACY
  8. 8. Campaign optimisation Across the full customer journey
  9. 9. CORPORATE CONTENT IN JOURNEY?
  10. 10. COLLECT2
  11. 11. DATALAYER
  12. 12. DATALAYER
  13. 13. DATA ENTERED 0 RETRIES NUMBER CHANGED CLICKED [+] ONCE HOUSETYPE CHANGED 3X POSSIBLE CONFUSING ICON? TOTAL TIME SPENT IN BANNER: 34s IDLE TIME: 7s, ON HOUSETYPE TOTAL: 2
  14. 14. DEMO DATA LOGGING
  15. 15. ACT3
  16. 16. PERSONALISATION
  17. 17. AND MORE DMP
  18. 18. DAG 1 DAG 2 DAG 3 No relevant new offer = No ad
  19. 19. DEMO bit.ly/relevantads
  20. 20. COLLECT EXTRACT ACT DMP DATA LAYER On-site personalisation

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