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Andrej Pancik - Scaling e-commerce with marketing automation

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Marketing Festival 2016

Published in: Marketing
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Andrej Pancik - Scaling e-commerce with marketing automation

  1. 1. Scaling e-commerce with
 custom Facebook marketing automation Andrej Pancik
  2. 2. My background Some marketing A data management platform for ads A lot of marketing
  3. 3. Represent platform
  4. 4. How do we make money? Margin for the creatorManufacturing Work
  5. 5. How do we make money? This is how we operate
  6. 6. How do we make money? This is how traditional e-commerce operates Margin for the creator
  7. 7. How do we make money? That is like our white glove marketing services
  8. 8. How do we make money? But usually it is this
  9. 9. Who are our users? We are a two sided marketplace One side is influencers like • Celebrities • People active in their community The other side is their buyer audiences
  10. 10. Venn diagrams of our audiences Influencer #1 audience Influencer #2 audience Influencer #3 audience
  11. 11. Venn diagrams of our audiences
  12. 12. Venn diagrams of our audiences
  13. 13. Why custom ads automation?
  14. 14. Not a reason A/B testing of ads and audiences Scheduling Rules Templates
  15. 15. 1. We had bad data We can't be investing a lot of money
  16. 16. 1. We had bad data Pixels reports – 15%+ error No Life Time Value tracking Not appreciating business nuances Data all over the place
  17. 17. 2. We didn't have the right data We wanted actionable data points that would guide our bidding and spending1. 2.
  18. 18. This is how I imagined the click cost distribution to be like Bid Volume
  19. 19. 50% This is how I imagined the click cost distribution to be like Bid Volume Your bid
  20. 20. What it is really like Bid Volume
  21. 21. 70% What it is really like Volume Bid Your bid
  22. 22. It's about the absolute volume you can capture Bid Volume Sometimes good enough A budget of the big company
  23. 23. 2. We didn't have the right data We wanted to estimate for each audience • The optimal long-term bidding strategy • Its true addressable size, and overlaps • Product affinities 1. 2.
  24. 24. 3. We were just slow Many products/audiences › many campaigns › a lot of mistakes Optimizing existing campaigns Exploring new opportunities Traditional rules not sufficient Traditional templates not sufficient 1. 2. 3.
  25. 25. Sample setup Creatives • set for newsfeed • set for sidebar Campaign setup (all dark posting) • Conversions for 300k+ audience • Clicks + CPC • Engagement – with direct CTA • Retargeting + CPM 1. 2. 3.
  26. 26. Sample setup #2 Audience tests & research 1. Owned pages data 2. Interest targeting 3. Past website data 4. Custom audiences 5. Lookalikes 6. Behavior on site Setup pixels Setup tracking urls Budget / Optimization • Initial $10 per ad set (rigorously tested) • Quick scaling up • Frequency cut-off • End of campaign/relaunch 1. 2. 3.
  27. 27. Why custom automation? 1. We had bad data 2. We didn't have the right data 3. We were just slow
  28. 28. Our custom tooling on top of Facebook API
  29. 29. Public tools Ad banner creator Facebook Ad launcher
  30. 30. Ad Banner Creator
  31. 31. Sample ad banners
  32. 32. Internal tools Dashboard Targeting finder
  33. 33. When using Facebook API – You are on your own.
  34. 34. Summary
  35. 35. TL; DR We did custom automation because it was • difficult to get the ground truth to existing tools • difficult to optimize bidding and spend with existing tools • still time consuming even with some automation We built our own stack on top of Facebook API to automate parts we didn't find elsewhere – it was tough but it worked
  36. 36. Good luck! Follow me at @AndrejPancik

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