Audience Targeting


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the ins and outs of audience and behavioral targeting

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  • Welcome.Thank you for joining us today at the IAC Roundtable.You’re among a select few to preview an exciting new development we’ve been working on for the past year to better serve your clients. I’d like to stress that today is intended to be an open conversation so please don’t be shy with your feedback, questions, and concerns.Let’s get started.
  • Yahoo Ad Interest Manager, Google Dashboard.Bluekai sept08
  • Yahoo Ad Interest Manager, Google Dashboard.Bluekai sept08
  • Yahoo Ad Interest Manager, Google Dashboard.Bluekai sept08
  • Yahoo Ad Interest Manager, Google Dashboard.Bluekai sept08
  • Audience Targeting

    1. 1.<br />Turning Audience Targeting into Revenue<br />
    2. 2. Audience Targeting:Definition (an attempt)<br />Targeting: The process of identifying segments of similar users, and incorporating them into the ad delivery decision<br />Behavioral Targeting: The process of identifying patterns of user interactions, and incorporating them into the ad delivery decision<br />Interaction<br />Recency<br />Frequency<br />
    3. 3. Audience Targeting: A Brief History of Starts and Stops<br />Facebook Beacon<br />Google announces BT effort<br />DSP’s<br />emerge<br />AOL acquires Tacoda<br />BT technologies emerge<br />Engage announces user profile DB<br />Yahoo and Google launch privacy tools<br />BT networks emerge<br />FTC publishes guidelines<br />Data co’s. emerge<br />RMX<br />10<br />02<br />98<br />01<br />04<br />08<br />09<br />05<br />Privacy investigation<br />The Industry Icons<br />…issues “last chance” to regulate<br />Tacoda winds down BT software sales<br />…then shuts it down<br />DCLK halts BT<br />DCLK acquires Abacus<br />Senate inquiry of NebuAd<br />…eventually shutters<br />…eventually shutters<br />Gator changes name to Claria<br />…eventually shutters<br />
    4. 4. Why BT Exists: Advertiser POV<br />Proxies are expensive<br />Exhibit A: 67% of visitors are Women *<br />* Comscore August 2010<br />
    5. 5. Why BT Exists: Publisher POV<br />A small amount of inventory generates the majority of revenue<br />$<br />$<br />ergo…<br />A majority of inventory generates a small amount of revenue<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />$<br />
    6. 6. A typical publisher scenario<br />Price<br />Direct Sales<br />Fill by ad networks<br />Volume<br />
    7. 7. A better scenario<br />Price<br />Direct Sales<br />Audience-based selling<br />New Revenue<br />Fill by ad networks<br />Volume<br />
    8. 8. An even better scenario<br />Price<br />Direct Sales<br />Audience-based selling<br />New Revenue<br />Fill by ad networks<br />Volume<br />
    9. 9. Ad<br />Ad<br />Ad<br />The New Supply Chain<br />The traditional ecosystem<br />Ad Net<br />
    10. 10. Ad<br />Ad<br />Ad<br />Ad<br />Ad<br />The New Supply Chain<br />DSP and Exchanges Dynamic<br />The new ecosystem<br />Ad Net<br />SSP<br />Agency<br />DSP<br />Data Co.s<br />
    11. 11. The New Supply Chain<br />DSP and Exchanges Dynamic<br />The future ecosystem?<br />Agency<br />Ad<br />Pub<br />Ad server<br />DSP<br />Media Planners<br />Sales<br />Team<br />Quant Team<br />Quant<br />Team<br />Data<br />Co.<br />Exchange<br />Ad Net<br />
    12. 12. Value Propositions<br />Every advertiser seeks any of these five categories:<br />Brand Association<br />Contextual Relevance<br />Audience Targeting<br />Metric-driven Goal<br />Creative Execution<br />
    13. 13. User Data Types<br />and the Data Providers<br />Demographic<br />Psychographic<br />Shopping<br />Social<br />Search<br />Contextual / Semantic<br />Behavioral / Interest<br />
    14. 14. Targeting Models<br />
    15. 15. IAC’s Approach<br />
    16. 16. IAC At A Glance<br />
    17. 17. Mapping our Data to Sellable Segments<br />Map these interactions…………………………………………………..…………to these attributes<br />View content<br />View product information<br />View ad<br />Click on ad<br />Purchase something<br />Search for something<br />Attend an event<br />Provide information about themselves<br />Interest<br />Life-stage<br />Lifestyle<br />Intent<br />Behavior<br />Demography<br />
    18. 18. Mapping our Data to Sellable Segments<br /> Turn these attributes…………………………..…………….into targets<br />Interest<br />Life-stage<br />Lifestyle<br />Intent<br />Behavior<br />Demography<br />Active Travelers<br />Affluents<br />
    19. 19. Mapping our Data to Sellable Segments<br />Package targets for advertisers<br />Active Travelers<br />Affluents<br />
    20. 20. Media Inventory Options<br />Audience Extension<br />Targeted Media<br />Site-Specific Inventory<br />Large Reach Vehicles<br />Verticals<br />Leveraging IAC’s O&O data across all IAC Properties<br />Sold by dedicated sales teams. Only select inventory available on non-guaranteed basis.<br />Reaches all of IAC’s brands and users.<br />Reaches IAC’s users anywhere on the Web<br />High volume content channels. Overlaying targeting is available.<br />Lifestyle<br />Run of<br />Active Shoppers<br /> Sports Fans<br />House<br />&<br />Home<br />Movie Fans<br />Enter-tainment<br />Parents<br />Personals<br />Affluents<br />
    21. 21. Cube Targeting Methodology<br />Analyzing multiple dimensions of attributes to achieve the highest level of insight into audience profiles<br />Declared Demographics<br />Interest<br />Behavior<br />Shopping Patterns<br />If consumers are multi-dimensional, then our targeting should be too<br />
    22. 22. Data as the Glue Across Properties<br />
    23. 23. Data Flow<br />DemographicData<br /><br />Site Data<br />Site Data<br />Site Data<br />Shopping History<br /><br />Browsing Patterns<br />Citysearch<br />
    24. 24. Data Flow<br />DemographicData<br /><br />Site Data<br />Site Data<br />Site Data<br />Shopping History<br /><br />Unified Database<br />Browsing Patterns<br />Citysearch<br />
    25. 25. Data Flow<br />Behavioral Logic and Segmentation<br />DemographicData<br /><br />Site Data<br />Site Data<br />Ad Engine<br />Makes delivery decision<br />Site Data<br />Ticket purchases<br />Ticketmaster<br />Unified Database<br />Targeted Ad<br />Browsing Patterns<br />Citysearch<br />Audience Cubes<br />
    26. 26. Tools<br /><ul><li>Ad server
    27. 27. Segmentation
    28. 28. Web Analytics
    29. 29. Data Warehouse
    30. 30. Audience Management
    31. 31. Reporting / BIRT</li></li></ul><li>What We’ve Learned<br />
    32. 32. Opposing Forces We Have to Tackle<br />Declared vs. Observed<br />Reach vs. Accuracy<br />Standard vs. Custom<br />Context vs. Audience<br />Dedicated vs. Integrated<br />Incumbent vs. Newcomber<br />
    33. 33. The problem with centralization<br /> So. Many. Products.<br />Head. Will. Explode…<br />
    34. 34. Pricing Strategy is Never Perfect at Launch<br />We started pricing near premium offerings<br />Adjusted prices after market feedback, observing volume<br />
    35. 35. Questions Pubs should ask themselves<br />How diverse is our audience?<br />Who do our clients want to reach?<br />What are we in short supply of?<br />How well do we know our audience?<br />Can we operationalize this?<br />What technology do we need?<br />What is the size of the opportunity?<br />
    36. 36. What is The Ad Ops Role in this New Era?<br />
    37. 37. Weigh in on the Strategy<br />Every strategy requires a different operational plan<br />
    38. 38. Audience-based Selling Requires a Modified DNA<br />Cookie-matching<br />User overlap<br />Cookie deletion<br />pixels<br />Look-alikes<br />Recency<br />Data modeling<br />Frequency<br />Semantic<br />Remarketing<br />Segment membership<br />Social graph<br />Cookie pools<br />
    39. 39. An Operational Plan is Integral to a Go-to-Market Plan<br /><ul><li>Execution
    40. 40. Make sure trafficking workflow syncs with systems’ integration
    41. 41. Standardization is important
    42. 42. Understand new limitations</li></li></ul><li>An Operational Plan is Integral to a Go-to-Market Plan<br /><ul><li> Inventory Management
    43. 43. Inventory lags targeting
    44. 44. Assess overlap
    45. 45. Adjust and articulate the margin of error
    46. 46. Take command of both UV’s and Imps
    47. 47. Flexibility begets complexity</li></li></ul><li>An Operational Plan is Integral to a Go-to-Market Plan<br /><ul><li>Productization
    48. 48. Level of targeting must align with sales strat
    49. 49. Determine impacts to order management
    50. 50. Make sure pricing fits with existing products</li></li></ul><li>Be Prepared to Execute Several Models<br />O&O Media<br />Aggregating your own media and data assets to create a Publisher-owned ad network<br />1<br />O&O Data<br />Selling your data assets into closed and open marketplaces<br />O&O Data<br />2<br />O&O Data<br />Non-O&O Media<br />Using your data assets, sell targeted media from anywhere on the Web<br />3<br />
    51. 51. What’s Next…and beyond<br />
    52. 52. Personalization<br /><ul><li>Opportunity for DSP’s
    53. 53. Amazon, eBay</li></li></ul><li>The Social Graph<br /><ul><li>Influencers
    54. 54. Connections
    55. 55. Conversations
    56. 56. Degrees</li></li></ul><li>Video and TV<br /><ul><li>Voice Recognition
    57. 57. Using Closed Captioning
    58. 58. Semantic modeling
    59. 59. Set-top box</li></ul>Source TNS Infosys TV, Simulmedia<br />
    60. 60. Privacy<br /><ul><li>Ad Notice and Ad Choices
    61. 61. Possible government intervention
    62. 62. Consumers get smarter</li></li></ul><li>Thankyou,<br />and don’t delete your cookies.<br />Ali C. Mirian<br />VP, Product and Technology<br />IAC<br /><br />