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Kantar utilise l'IA et le machine learning pour créer de nouvelles solutions innovantes qui aident les annonceurs à créer des publicités plus efficaces.

Nous testons des créations publicitaires rapidement et en grand nombre, ce qui permet à nos clients de lancer des campagnes en un temps record et d’accroitre leur ROI. Link AI, disponible sur Kantar Marketplace, est la solution la plus rapide, entièrement automatisée et alimentée par l'IA pour guider l'optimisation créative et média de Kantar. Elle s’appuie sur une base de données de plus de 250 000 publicités et 35 millions d'interactions humaines.

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Grâce à UMMO (Unified Marketing Measurement and Optimisation), vous pouvez avoir accès à évaluation complète de l'efficacité de toutes vos actions marketing. Nous déployons une approche unique de modélisation économétrique pour comprendre la contribution relative de tous les éléments du marketing mix, ainsi que leur impact à court et long terme sur les ventes et l'equity, afin de calculer un ROI marketing total.

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NeedScope a contribué à la croissance des marques dans plus de 15 000 études dans 115 pays. Vous pourrez comprendre les besoins fonctionnels, identitaires et émotionnels de votre marché et comment y répondre en vous appuyant sur un cadre psychologique validé et l'IA de Kantar. L’analyse des images et des vidéos de votre marque vous permettra de vérifier dans quelle mesure elles sont cohérentes sur l’ensemble des points de contact avec le positionnement émotionnel voulu.

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Kantar BrandNow intègre la technologie d'IA exclusive de Kantar, Trend AI, qui élimine le bruit dans les données et qui permet d'obtenir des données sans décalage et en temps réel. Vous pourrez également comprendre les principaux leviers de la croissance de votre marque, afin d'investir vos ressources au bon endroit et au bon moment, avec toute la confiance qu’offre le cadre d’analyse de Kantar, reconnu mondialement.

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Generative AI is a new and exciting type of artificial intelligence system capable of generating new content such as text, images or videos. Generative AI systems are based on "foundation models", that are capable of ingesting text, images, audio or videos and generating data of any or all of these types, in response to human prompting. Large Language Models (LLMs) are a special class of foundation models that work with text data.

 What makes foundation models very powerful is that they are "task agnostic", i.e., they can be highly proficient on seemingly unrelated tasks that they were not explicitly trained for. For example, LLMs are typically only trained to predict the next word in a sequence of words. However, this training when done at a massive scale, apparently allows the models to write poetry, do math, write code, solve puzzles, excel in exams and much much more.

 Experts agree that we are far from arriving at Artificial General Intelligence – the time when a machine will be able to understand or learn intellectual tasks as a human would. However, foundation models could be a possible first step.

Both Traditional AI and Generative AI are based on Machine Learning - algorithms that enable machines to learn from data without being explicitly programmed. However they differ in important ways.

 Traditional AI models are mostly focused on advanced analytic tasks such as prediction or clustering. They typically work with just one form of data and typically are trained for one specific task that they become very proficient at. They need to be trained from scratch or "fine-tuned" to perform well on a different task. Generative AI models on the other hand are based on foundation models that can ingest data of various types (text, image, video) and output any/all of these types. Generative AI models require orders of magnitude more data than traditional AI models but when provided with such data are more versatile - they can be good at activities they were not explicitly trained for.

 Neither form of AI is necessarily "better" or "worse" than the other. Each has its own benefits depending on the intended use case and in fact in many practical situations, the best solution involves a combination of both. For example, Link AI uses both Generative AI and Traditional AI as part of the same overall framework to issue its predictions.

There are quite a few known risks that we see in the market research industry. One is that the model starts making things up that are factually incorrect or fantastical - a phenomenon referred to as "model hallucination". A second is that the models might not be entirely up to date - we’ve seen this with time series examples where the previous version of ChatGPT gave wrong answers because it was only updated to 2021. More fundamentally, LLMs have no ‘knowledge of knowledge’, so there’s no such thing as a confidence level. And LLMs have no notion of time or temporality, or maths, which is rule-based, so they are currently limited in their interpretation of data to what they can discern through generic associations.

Aside from technical issues, there are also potential legal and ethical issues that arise. Intellectual property, for example: is this a creative act by the LLM, or is it re-hashing someone else’s IP? Does sharing your own data on the open web mean you give permission for it to be used by LLMs? And finally, the quality of the datasets the models use could easily reinforce biases and stereotypes without ‘knowing’.

Use cases for Generative AI fall into 3 broad categories:

1. Making things more efficient: Foundation models can free up a researcher to focus on what is important.

• Summarisation: market research collects a lot of data in the form of words – survey verbatims, qualitative interviews, and focus groups. LLMs could summarise, order and prioritise responses expediting the work of the researcher when creating a narrative for the client. LLMs and foundation models can even summarize videos and images!
• Automated reporting: market research also produces large volumes of quantitative data that need sorting, summarising, and presenting. LLMs could quickly organise and create draft headlines based on charts, tables, models, as well as executive summaries.

2. Doing things at higher quality: Foundation models can do what earlier AI models did at much higher quality, sometimes surpassing a human

• "Attribute" identification: LLMs can identify themes, assess sentiment, brand affinity, brand perceptions, identify emotions, fix for the researcher to refine.
• Prediction: Foundation models allow us to extract embeddings (mathematical representations) that other machine learning models can use to predict some outcome of interest. For instance, does the dialogue in a TV ad help predict its performance? How can we relate people’s qualitative experience interacting with a service representative to their brand loyalty or churn? The quality of such embeddings is often significantly superior to the previous generation of AI models

3. Doing entirely new things: Generative AI opens the door to many new applications

• Intelligent interviewing: already in use by the industry, conversational AI will come on in leaps and bounds, responding to previous answers and routing questions accordingly. And designing quant questionnaires will never be the same again, the machine can help with automating and standardising the process!
• Creative Writing: this could be anything from creating discussion guides, initial drafts of presentations, marketing copy, concept statements to video ads
• Conversational search queries: think of ’an intelligent agent’ that sits on top of data platforms you can query in natural human language. The agent then analyses potentially massive databases ’underneath’ and fetches back the results in natural language.