5. Quick Zappos FB Ads History
• Summer 2010
• Key Milestones
– >$1k /day October 2010
– ~$20k days December 2010
– ~$70k days November/December 2011
• ~$8M spend and 99% has driven traffic to
Zappos.com
5
8. Why?
• Reduce dependency on large direct marketing
channels
• New customer acquisition
• Over ¼ US display impressions
• Feed the funnel
8
9. Attribution Model Comparison
0% 2% 4% 6% 8% 10% 12% 14%
First Only
Prefer First
Distribute Evenly
U-Shaped
Prefer Last
Last Touch
9
10. Measuring Success
• Patience
• Realistic ROI expectations
• Will not compare to your mature, demand-
capture channels
– Not search
• What are your ROI goals on non-branded
search head terms?
• Cookie duration
10
16. Year One
• Core demographic targeting
– Common interests of brand fans
– Used existing market studies from our brand team
• Promote brands/categories with broadest
appeal
• Leverage signals
• Landing page - “The core and a little more”
16
18. Year One
• Core demographic targeting
– Common interests of brand fans
– Used existing market studies from our brand team
• Promote brands/categories with broadest
appeal
• Leverage signals
• Landing page - “The core and a little more”
• Predictive lifetime ROI
18
19. Year Two
• Larger targets
– Lower CPCs
– Longer lives
– Higher CTR
19
20. • 50 Million • 2 Million
• Cloned • Cloned
• 2x CTR
• ½ CPC
20
21. Year Two
• Larger targets
– Lower CPCs
– Longer lives
– Higher CTR**
• When > Who
– Time of day stronger influence than target
– Day of Week
• Continued landing page refinement
21
23. Year Two
• Larger targets
– Lower CPCs
– Longer lives
– Higher CTR**
• When > Who
– Time of day stronger influence than target
– Day of Week
• Continued landing page refinement
• Predictive modeling- how to follow natural weekly
traffic trend
• New customer acquisition
23
24. What’s next?
• Target discovery
– Display Networks
– Zappos.com visitors
• Close loop between what people like, what
they buy, inform ad creative
• Contextual relevance
24
We found that customers arriving to our site directly from facebook, are more likely to share/like than average customerFb thinks these are primary
1/3 profiles public, reveal likes
Predictiveroi: since camps don’t last nearly as long as they do in search, you don’t have as much time to let something run to collect enough data to make decisions.
Predictiveroi: since camps don’t last nearly as long as they do in search, you don’t have as much time to let something run to collect enough data to make decisions.
Year 1 was the year of “can we make this work?”The growth of our spend that year indicate that we did get it to work. But the biggest concern going forward was how do we get this to scale.With minimal upkeep, we were getting some campaigns to run longer than 3 months. Sacrifice a little on ROI for evergreen campaigns.Lp testing: reducing bounce rate x%Predictive modeling. Do we follow the natural weekly trend of traffic? Certain days organically get more impressions. Expansion of lifetime roi modeling, but used it to predict next day revenue and how much we could make if we spent $x more that day. Also took into account that lifetime roi was different by day of the week of click
Year 1 was the year of “can we make this work?”The growth of our spend that year indicate that we did get it to work. But the biggest concern going forward was how do we get this to scale.With minimal upkeep, we were getting some campaigns to run longer than 3 months. Sacrifice a little on ROI for evergreen campaigns.Lp testing: reducing bounce rate x%Predictive modeling. Do we follow the natural weekly trend of traffic? Certain days organically get more impressions. Expansion of lifetime roi modeling, but used it to predict next day revenue and how much we could make if we spent $x more that day. Also took into account that lifetime roi was different by day of the week of click
Landing page image here
Year 1 was the year of “can we make this work?”The growth of our spend that year indicate that we did get it to work. But the biggest concern going forward was how do we get this to scale.With minimal upkeep, we were getting some campaigns to run longer than 3 months. Sacrifice a little on ROI for evergreen campaigns.Lp testing: reducing bounce rate x%Predictive modeling. Do we follow the natural weekly trend of traffic? Certain days organically get more impressions. Expansion of lifetime roi modeling, but used it to predict next day revenue and how much we could make if we spent $x more that day. Also took into account that lifetime roi was different by day of the week of click
