THE TAPAD DEVICE GRAPH WHITEPAPER

Transcription

1 THE TAPAD DEVICE GRAPH WHITEPAPER

2 SUMMARY The purpose of The Device Graph is to unify a consumer s advertising, content and commerce experiences across all their digital screens. The goal is to optimize user experience and marketer, publisher and brand performance. This paper provides the following information about Tapad's unique technology: The methodology behind The Device Graph model Key data considerations Accuracy of The Device Graph How The Device Graph differs from other cross-device solutions How Tapad addresses privacy concerns

3 DIGITAL MARKETING CHALLENGES A decade ago, understanding consumers online presence was fairly straightforward they used a single desktop computer and a single browser to surf the web. The digital space has changed dramatically since then. There is significantly more digital content and more devices available with which to access that content. Reaching desired target audiences and measuring engagement has become more challenging as consumers interact with these digital offerings in a fragmented way, seamlessly switching between PCs, mobile devices and a seemingly endless number of other screens, and doing so multiple times a day. Even with a single device, a user may access the internet via different means either via an app or through a browser making the task of reaching consumers even more complicated. REMOVING THESE DEVICE SILOS IS ONE OF THE KEY CHALLENGES FACING MARKETING TECHNOLOGY TODAY, AS CROSS-DEVICE BEHAVIORS WILL ONLY PROLIFERATE OVER TIME. THE ULTIMATE GOAL IS TO UNIFY ALL OF A USER S SCREENS TO OPTIMIZE USER EXPERIENCE AS WELL AS MARKETER, PUBLISHER AND BRAND PERFORMANCE.

4 DEFINING THE DEVICE GRAPH In response to the evolving multi-screen marketplace, Tapad has created The Device Graph, a model designed to unify consumers with all of their digital devices. This model is similar to that of a social network. However, instead of connecting people, The Device Graph connects devices to each other (and to their owners). This enables marketers, publishers, brands and technology providers to understand and monetize digital engagement across all connected devices INPUTS Data is collected from three main sources: browsing and application usage behavior, obfuscated login data and partner-provided consumer behavior segments.

5 THE SOCIAL GRAPH METHODOLOGY The Device Graph methodology treats devices as nodes on the graph and the inferred connections between them as edges, similar to the way a social network treats individuals as nodes and connections to their friends (other nodes) as edges. (See Figure 1 for an illustration of a typical portion of a Social Network and Figure 2 for a typical illustration of a portion of The Tapad Device Graph ) Figure 1. Social Graph A typical setup for a social network (zoomed in on a small portion of the graph). Let user A be represented by the red node (circle) and user B by the grey. A typical social network is very interconnected and can hold many nodes in a single graph meaning that starting from a single individual (node) a path to any other node can usually be found by hopping along edges in the graph.

6 THE DEVICE GRAPH Figure 2. Device Graph Here, circles (nodes) are not individual users but rather distinct devices. The lines here have varied thickness because edges can have different confidence scores thicker lines suggest increased probabilistic similarity. Here, we show two isolated, disconnected subgraphs (clusters). The first contains three nodes all labeled with Frequent Traveler suggesting, perhaps, a single user using all of these devices. The other cluster contains two distinct sets of devices, some tagged as a Comic Book Enthusiast while others are labeled Dog Lover. There is also one device with both of these tags present perhaps a sign that this is a shared device in a household. The Device Graph is made up of hundreds of millions of these small subgraphs.

7 CO-OCCURRENCE SCORES Tapad assigns multiple layers of scores to these edges to assess relationships between devices. There are three major types of scores used by The Tapad Device Graph : CO-OCCURRENCE SCORES These are calculated using Heuristics and Bayesian methods. Devices that frequently co- occur on the same network blocks or in the same geographical area are more likely to belong to the same user than devices that concurrently appear in different parts of the country. Each time Tapad sees two or more devices in the same place, the confidence in them being used by the same user increases and therefore the edge score is increased. Tapad also works hard to ensure that the small clusters which make up The Device Graph are representative of devices that are used by connected individuals, without incorporating accidental or fleeting connections between clusters. This is done by looking at the structure of the subgraphs. For example, community detection can be performed on the clusters to look for connections between two groups which should be severed. Tapad leverages community detection algorithms to ensure that a visit to the local coffee shop doesn t have the consequence of connecting the visitor s tablet to every caffeine fiend s laptop on the block.

8 SIMILARITY SCORES & AUTHENTICATED SCORES SIMILARITY SCORES These types of scores are built on probabilistic inferred models by looking at behavioral features on each node. The features include several data points such as consumer segment data provided by data partners as well as visitation patterns observed in browsing and application usage data. Several different methods are used to determine the similarity score. One method is based on a Bayesian model which tries to infer missing features as described in the next section by undoing biases, another involves simple feature comparison between connected nodes, and a third relies on the OASIS algorithm originally used to score image similarity. AUTHENTICATED SCORES Tapad works with partners who provide obfuscated authentication that can be used to create high confidence edges between pairs of devices. These scores can also be used for assessing the accuracy of the inferred edge scores.

9 APPLYING THE SCORES The layered model of connecting devices is very powerful it allows for use of different features of The Device Graph for different purposes. For example, looking only at edges with high co-occurrence scores, or at devices that behave in very similar ways by keeping only devices with high similarity score links or even combining both of these measures to create an overall confidence score. The layered approach makes applying The Device Graph highly customizable, and for example, makes it easy to balance between scale and accuracy. SCALE ACCURACY Scale means how many devices can be targeted for a specific use. The lower the cutoff on confidence scores, the greater the scale. This is balanced by accuracy, since for certain needs it is necessary to narrow The Device Graph to the devices with high certainty of being related and connected. To do this, the value of the minimal allowed targeted confidence score for an edge is raised. This achieves higher targeting accuracy. In statistical terms, this balance is equivalent to trying to minimize the number of false positives (increasing accuracy) while maximizing the number of true positives kept in the set (increasing scale). This approach makes it easy to instantly and seamlessly transition between feature sets because the underlying data The Device Graph is so flexible.

10 DATA CONSIDERATIONS There are numerous challenges to be taken into consideration when analyzing such vast sets of data. Some notable examples are: DEVICE SCHIZOPHRENIA Since users are often switching between different environments (for example, switching between two different browsers or between an app and a browser) on a single device, they sometimes appear to have different identifiers thus creating a split-personality for a single device. This means that a device may end up connected to itself rather than unified into one device. Unifying a device, or merging sets of features, is desirable because it creates richer data for similarity scoring. Tapad merges identifiers in cases where there is strong evidence that they belong to the same device; when this is not the case, they are kept as separate nodes in the Device Graph.

11 DATA BIAS SOURCES Tapad also investigates the inherent biases in the data by looking at three main bias sources: PLATFORM BIAS It is known that people use their phones and computers very differently browsing different sites, or apps, for different information depending on the device they are using. This makes it necessary not only to look at similarity of the features associated with the different devices, but also to look at common cross-platform combinations of features. In order to tackle this, Tapad weighs features on a per-platform basis, giving features which are rare on certain platforms more leverage than those which are very common on that platform. DATA PARTNER BIAS Tapad has several different data partners each with their own unique audience segments and selection bias. This means a single device might not have coverage from all partners skewing feature- based modeling. Tapad has developed several ways to impute (probabilistically infer) the missing values using other devices for which both sources of information are present.

12 DATA BIAS SOURCES TEMPORAL BIAS Some devices in The Device Graph are ephemeral (possibly due to cookie churn caused by resetting of cookies or rejection of third party cookies) while others are more long-lasting. Long-lasting devices have the problem that they are snowballs the longer they persist the more data they aggregate. This leads to an imbalance in the number of features between long-lasting devices and ephemeral ones. To deal with this, Tapad works to ensure the correct temporal analysis of data making sure data is age-normalized and that the length of time the device has persisted is accounted for. Tapad does this by adding a decay time to the features to try to ensure all devices are on an even playing field. TAPAD MERGES IDENTIFIERS IN CASES WHERE THERE IS STRONG EVIDENCE THAT THEY BELONG TO THE SAME DEVICE.

13 DEVICE GRAPH ACCURACY Tapad performs several tests of its methodology. One of the most important is comparing probabilistically inferred connections with connections created from deterministic data. This is done using login data that is always obfuscated, since a user must connect with the same login information to access their account regardless of the device they are using. Tapad receives pairs of device identifiers known to be authenticated to the same user and uses this as a truth data set. The analysis then looks for those same devices in The Device Graph and compares these edges with edges from the truth data set.

14 DEVICE GRAPH ACCURACY There is selection bias in both data sets as the data points are collected from different sources. This means that the overlap numbers can be measured and interpreted in several ways. If devices are considered that: exist in the truth data set and have been observed in Tapad s inference source data sets and default scoring thresholds for advertising purposes A recent analysis shows that 87% of users in the truth data with at least two devices in our graph have at least one edge connecting at least two of their devices in The Tapad Device Graph. This translates into an: 87% + true negative rate 13% false negative rate The analysis also shows that only 0.5% of users were incorrectly connected to at least one device belonging to another distinct user which yields a: 0.5% false positive rate % true negative rate

15 OTHER CROSS-DEVICE APPROACHES NOT USED BY TAPAD TAPAD HAS BEEN DEFINING THE CROSS-DEVICE SOLUTIONS SPACE SINCE 2010, BUT WITH THE EVER-INCREASING SHIFT TOWARDS MULTI-SCREEN BEHAVIOR, SEVERAL OTHER VENDORS HAVE NOW ALSO BEGUN TO BRING CROSS-DEVICE PRODUCTS TO THE MARKETPLACE. Below is a summary of some of these other offerings, and their key distinctions from Tapad s more effective device graph model: THE DEVICE GRAPH IS NOT FINGERPRINTING The lack of a universally accepted standard for consistently identifying a unique device has made targeting mobile technologies more challenging as a multitude of identifiers have emerged even for a single device. The traditional desktop paradigm has also shifted, as there is no longer only one clear way to persistently identify a single computer. For example, the newest releases of some mainstream web browsers turn off third party cookies by default, thus often yielding no persistent identifiers. Fingerprinting tries to resolve this problem by employing various techniques to create unique, persistent identifiers that do not rely on browser cookies or operating system identifiers. Tapad s systems never use fingerprinting techniques. The Device Graph establishes connections between devices using privacy-safe, industry-standard identifiers, such as web cookies and IDFA.

16 THE DEVICE GRAPH IS NOT IP ADDRESSBASED HOUSE HOLDING IP address householding is the simplest and most ubiquitous way to group devices. This refers to retargeting an IP address with further advertising, comparing ad delivery and conversion events on a per-ip basis. This is known as householding based on the assumption that all devices that share an IP address connect via the same household router. The technique is powerful on a broad level but has several drawbacks, such as: The inability to target specific individuals within the household The inability to account for important sequences of events on mobile devices because they switch IP addresses in the middle of a sequence Events from unrelated devices on non-home networks such as business networks and local Wi-Fi hot-spots may appear to be related if networks are mis-categorized Tapad s Device Graph does not have these issues because while Tapad does use IP addresses as a signal to its inference engine, the Device Graph structure itself is completely decoupled from them and thus is able to: Accurately distinguish individuals, even within a household Determine connections between devices regardless of what network on which the devices appear

17 THE DEVICE GRAPH IS NOT CROSS-DEVICE LOOK-ALIKE MODELING Look-alike modeling is most often used for performance advertising. In this scenario, the system finds a device on one platform (e.g. a tablet) with usage patterns similar to clusters of users on another platform (e.g. a laptop) which exhibit a desirable behavior. This is arguably a cross-device technique as it leverages data collected on one platform to target devices on a different platform. However, finding devices that exhibit similar behavior is very different from pinpointing the devices that belong to an individual user, and a simple look-alike model is unlikely to be effective due to the platform data bias mentioned previously. Tapad s approach is different from other companies who seek to solve the multi-screen problem with straightforward look-alike modeling alone. Look-alike modeling cannot accurately target specific users because the search space is not adequately constrained. This approach may form a group of people with similar demographics and interests, but does not guarantee specific, individual targeting. For some campaigns, this kind of targeting may be sufficient because users with similar behaviors often exhibit similar purchasing patterns. But when the campaigns are targeting for something very specific say, targeting a user who placed a unique high-end purse in a shopping cart on an ecommerce site but then navigated away before purchase completion blanket-targeting users because they seem somewhat similar to that user would be unlikely to yield desired results

18 THE DEVICE GRAPH IS NOT CROSS-DEVICE LOOK-ALIKE MODELING The beauty of Tapad s Device Graph is that it constrains the space of similarity modeling to only connected devices. So if someone is looking at a unique luxury handbag on their phone, Tapad will retarget an ad for that product to a connected device in The Device Graph, rather than uniformly canvassing similar users not in the market for such a bag. TAPAD S SYSTEMS NEVER USE FINGERPRINTING TECHNIQUES. THE DEVICE GRAPH ESTABLISHES CONNECTIONS BETWEEN DEVICES USING PRIVACY-SAFE, INDUSTRY-STANDARD IDENTIFIERS, SUCH AS WEB COOKIES AND IDFA.

19 PRIVACY Protecting consumer privacy is paramount to Tapad. Industry-leading privacy protocols have always been built into Tapad s technology since its founding day. The Device Graph connects multiple related devices by analyzing collected data, but the means to collect this data and how this data is tied together is compliant with industry best practice standards. Data collection is done using standard identifiers such as web cookies and operating system provided identifiers (ios IDFA, Android Advertising ID). No data is collected if no such identifiers are available (no fingerprinting techniques are employed). This gives consumers the ability to avoid their data being collected using well-known, simple measures such as using their: BROWSER S INCOGNITO MODE THIRD-PARTY AD-BLOCKING COOKIE EXTENSIONS BLOCKING SELF-DESTRUCTING COOKIE EXTENSIONS and selecting the limit ad tracking option on ios devices. Tapad also provides persistent opt-out solutions for web browsers and was the first company in the industry to provide opt-out for apps through partnerships with Evidon. A more complete description of Tapad s privacy policy and privacy principles is available at

20 PRIVACY Tapad s Device Graph has helped define cross-device privacy standards: It is blind to personally identifiable information, including names, addresses and phone numbers It displays the DAA AdChoices notice to users on computers, mobile devices and tablets It has market-leading opt-out technology: Brings consumers into the conversation by ensuring persistent opt-out options on computers and mobile web Partnered with Evidon on first-to-market app opt-out solution Tapad continues to proactively innovate and lead in anticipating and addressing privacy concerns rather than reacting to the market climate. THE DEVICE GRAPH CONNECTS MULTIPLE RELATED DEVICES BY ANALYZING COLLECTED DATA, BUT THE MEANS TO COLLECT THIS DATA AND HOW THIS DATA IS TIED TOGETHER IS COMPLIANT WITH INDUSTRY BEST PRACTICES AND STANDARDS.

21 CONCLUSION TO ACHIEVE OPTIMAL RESULTS, MARKETERS AND PUBLISHERS ALIKE MUST TAKE INTO ACCOUNT CONSUMERS EVOLVING MULTI-SCREEN LIFESTYLE. The Tapad Device Graph provides a critical tool for unifying cross-device insights and optimizing cross-screen experiences. This offering: Is built using a unique combination of deterministic and inferred data to unify devices Leverages a flexible structure which enables customization based on objectives Is not without challenges; however, tests have confirmed very high accuracy levels Is distinctly different from other cross-screen solutions such as fingerprinting, householding and look-alike modeling Incorporates industry-leading privacy practices as a core component of Tapad s business philosophy

22 ABOUT TAPAD Tapad is the leader in cross-device content delivery. Our groundbreaking, proprietary technology assimilates billions of data points to nd the human relationship between smartphones, desktops, laptops, tablets, connected TVs and game consoles. The result: an unprecedented understanding of consumer behavior across related screens and the ability to reach the right people on the right device at the right time. With Tapad, publishers and advertisers can deepen consumer engagement with a more uid experience while increasing campaign cost-effectiveness. Backed by major venture firms, Tapad is based in New York and has offices in Atlanta, Chicago, Dallas, Detroit, Los Angeles, Miami and San Francisco. TechCrunch called the powerhouse Tapad team a hell of a list of entrepreneurs who created some of the most valuable online advertising companies of the last decade.

23 ABOUT US Tapad is the true cross-device data partner. Whether the goal is to amplify your message, engage your audience or transform data into dollars, we make it happen. By leveraging the power of our proprietary Tapad Device Graph, we unify data across all devices to understand the interests, passions and behaviors of the audiences that matter. REACH OUT TO US!