How are AI and machine learning helping deliver advertisers’ messaging to digital audiences?
We have been using artificial intelligence (AI) and machine learning over the past year in different audience creation and activation strategies, and one excellent example of their use is in the establishment of demographic-led segments. At DMS, we represent more than 35 publishers, the majority of whom are non-login, or have shy login data, resulting in a demo audience offering that is quite limited and unscalable. Since the demand for these audiences from big consumer packaged goods (CPG) advertisers is quite high, we partnered and worked with 1PlusX, a Swiss-German AI-powered data management platform (DMP) on their demo AI models to create demo-led segments. Simply put, the model looks at the behavioural traits of users across publishers to determine, for instance, their gender. In 2021, we went through a testing period when we adapted their advanced AI models to our region and established an accuracy measurement system. As a result, we were able to increase our demo audiences from 2.5 per cent to 33 per cent (x16), with an accuracy of 72 per cent for females and 75 per cent for males (both higher than the benchmarks used in our markets). We can now proudly say that we have 33 million addressable gender-based segments to be activated by our partners.
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With third-party cookies being phased out, what technologies or solutions can be used instead to ensure advertising reaches the right audiences?
At DMS, we have been scoping and testing different solutions over the past year and recently onboarded IBM Watson contextual targeting as part of our partnership with Permutive, our DMP partner. IBM Watson is an AI contextual targeting solution based on natural language processing (NLP) and works by crawling and classifying content based on different features such as categories, emotions, keywords, sentiment and concept. The classifications are used to enrich the pageview events of users who have read articles on any given topic, which in turn are used to create targetable cohorts. Moreover, contextual targeting does not rely on third-party cookies or any personal information. The use of machine learning to improve the relevance of contextual segments ensures brand safety and brings us closer to brand suitability.
In addition, we have been in talks with different ID solutions, such as ID5, Neustar Fabrik ID, and UID 2.0 (through our partner TTD), each of which has a different model and is based on different variables. Although Google seems to stand by its decision not to support or build alternative ID solutions to third-party cookies, the changes we are seeing from the king of data reveal that there is still much to discover.
How do you gather and segment your audience data?
We gather our users’ behavioural data using edge computing technology, allowing us to process the data in real-time and, most importantly, in a privacy-compliant environment through Permutive. Diverse algorithms are used to create segments based on a four-tier granularity and the fun begins once we create an audience. Fuelled by machine learning, we use audience discovery to uncover unique behaviours generated by the users within our ecosystem. For example, if we take the ‘fashion audience’, we can identify when they are active during the week, at what time of the day, their interests in terms of content, engagement level, content journey, and the list goes on.
What ad tech or data tech solutions are you excited to work with in 2022 and how do you see them affecting data collection and analysis?
2022 will be the year of data solutions for DMS, and we plan to focus on three solutions (alongside the AI-based demo audiences and AI-based contextual targeting mentioned earlier):
1. 0-party audiences, or declared audiences
The objective of 0-party audiences is to create effective audience segments using data collected via surveys. This is done by bridging two state-of-the-art technologies: Qualtrics as a survey engine and Permutive as a DMP. The responses captured in the survey are moved through a bridge integration to our DMP where advanced analytics and lookalike models are applied to scale the answers and create addressable audiences. We have tested this concept with auto, telco, F&B, and tourism brands, where results showed an increase in both media and brand impact metrics. This year, our focus is on optimising this solution and activating it with our advertising partners.
2. Data clean room
The concept behind data clean rooms is mapping advertiser and publisher data in an encrypted, secure and safe environment. We implemented our clean room, ‘Permutive Vault’, last year, and initiated testing with a global automotive brand through in Europe. This year, the focus is to provide advertisers with a safe passage for data partnerships.
3. Choueiri Group data transformation
Our top priority plan is building a data hub infrastructure fuelled by advanced analytics, AI and machine learning. We have already started with ingesting 75TB worth of DMS users’ yearly level data, and overlaying it with various data streams such as Doubleclick for Publishers (DFP) and Google Analytics (GA).
After testing one day’s worth of user-level data (200GB), we recorded astonishing results: on a campaign optimisation level, the performance increased by 450 per cent during the last week when the learning was applied.
