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A Case for Social Commerce:
Driving ROI through Social Graph Data
Agenda


• Open Graph Primer: Explicit Social Truths

• Case Study: 1-800-Flowers.com




                            22
Open Graph Primer:
Explicit Social Truths
What is possible at the convergence?

      • “Re-Imagination of Nearly Everything”


                          APIs
                          (Platforms)




            “Connected                   UX
            Data” (CRM)                 (Beauty)




             *Mary Meeker Internet Trends Report
Companies re-imagining everything.
Have a distinctly social and personalized feel.
Personalization has been around for awhile.

 • So what has social brought?
3 explicit social truths.

 • Names
 • Faces
 • Friends
Examples of Onsite Social Personalization: Travel
Examples of Onsite Social Personalization: Rentals
Examples of Onsite Social Personalization: Media
Examples of Onsite Social Personalization: Media
Examples of Onsite Social Personalization: Cause
Examples of Onsite Social Personalization: Cause
Examples of Onsite Social Personalization: Cause


 • More than $300 million dollars pledged
 • Over 30,000 successfully funded projects.
Examples of Email Social Personalization

 Friends’ recent activity with   Subject line customized
 commerce call to action         with friends’ names




                                  16
                                 16
Examples of Onsite Social Personalization: Retail
Names, Faces and Friends are made possible by:


                Facebook’s
                Open Graph
Big Channel

• Why you Like the Open Graph.
Big Data

• Why you also Like the Open Graph.
Actionable
 Move from broad conversation and sentiment analytics to more
      actionable customer level social data and analytics.
First Wave of Social...

 • Push phenomena
Second Wave… Existentialist…

• Pull phenomena
Social Graph Data
   Leveraging social graph data, product adoption can increase by up to 400%.
FB Historical Recap



       2007                       2010                          2012
    FB Platform               FB Open Graph                    FBX Ad
     Release                     Release                      Exchange

   Social Strategies


     Earned            Facebook               Open Graph           Paid Media
   Media / Apps        Commerce



     Leverage apps,     In News Feed             Customer’s           Facebook
     polls, posts to   Purchasing, fan        Facebook network    Retargeting, Spon
    engage audience    page commerce            utilized across     sored Stories
   on Facebook Page                            media channels




                                         25
Moving Forward…



     2012 and Beyond

   Social Strategies


     Earned Media              Open Graph             Paid Media


                                              

         Leverage                 Customer’s             Facebook
     apps, polls, posts        Facebook network      Retargeting, Spon
        to engage                        
                                 utilized across       sored Stories
       audience on              media channels
      Facebook Page




                                           26
Case Study:
1-800-Flowers.com
Smiles design and delivered.

• 1-800-Flowers.com is actually in the business of
  delivering smiles.




                      =
                        28
Justin Bieber Deluxe Totebag.

• Social connections – between
  friends, couples, coworkers – drive gift giving in
  all its forms…




                         29
Case Study
Background:
 1-800-Flowers.com was underwhelmed by
 Facebook Marketing results. They scaled
 their Facebook page to hundreds of
 thousands of Likes, built and deployed
 apps on their fan page and executed
 Facebook paid media. All with minimal
 impact on sales. They expected more.

Solution:
• Social Graph data capture through Social
  Emails
• Social Graph data capture through Social
  Sign On
• Social and CRM audience linking to power
  hyper-targeted Facebook Media
• Integrate Customer’s Facebook Network
  into the Shopping Experience


                                             30
Process: Creating Connections


1800Flowers.com           1800Flowers.com              Customer's
  Site or Email              Customer                   Friends




• Onsite Social App         •   Likes and Interests   • Common Likes
• Connection Experience     •   Demographics          • Close friends
• Value Exchange            •   Product Likes         • Influencers
                            •   Shares and Comments

• Audience                  • Activation              • Amplification
                                  31
Sample Flow - Social Emails

HTML Snippet in emails                                   • Ongoing messaging
power Social Graph Data                                  • CRM support &
Capture                                                    integration




                          • Fair value-exchange to
                            capture social crm profile
                          • A/B test permissions
1 Authentication ≈ 250 Social CRM User Profiles

 • FB Permissioning   Social:
                      • Gender: Male
                      • Age: 28
                      • Location: Chicago
                      • Brand Likes: ESPN, Adidas, Canon, Nike
                      • Likes & Interests: Football, basketball,
                        running, Chicago Bears
                      • Friends: Photos, Number of Friends, Names,
                        Birthday
                      • Open Graph Likes: Jeans, Shoes, Apparel

                       CRM:
                       • Email: High Email Engager
                       • Customer Segment: High Value

                             33
Results

Challenge:
Integrate connected-user
insights into email program
to drive higher engagement
and incremental sales

Solution:
Social Email integration &
personalization



 Results:
  Social Email drove 10X higher consumer
  engagement than standard product based
  newsletters
                                 34
Social Sign On




          Social Sign On widget for
          newsletter sign up
Facebook UIDs and Lookalike Targeting

                       Valuable Facebook
                         user and friend
                      profile data collected
                         by Social Amp

                                                       UIDs
                                               All your open graph data

                        High Value Social      • UID
                           Consumer            • Lookalike
                          • Likes H&M
                                                 retargeting
                          • 24 years old
                                               • Influencer targeting
                          • NYC                • Map to
                          • 400-500 friends      transactions/ROI
                          • Single
                          • High LTV



                                                Facebook Clusters

                                                  Facebook Sponsored Stories
                                                  and Marketplace Ads
Analytics Use Cases (non-CRM enabled)

 Social Analytics (non-CRM enabled)
    • Social Audience Profiling
        • Demographics (age, gender, location etc.)
        • Social Audience Segmentation (e.g. Fitness
          Fanatics, Skiers, Traveling Socialites)
        • Competitive Brand Analysis
        • Social Funnel Analysis
            • Frequency / recency of product or comparable product
              liked by user
            • Type of engagement on comparable products
              (like, want, “OG” verb)
Analytics Use Cases (CRM enabled)

 Social Analytics Tier (CRM enabled)
    • Influence analysis
        • Based on friends of users who have purchased or liked after
          the user
        • Profile analysis of influencers (demo, interests, activities)
    • Social CRM Analysis
        • Customer analysis overlaying social segments (e.g. Fitness
          Fanatics, Skiers, Traveling Socialites) with purchase history
          and loyalty
        • Social Profile analysis of high value or LTV audience segments
          (demo, interests, activities)
Lesson 1: Don’t Treat Social in a Silo
                              90% of the data that exists in the world                  It is projected that the amount of data
                               has been created in the past 2 years                     we create will grow by 40% each year




                                                                                                   Availability
                                                                                      Voice/
                                                                                      video




                                                                                                                                    Petabytes
                                                                             Social




                                                                                                               Knowledge
                                                                             data




                                                                                                                  gap
                                                                   Web log




                                                                                                                                    Terabytes
                                                                   data




                                                                                                                           Volume
                                                       Email and
                                                       text data




                                                                                                                                    Gigabytes
                                       CRM and
                                       enhanced data
                                                                                                   Usability




                                                                                                                                    Megabytes
                 Media data



  Transactions



                                                            39
Lesson 2: Plan for the Future State of CRM




                        40
Lesson 3: Trial Sponsored Stories

1      Kristen loves or                         3          Gianna loves and/or
       reviews a product                                   buys…
       integrated with Open                                Paid Media amplifies
       Graph app on VS.com                                 the action once more.



            1 action ≈ 250 unique FB
               sponsored stories
                              Paid Media to amplify action
                              to friends and friends of friends
       2         Gianna sees Kristen’s action on
                 Facebook as sponsored media




                                    41

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Merkle - iStrategy Chicago

  • 1. A Case for Social Commerce: Driving ROI through Social Graph Data
  • 2. Agenda • Open Graph Primer: Explicit Social Truths • Case Study: 1-800-Flowers.com 22
  • 4. What is possible at the convergence? • “Re-Imagination of Nearly Everything” APIs (Platforms) “Connected UX Data” (CRM) (Beauty) *Mary Meeker Internet Trends Report
  • 6. Have a distinctly social and personalized feel.
  • 7. Personalization has been around for awhile. • So what has social brought?
  • 8. 3 explicit social truths. • Names • Faces • Friends
  • 9. Examples of Onsite Social Personalization: Travel
  • 10. Examples of Onsite Social Personalization: Rentals
  • 11. Examples of Onsite Social Personalization: Media
  • 12. Examples of Onsite Social Personalization: Media
  • 13. Examples of Onsite Social Personalization: Cause
  • 14. Examples of Onsite Social Personalization: Cause
  • 15. Examples of Onsite Social Personalization: Cause • More than $300 million dollars pledged • Over 30,000 successfully funded projects.
  • 16. Examples of Email Social Personalization Friends’ recent activity with Subject line customized commerce call to action with friends’ names 16 16
  • 17. Examples of Onsite Social Personalization: Retail
  • 18. Names, Faces and Friends are made possible by: Facebook’s Open Graph
  • 19. Big Channel • Why you Like the Open Graph.
  • 20. Big Data • Why you also Like the Open Graph.
  • 21. Actionable Move from broad conversation and sentiment analytics to more actionable customer level social data and analytics.
  • 22. First Wave of Social... • Push phenomena
  • 24. Social Graph Data Leveraging social graph data, product adoption can increase by up to 400%.
  • 25. FB Historical Recap 2007 2010 2012 FB Platform FB Open Graph FBX Ad Release Release Exchange Social Strategies Earned Facebook Open Graph Paid Media Media / Apps Commerce Leverage apps, In News Feed Customer’s Facebook polls, posts to Purchasing, fan Facebook network Retargeting, Spon engage audience page commerce utilized across sored Stories on Facebook Page media channels 25
  • 26. Moving Forward… 2012 and Beyond Social Strategies Earned Media Open Graph Paid Media   Leverage Customer’s Facebook apps, polls, posts Facebook network Retargeting, Spon to engage  utilized across sored Stories audience on media channels Facebook Page 26
  • 28. Smiles design and delivered. • 1-800-Flowers.com is actually in the business of delivering smiles. = 28
  • 29. Justin Bieber Deluxe Totebag. • Social connections – between friends, couples, coworkers – drive gift giving in all its forms… 29
  • 30. Case Study Background: 1-800-Flowers.com was underwhelmed by Facebook Marketing results. They scaled their Facebook page to hundreds of thousands of Likes, built and deployed apps on their fan page and executed Facebook paid media. All with minimal impact on sales. They expected more. Solution: • Social Graph data capture through Social Emails • Social Graph data capture through Social Sign On • Social and CRM audience linking to power hyper-targeted Facebook Media • Integrate Customer’s Facebook Network into the Shopping Experience 30
  • 31. Process: Creating Connections 1800Flowers.com 1800Flowers.com Customer's Site or Email Customer Friends • Onsite Social App • Likes and Interests • Common Likes • Connection Experience • Demographics • Close friends • Value Exchange • Product Likes • Influencers • Shares and Comments • Audience • Activation • Amplification 31
  • 32. Sample Flow - Social Emails HTML Snippet in emails • Ongoing messaging power Social Graph Data • CRM support & Capture integration • Fair value-exchange to capture social crm profile • A/B test permissions
  • 33. 1 Authentication ≈ 250 Social CRM User Profiles • FB Permissioning Social: • Gender: Male • Age: 28 • Location: Chicago • Brand Likes: ESPN, Adidas, Canon, Nike • Likes & Interests: Football, basketball, running, Chicago Bears • Friends: Photos, Number of Friends, Names, Birthday • Open Graph Likes: Jeans, Shoes, Apparel CRM: • Email: High Email Engager • Customer Segment: High Value 33
  • 34. Results Challenge: Integrate connected-user insights into email program to drive higher engagement and incremental sales Solution: Social Email integration & personalization Results: Social Email drove 10X higher consumer engagement than standard product based newsletters 34
  • 35. Social Sign On Social Sign On widget for newsletter sign up
  • 36. Facebook UIDs and Lookalike Targeting Valuable Facebook user and friend profile data collected by Social Amp UIDs All your open graph data High Value Social • UID Consumer • Lookalike • Likes H&M retargeting • 24 years old • Influencer targeting • NYC • Map to • 400-500 friends transactions/ROI • Single • High LTV Facebook Clusters Facebook Sponsored Stories and Marketplace Ads
  • 37. Analytics Use Cases (non-CRM enabled) Social Analytics (non-CRM enabled) • Social Audience Profiling • Demographics (age, gender, location etc.) • Social Audience Segmentation (e.g. Fitness Fanatics, Skiers, Traveling Socialites) • Competitive Brand Analysis • Social Funnel Analysis • Frequency / recency of product or comparable product liked by user • Type of engagement on comparable products (like, want, “OG” verb)
  • 38. Analytics Use Cases (CRM enabled) Social Analytics Tier (CRM enabled) • Influence analysis • Based on friends of users who have purchased or liked after the user • Profile analysis of influencers (demo, interests, activities) • Social CRM Analysis • Customer analysis overlaying social segments (e.g. Fitness Fanatics, Skiers, Traveling Socialites) with purchase history and loyalty • Social Profile analysis of high value or LTV audience segments (demo, interests, activities)
  • 39. Lesson 1: Don’t Treat Social in a Silo 90% of the data that exists in the world It is projected that the amount of data has been created in the past 2 years we create will grow by 40% each year Availability Voice/ video Petabytes Social Knowledge data gap Web log Terabytes data Volume Email and text data Gigabytes CRM and enhanced data Usability Megabytes Media data Transactions 39
  • 40. Lesson 2: Plan for the Future State of CRM 40
  • 41. Lesson 3: Trial Sponsored Stories 1 Kristen loves or 3 Gianna loves and/or reviews a product buys… integrated with Open Paid Media amplifies Graph app on VS.com the action once more. 1 action ≈ 250 unique FB sponsored stories Paid Media to amplify action to friends and friends of friends 2 Gianna sees Kristen’s action on Facebook as sponsored media 41

Hinweis der Redaktion

  1. Names – Absolute IdentificationFaces – Emotive, influential and more of an ID than even the name. Milan Kundera, with the unrepeatable combination of features, “The serial number of the human specimen is the face.”Friends – Story of how value social graph is. 1) VC story, will always retain the social graph. 2) Friend’s age is extremely high predictor of your age 96% confidence level.
  2. Names – Absolute IdentificationFaces – Emotive, influential and more of an ID than even the name. Milan Kundera, with the unrepeatable combination of features, “The serial number of the human specimen is the face.”Friends – Story of how value social graph is. 1) VC story, will always retain the social graph. 2) Friend’s age is extremely high predictor of your age 96% confidence level.