The Little Black Book of Ads

Transcription

1 The Little Black Book of Ads JOUNCE MEDIA jouncemedia.com

2 Jounce Media s Little Black Book of Ads is your quick reference for all things programmatic. It contains descriptions of 15 fundamental concepts that underpin the ad tech ecosystem. Master these 15 concepts, and you ll be armed to tackle any programmatic advertising problem. For more information about Jounce Media s ad tech consulting services, visit jouncemedia.com

3 Must-Know Concepts This book contains descriptions of 15 fundamental concepts that underpin the ad tech ecosystem. Master these 15 concepts, and you ll be armed to tackle any programmatic advertising problem: Must-Know Terminology Throughout the book, we use the following industry-specific terms to describe the programmatic buying and selling of advertising inventory: Bidding Bid Request & Bid Response Second Price Auctions Brand Safety & Contextual Targeting Viewability Location Targeting Private Marketplaces Identity Management Cookie IDs & Device IDs ID Syncing CRM Onboarding Cross Device Matching Publisher Advertiser Ad Exchange A seller of ad inventory either a website or an app. Also referred to as the sell side and the supply side. A buyer of ad inventory. Also referred to as the buy side and the demand side. A piece of technology that conducts realtime auctions for ad inventory. Supplyside platforms (SSPs) all operate ad exchanges, but also have additional pieces of publisher technology. A piece of technology that buys inventory on ad exchanges. Demand-side platforms (DSPs) all operate bidders in addition to other pieces of advertiser technology. Advertising Applications Retargeting Frequency Capping Third Party Ad Serving Attribution Lift Measurement Client Side Server Side Everything that happens on a consumer s device. Everything that happens remote from a consumer, either in the network or a data center. Server side activities happen in the cloud.

4 Programmatic Components Each time a publisher makes the decision to sell an ad impression programmatically, five technology components communicate with each other. These five components are consistent across desktop and mobile advertising, and across display, video, and native ad formats. Buy-Side Technology Sell-Side Technology A Note About Real Time Bidding The real time bidding (RTB) ecosystem relies on separating the ad exchange and bidder into two independent pieces of technology. Walled gardens and ad networks often combine these buy-side and sell-side technology components into a closed auction environment.

5 Bid Request and Response For each available RTB impression, an ad exchange conducts an auction. The ad exchange sends a bid request (a description of the impression) to multiple potential bidders. s evaluate the bid request to determine whether it is a match for campaign targeting criteria, and if so, how much it is worth to the advertiser. s then send a bid response (a price) back to the ad exchange. The winning bidder is then awarded the impression by the ad exchange. In addition to sending bid requests to third party bidders, ad exchanges often operate their own internal bidders. Advertisers who have not licensed a DSP (an external bidder) can run campaigns directly with each ad exchange. Bid Request 1 2 Bid Response Win Notification 3 Ad Exchange 4 Ad Creative Advantages of external bidders: Consistent targeting across ad exchanges Global frequency capping across ad exchanges Easy access to advertiser data Advantages of internal bidders: Preferred inventory access Unique access to publisher data Lower fees

6 Second Price Auctions Price Reduction Blacklists Price Floors RTB auctions operate on a second price model in which the winning bidder pays the price offered by the second highest bidder. This is called price reduction. Publishers have the option to blacklist bidders from auctions. This preserves brand safety and eliminates channel conflict, but also increases price reduction. Publishers can also set price floors, below which bids are rejected. If only one bid clears the price floor, the highest bidder price reduces to the floor. $5 $5.00 Bid $5.00 Bid $5.00 Bid $4 $4.00 Floor $3 $2 $2.00 Bid $2.00 Bid $1 $1.00 Bid $1.00 Bid $1.00 Bid $0

7 Brand Safety & Contextual Targeting Every bid request contains a publisher identifier either a URL or an app name. s can analyze this identifier to determine whether the available impression meets campaign targeting criteria. By checking the publisher identifier against a ratings table, bidders can prevent ads from running alongside inappropriate content and can also target campaigns toward contextually-relevant content. Content Ratings Table xyz.com Bid Request Publisher = xyz.com Safety = PG Rated Bid Response Ad Exchange Context = sports, fitness, basketball Bid = $X.XX Publishers have the option issue blind bid requests, in which the publisher identifier is omitted. Blind bid requests typically capture weak demand, but this may be a rational choice for publishers who: 1. Want to limit channel conflict with direct sales 2. Want to prevent audience data leakage 3. Have NSFW content

8 Viewability There are three kinds of non-viewable impressions: ads that are out of view, ads that appear for a very short period of time, and ads served to non-human traffic: Advertisers can employ pre-bid viewability targeting solutions that predict inventory quality based on the publisher ID of each incoming bid request. Out of View Below the fold Inactive tab Covered by other content Short Duration Content Ratings Table xyz.com Bid Request Pub = xyz.com Slide show Back button Viewability = High Non-Human Traffic Bot networks / infected PCs Web crawlers The Media Rating Council (mediaratingcouncil.org) has defined precise viewability standards, and advertisers and publishers can license MRC-accredited technology to measure ad viewability.

9 Location Targeting Bid requests contain two sources of location information: IP address and GPS coordinates. The client device s IP address is included in every bid request, and bidders can use this information to infer the user s approximate location, typically accurate to the city level. Additionally, some bid requests from mobile devices specify the user s latitude and longitude, reported from the device s onboard GPS. When available, these GPS coordinates enable much more precise location targeting. Location Lookup Table Bid Request IP = City = New York, NY IP = Lat/Long = XX / YY Ad Exchange Bid Response Lat/Long = XX / YY Address = 201 Route 9 Point of Interest = Ford Dealer Bid = $X.XX It is also possible to build segments of users who have recently visited specific points of interest. These location segments allow advertisers to retarget users after visiting high value locations like auto dealerships and retail outlets.

10 Private Marketplaces Publishers are willing to provide preferred access to inventory in exchange for price and volume commitments. These commitments were traditionally negotiated via insertion orders. In the programmatic landscape, private marketplaces facilitate similar commitments. Once publishers and advertisers agree on the business terms of a private marketplace, the publisher s ad exchange assigns a deal ID to the transaction. All qualifying bid requests are populated with an optional deal ID parameter, and the advertiser s bidder targets this deal ID. Deal ID ABC Placement = Sports Section Priority = First Look Price = $2.00 CPM Volume = 5,000,000 Impressions $5 $5.00 Bid $4 Bid Request: Deal ID = ABC Ad Exchange $3 Bid Response: Bid = $2.00 $2 $2.00 Bid $1 $1.00 Bid $0 Private marketplaces disrupt the typical highest bid wins model of programmatic transactions. In a private marketplace, it is possible for a low bid to win the auction because the advertiser has made a broader business commitment to the publisher.

11 Cookies & Device IDs Cookie IDs and Device IDs are the connective tissue between the client (the user s device) and the server (everything happening in the cloud). Ad tech systems assign an ID to each client and then create matching server-side user profiles. Each time an ad tech system has access to a client, it reads the client s ID and then finds the matching server-side user profile, which contains information about who the user is and what advertising he/she has seen. Client Side Server Side Client Side Server Side User Profile User Profile Cookie ID: 123 User 123 Male Age 40 Minivan Intender Device ID: 456 User 456 Female Age 32 New Parent Cookie IDs Used for advertising within a browser (desktop & mobile) Each ad tech system sets a different cookie ID Cookie data is typically accessed via a tracking pixel Device IDs Used for advertising within native apps (mobile & OTT TV) All ad tech systems use the same device ID Device data is typically accessed via an app SDK

12 ID Syncing Because each ad tech system operates on a different web domain, they have different cookie IDs for the same web browser. ID syncing allows ad tech systems to share browserbased audience data with each other. By establishing that company A s cookie 123 and company B s cookie 789 are the same real world web browser, the two companies can merge together the matching user profiles to form a more complete understanding of the consumer. Client Side ID Sync Server-To-Server Data Transfer Company A User Profile Company B User Profile Company A Cookie ID: 123 User 123 User 789 Company B Cookie ID: 789 Male Age 40 Male Age 40 Minivan Intender Minivan Intender The process of ID syncing is necessary only for browserbased advertising. Native app inventory does not require ID syncing because each ad tech company uses the same device ID.

13 CRM Onboarding CRM onboarding enables offline-to-online data transfer. Data onboarding networks establish matches between offline identifiers like an address and online identifiers like a cookie ID. With the offline-to-online match established, advertisers can make their offline customer data actionable online. Everything that advertisers know about the customer joe@ .com can inform the ads that are delivered to cookie ID 123. Offline CRM File Match Network Online DMP User Profile User Profile User Profile PII (personally identifiable information) joe@ .com joe@ .com Anonymous Online ID Cookie ID 123 Cookie ID 123 Customer Information Male Age 40 Minivan Owner Male Age 40 Minivan Owner PII + customer information PII + anonymous ID Anonymous ID + customer info

14 Cross-Device Matching Because consumers use multiple devices, ad targeting and attribution systems need a unified way to identify each consumer across screens. Device graphs match multiple online identifiers to each other, providing a more complete view of a real world person s digital footprint. Device graphs are created using either deterministic matching data or probabilistic matching data: Device ID 999 Cookie ID 777 Deterministic Matching Deterministic matching systems use a verified crossdevice identifier like a user login to link multiple online IDs. Deterministic matching offers highly precise matches, but limited scale. vs. Probabilistic Matching Person ID 123 Probabilistic matching systems link multiple IDs using inferred data points like a shared location or a shared IP address. Probabilistic matching offers high scale, but is less precise than deterministic matching. Cookie ID 888 It is increasingly common for device graphs to include both deterministic and probabilistic matches. These hybrid graphs can be tuned to achieve the appropriate tradeoff between precision and scale for each advertiser.

15 Retargeting By including a tracking tag on their websites and in their apps, advertisers can collect the user ID of every visitor. The advertiser s bidder can then target this list of IDs, also known as a retargeting pool. All bid requests specify a user ID, and the advertiser s bidder can selectively bid for impressions whose user ID is a member of the retargeting pool. The consumer will then see ads from websites and apps he/she recently visited. Cookie ID: 123 IDFA: Tracking Tag User ID = 123 User Profile User ID 123 Bid Request User ID = 123 xyz.com visit 2 3 Ad Exchange Bid Response Price = $X.XX

16 Frequency Capping Each time an ad is served, the bidder updates the user s profile, keeping a running log of recent ad exposure. Advertisers can enforce rules to limit ad exposure to no more than X impressions per hour, day, week, etc. Once a user s profile indicates the frequency cap has been reached, the bidder will reject all new incoming bid requests. User Profile Ad Exchange Impressions delivered: cnn.com 7/18/15 8:49:48 webmd.com 7/18/15 9:05:22 weather.com 7/18/15 9:18:37 weather.com 7/18/15 9:18:55 cnn.com 7/18/15 9:52:05 Ad Exchange Ad Exchange By working with an external bidder that buys across all ad exchanges, advertisers can enforce global frequency caps. Internal bidders can only enforce local frequency caps within a single ad exchange.

17 Third Party Ad Serving Advertisers traditionally gave creative assets directly to publishers, who would serve ads on their websites. These site served ads enabled publishers to maintain control of their website and app user experience, but required publishers to directly traffic high volumes of advertiser campaigns. In programmatic environments, advertisers overwhelmingly use third party ad servers, which deliver ads across a broad range of publisher inventory. Third party ad serving streamlines campaign workflow and centralizes advertiser campaign performance data. Advertiser Ad Server Ad Exchange Publisher Ad Server 2. Conduct server-side auction and identify winner 1. Deliver selected ad exchange s ad tag 3. Deliver winning bidder s ad tag 4. Deliver advertiser ad server s ad tag 5. Deliver creative assets

18 Attribution Attribution systems require access to data sets that contain records of each consumer s path to purchase. Advertiser ad servers are typically the source of this attribution data. When a user converts, the attribution system can analyze the user s previous ad exposure and make a judgment about which ads should be assigned credit for influencing the purchase. T-28 Days T-21 Days T-14 Days T-7 Days Purchase Ad #1 Ad #2 Ad #3 Ad #4 Ad Clicked $ Attribution Window The three most common attribution methodologies are last-click, last-view, and multi-touch: Last Click Last View Multi-Touch 30% 50% 20% Attribution systems often combine these methodologies in custom ways. As an example, DoubleClick s attribution system assigns last-view conversion credit unless an ad is clicked, in which case the last clicked ad is assigned conversion credit.

19 Lift Measurement To isolate the incremental impact of advertising, marketers must conduct A/B experiments in which a random group of users is intentionally not exposed to branded advertising. The holdout group is typically managed by the advertiser s ad server. When the advertiser s media buying systems purchase impressions for the holdout group, the ad server delivers an unbranded ad, often a public service announcement (PSA). The advertiser can then measure conversion rates for the holdout group vs. exposed group to determine the number of purchases caused by advertising. Exposed Group Holdout Group 11% of audience converts 10% of audience converts Advertising drives 1% lift in conversion rate Advantages of lift experiments: Perfect measurement of advertising impact Limitations of lift experiments: Cost of delivering unbranded ads to holdout group and inability to isolate impact of different tactics

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