{"id":5109,"date":"2023-06-26T09:02:32","date_gmt":"2023-06-26T07:02:32","guid":{"rendered":"http:\/\/nextbrain.ai\/?p=5109"},"modified":"2024-10-23T12:55:55","modified_gmt":"2024-10-23T10:55:55","slug":"unlocking-the-future-of-marketing-mix-modelling","status":"publish","type":"post","link":"https:\/\/nextbrain.ai\/fr\/blog\/unlocking-the-future-of-marketing-mix-modelling","title":{"rendered":"Naviguer dans les d\u00e9fis du Marketing Mix Modelling dans un monde num\u00e9rique"},"content":{"rendered":"
\n

Comprendre le Marketing Mix Modelling \u00e0 l’\u00e8re num\u00e9rique d’aujourd’hui<\/h2>\n
\n \"D\u00e9bloquer
D\u00e9bloquer l’avenir du Marketing Mix Modelling<\/figcaption><\/figure>\n

Le Marketing Mix Modelling (MMM) est actuellement un sujet br\u00fblant pour de nombreuses raisons. \u00c0 l’\u00e8re num\u00e9rique, il fait face \u00e0 des d\u00e9fis critiques que les marques doivent surmonter. La prolif\u00e9ration des plateformes a compliqu\u00e9 la t\u00e2che d’\u00e9tablir la voix et la personnalit\u00e9 d’une marque. En m\u00eame temps, la croissance exponentielle des r\u00e9seaux sociaux a renforc\u00e9 le pouvoir des consommateurs, entra\u00eenant l’\u00e9mergence de nombreuses nouvelles niches et audiences. En cons\u00e9quence, construire une relation significative entre les marques et leurs audiences, tout en mesurant avec pr\u00e9cision son impact, est devenu de plus en plus difficile. Les jours des relations durables entre marques et consommateurs ont c\u00e9d\u00e9 la place \u00e0 des sc\u00e9narios en \u00e9volution rapide, n\u00e9cessitant un rafra\u00eechissement des strat\u00e9gies de Marketing Mix Modelling.<\/p>\n

D\u00e9fis cl\u00e9s dans le Marketing Mix Modelling<\/h3>\n

Ce paysage complexe pr\u00e9sente plusieurs d\u00e9fis, notamment :<\/p>\n

Qualit\u00e9 des donn\u00e9es<\/h3>\n

Avec des campagnes plus courtes ciblant des segments plus petits, la disponibilit\u00e9 des donn\u00e9es a diminu\u00e9. Les mod\u00e8les statistiques traditionnels et les algorithmes de machine learning standard d\u00e9pendent de grands ensembles de donn\u00e9es. Pour rem\u00e9dier \u00e0 cette limitation, de nouvelles Mod\u00e9lisation du Mix Marketing<\/strong> techniques telles que les mod\u00e8les bay\u00e9siens, les mod\u00e8les graphiques, et les mod\u00e8les de Deep Learning causalement explicables doivent \u00eatre adopt\u00e9es. Les donn\u00e9es synth\u00e9tiques, qui offrent des ensembles de r\u00e9sultats \u00e9quivalents aux donn\u00e9es d’entr\u00e9e, peuvent \u00e9galement aider \u00e0 g\u00e9n\u00e9rer de plus grands ensembles d’\u00e9chantillons adapt\u00e9s aux algorithmes de machine learning.<\/p>\n

Effets d’entonnoir<\/h3>\n

Dans un environnement multicanal o\u00f9 la personnalit\u00e9 de la marque se construit \u00e0 travers de nombreux canaux, les effets de funnel jouent un r\u00f4le significatif. \u00c0 mesure que le nombre de canaux augmente, s'\u00e9tendant sur des plateformes en ligne et hors ligne, le Marketing Mix Modelling devient plus complexe, et l'interaction entre les diff\u00e9rents canaux devient in\u00e9vitable. Les mod\u00e8les MMM existants ont souvent du mal \u00e0 comprendre ce m\u00e9lange, ce qui conduit \u00e0 une sous-estimation de certains effets de canal. Comprendre et prendre en compte les effets de funnel devient crucial lors de l'\u00e9tablissement d'une personnalit\u00e9 de marque \u00e0 travers plusieurs canaux.<\/p>\n

Biais de s\u00e9lection et causalit\u00e9<\/h3>\n

La relation entre la publicit\u00e9 et la demande n'est plus simple en raison de l'abondance de canaux, de m\u00e9dias et d'audiences. Le biais de s\u00e9lection se produit lorsqu'une variable m\u00e9dia d'entr\u00e9e est corr\u00e9l\u00e9e \u00e0 un ou plusieurs facteurs de demande cach\u00e9s. L'incapacit\u00e9 \u00e0 identifier ces facteurs peut entra\u00eener des interpr\u00e9tations erron\u00e9es des contributions des m\u00e9dias. Les nouveaux mod\u00e8les MMM doivent avoir la capacit\u00e9 d'incorporer d'autres variables pour \u00e9valuer avec pr\u00e9cision l'efficacit\u00e9 des diff\u00e9rentes campagnes.<\/p>\n

Mesurer la cr\u00e9ativit\u00e9<\/h3>\n

Dans le domaine num\u00e9rique, les consommateurs sont devenus des auditeurs tr\u00e8s engag\u00e9s. Si une marque ne communique pas avec ses clients, une autre marque saisira l'opportunit\u00e9. Les marques doivent constamment d\u00e9velopper de nouvelles strat\u00e9gies cr\u00e9atives tout en trouvant des moyens efficaces de mesurer leur impact. Les mod\u00e8les MMM ne devraient pas seulement \u00e9valuer les contributions des canaux, mais aussi \u00e9valuer l'efficacit\u00e9 des diff\u00e9rentes approches cr\u00e9atives.<\/p>\n

\n

En relevant ces d\u00e9fis et en adoptant des techniques innovantes, l'avenir du Marketing Mix Modelling peut \u00eatre fa\u00e7onn\u00e9 pour r\u00e9pondre aux exigences d'un paysage marketing en \u00e9volution.<\/p>\n<\/blockquote>\n

L'avenir du Marketing Mix Modelling<\/h3>\n

Pour rester comp\u00e9titives dans cet environnement dynamique, les marques doivent tirer parti d'outils et de m\u00e9thodologies avanc\u00e9s. NextBrain AI<\/strong> offre une plateforme de machine learning sans code qui peut aider les entreprises \u00e0 r\u00e9volutionner leurs strat\u00e9gies de Marketing Mix Modelling. En utilisant nos solutions, les marques peuvent obtenir des insights plus profonds sur leur performance marketing et prendre des d\u00e9cisions \u00e9clair\u00e9es.<\/p>\n

D\u00e9couvrez-en plus sur notre solutions<\/a> pour am\u00e9liorer l'efficacit\u00e9 de votre marketing. Restez en avance sur la concurrence et d\u00e9bloquez le potentiel de votre strat\u00e9gie marketing.<\/p>\n

Pour plus d'informations sur le marketing et la technologie, lisez-en plus sur notre blog<\/a> et d\u00e9couvrez les derni\u00e8res tendances et strat\u00e9gies.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

Understanding Marketing Mix Modelling in Today’s Digital Age Unlocking the Future of Marketing Mix Modelling Marketing Mix Modelling (MMM) is currently a hot topic for […]<\/p>\n","protected":false},"author":1,"featured_media":5112,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[70],"tags":[],"class_list":["post-5109","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"yoast_head":"\nNavigating the Challenges of Marketing Mix Modelling in a Digital World - NextBrain AI | No-Code Machine Learning<\/title>\n<meta name=\"description\" content=\"Looking to revolutionize your marketing mix modelling? NextBrain AI's no-code machine learning platform is here to help. 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