{"id":5232,"date":"2023-07-04T12:56:46","date_gmt":"2023-07-04T10:56:46","guid":{"rendered":"http:\/\/nextbrain.ai\/?p=5232"},"modified":"2023-07-07T14:47:33","modified_gmt":"2023-07-07T12:47:33","slug":"synthetic-data-generation-for-digital-marketers-and-brand-strategists","status":"publish","type":"post","link":"https:\/\/nextbrain.ai\/fr\/blog\/synthetic-data-generation-for-digital-marketers-and-brand-strategists","title":{"rendered":"G\u00e9n\u00e9ration de donn\u00e9es synth\u00e9tiques pour les marketers digitaux et les strat\u00e8ges de marque"},"content":{"rendered":"
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Image par Photo Mix<\/a> de Pixabay<\/a><\/figcaption><\/figure>\n<\/figure>\n\n\n\n

Le marketing digital et la strat\u00e9gie de marque pr\u00e9sentent des d\u00e9fis significatifs pour les marketeurs et les strat\u00e8ges face \u00e0 l'\u00e9volution des tendances et des comportements des consommateurs. Le succ\u00e8s d\u00e9pend d'atteindre le bon public avec un contenu pertinent et d'optimiser les co\u00fbts. Cependant, toutes les marques n'ont pas un acc\u00e8s \u00e9tendu aux donn\u00e9es pour \u00e9clairer leurs d\u00e9cisions. C'est ici que la g\u00e9n\u00e9ration de donn\u00e9es synth\u00e9tiques joue un r\u00f4le crucial, en augmentant les \u00e9chantillons limit\u00e9s et en extrayant des insights pr\u00e9cieux.<\/p>\n\n\n\n

Le R\u00f4le des Donn\u00e9es dans le Marketing Digital<\/h5>\n\n\n\n

Les donn\u00e9es renforcent le marketing digital, permettant aux marques de prendre des d\u00e9cisions bas\u00e9es sur les donn\u00e9es et d'optimiser leurs strat\u00e9gies. Pourtant, la collecte de donn\u00e9es d\u00e9taill\u00e9es dans des contextes en \u00e9volution rapide pose des d\u00e9fis. En effet, les marketeurs travaillent souvent avec des ensembles de donn\u00e9es petits ou moyens manquant de signification statistique, ce qui entrave la pr\u00e9vision pr\u00e9cise du succ\u00e8s des campagnes et la d\u00e9termination du point de saturation des d\u00e9penses publicitaires.<\/p>\n\n\n\n

Donn\u00e9es Synth\u00e9tiques Tabulaires : D\u00e9verrouiller son Potentiel<\/h5>\n\n\n\n

Alors que la g\u00e9n\u00e9ration de donn\u00e9es synth\u00e9tiques attire l'attention dans les domaines de l'image et du texte, son application aux donn\u00e9es tabulaires est souvent n\u00e9glig\u00e9e. Les donn\u00e9es synth\u00e9tiques tabulaires, structur\u00e9es en lignes et en colonnes, d\u00e9tiennent un immense potentiel pour les marketeurs num\u00e9riques et les strat\u00e8ges. En tirant parti des generative adversarial networks (GANs), une technologie de machine learning \u00e0 la pointe, les donn\u00e9es synth\u00e9tiques \u00e9largissent les ensembles de donn\u00e9es et am\u00e9liorent la \u00ab r\u00e9solution \u00bb, r\u00e9v\u00e9lant des insights suppl\u00e9mentaires.<\/p>\n\n\n\n

R\u00e9seaux Antagonistes G\u00e9n\u00e9ratifs (GANs) : Autonomiser la G\u00e9n\u00e9ration de Donn\u00e9es Synth\u00e9tiques<\/h5>\n\n\n\n

Les GANs sont une innovation puissante en machine learning impliquant deux r\u00e9seaux de neurones, un g\u00e9n\u00e9rateur et un discriminateur, qui s'affrontent. Le g\u00e9n\u00e9rateur cr\u00e9e de nouveaux \u00e9chantillons de donn\u00e9es statistiquement similaires aux donn\u00e9es d'entr\u00e9e, tandis que le discriminateur distingue les \u00e9chantillons r\u00e9els des \u00e9chantillons synth\u00e9tiques. Ce jeu adversarial motive l'entra\u00eenement, g\u00e9n\u00e9rant des donn\u00e9es synth\u00e9tiques de haute qualit\u00e9 ressemblant \u00e0 l'ensemble de donn\u00e9es original.<\/p>\n\n\n\n

Comprendre le point de saturation en marketing digital<\/h5>\n\n\n\n

Le point de saturation est crucial en marketing digital, emp\u00eachant les rendements d\u00e9croissants dus \u00e0 des d\u00e9penses publicitaires excessives. La courbe S de la publicit\u00e9 illustre la relation entre les d\u00e9penses et leur impact sur les ventes, les revenus ou la part de march\u00e9. Au-del\u00e0 d'un certain point, l'augmentation des d\u00e9penses ne produit plus de rendements proportionnels. Estimer le point de saturation avec pr\u00e9cision est difficile, surtout avec des donn\u00e9es limit\u00e9es. Les donn\u00e9es synth\u00e9tiques r\u00e9pondent \u00e0 cela en fournissant un ensemble de donn\u00e9es plus large pour l'analyse et en permettant des pr\u00e9visions plus pr\u00e9cises.<\/p>\n\n\n\n

Un cas d'utilisation pratique : tirer parti des donn\u00e9es synth\u00e9tiques<\/h5>\n\n\n\n

Consid\u00e9rons une nouvelle marque lanc\u00e9e il y a deux ans, menant diverses campagnes publicitaires sur diff\u00e9rentes plateformes avec des donn\u00e9es limit\u00e9es. D\u00e9terminer s'ils ont atteint le point de saturation et planifier les prochaines \u00e9tapes strat\u00e9giques est vital. G\u00e9n\u00e9rer un ensemble de donn\u00e9es synth\u00e9tiques \u00e0 partir des donn\u00e9es originales \u00e9largit la taille de l'\u00e9chantillon et offre des perspectives sur le point de saturation et d'autres m\u00e9triques cl\u00e9s.<\/p>\n\n\n\n

En utilisant la biblioth\u00e8que Python open-source nbsynthetic <\/em>par l'\u00e9quipe de NextBrain.ai, des donn\u00e9es synth\u00e9tiques sont g\u00e9n\u00e9r\u00e9es \u00e0 partir de l'ensemble de donn\u00e9es original. Un ensemble de donn\u00e9es synth\u00e9tiques de 2000 \u00e9chantillons est cr\u00e9\u00e9, et une comparaison visuelle entre les donn\u00e9es originales et synth\u00e9tiques est r\u00e9alis\u00e9e. De plus, un mod\u00e8le de machine learning, comme un Random Forest Regressor, est entra\u00een\u00e9 sur les deux ensembles de donn\u00e9es pour pr\u00e9dire des m\u00e9triques cl\u00e9s telles que le Revenu Mensuel R\u00e9current (MRR). Les r\u00e9sultats (chiffres ci-dessous) montrent que le mod\u00e8le entra\u00een\u00e9 sur les donn\u00e9es synth\u00e9tiques atteint une plus grande stabilit\u00e9 et une meilleure pr\u00e9cision pr\u00e9dictive par rapport au mod\u00e8le entra\u00een\u00e9 sur les donn\u00e9es originales \u00e0 faible taille d'\u00e9chantillon.<\/p>\n\n\n\n

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\"\"<\/figure><\/div><\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
\u2554\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2557  \n     R\u00c9SULTATS  \n\u255a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255d  \n\nDonn\u00e9es originales  \n-------------  \nScore sans validation crois\u00e9e = 0.32<\/strong>\nScores avec validation crois\u00e9e = [ 0.19254948 -7.0973158   0.1455913   0.18710539 -0.14113018]\n\nDonn\u00e9es synth\u00e9tiques\n--------------------\nScore sans validation crois\u00e9e = 0.80<\/strong>\nScores avec validation crois\u00e9e = [0.8009446  0.81271862 0.79139598 0.81252436 0.83137774]\n\nAlgorithme entra\u00een\u00e9 avec des donn\u00e9es synth\u00e9tiques et test\u00e9 avec des donn\u00e9es originales\n-------------------------------------------------------------------\nScore avec pr\u00e9diction de validation crois\u00e9e = 0.71<\/strong>\n<\/code><\/pre>\n\n\n\n
Cet article a \u00e9t\u00e9 initialement publi\u00e9 dans Towards Data Science. Vous pouvez trouver l'original ici ici<\/a>.<\/em><\/p>","protected":false},"excerpt":{"rendered":"
Digital marketing and brand strategy present significant challenges for marketers and strategists amid evolving trends and consumer behaviors. Success depends on reaching the right audience […]<\/p>\n","protected":false},"author":3,"featured_media":5267,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"footnotes":""},"categories":[70],"tags":[],"class_list":["post-5232","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"yoast_head":"\nSynthetic Data Generation for Digital Marketers and Brand Strategists<\/title>\n<meta name=\"description\" content=\"Discover the power of synthetic data generation for digital marketers and brand strategists with NextBrain AI - a no-code machine learning platform.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/nextbrain.ai\/fr\/blog\/synthetic-data-generation-for-digital-marketers-and-brand-strategists\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Synthetic Data Generation for Digital Marketers and Brand Strategists\" \/>\n<meta property=\"og:description\" content=\"Discover the power of synthetic data generation for digital marketers and brand strategists with NextBrain AI - 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