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.

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

3. Represent platform

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

30. Ad Banner Creator

31. Sample ad banners

33. Internal tools
Dashboard
Targeting finder

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