From Audiences to Outcomes: The advertising industry is at a turning point. AI isn't just another optimization tool, it's fundamentally changing how we plan, create, and measure video campaigns.
In this in-depth conversation, two Dailymotion experts break down what's really happening in AI-powered advertising, and why the old playbook no longer works.
Hamza Kourimate, Chief Marketing Officer explores why the industry must move beyond vanity metrics, what "Promptable Brands" mean for the future, and how AI transforms the three core pillars of marketing.
Cyrille Brun, Chief Data & AI Officer, explains the technical shift from traditional machine learning to agentic AI: how specialized agents work together to balance competing objectives and optimize for what truly matters: real business outcomes.
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00:00Je m'appelle Hamza Kouhimat, le chef marketing officier de Dailymotion pour le business.
00:17Generating un vrai résultat pour nos clients
00:19requires plus que ajouter un layer d'AI sur nos solutions existent.
00:23C'est pourquoi je crois que l'AI doit transformer le cœur de notre business
00:27par réinventing trois main pillars de marketing.
00:31The first one is market research and media planning
00:33where AI can speed up the process from a multi-week journey to a matter of minutes
00:39while removing the human bias and analyst bias
00:42and also introducing a concept of synthetic data
00:45on top of the first-party data that we use to analyze consumer behavior.
00:49The second one is KPI definition and optimization
00:52where AI can help us move beyond vanity metrics
00:55like completion rates, CTR, to optimize both media efficiency
01:00and marketing impact on real outcome KPIs
01:04like brand lifts, awareness, sales, for example.
01:08The third one is ad experience or creative experience
01:12where AI can reinvent how brands experience connections with their audiences
01:17moving from mass communication to one-to-one interactions.
01:20I'm quite critical about what I call vanity metrics.
01:30I really believe AI can help advertisers move beyond those vanity metrics
01:35by closing the gap between having an efficient media optimization plan
01:39and optimizing real impact on their marketing outcome that they measure themselves.
01:44The problem with our industry is that media KPIs today are really simple to optimize
01:50and do not really guarantee a measurement of impact, real impact.
01:54So today it's no longer about only optimizing reach and completion rates
01:58but finding the perfect balance between media performance on one hand
02:02and reaching the right audience and optimizing the correct outcome on the other hand.
02:06We are entering today an era where consumers accustomed to interacting with tools like ChatGPT
02:18expect the same level of personalized interactions with brands.
02:21I have introduced the concept of promptable brand
02:24which is a brand that one can simply ask a question, express a need
02:29and receive personalized information and responses
02:32or any piece of content within the ad experience itself.
02:36So today it's the end definitely of a top-down, one-to-many communication process
02:42to a one-to-one communications
02:44where brands can interact and get instant feedback from their audiences
02:48and vice versa by the way
02:50and which changes completely the codes of user engagement within the ad experience itself.
02:55We have launched Ray, the agentic advertising platform by Dailymotion, which basically helps marketing leaders, media buyers, optimize their campaigns
03:11by leveraging four main pillars.
03:14The first one is the agent that helps do the media planning and market research.
03:20The AI leverages tons of first-party data from Dailymotion, from video consumption, demographics, browsing patterns
03:29but also data that comes from their interactions from existing or past video ads.
03:35The data that comes from our DMP is leveraged to transform those signals
03:40into real consumer behavioral patterns that can help identify and define audience segments.
03:46The second pillar is the engagement pillar
03:49which basically transforms the video asset coming from a client
03:53into a way more engaging video ad experience, leveraging neuroscience KPIs, sentiment analysis
03:59to make sure that we are hitting the right concept and experience
04:04that will engage with the audience that we have defined.
04:06The third one is the activation part, which is our smart reach activation engine
04:10that finds the perfect balance between optimizing the media KPI
04:14which is required by the agency and the media buyer
04:17and reaching the right audience and getting the right outcome.
04:19And finally, the fourth one, the post-campaign measurement part
04:23is a way for us to provide consistency
04:27and a clear and comprehensive media report or campaign report for our clients
04:32that move beyond only showing how the vanity metrics and media KPIs have performed
04:38but more towards understanding the outcome that the campaign itself has generated for the client.
04:44I do believe that those four pillars are fueled by one unique selling point of Dailymotion
04:51which is our first-party data added to the synthetic data
04:54that we're able to capture leveraging AI today.
04:56And the combination of those data points are what makes our AI unique.
05:01AI is as good as the data you give it.
05:03Ray today helps marketing professionals and media buyers focus on what really matters.
05:09Moving from audiences to outcome
05:12while optimizing KPIs on both the media part and the marketing part.
05:16Hey, hi, I'm Cyril Brun. I'm a Chief Data and AI Officer at Dailymotion.
05:20Our strength at Dailymotion doesn't come from the fact that we're building our own large language models.
05:31Our strength comes from the fact that we'll build many highly specialized AI agents
05:37that are called upon by supervisors, but also AI agents,
05:41that are able to understand language and nuances within questions to call the specialized agent
05:47that is the best suitable for the task requested.
05:50Some agents will actually use machine learning models to answer specific tasks,
05:55but we also have other AI agents that just understand language
05:59that are plugged with our own data or build a little bit differently.
06:02Agentic AI doesn't replace machine learning models.
06:05However, it brings a more holistic view on solving a problem with different tasks.
06:12Usually, machine learning models will actually optimize for specific metrics
06:16in a very greedy and crude way, going to optimize that metric at all costs,
06:21while AI agentic will try to find different ways to optimize in a more human way the question being asked.
06:32One key use case of the AI agentic is that it helps us solve the vanity metrics problem
06:42by optimizing for more complex objectives for advertisers.
06:47Classical machine learning models will optimize for specific metrics like completion rate in a very crude way.
06:55For example, by delivering massively on contexts where the player is on autoplay,
07:01where the attention rate is pretty low.
07:04So it will hit the KPI, but not the advertiser objectives.
07:09So the idea is to be able, through the supervisor work,
07:14to balance a little bit different objectives,
07:17whether it's the KPI for completion rate,
07:20but also more complex marketing objectives like attention or brain lift.
07:27The idea is to build a system that is able to understand those advertiser marketing KPI
07:33and to ask upon the right specialist to solve the right problem at the right time with Nuance.
07:39The supervisor will actually guide the different agents towards different objectives,
07:44sometimes pushing more for the completion rate and sometimes more for more advertiser marketing objectives.
07:57Our key advantage today in the market is that we are able to combine the AI agentic system,
08:04machine learning expertise that we've built over the years,
08:07and historical data we gathered over the past years.
08:11So large language models, they are great to answer natural language questions,
08:17but usually they are not fine-tuned to our business use cases, nor to our own data.
08:23So Dailymotion has invested over the past years in delivering machine learning product at scale,
08:29whether it's for optimizing advertising campaign or recommendation algorithms.
08:34We also have a lot of data, for example panel data, creative data, campaigns data,
08:40which we use to fine-tune the large one language models we use.
08:44And overall, what we see as a key advantage is the combination of those three assets.
08:56We see in the market a big shift in the way we define our jobs.
09:00For example, developers will actually spend much less time with tools like cursor, with cloud code,
09:08defining basic functions, they will spend much more time thinking about overall architecture.
09:14The media planner, for example, will spend much less time in back and forth with panels, with agencies,
09:22which we've built, spend much more time answering, getting questions from the tools to iterate on better understanding their personas.
09:34Before, back and forth with agencies and panels was limiting the quality of their work.
09:39Now, they will spend time where it's most interesting, understanding their persona, fine-tuning them and going into strategic thinking about how to use them.
09:50The jobs become less operational and much more strategic.
09:59Make AI work for you.
10:01Make every impression count today.
10:03Reach out to us to schedule a demo and see how Ray can be beneficial for your business.
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