{"id":124,"date":"2017-03-10T12:17:20","date_gmt":"2017-03-10T20:17:20","guid":{"rendered":"https:\/\/www.microsoft.com\/fr-ca\/industry\/blog\/industry\/retail\/machine-learning-retail-execution-supercharged-store-visits-and-data-analysis\/"},"modified":"2017-03-10T12:17:20","modified_gmt":"2017-03-10T20:17:20","slug":"machine-learning-retail-execution-supercharged-store-visits-and-data-analysis","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/fr-ca\/industry\/blog\/retail\/2017\/03\/10\/machine-learning-retail-execution-supercharged-store-visits-and-data-analysis\/","title":{"rendered":"Apprentissage automatique + marchandisage au d\u00e9tail = visites en magasin et analyses des donn\u00e9es charg\u00e9es \u00e0 bloc"},"content":{"rendered":"

Avez-vous d\u00e9j\u00e0 imagin\u00e9 \u00e0 quel point votre journ\u00e9e serait diff\u00e9rente si vous disposiez d\u2019un assistant personnel d\u00e9di\u00e9? Il pourrait arriver t\u00f4t et travailler tard et s\u2019occuper des t\u00e2ches administratives sur le terrain pour vous permettre de profiter au maximum de votre temps. La solution, c\u2019est l\u2019apprentissage automatique, une technologie \u00e9mergente qui permet aux ordinateurs d\u2019apprendre \u00e0 partir de donn\u00e9es et d\u2019effectuer des pr\u00e9dictions \u00e0 partir de celles-ci sans que vous ayez \u00e0 leur fournir des directives explicites. En ce qui concerne les entreprises de biens de consommation, l\u2019apprentissage automatique peut permettre d\u2019affecter les repr\u00e9sentants sur le terrain \u00e0 des visites en magasin plus efficacement et de leur fournir les capacit\u00e9s n\u00e9cessaires pour effectuer rapidement leurs t\u00e2ches quotidiennes. Imaginez un robot qui pourrait faire le tour des rayons pour v\u00e9rifier la conformit\u00e9 des produits et lib\u00e9rer ainsi du capital humain pour vos t\u00e2ches \u00e0 plus grande valeur ajout\u00e9e. Gagner du temps en magasin<\/a> permet aux repr\u00e9sentants d\u2019effectuer davantage de visites et donc potentiellement de r\u00e9aliser des \u00e9conomies importantes. Mais l\u2019apprentissage automatique ne sert pas qu\u2019\u00e0 simplifier les visites en magasin\u00a0: c\u2019est un moyen efficace d\u2019appliquer un historique de donn\u00e9es \u00e0 un probl\u00e8me en cr\u00e9ant un mod\u00e8le et en utilisant ce dernier pour pr\u00e9dire des comportements futurs. Au fil du temps, l\u2019apprentissage automatique d\u00e9tecte les habitudes et les tendances, par exemple la p\u00e9riode o\u00f9 une promotion conna\u00eet du succ\u00e8s et les param\u00e8tres connexes, sur lesquels les intervenants peuvent se fonder pour am\u00e9liorer la strat\u00e9gie de l\u2019entreprise. Poursuivez votre lecture pour d\u00e9couvrir comment l\u2019apprentissage automatique peut aider les entreprises de biens de consommation \u00e0 d\u00e9terminer o\u00f9 aller et quoi faire, \u00e0 en faire plus avec moins de saisies de donn\u00e9es et \u00e0 approfondir leur analyse de donn\u00e9es.<\/p>\n

Automatisez et optimisez la planification des itin\u00e9raires<\/h2>\n

Beaucoup d\u2019efforts sont mis pour d\u00e9terminer quel point de vente doit faire l\u2019objet d\u2019une visite et \u00e0 quelle fr\u00e9quence et ce qui doit \u00eatre fait sur place. Imaginez disposer d\u2019un logiciel qui tient compte de plusieurs facteurs autres que la localisation pour pr\u00e9dire automatiquement le meilleur moment pour se rendre \u00e0 un magasin et am\u00e9liorer l\u2019efficacit\u00e9 globale des itin\u00e9raires. Si un magasin conna\u00eet une p\u00e9riode creuse chaque mois de juillet, a r\u00e9cemment embauch\u00e9 un nouveau g\u00e9rant ou s\u2019appr\u00eate \u00e0 lancer une promotion, le logiciel adaptera les itin\u00e9raires en cons\u00e9quence pour optimiser le temps de travail des employ\u00e9s. La technologie d\u2019apprentissage automatique peut \u00e9galement cr\u00e9er des listes de t\u00e2ches \u00e0 effectuer en magasin en fonction des besoins particuliers de chaque magasin. Ainsi, avant d\u2019arriver en magasin, le repr\u00e9sentant sera d\u00e9j\u00e0 au courant des probl\u00e8mes, par exemple ceux relatifs \u00e0 de l\u2019\u00e9quipement en panne, et n\u2019aura pas \u00e0 perdre du temps \u00e0 d\u00e9terminer les t\u00e2ches \u00e0 effectuer. Lorsque les repr\u00e9sentants arrivent sur place, le logiciel peut \u00e9galement les aider \u00e0 simplifier leurs activit\u00e9s de v\u00e9rification.<\/p>\n

Ayez la possibilit\u00e9 d\u2019effectuer la visite en magasin \u00ab\u00a0parfaite\u00a0\u00bb<\/h2>\n

Imaginez que les repr\u00e9sentants disposent d\u2019une technologie qui leur permette d\u2019employer leur temps \u00e0 bon escient lorsqu\u2019ils effectuent une v\u00e9rification en magasin. Ils pourraient non seulement travailler sans stylo ni papier (qu\u2019utilisent encore 64\u00a0% des professionnels du marchandisage au d\u00e9tail[1]<\/a>), mais \u00e9galement \u00e9viter d\u2019avoir \u00e0 saisir des donn\u00e9es num\u00e9riques en employant la reconnaissance des images num\u00e9riques et une fonctionnalit\u00e9 parole-texte. La reconnaissance des images num\u00e9riques permet aux repr\u00e9sentants de prendre des photos des pr\u00e9sentoirs de produits en magasin plut\u00f4t que de consigner manuellement les r\u00e9sultats d\u2019inspection. Un mod\u00e8le peut ensuite \u00e9valuer les articles en rupture de stock, la pr\u00e9sentation, les prix, l\u2019espace sur les tablettes et la conformit\u00e9 au planogramme \u00e0 partir d\u2019une image. Alors qu\u2019un agent humain devrait \u00e9valuer visuellement chaque \u00e9l\u00e9ment pour d\u00e9celer un probl\u00e8me de placement de produit, le logiciel, lui, peut trouver les erreurs et les incoh\u00e9rences en quelques secondes. L\u2019apprentissage automatique permet \u00e9galement aux repr\u00e9sentants de dicter des notes, des ordres et des commandes dans un appareil portable comme une montre ou un casque d\u2019\u00e9coute intelligents. Le syst\u00e8me isole les mots-cl\u00e9s des paroles enregistr\u00e9es, et ces mots d\u00e9clenchent des actions dans le logiciel de marchandisage au d\u00e9tail. Les donn\u00e9es enregistr\u00e9es num\u00e9riquement permettent aux professionnels du marchandisage au d\u00e9tail de gagner du temps et d\u2019\u00e9viter les erreurs inh\u00e9rentes \u00e0 la collecte de donn\u00e9es manuelle. Comme les donn\u00e9es recueillies lors des visites sont diss\u00e9min\u00e9es en temps r\u00e9el, les gestionnaires re\u00e7oivent les r\u00e9sultats des v\u00e9rifications sur-le-champ plut\u00f4t que plusieurs mois apr\u00e8s celles-ci.<\/p>\n

Approfondissez l\u2019analyse de donn\u00e9es<\/h2>\n

En plus de recueillir les donn\u00e9es, l\u2019apprentissage automatique employ\u00e9 dans le domaine du marchandisage au d\u00e9tail peut d\u00e9celer des tendances dans les donn\u00e9es pour pr\u00e9dire la meilleure marche \u00e0 suivre pour la suite des choses. Les entreprises de biens de consommation g\u00e8rent d\u2019\u00e9normes quantit\u00e9s de donn\u00e9es concernant les ventes, les stocks en magasin, les livraisons et les promotions chez des milliers de d\u00e9taillants. L\u2019utilisation de feuilles de calcul pour effectuer un suivi et une analyse demande beaucoup de temps, et une feuille de calcul peut uniquement faire ce que vous lui demandez explicitement. L\u2019apprentissage automatique, lui, reconna\u00eet automatiquement les mod\u00e8les et tendances courants qui seraient normalement difficiles \u00e0 d\u00e9celer. Par exemple, une solution d\u2019apprentissage automatique peut analyser des donn\u00e9es pour pr\u00e9dire l\u2019effet exact d\u2019une promotion chez une importante cha\u00eene de magasins ou d\u00e9terminer le rendement d\u2019un programme de fid\u00e9lit\u00e9 au sein d\u2019un magasin pr\u00e9cis. Comprendre les donn\u00e9es \u00e0 l\u2019\u00e9chelle granulaire facilite le calcul du rendement d\u2019un produit, la d\u00e9tection de probl\u00e8mes et la mise \u00e0 l\u2019\u00e9chelle de pratiques exemplaires \u00e0 l\u2019ensemble des activit\u00e9s.<\/p>\n

Microsoft Machine Learning<\/h2>\n

L\u2019apprentissage automatique peut grandement aider \u00e0 affecter efficacement les employ\u00e9s sur le terrain, \u00e0 automatiser les proc\u00e9d\u00e9s manuels r\u00e9p\u00e9titifs et \u00e0 am\u00e9liorer l\u2019analyse de donn\u00e9es et l\u2019obtention de renseignements pratiques \u00e0 l\u2019\u00e9chelle d\u2019une organisation. Au final, ces avantages vous aident \u00e0 suivre le rythme de la croissance du march\u00e9 de votre produit et \u00e0 prendre de meilleures d\u00e9cisions en mati\u00e8re de promotions, de campagnes et d\u2019investissements. Microsoft et ses partenaires continueront \u00e0 r\u00e9aliser des investissements continus en apprentissage automatique et en marchandisage au d\u00e9tail pour aider les entreprises de biens de consommation \u00e0 mieux s\u2019adapter \u00e0 notre \u00e9poque de plus en plus num\u00e9rique<\/a>. Pour en savoir plus sur nos solutions en march\u00e9 de marchandisage au d\u00e9tail actuelles, jetez un coup d\u2019\u0153il \u00e0 AFS POP Retail Execution<\/a> et \u00e0 AFS Retail Execution<\/a> sur AppSource<\/a> d\u00e8s aujourd\u2019hui. [1]<\/a>EKN Outlook, 2016<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

L\u2019apprentissage automatique aide les \u00e9quipes de marchandisage au d\u00e9tail \u00e0 optimiser les itin\u00e9raires, \u00e0 acc\u00e9l\u00e9rer les visites en magasin et \u00e0 approfondir l\u2019analyse de donn\u00e9es. Apprenez-en plus sur les solutions en march\u00e9 d\u00e8s aujourd\u2019hui.<\/p>\n","protected":false},"author":223,"featured_media":226,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ep_exclude_from_search":false,"_classifai_error":"","_classifai_text_to_speech_error":"","footnotes":""},"categories":[70],"post_tag":[],"content-type":[],"coauthors":[52],"class_list":["post-124","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-retail"],"yoast_head":"\nApprentissage automatique + marchandisage au d\u00e9tail = visites en magasin et analyses des donn\u00e9es charg\u00e9es \u00e0 bloc - Blogues de l'industrie Microsoft<\/title>\n<meta name=\"description\" content=\"For retail execution teams, Machine Learning helps optimize route plans, expedite store visits, and deepen data analysis. 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