the ins and outs of audience and behavioral targeting

1. www.iacadvertising.com Turning Audience Targeting into Revenue

2. Audience Targeting:Definition (an attempt) Targeting: The process of identifying segments of similar users, and incorporating them into the ad delivery decision Behavioral Targeting: The process of identifying patterns of user interactions, and incorporating them into the ad delivery decision Interaction Recency Frequency

3. Audience Targeting: A Brief History of Starts and Stops Facebook Beacon Google announces BT effort DSP’s emerge AOL acquires Tacoda BT technologies emerge Engage announces user profile DB Yahoo and Google launch privacy tools BT networks emerge FTC publishes guidelines Data co’s. emerge RMX 10 02 98 01 04 08 09 05 Privacy investigation The Industry Icons …issues “last chance” to regulate Tacoda winds down BT software sales …then shuts it down DCLK halts BT DCLK acquires Abacus Senate inquiry of NebuAd …eventually shutters …eventually shutters Gator changes name to Claria …eventually shutters

4. Why BT Exists: Advertiser POV Proxies are expensive Exhibit A: 67% of iVillage.com visitors are Women * * Comscore August 2010

5. Why BT Exists: Publisher POV A small amount of inventory generates the majority of revenue $ $ ergo… A majority of inventory generates a small amount of revenue $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $

6. A typical publisher scenario Price Direct Sales Fill by ad networks Volume

7. A better scenario Price Direct Sales Audience-based selling New Revenue Fill by ad networks Volume

8. An even better scenario Price Direct Sales Audience-based selling New Revenue Fill by ad networks Volume

9. Ad Ad Ad The New Supply Chain The traditional ecosystem Ad Net

10. Ad Ad Ad Ad Ad The New Supply Chain DSP and Exchanges Dynamic The new ecosystem Ad Net SSP Agency DSP Data Co.s

11. The New Supply Chain DSP and Exchanges Dynamic The future ecosystem? Agency Ad Pub Ad server DSP Media Planners Sales Team Quant Team Quant Team Data Co. Exchange Ad Net

12. Value Propositions Every advertiser seeks any of these five categories: Brand Association Contextual Relevance Audience Targeting Metric-driven Goal Creative Execution

13. User Data Types and the Data Providers Demographic Psychographic Shopping Social Search Contextual / Semantic Behavioral / Interest

14. Targeting Models

15. IAC’s Approach

16. IAC At A Glance

17. Mapping our Data to Sellable Segments Map these interactions…………………………………………………..…………to these attributes View content View product information View ad Click on ad Purchase something Search for something Attend an event Provide information about themselves Interest Life-stage Lifestyle Intent Behavior Demography

18. Mapping our Data to Sellable Segments Turn these attributes…………………………..…………….into targets Interest Life-stage Lifestyle Intent Behavior Demography Active Travelers Affluents

19. Mapping our Data to Sellable Segments Package targets for advertisers Active Travelers Affluents

20. Media Inventory Options Audience Extension Targeted Media Site-Specific Inventory Large Reach Vehicles Verticals Leveraging IAC’s O&O data across all IAC Properties Sold by dedicated sales teams. Only select inventory available on non-guaranteed basis. Reaches all of IAC’s brands and users. Reaches IAC’s users anywhere on the Web High volume content channels. Overlaying targeting is available. Lifestyle Run of Active Shoppers Sports Fans House & Home Movie Fans Enter-tainment Parents Personals Affluents

21. Cube Targeting Methodology Analyzing multiple dimensions of attributes to achieve the highest level of insight into audience profiles Declared Demographics Interest Behavior Shopping Patterns If consumers are multi-dimensional, then our targeting should be too

22. Data as the Glue Across Properties

23. Data Flow DemographicData Match.com Site Data Site Data Site Data Shopping History Pronto.com Browsing Patterns Citysearch

24. Data Flow DemographicData Match.com Site Data Site Data Site Data Shopping History Pronto.com Unified Database Browsing Patterns Citysearch

25. Data Flow Behavioral Logic and Segmentation DemographicData Match.com Site Data Site Data Ad Engine Makes delivery decision Site Data Ticket purchases Ticketmaster Unified Database Targeted Ad Browsing Patterns Citysearch Audience Cubes

27. Segmentation

28. Web Analytics

29. Data Warehouse

30. Audience Management

32. Opposing Forces We Have to Tackle Declared vs. Observed Reach vs. Accuracy Standard vs. Custom Context vs. Audience Dedicated vs. Integrated Incumbent vs. Newcomber

33. The problem with centralization So. Many. Products. Head. Will. Explode…

34. Pricing Strategy is Never Perfect at Launch We started pricing near premium offerings Adjusted prices after market feedback, observing volume

35. Questions Pubs should ask themselves How diverse is our audience? Who do our clients want to reach? What are we in short supply of? How well do we know our audience? Can we operationalize this? What technology do we need? What is the size of the opportunity?

36. What is The Ad Ops Role in this New Era?

37. Weigh in on the Strategy Every strategy requires a different operational plan

38. Audience-based Selling Requires a Modified DNA Cookie-matching User overlap Cookie deletion pixels Look-alikes Recency Data modeling Frequency Semantic Remarketing Segment membership Social graph Cookie pools

40. Make sure trafficking workflow syncs with systems’ integration

41. Standardization is important

43. Inventory lags targeting

44. Assess overlap

45. Adjust and articulate the margin of error

46. Take command of both UV’s and Imps

48. Level of targeting must align with sales strat

49. Determine impacts to order management

51. What’s Next…and beyond

54. Connections

55. Conversations

57. Using Closed Captioning

58. Semantic modeling

59. Set-top boxSource TNS Infosys TV, Simulmedia

61. Possible government intervention