Introduction to online advertisting

04/08/2304/08/23

Jason Shulman, [email protected] Chief Revenue Officer

Introduction to Data Driven Advertising

mailto:[email protected]

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.”

Proprietary and Confidential

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

GawkerAdify

User

AOLYahoo


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 CPMExchange 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.


Targeting is enabled by both knowing something about a user and by dropping a cookie

1. Choice Hotels sends page to userwith 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: 1000121For: 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

Data you can know directly about a user:Last time they visited your site Operating systemComputerBrowserIP Address

Data you can derive from what you know (in order of accuracy):

StateDMACityZip-ishZip level segmentation solutions

Data that is available from third parties:DemographicsGeographicIn-Market (Buyer intent)

TravelAutoLocal ServicesEducationConsumer Products

Frequent BuyerAuto TypeDisease PropensityLifestyle Segments


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.


Building Look-A-Like models1. [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 HotelsUserID: 1000121UserID: 1000122UserID: 1000123UserID: 1000124UserID: 1000125UserID: 1000126UserID: 1000127UserID: 1000128UserID: 1000129UserID: 1000131

Days4. Similar users sees highly relevant ads.

Great Hotel

3. Build Look-A-Like Targeting List


Next Generation Targeting

Specific Audience Behavioral, Blue Kai/ExelateSearch Terms to DisplaySocial Graph to DisplayDigital Direct Mail to Display


BlueKai User Cookie:UserID: 1000121Depart:WASArrive: LGADepartDate: 12/7ReturnDate:12/9AdvancePurchase:6 Days

Data providers are identifying users who are in-market by capturing web site usage (BT) 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

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

Potential Tactic: 1.Identify markets with unsold inventory and buy data to identify users who are looking for hotels in those markets 2.Build look-a-like models using BlueKai’s 14,000 catagories


Jackson Hole Hotel In MarketUserID: 1000121UserID: 1000122UserID: 1000123UserID: 1000124UserID: 1000125UserID: 1000126 Days

Search2Display – using the search terms that bring people to publisher sites to build highly effective retargeting lists

1. 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


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.


Social Group Of Users With High CLVUserID: 1000121UserID: 1000122UserID: 1000123UserID: 1000124UserID: 1000125UserID: 1000126 Days

Social Graph Targeting – Using social graph data to target highly similar users and grow valuable retargeting lists

1. 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


Why does this work?

Birds of a feather – users are highly likely to share demographics, like income, interests and needs with close friendsWhen combined with demographic data, can be very strongEspecially powerful for products that move through social groups via recommendation


On list “Likely to buy printer ink in next 6 months”?UserID: 1000121 - NOUserID: 1000122 - YESUserID: 1000123 - YESUserID: 1000124 - NOUserID: 1000125 - YESUserID: 1000126 - NO

Digital Direct Mail– Using powerful offline Direct Marketing lists for online targeting

1. 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: 1000124UserID: 1000125UserID: 1000126


Appendix

Documents

Introduction to online advertisting