Ogsf mon 1430 nathan luman
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Zappos has driven over $8 million in traffic to their website through Facebook ads since 2010. They aim to reduce dependency on large direct marketing channels and acquire new customers through Facebook's large display network. Their attribution model compares metrics like first click and last touch conversions. They focus on core demographics and broad appeal categories in year one, then expand targets in year two. Ongoing refinement of landing pages and predictive modeling helps optimize the strategy over time.
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Ogsf mon 1430 nathan luman
- 1. Zappos Drives em in with Facebook Ads social@zappos.com @ripcityrhapsody 1
- 2. Hello! 2
- 3. 3
- 4. 4
- 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
- 6. Why? • Reduce dependency on large direct marketing channels 6
- 7. 7
- 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
- 11. 11
- 12. Measuring Success • Secondary conversions – Shares – Brand Like – Post-purchase share • Share attribution 12
- 13. Click Post Click Like -> Post • Are your measurement tactics undervaluing your facebook efforts? 13
- 14. Year One • Understand your core demographic targeting – Common interests of brand fans – Use existing market studies 14
- 15. 15
- 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
- 17. Landing page - “The core and a little more” 17
- 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
- 22. Landing Page Refinement 22
- 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
- 25. Thank You • social@zappos.com • @ripcityrhapsody • http://www.zapposinsights.com/ • http://vegastech.com/ 25
Editor's Notes
- Breakdown of how long it takes for ads to convert
- 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