Deterministic vs. probabilistic. At LiveRamp, our position is clear: we believe deterministic matching should be the backbone of marketing. true or false. Deterministic and probabilistic are opposing terms that can be used to describe customer data and how it is collected. But relying exclusively on deterministic methodologies limits the use cases available to marketers. s, detailed audience profiles can be achieved from incomplete information. 1 0 obj Leverage your first-party data in the digital and TV ecosystems. The leading data connectivity platform for the safe and effective use of data. 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. 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. This allows lifestylewebsite.com to operate under the assumption that Jennifer and Lauren share other demographic, psychographic or interest-based traits and characteristics as Angie, which in turn allows lifestylewebsite.com’s advertisers to reach more of their desired audience. Build better products with embedded identity resolution. 2 0 obj Today we’ll walk through the differences between deterministic and probabilistic data methodologies and what really matters. 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� There are two primary methodologies used to resolve devices to consumers: probabilistic and deterministic. A probabilistic approach of the hazard was also included. The Skinny on Audience Buying and How it Differs from Contextual, The Latest Digital Video Viewership and Ad Spend Trends: November 2020, A Streaming Wars State of the Union: Unexpected Successes and Struggles of Subscription Video on Demand in the Year 2020, A Look at the OTT and CTV Video Streaming Landscape in Japan with CCI, Why the IAB UK Gold Standard Marks a Critical Point in the Advertising Industry. 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. Mobile? Deterministic and Probabilistic models in Inventory Control Examples include email addresses, phone numbers, credit card numbers, usernames and customer IDs. Deterministic modeling relies on definitive proof of a user’s identity, such as through a user login. Deterministic Identity Methodologies create device relationships by joining devices using personally identifiable information (PII) , such as email, name, and phone number. A3.3 Excavation - Uncertainty related to characteristic shear strength Now that we’ve covered the different types of data modeling, next week we’ll explore the differences between audience buying and contextual buying. Access unique, trusted data to enhance and extend customer knowledge. Explore our educational videos, webinars, e-books, and more. Probabilistic methodologies can complement a deterministic identity solution in two major ways: expanded reach (finding people who have been matched deterministically across more devices) and linkage curation (confirming device linkages and resolving identity conflicts). 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. Leveraging only one or the other will likely leave you at a distinct disadvantage. 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. Discover the latest about martech and LiveRamp. 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. Probabilistic data modeling identifies users by matching them with a known user who exhibits similar browsing behavior. %���� But it’s certainly not insurmountable. Uncover omnichannel insights and make actionable and informed decisions on your business or marketing data. Deterministic modeling relies on definitive proof of a user’s identity, such as through a user login. See what’s possible when your data is more accessible and meaningful. <> What Are Cookies and How Do They Work on Desktop Vs. This means that the majority of first party publisher data falls in the deterministic category. The average person today owns multiple connected devices. Deterministic data: Information about people that is known for sure Deterministic data is digital facts about people that we trust are 100% true. Watch Now: An Introduction to LiveRamp Identity, Featured Case Study: Fitbit achieves 2x higher return on ad spend without cookies. As you can see, both deterministic and probabilistic data are necessary to fuel these modern marketing initiatives. (Stay tuned for a future post on the key differentiators of the best identity solutions.). Probabilistic data offers the … Learn more about the largest deterministic graph for activating people-based marketing anywhere. Figure 3 Comparing Deterministic Vs. Probabilistic Proved Reserves. Probabilistic data modeling identifies users by matching them with a known user who exhibits similar browsing behavior. Earlier in our audience series, we defined the various types of data. SpotX is the trusted platform for premium publishers and broadcasters. This fragmented, device-based customer data footprint creates a tremendous challenge for marketers seeking to deliver personalized experiences across channels. Probabilistic vs Deterministic Matching: What’s The Difference? However, that does not mean that probabilistic isn’t valuable. Our deterministic linkages continuously move and change, while the people-based ID they are anchored to stays persistent. The reason first party data is so valuable is because it can be. LiveRamp identity resolution enables marketers to tie disparate data sets together and match consumers to their proxies in a privacy-conscious manner. Deterministic vs Probabilistic Data In our latest Insights Post, we defined 1st, 2nd and 3rd party data. Probabilistic data offers the element of scale. Smartphones, tablets, laptops, desktops, smart TVs, video game consoles, streaming sticks—consumers may use any combination of these and other devices every day. The case of natural decrease of TBT concentration in the Drammen fjord was investigated as a test case. Accurately measure ROI and improve your bottom line. LiveRamp Enhances Identity Infrastructure to Include Unified ID 2.0, RampUp: Worldwide Virtual Summit – Watch On-Demand, personally identifiable information (PII), Conquering the Basics of Account-Based Marketing. 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. THE destination for marketing technology thought-leadership and events. Probabilistic modeling is much more complex and nuanced in the way it identifies a user as it relies, as the name suggests, on probability. In 2016 a methodology to communicate the results of a probabilistic approach will be developed. Probabilistic evidence can also be used to corroborate incoming deterministic links to improve accuracy. Transform consumer data into deterministic person-based IDs. This data is generated through collecting anonymous data points from a user’s browsing behavior and comparing them to deterministic data points. %PDF-1.4 For example, a luxury brand that wants to target a new premium product at high-net-worth consumers could opt for this approach as there is no harm in also reaching that audience’s network since they likely share a similar wealth profile. The reason first party data is so valuable is because it can be determined true or false. This data is generated through collecting, a user’s browsing behavior and comparing them to deterministic data points. 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. 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.
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