deterministic vs probabilistic approach

Smartphones, tablets, laptops, desktops, smart TVs, video game consoles, streaming sticks—consumers may use any combination of these and other devices every day.

This fragmented, device-based customer data footprint creates a tremendous challenge for marketers seeking to deliver personalized experiences across channels.

By aggregating these data points and plugging them into deduplication algorithms, detailed audience profiles can be achieved from incomplete information. <> stream Leverage our data monetization solutions to strengthen your position in the market. LiveRamp uses device groupings from probabilistic links to expand our deterministic matches. A probabilistic approach of the hazard was also included. By aggregating, these data points and plugging them into deduplication algorithm. For obvious reasons, deterministic may seem like the better option since the goal of collecting data is to always come as close as possible to identifying who your audience is. Learn how our technology powers programmatic video across all screens. Deterministic identity solutions complement probabilistic graphs for reach expansion and incorporate probabilistic corroboration for incoming deterministic data. This means that the majority of first party publisher data falls in the deterministic category. From this information, is able to indentify other fasionistas like Jennifer and Lauren because they exhibit similar browsing behavior. Another key benefit of deterministic modeling is the implication for cross-device tracking. Utilizing both models – with reasonable expectations for each – can provide added context about who your prospective buyers are and the best ways to engage them. For example, is able to recognize Angie because she has a user login to the site. Watch Now: An Introduction to LiveRamp Identity, Featured Case Study: Fitbit achieves 2x higher return on ad spend without cookies. Uncover what’s unsaid about technology, data, and business.

1 0 obj Although it is not certain that you are reaching your exact user or household you desired, it is likely and your best bet when a deterministic match is not available. Transform consumer data into deterministic person-based IDs.

Deterministic linkages can sometimes be misleading if misinterpreted. However, that does not mean that probabilistic isn’t valuable. When using probabilistic methodologies, a deterministic vendor can and should filter incoming linkage data sources. The case of natural decrease of TBT concentration in the Drammen fjord was investigated as a test case. �۟��96�����x��h�D`T|pS��D�,K2R"�'��D,�5K�> ��R��?S���ϖ��Mȥ��^��R�ǬȞ�T���c��@J��?� N�����2�e��Ld���'�W2 )Q�>dI8��9T� v�o+��1��Y;��Ѧ )�m�h[e/ )AD4��Ǝ�+��PuU�t����m���q�p؎�)��.�o�t�Sr�k2*_:�����d�$Mw��'0o�i��?�b}'q�����tϩ�UK4t��o_��FJ/Y� a��������� o2�R7����8��}���\�i��MpԆ,��4��硶��oM��H�VYtV��q�n���� �HC��|�T�6�9iU���Yϱb��L7j��c����P�e��,?������]���&�9�?��������~��1�go[��Q�e���������D������2vc�_�M}蚞QP>%���p�ؗch����h塱clyom�mw���ұ�����:�+���Bc��uゎ�e.+ȇF���q��3���g%g˽��Z�!�c�s��A>R��s���A�?4v.0���|�5ӎ�W~��^��1���WJl)��`��㾍�u��:r����{�A,�����g�l�z���8g]ϡ�OC֌{���snc������Ue;ƃ��X�����_�R/G���E���[��X)�s�O?r0�xPz�r�� �����}���9����}k�^��& �{�k�y�o;�s='C. Now that we’ve covered the different types of data modeling, next week we’ll explore the differences between audience buying and contextual buying. We affirm this approach is imperative to executing people-based marketing, and our customers agree. Through our people-based identity graph—reaching over 200 million unique users on the web as well as more than 600 million matched mobile devices—we have planted our flag firmly in the fully deterministically sourced matching camp. Our deterministic linkages continuously move and change, while the people-based ID they are anchored to stays persistent. We believe a solution based on probabilistic matches, even when using a knowledge base of PII linkages for machine learning, cannot achieve the same level of accuracy and recency of identity as a truly deterministic identity graph. x��}ݪ.9��}=Ź6���of�1L�Lϵ�?��c������+#��*(v�GR��P��H����L�?�����#�¿��O�����Oۯ�?��H�W�Z�����_�O6����O����2�~��_�H�й����_�����m�����_���-���w��s���O�����q�O������MS�M14���~Ӵ�����Ǟ�S���������IGigyh�ai�g/���8��9>g۷��q��i�~����Y��I{:���͙>�/���liʧ�@�8���D�㜟��G]�Ӻ=iy|���X�� �����s�m��T�z�\i�����xh�bi2Ѵ��t��s���}zh�� E!��i L����x��M+��g�);Ә[T���ͭ�qje�W��W�biN:�4N�L��*�{���8.G4�A��W�-�փ]�h�\)�����Tv+��9���t�ƣп�h�A����'����K��y�z���r�x#2�����1$��0D����Gh��Mv#GD�G?�kEo���2��"K��G��Z����mb"��~~���yW�9b"{����Ȍtl�i��9���1��Uä���d4^�����I[��&��gw� endobj s, detailed audience profiles can be achieved from incomplete information. What Are Cookies and How Do They Work on Desktop Vs. Deterministic data, also referred to as first party data, is information that is known to be true; it is based on unique identifiers that match one user to one dataset. Read previous posts from our audience series: This article was written by Lexie Pike, product marketing manager at SpotX. Whether a user is logged in on their phone, tablet or laptop, a publisher or brand definitively recognizes that user across devices and can provide a holistic, rather than a fragmented, user experience. (Stay tuned for a future post on the key differentiators of the best identity solutions.). The leading data connectivity platform for the safe and effective use of data. Angie frequently browses the fashion content of and other fashion sites. Probabilistic data modeling identifies users by matching them with a known user who exhibits similar browsing behavior. Deterministic Identity Methodologies create device relationships by joining devices using personally identifiable information (PII) , such as email, name, and phone number. Learn more about the largest deterministic graph for activating people-based marketing anywhere. 2 0 obj Deterministic vs Probabilistic Data In our latest Insights Post, we defined 1st, 2nd and 3rd party data. The average person today owns multiple connected devices. This data is generated through collecting anonymous data points from a user’s browsing behavior and comparing them to deterministic data points. Because probabilistic matching may introduce more false positives, marketers should reserve it for specific use cases, such as when they need extra reach and are comfortable with sacrificing a small amount of deterministic accuracy. The Big Debate: Deterministic vs. Probabilistic 11/21/2016 03:02 pm ET Updated Nov 22, 2017 Some time ago we passed a tipping point where marketers realized that targeting by device didn't make much sense and a cross-device "people-focused" approach worked better.

For […] But it’s certainly not insurmountable. We work with preferred partners, such as Drawbridge and Tapad, to layer these probabilistic capabilities on top of our graph. Probabilistic data offers the … Crucially, these facts will never change and the probability that they are true will always be 100%, thus they provide a solid foundation for a multitude of applications in online marketing. For example, if a father’s Spotify account is accessed by his son and his son’s college roommate, one account is now attached to multiple devices, resulting in identity conflict. If a publisher possesses data about a user through a login, the publisher can definitively identify the user next time he or she visits or logs in. SpotX is the trusted platform for premium publishers and broadcasters. Deterministic vs. probabilistic. Deterministic and probabilistic are opposing terms that can be used to describe customer data and how it is collected. Indeed, framing the debate as  probabilistic vs. deterministic matching neglects to take into account that these methodologies complement each other. For obvious reasons, deterministic may seem like the better option since the goal of collecting data is to always come as close as possible to identifying who your audience is. The reason first party data is so valuable is because it can be. Access unique, trusted data to enhance and extend customer knowledge. What we haven’t yet explored is the deterministic and probabilistic data models that are used to produce and analyze this audience data. <> Probabilistic data modeling identifies users by matching them with a known user who exhibits similar browsing behavior.

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