Your SlideShare is downloading. ×
Introduction to online advertisting
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.


Introducing the official SlideShare app

Stunning, full-screen experience for iPhone and Android

Text the download link to your phone

Standard text messaging rates apply

Introduction to online advertisting


Published on

1 Like
  • Be the first to comment

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide


  • 1.
    • Jason Shulman, [email_address]
    • Chief Revenue Officer
    • Introduction to Data Driven Advertising
  • 2. Marketers are focused on capturing and measuring:
    • Intentions … serving messages – in real-time – based on the assumed intentions of the consumer.
    • Mentions … the things people say about your brand are more important than what you say.
    “ The challenge is to build technological capabilities that allow you to see the complete digital footprint a customer leaves when they engage with your brand.”
  • 3.  
  • 4. The eco-system around serving an ad impressions is...complicated. Publisher ` Exchange [x+1] Right Media Exchange Ad Network 2 AppNexus Exchange Ad Network 3 Content Network
      • Gawker
      • Adify
      • AOL
      • Yahoo
  • 5. Ad inventory is priced as a function of perceived inventory quality
    • Direct Buy/ Guaranteed – Advertisers negotiate directly with publishers for guaranteed placements. Tier 1. Endemic, Homepages, Above the fold. $10 CPM +
    • Direct Buy/ pre-emptable – Advertisers negotiate directly with publishers for pre-emptable ad placements. Tier 2. Secondary pages, $4 CPM
    • Exchange Buy – pre-emptable, non-guaranteed. May be blind to page before purchase. Typically done through Ad Networks and consist of RON inventory. Tier 3. $.25-$2.00 CPM from exchanges.
    • Supplementary elements that affect price:
      • Endemic vs. Non-Endemic – Content on endemic sites or pages that relate in some way to the product being advertised.
      • Run of Network (RON) or Run of Site (ROS) – Ads are served on unsold inventory across the network or site. Often given to exchanges.
      • Brand safe – Impressions can only be shown on sites advertiser considers safe for the brand. Typically Comscore 500 or some subset. More expensive.
  • 6. Targeting is enabled by both knowing something about a user and by dropping a cookie 1. Choice Hotels sends page to user with code to request for image pixel 2. Request asks retargeting pixel server for “image” and sends data as part of every request. 3. Response includes 1X1 pixel and sets a cookie that contains unique ID. Choice Hotels Targeter ( [x+1] ) 4. Send list of cookies IDs and bid price to exchange Exchange Retargeting Example [x+1] Cookie: UserID : 1000121 For: ChoiceHotels Group1
  • 7. Exchange buyers can bid on ad inventory that is being served to site visitors 1. A user with a Choice Hotels cookie visits a publisher site. Instead of serving an ad itself, site informs exchange of impression to be auctioned and includes cookie ID’s 2. Exchange offers the impression up for bidding. Offers cookie ID’s to all bidders. Exchange Retargeter 4. Retargeter serves Choice Hotels Ad Retargeting Example 3. Retargeter bids on impression containing its cookie, awards ad impression to retargeter (assuming winning bid)
  • 8. Ad Targeting is based on knowing something about a user and making that knowledge persistent
    • Data you can know directly about a user:
    • Last time they visited your site
    • Operating system
    • Computer
    • Browser
    • IP Address
    • Data you can derive from what you know (in order of accuracy):
    • State
    • DMA
    • City
    • Zip-ish
    • Zip level segmentation solutions
    • Data that is available from third parties:
    • Demographics
    • Geographic
    • In-Market (Buyer intent)
        • Travel
        • Auto
        • Local Services
        • Education
        • Consumer Products
    • Frequent Buyer
    • Auto Type
    • Disease Propensity
    • Lifestyle Segments
  • 9. Common tactics
    • Remarketing – Targeting visitors of your site for advertising.
      • Should only have one vendor. Otherwise they bid against each other
    • Building Look-A-Like models – Profiling users and finding users with similar profiles
      • Number of data elements that are available are much greater than even a year ago.
  • 10. Building Look-A-Like models
    • [x+1] analysis identifies users likely to respond to particular products and builds profile.
    User Database User Database User Database Age … Location… Demos…. Level… Interests…. Purchases…. 2. Query databases – in-house and third party - for users with similar characteristics and their cookie IDS Look-A-Like Model for Atlanta Hotels UserID: 1000121 UserID: 1000122 UserID: 1000123 UserID: 1000124 UserID: 1000125 UserID: 1000126 UserID: 1000127 UserID: 1000128 UserID: 1000129 UserID: 1000131 Days 4. Similar users sees highly relevant ads. Great Hotel 3. Build Look-A-Like Targeting List
  • 11. Next Generation Targeting
    • Specific Audience Behavioral, Blue Kai/Exelate
    • Search Terms to Display
    • Social Graph to Display
    • Digital Direct Mail to Display
  • 12. Data providers are identifying users who are in-market by capturing web site usage (BT) BlueKai User Cookie: UserID: 1000121 Depart:WAS Arrive: LGA DepartDate: 12/7 ReturnDate:12/9 AdvancePurchase:6 Days 1. User visits travel site 2. Part of the site captures user info in a cookie 5. [x+1] determines right price on that impression to that user Bids for User ID 1000121 : MB 1 – .002 ¢ MB 2 – .003 ¢ [x+1] – .004 ¢ BlueKai [x+1] 3. Data Provider and [x+1] cookies are synched up 4. [x+1] finds user on Ad Exchange or other media source 1000121 Bidding Open For: User ID 1000121 1000121 6. User sees highly relevant travel ad Best flights
  • 13. Data providers are allowing marketers to reach very specific market segments
    • Potential Tactic:
    • Identify markets with unsold inventory and buy data to identify users who are looking for hotels in those markets
    • Build look-a-like models using BlueKai’s 14,000 catagories
    Segment Description Approximate Targetable Users (snapshot) In-Market --> Travel --> Hotels & Lodging --> By City --> Domestic --> Alabama 22,000 In-Market --> Travel --> Hotels & Lodging --> By Hotel Class --> 3 Stars or more 170,000 In-Market --> Travel --> Hotels & Lodging --> Length of Stay --> 1-2 days --> 2 days 580,000 In-Market --> Travel --> Vacation Packages --> By Length of Trip --> 10 or more days 69,000 In-Market --> Travel --> Air Travel --> By Day of Departure --> Saturday Departure 1,032,000
  • 14. Search2Display – using the search terms that bring people to publisher sites to build highly effective retargeting lists Jackson Hole Hotel In Market UserID: 1000121 UserID: 1000122 UserID: 1000123 UserID: 1000124 UserID: 1000125 UserID: 1000126 Days
    • User performs a search:
    3. Relevant search terms trigger adding user to intender list 4. User sees highly relevant ad Great Hotel Detect that User searched “ Great Jackson Hole Hotel” 2. User arrives at publisher site
  • 15. Search2Display - Performance
      • Search2Display has similar Cost Per Acquisition to most effective tactic, remarketing and outperforms RON ad buys.
      • Compared to other tactics, click through and response rates were on the high-end of performance.
  • 16. Social Graph Targeting – Using social graph data to target highly similar users and grow valuable retargeting lists Social Group Of Users With High CLV UserID: 1000121 UserID: 1000122 UserID: 1000123 UserID: 1000124 UserID: 1000125 UserID: 1000126 Days
    • Identify high value “seed” user
    3. Add friends to targeting list 4. Group sees highly relevant ads Sign Up 2. Use social graph data to identify valuable individuals within immediate social group (NON PII) User Conversion Data
  • 17. Why does this work?
    • Birds of a feather – users are highly likely to share demographics, like income, interests and needs with close friends
    • When combined with demographic data, can be very strong
    • Especially powerful for products that move through social groups via recommendation
  • 18. Digital Direct Mail– Using powerful offline Direct Marketing lists for online targeting On list “Likely to buy printer ink in next 6 months”? UserID: 1000121 - NO UserID: 1000122 - YES UserID: 1000123 - YES UserID: 1000124 - NO UserID: 1000125 - YES UserID: 1000126 - NO
    • Begin with offline DM list, purchased or in-house
    3. During ad call, partner tells [x+1] that user is on list, but not who user is 4. Users see highly relevant ads Printers 2. [x+1] partners append list to their US House Hold file Printer Purchasers ………… . ………… . ………… . ………… . ………… . ………… . ………… . ………… . ………… . ………… . ………… . ………… . ………… . UserID: 1000124 UserID: 1000125 UserID: 1000126
  • 19. Appendix