Introduction to online advertisting

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Marketers are focused on capturing and measuring: ,[object Object],[object Object],“ 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.”

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 ,[object Object],[object Object],User ,[object Object],[object Object]

Ad inventory is priced as a function of perceived inventory quality ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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

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)

Ad Targeting is based on knowing something about a user and making that knowledge persistent ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Common tactics ,[object Object],[object Object],[object Object],[object Object]

Building Look-A-Like models ,[object Object],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

Next Generation Targeting ,[object Object],[object Object],[object Object],[object Object]

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

Data providers are allowing marketers to reach very specific market segments ,[object Object],[object Object],[object Object],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

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 ,[object Object],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

Search2Display - Performance ,[object Object],[object Object]

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 ,[object Object],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

Why does this work? ,[object Object],[object Object],[object Object]

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 ,[object Object],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

Introduction to online advertisting

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Introduction to online advertisting