---
title: UserJournAI – IA comportementale pour comprendre, valider et activer les audiences
description: UserJournAI est une plateforme d’IA comportementale qui aide les marques, agences et régies à identifier, comprendre et activer des audiences à partir de comportements réels, avec validation avant activation et approche privacy-first conforme au RGPD.
image: https://userjournai.com/hubfs/1.UJ-Home-2.jpg
---

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)](https://userjournai.com/fr/accueil?hsLang=fr)

- [Plateforme](https://userjournai.com/fr/plateforme?hsLang=fr)
- [Comment ça marche](https://userjournai.com/fr/comment-ca-marche?hsLang=fr)
- [Cas d'usage](https://userjournai.com/fr/cas-dusage?hsLang=fr) 
    - [Retail](https://userjournai.com/fr/cas-dusage/retail?hsLang=fr)
    - [E-commerce](https://userjournai.com/fr/cas-dusage/e-commerce?hsLang=fr)
    - [Agences Media](https://userjournai.com/fr/cas-dusage/agences-media?hsLang=fr)
    - [DOOH](https://userjournai.com/fr/cas-dusage/dooh?hsLang=fr)
- [Ressources](https://userjournai.com/fr/blog?hsLang=fr)

[FR](https://userjournai.com/fr/accueil) [EN](https://userjournai.com/en/home) 

[Accès plateforme](https://platform.userjournai.com) [Demander une démo](https://userjournai.com/fr/contact?hsLang=fr)

[Plateforme](https://userjournai.com/fr/plateforme?hsLang=fr) [Comment ça marche](https://userjournai.com/fr/comment-ca-marche?hsLang=fr)

Cas d'usage

[Retail](https://userjournai.com/fr/cas-dusage/retail?hsLang=fr) [E-commerce](https://userjournai.com/fr/cas-dusage/e-commerce?hsLang=fr) [Agences Media](https://userjournai.com/fr/cas-dusage/agences-media?hsLang=fr) [DOOH](https://userjournai.com/fr/cas-dusage/dooh?hsLang=fr)

[Ressources](https://userjournai.com/fr/blog?hsLang=fr)

[FR](https://userjournai.com/fr/accueil) [EN](https://userjournai.com/en/home)

[Accès plateforme](https://platform.userjournai.com) [Demander une démo](https://userjournai.com/fr/contact?hsLang=fr)

IA comportementale pour comprendre, valider et activer les audiences

# Comprenez, validez et activez vos audiences *à partir de comportements réels.*

UserJournAI aide les marques, agences et régies à passer d’un ciblage fondé sur des profils supposés à une activation construite sur des comportements réels. La plateforme permet de comprendre les audiences, valider les décisions marketing et activer les bons segments avant l’investissement média.

Comportements réels Décisions validées avant activation Activation sans dépendance aux cookies tiers Approche privacy-first conforme au RGPD

[Demander une démo](https://userjournai.com/fr/contact?hsLang=fr) [Comprendre la plateforme](https://userjournai.com/fr/accueil#comment)

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Group](data:image/png;base64,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)](https://www.mci-group.com/) 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Group](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArgAAAK4CAYAAACfwI4vAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAADSFSURBVHgB7d3tldRGtzZg+V3+/5gIMBGAIwAiAEcAjmBwBOAIgAgwEcBEAEQARICJAIhg3rnbp+xhPAPz0a0qbV3XWlqNn3NsWi2VdGtrq/TDwaEJAACK+H8TAAAUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFDKjxOL8ddff02vX7+e3r9/P33+/Hl69+7d5rMtR/3000+b5eeff978840bNzZ/vn79+ubP+b8tSVvfrHt+h7acZf3zmXXOuuef82e2J799tkW2z8ePH//ZLu0z8ueTtO0UbV/NZ9tubZu1/xsAnMUPB4cmhpRw8Pz5802ozXI8xF1GQl6WO3fuTLdu3Rou8LZAu7+/v1n3/HlbWuC9e/fudPPmTYH3HLJdsj3evHnzzwXXNvfL07RtlqVtM6EXgNOsIuD+9ttv05xyMn78+PF0UQkNf/zxx+ZzLvfv39+E3YS+nrLOCfUvX76cJThFgtKjR482wUlo+q9sk11caFxWu0i5d+9e9+32559/bkL/nHKMWdqdGIDZHKxAVnPO5fBke3Benz59OjgMWQeHJ6zZv+/x7354sj6Y0yjrnuUw6B98+PDhYO1evXq1+S1G2CZnWQ7D7ma/7bXt8lvNvc72U4DTCbg7WM4bcF+8eLH5d+b+nt9bh10H3QTbBw8eDBmiErjXZqQLjcssPS5SBFyAsZhFoaPcgv/999+nX3/99dSHcHrJ90nbQto7tv3dst5pwbh27dr05MmT2VoRziMtC/l+o22XXUjrwe3bt6crV65s1nvE7XEeaRfItss6zdnmA8A4BNxOEpx++eWXTcAbWcJCgsK2gl4CR9Z7CUGqbaP0A1fUgm3VIHh0/dZwoQLAvwTcDvKgToLTUk662wp6qdouLWwkhKfC/vTp06mK6sH2uKxjKrq7uBsBwJgE3JllhoCExaXdBm5BL9//Iv9uwlSqtkv14MGDxYfchLs1Bdvj2t2Ii+zDACyLgDujnFjT17pk+f7nCUctVFUIVAm5SwxHR3ue196T2nrL83sAUJeAO5O0JSw93DZnfSiuhduR5k69rITcJd3mPtrzzL/ye2TfBKAmAXcGCUQJhVW0loNvtVm0loZqPY9nWfdRZIYOD1idzgwLAHUJuDOoGDKyPt+6zZsHeipVbo/63rr31irno8/QAQC7IuDuWKpoVStoCVAnVcES/qpOrdWctu695aLC/K8ArJ2Au0MJttWraMcrmQm2a+n3HK2Km99eSwIACLg7tYaHWFIpbNXCBKtUrNfi6Lr3ltkd0vO89LeQAcA2CLg7kjk311JJa5XMfK6tejhCFTfz81aZoQMAtkHA3ZE1zbOZKmZCVkL92vSu4qZym6nLAIB/Cbg7kCrm2iqZaw5ZvS5m0nOrcgsA/yXgwiWlgjt372suoDIVGwDwXwIubEFaNObS5rn1QBkAnOzHieH8/PPP008//bRZmoSZBJs1hJqsf5ajst4jvzgiVdyHDx9Oc0jl1lRgAHA6AXcACbLppbx58+Z069atr4LtcQk2CVNv3rwp81BX1rmt+40bN765/gm5WfJw1UgvM2htCt/67tuQfl8vcQCA7zhYgazmiMthoDt49erVwUV9+PDh4DAYD7t+c6z/3bt3h1mfJ0+eHOxS1neUda2ybEuPcZj9AYCTqeB2kCpfbmdfduaB3MZ/9uzZtLe3t5nkfym3rdv3TsX2sv+dFy9ebN4WN8ILJnbdQrHkF4e0lpvj7SeperdlLS04AOyegDuz3IJPKDveY3rZ/+ZhJXQRr2lNGM+rfLd5Kz8XCvnv9Z5VYJetA0t7iUb278Pq+nT9+vXNhcxZ9/fWa53l/fv3//wZAM7jh5Rxp+J++OGHaQQtiO6qTzNBYOSn61O1TrjdlREquYe3jbd68RIJtteuXZtGl/W+d+/epp98m79B9udcPOzv728+txn0t3X4y8XV3D3xu9jXAKowTdhMciLaZbiNBOhRX7jQKre7lHW/bNvDZe2i2jj6W/Hym2ffTuDKNt526MqYSTU4bS35O/J3bTtEA1CLgDuDnKB3HW6bBMnRTvz5PqmuzmGuqbpO8/Hjx2mb8rayUWfLaME2y5wXFvm7Enbfvn27+RR0AThOwJ1BQtdcJ+GE6NGquAlAc0n46VnF3XYFd4SH547LPvb48ePZg+1J3yOV3FR1BV0AjhJwdywn3bkDZ/og56gWn0WPW8k9q7jbDLip3I72YFnaYFI5He0iqgXdBG9BFwABd8d6hK3Ws9hbgkaP9f/eyzJ2aZsP+OVlFiNJ+0vC7cgBMsG79egCsF4C7g4lCPQ60ebNYL2dZ3qobev1u2+r4prZAkZ6Y1kuVObqo76sNs+ytgWA9RJwd6jnrfIRKrg91z/zr/ayjZA7UvV219O77Uoucnr3CQPQh4C7Q70fwOlZvUqvZs+/f8mhJgF5lJkTlhpumzY9X+/ZNQCYl4C7Iz1vzx/9Dr30riDnt+/Vh3vZCu4orQlLD7dHZT2EXID1EHB3ZIQKYs+APUIP8FL7L0doT8gFSpVw2wi5AOsh4O5Izx7Q5urVq1MvaVHordd3uMzLHlL97V3BzYVBptuqKCFXTy5AfQLujoxQPbxy5crUQ4LlCPPwjjIX8HmM0J6QntXKsw+8ePHC7AoAxQm4OzJCBbNXBXeUYLnEgNu7PWHOt+71kv0iU4gBUJeAuwOjBIReAW+U9e/ZonFRPSu4Pd6610vaFNayrgBrJODuwBIrh9vk9u/F9G5PSPV2TfuuB84A6hJwd2DtAZeLef/+/dRLz7fu9WKcAtQl4MIgelZw9/b2JgCoQsCFQbx7927qZYRXOwPAtgi4MIDPnz9f+g1oFzXCW/cAYJsEXBhAz+rtvXv3JgCoRMCFAXz58mXqZYQ5mwFgmwRcGECvCm5mEhBwAahGwIUBpAe3B+EWgIoEXBhArwfMBFwAKhJwYQC9KrhmTwCgIgEXVuz69esTAFQj4MIAerUoAEBFAi6sWGZRAIBqBFzK+vjx48S3CbgAVCTgAgBQioALK9Zr9gYA2CUBFwbQa7ouAReAigRcWDEBF4CKBFwYQK8K7pcvXyYAqEbAhQH0ms3g3bt3EwBUI+DCAHpVcL1gAoCKBFwYwP/+97+pBxVcACoScGEAN27cmHpIBVcVF4BqBFwYQK8Whdjf358AoBIBFwaQh8x6hdyXL19OAFCJgAuD6NWmkD5c8+ECUImAC4O4devW1EPC7dOnTycAqELAhUFcv3596uXJkyfT2ni4DqAuARcGkQpurxc+rK2K++eff07Pnz+fAKhJwIWB9OrDjUePHq2iFzeV2z/++GMCoC4BFwZy9+7dqZeE2zUEv6yj9gSA2gRcylpiiLl3797UU3pxX79+PVWVcJv2BABqE3BhIOnB7TWbQvPbb7+VbFVIuE0bBgD1CbgwmJ5tCpHK96+//jpVItwCrIuAC4NJm0Kv2RSatCn8/vvvUwXCLcD6CLgwmITbBw8eTL2lH3fpD50JtwDrJODCgPb29qYRJBwuNeSmAi3cAqyTgAsDShX3/v370wgSEpfUrpAe4l9++WWVb2cD4G8CLgzq4cOH0ygSFq9duzb01GttHt+E23fv3k0ArJeAC4P6+eefh+jFbRJub9++PeQrbjO3bYLtWt7GBsC3CbgwsFRxe8+ocFRCblonMo1Y72puguzTp083leXM3evtZAA0Ai4MLOF2pFaF5uXLl/8Ey7nffNZaEfL3p8It2AJwnIALg0uI6/12s9OkNSBtCwmbaV3YVdhMqE2Qzt915coVrQgAfNOPEzC8x48fb3pMR9VaF+LGjRubQH7z5s3Nn9NLfF4Jr/lvvnnzZhNsswi0AJyVgAsLkKCYkLuE6boyg0GWNk1X2iwSco9+Hu0rblXfFmrbJwBclIALC5FWhf39/dl7Xi8rgdW0XQDMSQ8uLMizZ8+GmlUBAEYk4MKC5Bb/ixcvJgDgdAIuZVV9KCkPcKUfFwA4mYBLWZWfuk8/7ojz4wLACARcWKjMBXvv3r0JAPiagAsLlhctCLkA8DUBFxYuITctCwDA3wRcKCAPnenJBYC/CbhQRHpyza4AAAIulJJWhbdv327mywWAtRJwoZgbN25Mr1692syXCwBrJOBCQangJuTqyz3d1atXJwBqEnChsPTlfvjwQcvCMelVvn///gRATQIuFJdwm5D77Nmz1QfdrH96lE2rBlCbgAsrkYpl2hbWWrnc29vbhNv0KANQm4ALK5IKZiq5qeiuJejmYbsE2ydPnkw//fTTBEB9Ai6s0PGgWzH4JdimYp1F1RZgXQRcWLEWdFPhrNKjezTYmioNYJ0EXGATbFPJTUW39ekuKeymAp0p0T59+iTYAjD9OFFWTvqpys1tlNvBCWg9Qtr//ve/ackSDltAfP369bS/v7/5fPfu3TSS7N/Z1xJsewfa7Gdzfwf9xACn++Hg0FTcX3/9Nc3NvKNUk3GUoPvmzZtN2O0ReBNoEyRv3ry5+RTyADjJKgIusH2fP3/+J+h+/Phx89n+t8tIaG3V2Vwo5o1j+XMWgRaAsxBwga1L0M1y9O7JSXdS2p2OFmrbAgCXIeACAFCKWRQAAChFwAUAoBQBFwCAUgRcAABKEXABAChFwAUAoBQBFwCAUgRcAABKEXABAChFwAUAoBQBFwCAUgRcAABKEXABAChFwAUAoBQBFwCAUgRcAABKEXABAChFwAUAoBQBFwCAUgRcAABKEXABAChFwAUAoBQBFwCAUgRcAABKEXABAChFwAUAoJQfJ4b1+fPnr5bjfv75580CAMC/BNwBvHv3brO8f/9++uuvvzZ/Pi3UnuSnn36abty4sfm8devWdP369X/+GQBgbX44ODQxq9evX2/C7MuXL/8Js7uQsHv37t3p5s2bm8ALAJzseLEpS87P+TxNu5PaCk0pMOWfnXP7E3BnklC7v78//fnnnzsLtN+SAXf//v3p3r17W2tryHr8+uuv05wS2h8+fDjBNh29c/Lx48d/TmzfO7lFG0/tJJfPq1evOsnB4HJefvPmzeZzF8WmnK+ypMiUT+Yl4O5QBsvTp0//qdSOIlXdvb29Sw+4nPivXbs2zSkh/dmzZxNcVPbbdhclny3M7kpCbsKu9iHor52XU2z63sXrNmXM59x7586dzSe7J+DuQAZNG0A9qrVnlZPuo0ePNlXdixBwazhaqTze+51q5jar/j1kfXKRmUpNPkcYk5UrO3/88cfUQwJELtxHlPPB3Ptd7iLkeLktGT85X8xtW3ftcjGbfTOfvbUL3uyv7vLs0AFb8+HDh4PDA0ouGBa1HA62g1evXh2cV9Z37u+a35fz+/Tp02YbP3ny5ODBgwcHhxWEzXY/DAXf/c0vsm/0lvXNuh6eRGbfRy8y/rJfL/F3PknP33FU+W5z/x7Z97fpsLDQZbte9pifcTXyceAw4B4cFsMO2D4BdwtyMk1oGHUAnedAktB6VgLuuN6+fbsJePm9LntyXUrwyjh88eLFIkLtaUu21WGV7FzjcDQ9f7tRCbgXXy56zM8YWtKxIPuIoLtdXvRwSbn1lNv0h2FiWrq0VNy+fXuIWzicT+sry0N/V65cmX755Zfp8KJr9j6zHrLuufWYcZj1X/L+m22V28BZl99++81YhAvIsTDHwCWNn4z9tJRk7D9//nzi8gTcC8rOmDCYEDFyn+15tfXq1UfH2WVbZTtleyXUZl8cpcd0Dln/hMCse0JhtfVuF5wuOuFsKpyXjwbd6sWJXRNwLyBXV0u7OjyvBIbff/99YiytUpuDeA6A2U5rCz8t2Gb9EwKry/ZtQXek2VhgJLm4r3Rebg9xKzZdnIB7Tgl9ubpaQ5UsbRc5YKylIjiyHLRz+z0HvFQn1ljRa60I2SfXEGyPyzbPuifcq+zAv3JcyPGx4rmqtSwZ8+cn4J5RBk4qKBV6bc8jFaOcVA2uPlKVaNW7NbUfHNfCXcVWhPNKuFfZgb9lHPSYvmxOOf/m+JdzAGcn4J5B27nW2gfX+ppUcufR2hAqPDR1Wfktctck+5+LrK+p7LB2awi3TXtzqAvbsxNwv6OFu7WfRITceaQ612ZAsM/9fWG5trsm56FPj7VaU7g9KutsvJ+NgPsNwu3X0q7gwbPdSJW2TQ1lf/v3QU6/xdnkpJd9xwUoa5A7XGsMt42HwM9GwD2FcHuyVBhdPW5P28/sa//K/rWWBzm3qVX/7UdUlkJL7nCtXe5s5aKW0wm4J2gPlDlRnCxXj5rdL+foywnMcfq3/CY5YK+5MnNZ7YLJdGJUlP07faj8TcHp2wTcE2QACbff5lb6xR2dEYC/tYvKNU7/tW0t5HobEtXktrzzztf05J5OwD0mO4qK2ve1ahtnZ0aAk7Vwq+q4PflN0+Yh5FJFzs3uHJ4sIddY/y8B94hUj1TVzk5IO7tWVTMjwNeE290Scqkgx0/n5m8z885/Cbj/JzuGpxLZhTzxm5YEIe6/hNvdy4nPb8yS6bv9vlYs8HDuvwTc/2PHYBdy0ZSAYd/6r/w2gtfueWiWJUtbguPE2SjUfU3Anf7u7XHwZ5u8pODbMub8NvNR3WGp7LPnk1bL3DVEwNXbw9aZqunb9Lr3YYolWIccXxXtBNxNEIFtaVOAObiczC20vrJ/qu5AbWY5+tuP04qlkrTEIPLzzz9PN27c2HxevXp1+umnn/753yM7d5aPHz9uqohZBK7dy9PqeWqd07lN3l96wm/evLk5hgA15WI2/ct3796d1mrVAXcpkyMnwCY45aR069atfwLtebSgmxBmnt/tS1XM6yO/Ta/7ONKq8Pbt2wsdS4BlyN2yi2aGClYbcJdQvc2Oube3t5UdNNWaLAnKWe+EXIFjO/I76in9tuxvS/qNMt4y7o7eJWl3SJpUojN+vnz5srg7JfmeuSh7+PDhBNS0+nF+sFKHJ6uDrP6Iy+GJ9eDVq1cHc3j27NnQv8Xx5TCgH4zkMLQt5re76LKNfXEJ+1jG3ePHjw8+fPhwcFH5dzOmDm8LDr++h6H9Uut6ml7rk31sVD32/+zP25T9ute23cW+knNJxvuLFy824+DTp09frW/++fAux+b4l/+//P8fFokWtZ5ZMs6Pr9tarDLgZocdcUfMoJsr2B53eIU35G9yfBkp4D558mQRv9lll8vukyNfBOTgn31/FyeAFnZHv5jetl7rIuDudtsuPeBu4wI2lnQR25acq9ZolQE3IWm0HXBvb++gtwzc0SttowTcXPWP/Dttc7lMwB11n9plsD3JyEE3+/I29VoPAffrRcD9e5w/ePBgZ4WjJVzE7mJfWIrVBdzskCPteD2rtifJ7zPybZgRAm5uW+XAOepvtO3lMvvnqBeTvW7ZjXinZNvBsMp6bJOAO/8y5wVs5Dg5ctAdKWfMZXXz4I40g0AeWjnc6TYPs4yifSdTCJ2sTZZvqqvvy2+VhzlHkQfFsm/nDWq9nirOg3aHF5H/eWCtp2ynpcwoA9+T82nGWMbanOO8/b2jPtCVGZTWZnUBd5SN3ILkSCe6JgeFTCF07969iX+1152aeeJsRgpNGWfZp0e4mGxjf6T5Kb02mQoSLnufV0e8iI3Mibu2wsyqpgnLxh2hgjtyuD2qVd/WeOV3kswpWC3c5mKmTYmVzzYlVlviItX8UcZa5PtnvI00F2TG/osXLzYnwxEuBLK9Ms5d1LJUz549G+ZFO+0cn7t9o7yyPWN8f39/XWP8YEUOr2CG6IVJD+eSpJdrhN8tS68e3CrTgbXpcdJPt8v9cJR+vfSTjz5Fzih9udvq2ez1/fXg7mZ7NqP24OZ5iJHPqYeBcpjfam0Pm60q4OZpyt47WE5mS5OAMErzfI+Am4PnCOt+mYPaNqbHOY8R9pd8hznX+TJGOQlu40GUntt7VALu7pZtzwKyC6MUidY2J+6qenB73zLNbYslvvEqt3Zz+2eN2kNlS5Ntln60w4PZ5lZZXiM8V0vMCG/0ag+Ujd4G1KQHdoQHO3MLE5bi8MJ9qF7206QdaYRjUdoURmmZmMNqAu4IGzY7+VKlRzMhaW2W9jrjbKcEuwTbuZ8ibvJqyN4S7pcSbiPbKceH3n3CI816Ad+S19gv5ZzULrhHeA5gTRexqwm4vcNtmt+XPvVWQsNID+rsWk72Sznht2A7wrRzve+UZKwt8WIsgbz3FEMjPRwIp8lYWdrMHyOM78hsCmuxmoD7/v37qadcbS5dwu0oT6nu2lLmBm1P644yn3LCUe+K96jzUJ5Fgnnv7ahNgdHleLdEI4zvHJ/XMl3YagJuz5NudugqL064c+fOtAajtya0HtvMtzjSi0LevHkz9bS01oST9A7oa6rwsDxLH+MjXICv5S6NFoUZVJp3rlJYP83orQnZBnlpwYgPLPY8cOakV+EOQ7Zvz4uWXNh5mQkjWuqD2kf1Ht/RuxAxFxXcGYxUYduG6m0KI7cm5KnhUWcH6N2/mQvJpVdvm95VnrWcAFmWJbcfHdV7PdZyASvg7ljCbZWTblP5TSijtia0XtuRH54a4UHOKnrfKVnTVEIsQ5U7NNHeHNnLWsb3KgJuz8BS8XZ+Bma10B7ZT0ZsTWivmh39TkDPql9OfNX2yZ7ze5pJgdEsYb7b8+hZrFjLg2arCLg9N+TNmzeniqq1XcSI1dtUy5fywoKeVYGKDz/2nHlFDy6jqTAT0VG9s8EaxriAu2MVK51RrTI9YvU2B/R8p6XMPdzzgFmtuhPZ7r3GWY6ZQi6jyHm02rm0d/ti76lT57CKgPvly5eph54nqF2rdrAZ7cGyPISwtInMe1VwK95NaHqumz5cRlF1jPe8MNeiUEReW9pD1eptXL9+fapitOptKrdLmwqnZxgScHfj48ePE4yg6vzrPc+jWhS4lMqvtU14r7J+I1Vvc0W/tMpt9KwGVLrYOs4JEOoWi3pewKrgFtGrElG5ghuZk3XpchIf5Ynx7C/Pnj2blqhnGKo8znpeSK7ldZ6Mr3Krn/G9Oyq4XFiFW8MJtyNUqto8t0utive8nV39zXq9ArwKLiOoXijqtX4CLpdSuUWhilHaE168eFH+QL4La/jN7BesWfX9v9cFuh5cLkXAHdso1dvMmLD0KmSv33ENY6zXOqrgMgLnUS5KwN0hPWxje/78+dRbqhNLmzFhJAIu1FZ9/3eHZndWEXCvXr069VA94C55/fLdR5gaLH238C0CLsD5qeByYUsOuC9fvpx6S2uCq3cA5uYhMy6leg9brzfEbUPv9gStCQD0IuAWYZqd3Vjq+o0w922qt1zeGg7SevkBzk/A3aHqJ6alBtze4Tb74/379ycuT8DdHe0zjMB5lItaRcDt+aaQyjvvu3fvpiXa39+feqpYvXURuTtOgKxZ9f3fBezurCbg9gq5Sw2BZ7HUdetZwVW93a6cHMxWshtmb2AEAi4XtZqHzHodrN+8eTNVlHC7xIGZcNvze1ftve01FV9UPwH2upDUosAI3AndDRXcQnq9KapqBXepwf39+/dTL5Wrt1euXJl6qXoRGT2PHwIuo6h6Hl3DHaieVhNwex2se1cMd2WEeWQvouf3vnXr1lTV9evXp160Ae1Gz6o8HFX5TmgvKriF9DwBj/BK2G0aYZqti+p5QNnb25uq6tmvudR98Sx6nthVcBlF1YvYng88C7iF9KyeLbXaeZo//vhjWqKefcM5mPRqk5lDAm7P+aar9uj1DO+V91eWpeqd0J7BfQ0Pka6qRaHXBs3grFJlSpD4888/pyX6+PHj1Mvdu3en6noGomp3SSLHjF7BXbhlNO6EblfPu9pzWdWrep2AL2+p1dvoebV8586dqTp3Sbar5zFDwGU01cZ476KXCm4xPU/AqXou/Tbqkqu30Svg5kBS+QGzpveDZpV6cTPWep7Qb968OcFIMr4r9eL2Lhat4SJ2VQG390H7119/nZbs9u3b05L1usBYQ7iN3gfMJd9dOK53z+EST34jFxBMBbUdVe6E9mw/irXcoVldBbdnWT5Xn7///vu0RAkPS69A97r6X0vA7V2prtLrnnHWM6wv+YHIUYOkgLsduYNY4bfsHdTXMkPKqgJu9H7Y58mTJ9PTp0+nJUloePTo0bRkPcP5Gpr5G1Xcy8vJr+f+uuQLshHDT+V5mueW7bv0MZ7zae9Wv7UUXVYXcO/duzf19uDBg8VUmnKi/e2336alcztoHr0fpsu4WvLDKNlPe19MXnYb9qwOjRgme87eUlGKREu+aBjhfLqWosvqAm7CxghPD6Yfd/RBmpNt+m4rzDH65cuXqYfsa2t4WrXp3QYUOYEs9TZm75Nftt2Sp7QbMUxWfhFJL1r9LkcFt6gcwO/fvz/1lhNwwuOoB79K4TbMJzqf3uMrY2uJD3Tm5Nf7eLD0+ZpHLBoIuNuX33RprX7ZN0do9VtLuI3VBdwYZU7SFnJHG6jVwm30fIPZ2owwvnICXFKv3ignv220cPXc50cLkzmG6sHdjaW1+o1y0b2Glw41qwy4uYIZ6SomAzW3XEa4rZr+xV9++aVUuI1e67PGgDvK+EpgXEKVZ5STX/bVbWy3nvv8aK9trviGvZFk3Ix+rhqtYLSmOa5XGXBjtKuYNM4nWPa6Ik24TsjOAcOUNtuzpv7bo0YZX7l4HDnkjnTye/jw4bQNvff5/f39aRRLfjHOErS7oKOG3NHC7ZKnALyI1Qbc3IobLXy0wZAHTeYaEG3alWvXrm1CdlW9QvtaA+5I4yshd8R2hZFOftucw7j3XYtRZtGo8PbKJchv3LM4dJoRW/3W1H8bqw24OaDnxDeiHBgTODM4dnWLq00mn78nt3KrV21Vpec12vjKPj5SyB2tFSgV920F094Bd4QXfvzV+WUda9MquaP85jlvj9jqt7e3N63KwYp9+vTp4PBEfJCfYeQl3/H+/fsHh8H34MOHDwcXlX/3sEp7cHgVN/w6n7bkd7iIXuv86tWrg7UacXwdhq9LjaFt/CaHwX+o3yTLNn+T/Ld6r0/Ge085TlX7DZ49e9Z9nc6y9Bzjo47vLDdu3DhYmx+nFUuVKX1no8+pl6vTVHVbP1d6aFovTSZsbnOtHr0lnH8nT+9m/td89n73dW+PHz/uUsVd4zRhTavijvQWvIyBdtdiWz2nZ5WqbY41o43DTOu2zapr/lvZ9j3vmrRppHpUrPL36r3tp43x7NcZ43PdUcg+N2d74XmtrnobByy6orm25aIVXPpJRWXEfSnfK3dFdi1V/FGPMbuqdo2wvrl7MHclL/tT7/Vuy1oruMeXnDN2eSdt5PHdlozzNVptD+5RhwN3tQ8Dwa5lfI0olZZUeVLtSeVlm/OVpnqZSl76Akd+oUseBtxFhWuEOxfthR9zVZKzvUd4iRBfSzU9YzDjPHdQtjEW89/If+vKlStDj+9mG/NbL9EPSbkTmxkElvr6vzXJCWTUwMTpEiCXcNu2zQWbl1WcZ0qdNqH/mzdv/mkJGl3W77DCOe1C1j8n/hFkGx5W2XZWxGgz0Yw2C03246z3tmT89n6V9La0WUNam19rq2kXe9mm7cKoza38/v37zdjOsqSHlrNO2Q/WOCe7gHvEEq7E1k7AXaacEFJBWeJsFjkxtJPD8RNgTnxHT4ZLknG0q4rjaNt7Fyf5VqVPsB1x+wu4RPqQR3oOYlYH/CP9WqP2C1r+XvTgLtfjx48Xta8ZR5czYl9i1vuyfbn59w8Dw/Az8OjBtSTPZGaHtdKDe0Su7l+8eDFxNmubNJrLyYwKa3oP+qhynJtjBokRt/XROcbTVpA7dt+rvqZK32bAyNyma5k7nOXLOF/180UH/IdK09muDPP06Nx/rwrusqWa4C5J3yWVuLm29VJ+k1Rjs18eX5by/U9aVHDXvfSeC3oEKrgnSKVp7jkyl2bO+QWpI9WE3CUxa0kfGbdzPem/zdf/7lrrpz6+wBJl7HlWZcWv6v2eHhPBL0UmjDYdDheVJ5fz4g3mld997odNHENhfgpQfxNwv0HI/a+cJEebDoflaW8ZYh69ni9IBVevPpfRpu7jbNL7nrvQCLjfJeT+y0N4bFPGlgPx7vWeB3OVrwg94vhr1DmfhFsvYzqbjHF3x/4l4J6BkLvuyaLZnRyM1/qWnTm0nuee4zYVpTVX4BLwhbPLmWvmj6Vzjv6agHtGCblr3XmEW3YpUzcJuduXUJVxO8Jrc9caTnLMXO0k+1uWuz2OE6dLscA5+msC7jm0N8OMcMKYi3DLHBJyVWi2J+P17du3wxyrcuxc4xzI23yTGNPm+Q/nov/KsVO7138JuOfUThxrOBnnpJR1dUBhDlqBtmPUi9K19VF6kn372l0JLR//WvWreL9DwL2g7FAfPnwoewBL35gDCXPLuDJP7sXlFu6oF6Vrmpsz1WqhYzc87Pwv4fbbBNxLyEBLyM1Bu0rQbdUfU4HRS8KBOwfnk/CYHry0eox8cbCGKYw8yb57bWaFNRNuv0/A3YLM6dnaFpZ6Us5JMd8/62HOQXprF49aFr4vfba5KF1KcEz4q3qM8czCfHLeXeuFRNZbuP0+AXdLEhDbTAtLC7otoOf7uzXMSNY8e8n3HL0oXdqDr7nFXO1hXeF2frmoW1Mlt+1jHig7GwF3y9q0MEtoXUiwzWCp1GJBPan2qeZ+rT0AutQqzkhTmG2DcNtPzmNr6Ntvszi5w3p2Au4OZeDlxJydMn8e4eDXqj6fPn3aBFuDhaVoF44ZS2vVTnIVwlQLuUufPky47a9y337rr7ePnZ+AO4PWEN/Cbm4vzFm5yKDI35m/O8FWKwJLlX25jaU1XZwdDbaV1ru9aW2p1XlTKY6jXWhUugBu+5eWhIv5cWJW2WHbCeqvv/6a3r17N71582bzmeXz58/TZeSEkYGev+P69eubTwdfqmkns9evX0/Pnz/fzB5QUcZvwl/1MJ+L7gST27dvb46LS5CpFM02M5Z2AXzz5s3pjz/+WMy+dFwKYJUfxpyLgNtRBmOWo7foEnBb0M3g/PLly+Z/Pz5QE2Sz/O9//9t8ZkC0cDuXy4ZxuKx2wZgQmBNaAu9ST2pNxnHC3p07d1Z1gmszZyTsPn36dNjjSwtRwse4Mn6yfXJMWNLFb2shVLHdkgO4oMMK2kF2oTmXw4F/AN9yGD4ODk9us++bl13ynQ+rNgefPn06WLvDoHtwGFKG2j6H4WOzfS7iMBR32Z+2KeNq7nXIkv3gMrIvjX48yL51GGyN/S3Tg8ui6B3me9rsIG0mk1ErbdmX891yKzK98a0/3z7+da9171lejj5DobK2PK2dacT+3Oxb6UH3bMxuaFHgwpZ+K5jacmLLCa2d1NK+sL+/v2kByp97yAkt7URpP2htRZzu6DbMdku/dbZd/rxL2U7p40z72DYeCM5FzNwtF/atr43SzpR9Oq/U3ta+xekEXC7s48eP09wctLmoow94Rnuw8/379//0vW8rOOUklpNXPvOwZ/uz/ffi8hu2QJBtlYDStl17YPcijm+rBI9tb6elT4VWSbs7EO2id5cXTe0ZmdZTL9TOR8DlwnZdRTmJgMC2HA1MRyUstcpO+zztYu7q1aubz/aA59wPeq5VfueExuPB8aRtd5L2gG97WJd1OnrRe9JFU5azVt7bvnT8wlag7UfA5cJ63N5pgQJ2pYUflse246JOu2hKwG2zGp307xxdGMuqAm6P6UIqv3VJBReAylp4deG0PKsKuD0mfq4acHs9pOMgAwB8z6qmCetR/av6MoT0Kc3NbSAA4CxWFXB7VP+qBtyXL19Oc9OsDwCchYC7Yz36VHctbR49WhRUbwGAsxBwd6zHrfxd6zlJPgDA9wi4O9YrDO5SHtbrIfMKAgB8z6oCbo+AlIBbqQ83U631ekWvHlwA4CxWV8Ht0ceZ96dX0at6m3CrBxcAOItVBdzo0cfZY8aBXVC9BQCWYHUBt0dQSpvC0ntxE2x7VW/jzp07EwDAWawu4N68eXPq4bffflt0L26Pt8AdpYILAJzVKiu4PXo5e1dALyOtCVl6SVuJV/QCAGe1uoCbcNurGvjkyZPp6dOn05IkmP/+++9TT3fv3p0AAM5qdQE3egamR48eLebtZgm3t2/f7t5aof8WADiPVQbcnoEpYfHXX3/t2s96Fi3c9v6eqbZrTwAAzmOVATeBqedrX0cJj6cZ6fvt7e1NAADnscqAG737OhMef/nll+HmyE37xEjhu+eFCACwTKsNuPfu3Zt6a+0Ko8yukO+R0D1KuL1//772BADg3FYbcDObwijVwTx4du3atW6v9M1LKBJs8z1G8vDhwwkA4LxWG3BjpACVqmkqlnMG3QTbtCNkGW1mB9VbAOCiVh1wU8EdrcfzaNDN28+2HTzTFpG5eFuwHfUVwqq3AMBF/TitXILUiCEvQbe9QazN+pAps65fv36ut7Hlv5OQ/P79+816jhpoj1K9BQAuY/UBt1VxRw5+LewelYCb5aQgmCrt0WVpVG8BgMtYfcCNUau439LC6+gvjDivzHuregsAXMaqe3CbEXtx1yjBdrSZHACA5RFw/8+zZ8/O3NfKbtgGAMA2CLj/J9VDvZ/95LdXRQcAtkHAPeLBgwdDvOFsbbQmAADbJOAe8+TJEw85zSi/9atXryYAgG0RcI9JD2gCl17Q3ctv/OLFCxcUAMBWCbgnaFVFIXe3Hj9+vHlpBQDANgm4p0jwSgBjNzJjQt5YBgCwbQLuNySAJYixXcItALBLAu53CLnbk5YP4RYA2DUB9wwSyN6+fethqEtofc3CLQCwawLuGaUnNwFNyD2/9tt5oAwAmIOAew4Jt6nkqkKe3d7enuo3ADArAfecWh9pFqHtdK0lIS/OAACYk4B7Qani6ik92cOHDzdV21u3bk0AAHMTcC8hVcpUcvXm/u3u3bvThw8fpkePHnlJBgDQjYC7BalUJtittW0hwTYh32t3AYARCLhblHaFBN2EvYS+ylKhTSvCp0+fNsFWOwIAMIofJ7YuYS/LX3/9Nf3555/T8+fPN3+uIOuVmRHyqQ0BABiRCu4O5XZ9+lFT1c1DVw8ePFjkXLAJs48fP95Ua1t1WrgFAEalgjuTBNsWblPNff369bS/vz+9e/duuOpuwmtC7M2bN4VZAGBxfjg4NNFVAm6C7ps3bzafnz9/3nzOIeE1leZUaa9fv7759KAYAEelKJN2u7ml0GI6Ti5CwB1Yq+5m+fLlyz9/jvaZMJzlJAmvrfqa0Jol/3z16tXNn1NRFmYBgGoEXAAASvGQGQAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFCKgAsAQCkCLgAApQi4AACUIuACAFDK/wdUCo9DIhaw5wAAAABJRU5ErkJggg==)](https://www.mci-group.com/)

---

Le problème

## Vous ciblez des profils. Vous mesurez des résultats. Entre les deux, le comportement réel reste invisible.

Les plateformes publicitaires optimisent après diffusion. Les campagnes sont donc lancées avant d’être réellement comprises, ce qui crée un décalage structurel entre ciblage, message et performance.

La majorité des décisions marketing repose encore sur des hypothèses : centres d’intérêt, audiences similaires ou ciblage socio-démographique. Ces approches décrivent des profils, mais ne traduisent pas la manière dont les consommateurs agissent réellement.

Des audiences approximatives construites sur des signaux faibles

Des messages diffusés sans validation réelle en amont

Des performances instables selon les campagnes et les périodes

Des budgets média partiellement gaspillés faute de certitude initiale

Changer de logique marketing · Avant → Après UserJournAI

Profils supposés→Comportements observables

Test en production→Validation avant diffusion

Ciblage large→Audiences réellement activables

Performance incertaine→Décisions plus prévisibles

Intuition seule→Compréhension fondée sur des signaux réels

Le changement

## Comprendre avant d’activer

UserJournAI inverse la logique du ciblage marketing : avant d’investir, vous comprenez ; avant d’activer, vous validez. La plateforme modélise des comportements réels et transforme des signaux agrégés en décisions marketing mesurables.

Le marché aujourd'hui

UserJournAI

Profils déclaratifs

Comportements réels

Optimisation après lancement

Validation avant activation

Décisions intuitives

Décisions fondées sur des signaux observables

Budget exposé à l’incertitude

Budget engagé avec plus de certitude

Fonctionnement

## Du signal brut à l’activation

La plateforme transforme des signaux comportementaux agrégés en segments activables, insights consommateur et décisions marketing validées grâce à une séquence en six étapes. Les modèles n’inventent pas : ils interprètent des signaux réels, croisés et contextualisés.

01

### Collecte des signaux

Données agrégées issues de la mobilité physique anonymisée, du digital, des données socio-démographiques, des panels consommateurs et des environnements média.

Données agrégées

02

### Modélisation comportementale

L’IA comportementale croise les signaux pour détecter des corrélations : déplacements, rythmes de vie, zones fréquentées, affinités et logiques de consommation.

IA comportementale

03

### Construction des audiences

Les segments sont créés à partir de comportements observables pour représenter des audiences probabilistes réellement activables, sans identification individuelle.

Segmentation

04

### Compréhension des insights

La plateforme aide à comprendre qui sont les audiences, où elles se trouvent, ce qui les motive, ce qui les freine et pourquoi elles agissent.

Insights

05

### Validation des décisions

Les audiences et les messages sont testés avant diffusion pour réduire le risque marketing et renforcer la pertinence.

Pré-activation

06

### Activation et mesure

Les campagnes sont activées sur les principaux environnements média, puis mesurées en continu jusqu’aux visites, leads ou ventes selon les cas d’usage.

Activation continue

Sources de données

## D’où proviennent les signaux utilisés ?

UserJournAI combine plusieurs familles de signaux complémentaires pour construire une lecture probabiliste des audiences. L’objectif n’est pas d’identifier une personne, mais de comprendre des dynamiques collectives, anonymisées et activables.

**Mobilité physique anonymisée**Flux, zones de fréquentation et bassins de mobilité

**Données socio-démographiques agrégées**Contexte territorial, IRIS/INSEE et caractéristiques de zones

**Panels consommateurs**Affinités, intentions et comportements déclaratifs structurés

**Signaux contextuels**Tendances, environnements média et signaux d’intérêt

Cas d’usage

## Adapté à vos enjeux

Réseaux de magasins, e-commerce, agences média et régies DOOH utilisent UserJournAI pour réduire l’incertitude, affiner le ciblage et mieux piloter la performance.

Enjeu

### Réseaux de magasins

Attirer les consommateurs qui se déplacent réellement

- Identifier les zones de chalandise réelles à partir des flux
- Cibler les audiences les plus susceptibles de visiter les points de vente
- Comprendre les parcours physiques et les dynamiques locales
- Mesurer l’impact des campagnes sur les visites et le trafic utile

⇒ Plus de trafic qualifié, moins de déperdition

[Optimiser le trafic en magasin](https://userjournai.com/fr/cas-dusage/retail?hsLang=fr)

Enjeu

### e-Commerce

Cibler des intentions d’achat plus concrètes

- Identifier des audiences à forte affinité réelle avec l’offre
- Mieux comprendre les signaux offline qui influencent l’achat online
- Valider les messages avant diffusion pour limiter les tests inutiles
- Améliorer la conversion grâce à des décisions plus précises

⇒ Plus de conversion, moins d’incertitude

[Comprendre les enjeux e-commerce](https://userjournai.com/fr/cas-dusage/e-commerce?hsLang=fr)

Enjeu

### Agences média

Renforcer la performance et la différenciation

- Apporter une intelligence audience plus fine aux clients
- Justifier les recommandations média par des signaux comportementaux
- Réduire l’écart entre stratégie, ciblage et résultats
- Créer plus de valeur grâce à une meilleure lecture des comportements

⇒ Plus de performance, plus de crédibilité

[Améliorer la performance client](https://userjournai.com/fr/cas-dusage/agences-media?hsLang=fr)

Enjeu

### Régies DOOH

Mesurer l’impact réel sur les flux physiques

- Comprendre les audiences réellement exposées aux écrans
- Qualifier les flux en fonction des comportements et affinités
- Mieux valoriser les inventaires avec une lecture plus utile du terrain
- Démontrer l’impact des campagnes au-delà de l’exposition brute

⇒ Plus de valeur démontrée, plus d’impact mesuré

[Mesurer l’impact réel](https://userjournai.com/fr/cas-dusage/dooh?hsLang=fr)

Privacy & conformité

## Conçu pour fonctionner sans données personnelles individuelles

UserJournAI fonctionne à partir de données agrégées et anonymisées. La plateforme a été pensée pour améliorer la performance marketing sans dépendre d’identifiants individuels, sans cookies tiers et avec une approche privacy-first conforme au RGPD. Elle ne cherche pas à reconnaître les personnes : elle modélise des comportements collectifs pour produire des représentations probabilistes d’audiences.

Aucune donnée personnelle individuelle

Décisions marketing construites à partir de données agrégées et anonymisées

Approche privacy-first

La conformité n’est pas une couche ajoutée, elle fait partie de l’architecture

Activation compatible avec l’existant

La plateforme s’intègre à vos environnements média et à vos outils actuels

Performance sans compromis

Mieux comprendre et activer les audiences sans sacrifier la confidentialité

Questions fréquentes

## Les réponses essentielles pour comprendre UserJournAI

Une synthèse claire pour comprendre le positionnement, le fonctionnement, les usages et la logique privacy-first de la plateforme.

Qu’est-ce que UserJournAI ?

UserJournAI est une plateforme d’intelligence marketing basée sur l’IA comportementale. Elle permet aux marques, agences et régies d’identifier, comprendre et activer des audiences à partir de comportements réels, en croisant mobilité physique et signaux comportementaux, sans utiliser de données personnelles individuelles.

En quoi UserJournAI se différencie-t-il des plateformes publicitaires classiques ?

Les plateformes classiques ciblent surtout des profils, des intérêts déclaratifs ou des audiences similaires. UserJournAI cible des comportements réels et permet de valider les audiences et les messages avant l’activation média, afin de réduire l’incertitude et d’améliorer la performance dès le lancement.

Comment fonctionne la plateforme ?

La plateforme collecte des signaux agrégés, applique des modèles d’IA comportementale, construit des audiences activables, valide les décisions marketing avant diffusion, puis active et mesure les campagnes dans une logique continue d’amélioration.

D’où proviennent les données utilisées ?

UserJournAI combine des signaux de mobilité anonymisés, des données socio-démographiques agrégées, des panels consommateurs et des signaux contextuels. Ces sources servent à construire une compréhension probabiliste des audiences, sans identification individuelle.

Que signifie “jumeaux numériques comportementaux” ?

Il ne s’agit pas de copies numériques d’individus. Ce sont des représentations probabilistes d’audiences, construites à partir de signaux collectifs, anonymisés et contextualisés, pour comprendre les comportements, tester des hypothèses et qualifier l’intention avant activation.

Pour quels types d’acteurs UserJournAI est-il adapté ?

UserJournAI s’adresse aux réseaux de magasins, aux e-commerçants, aux agences média et aux régies DOOH qui veulent mieux comprendre leurs audiences, réduire le risque marketing et améliorer la précision de leurs campagnes.

UserJournAI est-il conforme au RGPD ?

Oui. UserJournAI fonctionne à partir de données agrégées et anonymisées, sans recourir à des données personnelles individuelles. Son architecture est privacy-first par design et pensée pour un usage conforme au RGPD.

## Arrêtez de deviner, commencez à *comprendre*

Demandez une démonstration pour découvrir comment UserJournAI aide vos équipes à prendre des décisions plus fiables, mieux cibler les audiences et améliorer la performance des campagnes.

[Demander une démonstration](https://userjournai.com/fr/contact?hsLang=fr) [Contacter l’équipe](https://userjournai.com/fr/contact?hsLang=fr)

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)](https://userjournai.com/fr/accueil?hsLang=fr)

UserJournAI permet de comprendre les comportements réels, de valider les décisions marketing et d’activer des campagnes plus performantes dès leur lancement.

[YouTube](https://www.youtube.com/@UserJournAI)[Facebook](https://www.facebook.com/Userjournai)[LinkedIn](https://www.linkedin.com/company/userjournai/)[Instagram](https://www.instagram.com/userjournai/)[X](https://x.com/UserJournAI)

Membre du collectif [Hub&Up](https://www.hubandup.com)

### Entreprise

- [A propos](https://userjournai.com/fr/nous-sommes-user-journai?hsLang=fr)
- [Manifeste](https://userjournai.com/fr/manifeste?hsLang=fr)

### Ressources

- [Blog](https://userjournai.com/fr/blog?hsLang=fr)
- [Privacy](https://userjournai.com/fr/privacy-rgpd?hsLang=fr)
- [FAQ](https://userjournai.com/fr/faq?hsLang=fr)

[Mentions légales](https://userjournai.com/fr/mentions-legales?hsLang=fr) — Copyright © 2026, User JournAI

```json
{
  "@context" : "https://schema.org",
  "@type" : "Organization",
  "name" : "UserJournAI",
  "sameAs" : [ "https://www.youtube.com/@UserJournAI", "https://www.facebook.com/Userjournai", "https://www.linkedin.com/company/userjournai/", "https://www.instagram.com/userjournai/", "https://x.com/UserJournAI" ],
  "url" : "https://userjournai.com"
}
```