Andrej Pancik - Scaling e-commerce with marketing automation
2 likes2,636 views
1. The document discusses custom Facebook marketing automation for scaling e-commerce. It describes challenges with existing tools, like bad data, lack of the right data metrics, and slow optimization processes. 2. Custom automation was built on top of the Facebook API to automate tasks like A/B testing, scheduling, rules, and templates. This helped address issues and guide bidding and spending more effectively. 3. Sample setups and tools are shown, including ad banner creation, targeting tools, and dashboards. In summary, custom automation was necessary due to limitations of existing tools for optimizing complex e-commerce campaigns at scale.
Andrej Pancik - Scaling e-commerce with marketing automation
- 1. Scaling e-commerce with custom Facebook marketing automation Andrej Pancik
- 2. My background Some marketing A data management platform for ads A lot of marketing
- 4. How do we make money? Margin for the creatorManufacturing Work
- 5. How do we make money? This is how we operate
- 6. How do we make money? This is how traditional e-commerce operates Margin for the creator
- 7. How do we make money? That is like our white glove marketing services
- 8. How do we make money? But usually it is this
- 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. Venn diagrams of our audiences Influencer #1 audience Influencer #2 audience Influencer #3 audience
- 11. Venn diagrams of our audiences
- 12. Venn diagrams of our audiences
- 13. Why custom ads automation?
- 14. Not a reason A/B testing of ads and audiences Scheduling Rules Templates
- 15. 1. We had bad data We can't be investing a lot of money
- 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. 2. We didn't have the right data We wanted actionable data points that would guide our bidding and spending1. 2.
- 18. This is how I imagined the click cost distribution to be like Bid Volume
- 19. 50% This is how I imagined the click cost distribution to be like Bid Volume Your bid
- 20. What it is really like Bid Volume
- 21. 70% What it is really like Volume Bid Your bid
- 22. It's about the absolute volume you can capture Bid Volume Sometimes good enough A budget of the big company
- 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. 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. 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. 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. Why custom automation? 1. We had bad data 2. We didn't have the right data 3. We were just slow
- 28. Our custom tooling on top of Facebook API
- 29. Public tools Ad banner creator Facebook Ad launcher
- 36. When using Facebook API – You are on your own.
- 38. Summary
- 39. 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
- 40. Good luck! Follow me at @AndrejPancik