{"id":11540,"date":"2023-07-03T14:05:00","date_gmt":"2023-07-03T14:05:00","guid":{"rendered":"https:\/\/datama.io\/category-management-comment-identifier-les-plus-grosses-opportunites-business\/"},"modified":"2025-03-05T11:17:41","modified_gmt":"2025-03-05T11:17:41","slug":"category-management-comment-identifier-les-plus-grosses-opportunites-business","status":"publish","type":"post","link":"https:\/\/datama.io\/fr\/category-management-comment-identifier-les-plus-grosses-opportunites-business\/","title":{"rendered":"Category Management : Comment identifier les plus grosses opportunit\u00e9s business ?"},"content":{"rendered":"\t\t
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Category: Category Management |Solution: Datama Compare | Type : Recurring | Instries: all ie FMCG, Beauty, Toys | Extension: None<\/p>\n\n

Tags<\/strong>: Tags: #CategoryManagement #PerformanceEnseigne #RelationRetailer#OpportunitesBusiness#waterfall#Quantification<\/em><\/p>\n\n

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\u00ab\u00a0Data in FMCG businesses has always been especially perishable; its value quickly diminishes with time. Datama tools can help category managers transform data into actions in a faster and more precise way to impact category results and to get a competitive advantage<\/em>\u00ab\u00a0<\/p>\nBrian Harris<\/strong>
Creator of Category Management Concept<\/cite><\/blockquote>\n<\/figure>\n\n

Le contexte<\/h2>\n\n

A chaque fin de p\u00e9riode, les category managers font l\u2019analyse de la performance de leur cat\u00e9gorie au sein de leur enseigne, avec une analyse plus ou moins d\u00e9taill\u00e9e de la comp\u00e9tition, et une analyse en profondeur de leurs marques et produits.<\/p>\n\n

Cela prend beaucoup de temps, cr\u00e9e du stress, et la qualit\u00e9 du diagnostic est variable en fonction du niveau d\u2019analyse du cat man, de son expertise business, de son temps disponible, de l\u2019importance de la data au sein de l\u2019\u00e9quipe\u2026<\/p>\n\n

Cet article se concentre sur 2 points cl\u00e9s qui impactent fortement la qualit\u00e9 de l\u2019analyse et la confiance que l\u2019on peut avoir dans son diagnostic de performance:<\/p>\n\n

    \n
  • Les analyses sont majoritairement faites \u00e0 base de % d\u2019\u00e9volution<\/li>\n\n
  • L\u2019\u00eatre humain a une capacit\u00e9 d\u2019analyse et de synth\u00e8se limit\u00e9e<\/li>\n<\/ul>\n\n

    Ne pas quantifier les % peut faire tirer les mauvaises conclusions<\/h2>\n\n

    Dans 99% des cas, par habitude et manque d\u2019outil appropri\u00e9, les analyses sont faites en ne regardant que des % d\u2019\u00e9volution, ce qui peut \u00eatre trompeur, par exemple :<\/p>\n\n

      \n
    • Un petit % d\u2019\u00e9volution sur une grosse masse (volume, valeur) peut avoir un impact fort sur le business<\/li>\n\n
    • A l\u2019inverse un gros % d\u2019\u00e9volution sur une petite masse n\u2019aura qu\u2019un impact minime sur le business (si ce % est fort chaque p\u00e9riode, c\u2019est bien \u00e9videmment un trend de fond \u00e0 suivre m\u00e9ticuleusement)<\/li>\n<\/ul>\n\n

      Par exemple, dans le cas ci-dessous, une perte de 5 points de DV sur le produit A entra\u00eene une perte de 5500 unit\u00e9s alors qu\u2019une baisse de 9pts de DV fait perdre 4800 unit\u00e9s au produit B, soit un \u00e9cart de 15% d\u2019unit\u00e9s.<\/p>\n\n

      \"\"<\/figure>\n\n

      D\u2019o\u00f9 l\u2019importance de quantifier l\u2019impact de l\u2019\u00e9volution des KPI explicatifs (DV, prix, lin\u00e9aire etc) sur les ventes valeur et\/ou volume (ou la marge pour les compte-cl\u00e9s).<\/p>\n\n

      Et ce sur l\u2019ensemble des business drivers : enseignes, cat\u00e9gories, marques, sous marques, gammes, formats, jusqu\u2019\u00e0 l\u2019ean.<\/p>\n\n

      Cette quantification (ou massification) des % d\u2019\u00e9volution permet d\u2019identifier les plus gros gains absolus<\/strong> mais surtout les plus grosses pertes absolues<\/strong>, et donc d\u2019identifier les priorit\u00e9s sur lesquelles se concentrer pour une meilleure performance<\/strong>.<\/p>\n\n

      Bien \u00e9videmment, la connaissance qu\u2019a le cat man de l\u2019entreprise (process, possibilit\u00e9s d\u2019action, budget\u2026), du march\u00e9 et des enseignes lui permet de d\u00e9terminer si les grosses sources de perte identifi\u00e9es sont actionnables\u2026<\/p>\n\n

      M\u00eame si elles ne sont pas actionnables il est n\u00e9anmoins cl\u00e9 de les avoir identifi\u00e9es pour comprendre au mieux sa performance et travailler sur des actions futures \u00e0 prendre.<\/p>\n\n

      Les limites analytiques humaines entra\u00eenent des opportunit\u00e9s et menaces manqu\u00e9es<\/h2>\n\n

      Une analyse structur\u00e9e de bout en bout, qui suit le m\u00eame sch\u00e9ma \u00e0 chaque p\u00e9riode<\/p>\n\n

        \n
      • Une analyse qui commence par un sch\u00e9ma habituel mais qui ensuite diverge en fonction de ce que les data montrent<\/li>\n\n
      • Une analyse sans structure, o\u00f9 l\u2019on s\u2019attarde sur les % d\u2019\u00e9volution les plus importants<\/li>\n\n
      • Une analyse centr\u00e9e sur les hypoth\u00e8ses que l\u2019on \u00e9met en fonction de nos connaissances du business<\/li>\n<\/ul>\n\n

        Ces 4 approches ont des forces et des faiblesses. Certaines sont plus puissantes que d\u2019autres mais aucune n\u2019est parfaite car les capacit\u00e9s d\u2019analyse et de synth\u00e8se de l\u2019information de l\u2019\u00eatre humain sont limit\u00e9es.<\/p>\n\n

        En effet, il est extr\u00eamement complexe et chronophage d\u2019analyser chaque marque, chaque gamme, chaque sous gamme, chaque format, chaque produit etc sur chaque KPI, et de les comparer \u00e0 la comp\u00e9tition.<\/p>\n\n

        Et ensuite d\u2019en faire une synth\u00e8se avec des axes prioritaires.<\/p>\n\n

        Par cons\u00e9quent, aucun cat man ne pouvant tout analyser et synth\u00e9tiser par manque de temps, de capacit\u00e9 ou d\u2019envie, cela entra\u00eene in\u00e9vitablement de rater des opportunit\u00e9s et menaces.<\/strong><\/p>\n\n

        DataMa apporte une solution cl\u00e9 en main \u00e0 cette complexit\u00e9<\/p>\n\n

          \n
        • Quantification des impacts<\/strong> de chaque business driver sur chaque KPI<\/li>\n\n
        • Visualisation rapide et impactante<\/strong> de l\u2019ensemble de ces impacts<\/li>\n\n
        • Mise en avant des impacts les plus int\u00e9ressants et donc prioritaires<\/strong> d\u2019un point de vue data (la petite barre verte \u00e0 c\u00f4t\u00e9 de chaque business driver)<\/li>\n<\/ul>\n\n

          Voici un exemple d\u2019analyse qui permet, en quelques secondes, de comprendre que la baisse de ventes valeur est li\u00e9e \u00e0 une forte perte de DV chez Carrefour, qui doit donc \u00eatre le focus de l\u2019analyse.<\/p>\n\n

          \"\"<\/figure>\n\n

          DataMa vous permet donc d\u2019identifier imm\u00e9diatement les plus grosses opportunit\u00e9s business, pour que vous puissiez vous concentrer sur les solutions et recommendations.<\/p>\n\n

          Un diagnostic pr\u00e9cis et rapide pour une plus forte valeur ajout\u00e9e<\/h2>\n\n

          La valeur ajout\u00e9e des category managers<\/strong> ne devrait pas se situer dans leur capacit\u00e9 \u00e0 faire des analyses de data pr\u00e9cises mais dans leur apport au business<\/strong>.<\/p>\n\n

          Le probl\u00e8me est qu\u2019ils passent tellement de temps sur ces analyses de data qu\u2019ils en ont trop peu pour se concentrer sur leur vraie valeur ajout\u00e9e :<\/p>\n\n

            \n
          • L\u2019histoire (le storytelling)<\/strong> qu\u2019ils racontent autour de la data, pour mettre en avant les points cl\u00e9s (on sait \u00e0 quel point une mauvaise histoire peut noyer une superbe analyse)<\/li>\n\n
          • Les recommandations<\/strong> d\u2019actions \u00e0 prendre et de strat\u00e9gie cat\u00e9gorielle<\/li>\n\n
          • Les relations<\/strong> internes mais surtout avec les retailers<\/strong><\/li>\n<\/ul>\n\n

            L\u2019histoire et les recommandations, \u00e0 l\u2019inverse de l\u2019analyse de la data, sont par nature sujettes \u00e0 la subjectivit\u00e9 car elles font appel \u00e0 la cr\u00e9ativit\u00e9, \u00e0 l\u2019expertise business et \u00e0 la connaissance des consommateurs, de l\u2019entreprise et des clients.<\/p>\n\n

            Ce sont des \u00e9l\u00e9ments qui doivent donc \u00eatre d\u00e9battus en interne pour \u00eatre optimis\u00e9s et pour permettre d\u2019avoir l\u2019impact le plus fort sur le chiffre d\u2019affaires.<\/p>\n\n

            Conclusion<\/h2>\n\n

            L\u2019analyse cat\u00e9gorielle et de performance est cl\u00e9 mais \u00e9galement particuli\u00e8rement complexe.<\/p>\n\n

            Tellement complexe et chronophage qu\u2019elle ne laisse pas assez de temps aux \u00e9quipes pour apporter une forte valeur ajout\u00e9e autour d\u2019une histoire impactante, de recommandations puissantes et du d\u00e9veloppement d\u2019une relation client profonde.<\/p>\n\n

            Cette relation client est la base de tout bon business car toutes les \u00e9tudes montrent que les \u00eatres humains prennent des d\u00e9cisions \u00e9motionnelles qu\u2019ils post-rationalisent.<\/p>\n\n

            Nous \u00e9voquerons dans un prochain article d\u2019autres aspects critiques de la relation avec les retailers que DataMa permet de renforcer : confiance et transparence dans le diagnostic, approche objective et r\u00e9activit\u00e9.<\/p>\n\n

            Alors si vous aussi vous avez envie de r\u00e9duire drastiquement le temps d\u2019analyse de votre \u00e9quipe et d\u2019avoir un impact sur le business encore plus fort, n\u2019h\u00e9sitez pas \u00e0 me contacter<\/p>\n\n

            Vous pourrez tester gratuitement DataMa sur vos data pour en valider la puissance !<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

            Category: Category Management |Solution: Datama Compare | Type : Recurring | Instries: all ie FMCG, Beauty, Toys | Extension: None Tags: Tags: #CategoryManagement #PerformanceEnseigne #RelationRetailer#OpportunitesBusiness#waterfall#Quantification \u00ab\u00a0Data in FMCG businesses has always been especially perishable; its value quickly diminishes with time. Datama tools can help category managers transform data into actions in a faster and more […]<\/p>\n","protected":false},"author":19,"featured_media":16798,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[139],"tags":[220,135],"class_list":["post-11540","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles-fr","tag-category-management-fr","tag-waterfall-fr"],"yoast_head":"\nCategory Management : Comment identifier les plus grosses opportunit\u00e9s business ? - Datama<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datama.io\/fr\/category-management-comment-identifier-les-plus-grosses-opportunites-business\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Category Management : Comment identifier les plus grosses opportunit\u00e9s business ? - Datama\" \/>\n<meta property=\"og:description\" content=\"Category: Category Management |Solution: Datama Compare | Type : Recurring | Instries: all ie FMCG, Beauty, Toys | Extension: None Tags: Tags: #CategoryManagement #PerformanceEnseigne #RelationRetailer#OpportunitesBusiness#waterfall#Quantification \u00ab\u00a0Data in FMCG businesses has always been especially perishable; its value quickly diminishes with time. 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