{"id":14051,"date":"2023-07-13T08:18:22","date_gmt":"2023-07-13T08:18:22","guid":{"rendered":"https:\/\/datama.io\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/"},"modified":"2025-04-02T13:13:18","modified_gmt":"2025-04-02T13:13:18","slug":"using-datama-with-google-analytics-4-a-guide-for-web-analysts","status":"publish","type":"post","link":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/","title":{"rendered":"Utiliser Datama avec Google Analytics 4 : un guide pour les Web Analystes"},"content":{"rendered":"\t\t
\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

<\/p>\n

Avant de se lancer dans l\u2019analyse du funnel de performance d\u2019un site web, une \u00e9tape essentielle consiste \u00e0 optimiser les donn\u00e9es pour mener des analyses plus pertinentes. Avec la r\u00e9cente migration d’Universal Analytics vers Google Analytics 4 (GA4), la pr\u00e9paration des donn\u00e9es est plus importante que jamais, car les donn\u00e9es doivent \u00eatre configur\u00e9es d’une mani\u00e8re compl\u00e8tement diff\u00e9rente. Cependant, cela repr\u00e9sente une opportunit\u00e9 de repenser et d\u2019am\u00e9liorer nos m\u00e9thodes de pr\u00e9paration des donn\u00e9es.
<\/span><\/span><\/h4>\n

<\/p>\n

Deux options principales sont disponibles pour la pr\u00e9paration des donn\u00e9es GA4. La premi\u00e8re consiste \u00e0 utiliser le connecteur natif de GA4 vers BigQuery, o\u00f9 vous pourrez interroger votre base de donn\u00e9es en SQL et l’int\u00e9grer dans vos outils de visualisation, ou dans DataMa. La deuxi\u00e8me option consiste \u00e0 utiliser le connecteur GA4 natif de DataMa avec la solution Prep, qui permet d\u2019extraire en quelques clics le jeu de donn\u00e9es n\u00e9cessaire \u00e0 votre analyse.<\/span><\/span><\/h4>\n

<\/p>\n


DataMa offre une valeur ajout\u00e9e significative en facilitant l\u2019explication des variations de performances de votre site internet pour prendre des d\u00e9cisions \u00e9clair\u00e9es et ainsi am\u00e9liorer l\u2019efficacit\u00e9 de votre site internet.<\/span><\/span><\/h4>\n

<\/p>\n


Une fois configur\u00e9, vous pouvez programmer la fr\u00e9quence des analyses et recevoir des notifications par email ou directement dans vos canaux Slack.
<\/span><\/span><\/h4>\n

<\/p>\n

\"\"<\/figure>\n

<\/p>\n

 <\/h1>\n

<\/p>\n

 <\/h1>\n

<\/p>\n

Pr\u00e9paration des donn\u00e9es dans BigQuery<\/strong><\/h2>\n

<\/p>\n

Pour recevoir des donn\u00e9es GA4 dans BigQuery, vous devrez activer le connecteur natif dans vos param\u00e8tres GA4.<\/a><\/span><\/span><\/p>\n

<\/p>\n

Une fois les donn\u00e9es disponibles dans BigQuery, elles sont regroup\u00e9es dans une seule table, dans laquelle certaines valeurs, telles que les param\u00e8tres, se trouvent dans des tableaux que vous devrez \u00ab dissocier \u00bb. La d\u00e9simbrication d\u00e9compile le tableau afin que les diff\u00e9rentes valeurs n’apparaissent pas dans une seule colonne, mais sous plusieurs colonnes pour chaque param\u00e8tre s\u00e9lectionn\u00e9. Pour disposer des diff\u00e9rents champs cit\u00e9s ci-dessus, vous pouvez compter sur :<\/span><\/span><\/p>\n

<\/p>\n

    <\/p>\n

  1. Les champs natifs GA4 tels que Sessions et Revenu<\/span><\/li>\n

    <\/p>\n

  2. Le type de propri\u00e9t\u00e9s utilisateur que vous avez d\u00e9fini
    MAX(CASE WHEN up.key=’userPropertyName’ THEN COALESCE(up.value.string_value, CAST(up.value.int_value AS STRING),CAST(up.value.float_value AS STRING),CAST(up.value.double_value AS STRING)) END) as userPropertyName<\/span><\/span><\/li>\n

    <\/p>\n

  3. Sur les param\u00e8tres des events (anciennement connus sous le nom de dimensions custom dans Universal Analytics)
    <\/span><\/span>MAX(CASE WHEN ep.key=’eventParameterName’ THEN COALESCE(ep.value.string_value, CAST(ep.value.int_value AS STRING),CAST(ep.value.float_value AS STRING),CAST(ep.value.double_value AS STRING)) END) en tant que eventParameterName<\/span><\/li>\n

    <\/p>\n<\/ol>\n

    <\/p>\n

    \n

    <\/p>\n

    Remarque : il est important de choisir avec soin les dimensions qui seront utiles pour l’analyse, car elles peuvent augmenter consid\u00e9rablement le nombre de lignes de votre ensemble de donn\u00e9es et, par cons\u00e9quent, le temps de calcul.<\/span><\/span><\/p>\n

    <\/p>\n<\/blockquote>\n

    <\/p>\n

    Vous pouvez ensuite compl\u00e9ter votre requ\u00eate avec cette requ\u00eate de base :<\/span><\/span><\/p>\n

    <\/p>\n

    SELECT
    <\/span>#native GA4 properties
    <\/span> DATETIME(TIMESTAMP_MICROS(event_timestamp)) as event_timestamp,
    <\/span> DATE(TIMESTAMP_MICROS(event_timestamp)) as event_date,
    <\/span> event_name,
    device.category as device_category,
    <\/span> device.language as device_language,
    <\/span> device.browser as device_browser,
    <\/span> geo.country as geo_country,
    <\/span> traffic_source.name as traffic_source_name,<\/span>
    traffic_source.medium as traffic_source_medium,
    <\/span> traffic_source.source as traffic_source_source,<\/span><\/p>\n

    <\/p>\n

    #user properties unnest<\/span><\/p>\n

    <\/p>\n

    MAX(CASE WHEN up.key=’userPropertyName’ THEN COALESCE(up.value.string_value, CAST(up.value.int_value AS STRING),CAST(up.value.float_value AS STRING),CAST(up.value.double_value AS STRING)) END) as userPropertyName,
    <\/span><\/p>\n

    <\/p>\n

    #event parameters unnest<\/span><\/p>\n

    <\/p>\n

    MAX(CASE WHEN ep.key=’eventParameterName’ THEN COALESCE(ep.value.string_value, CAST(ep.value.int_value AS STRING),CAST(ep.value.float_value AS STRING),CAST(ep.value.double_value AS STRING)) END) as eventParameterName<\/span><\/p>\n

    <\/p>\n

    FROM<\/span><\/p>\n

    <\/p>\n

    `ProjectName.analytics_XXXXXXXXX.events_*` ,
    <\/span>unnest(user_properties) as up,
    <\/span> unnest(event_params) as ep<\/span><\/p>\n

    <\/p>\n

    WHERE regexp_extract(_table_suffix, r'[0-9]+’) BETWEEN format_date(‘%Y%m%d’,date_sub(current_date(), interval 8 day)) #To extract over the 7 last days
    <\/span>AND FORMAT_DATE(‘%Y%m%d’,DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY))
    <\/span>GROUP BY 1,2,3,4,5,6,7,8,9,10<\/span><\/p>\n

    <\/p>\n

    Notez que dans cette requ\u00eate, le nombre de variables n’est pas exhaustif ; vous pouvez ajouter autant de variables que n\u00e9cessaire pour votre analyse.<\/span><\/p>\n

    <\/p>\n

    Pour cr\u00e9er un ensemble de donn\u00e9es pour DataMa, vous devrez cr\u00e9er deux types de colonnes : <\/span><\/p>\n

    <\/p>\n

    Dimensions<\/strong>: variables qui vous permettront d’analyser les donn\u00e9es selon diff\u00e9rents segments (y compris des champs tels que la date, une version de test A\/B, le canal d’acquisition, le pays, le type de produit, etc.)
    <\/span>
    M\u00e9triques<\/strong>: variables qui vous permettront de quantifier chaque \u00e9tape du funnel. Ne mettez pas de ratios, mais seulement des chiffres sommables<\/span><\/p>\n

    <\/p>\n

    Les dimensions sont relativement simples, car ce sont les valeurs des diff\u00e9rents champs que vous avez pr\u00e9par\u00e9s lors de la phase pr\u00e9c\u00e9dente ; pour les m\u00e9triques, cela d\u00e9pend des mesures que vous souhaitez avoir :
    <\/span>
    – Soit ils seront natifs de GA4, comme Revenue, qu\u2019il faudra additionner pour agr\u00e9ger les donn\u00e9es et limiter le nombre de lignes dans le jeu de donn\u00e9es. Ou encore, vous devrez compter le nombre d’utilisateurs\/sessions qui ont effectu\u00e9 une certaine action ou atteint une certaine page.
    <\/span>
    COUNT(DISTINCT user_id) AS Number_Users<\/span><\/p>\n

    <\/p>\n

    – Vous pouvez \u00e9galement annuler le pivotement d’une dimension pour compter le nombre d’occurrences de chacun de ses segments. Par exemple, si une dimension Funnel_Step contient la valeur des \u00e9tapes de l’entonnoir, vous pouvez utiliser la fonction PIVOT pour transformer un ensemble de donn\u00e9es avec trois colonnes Date, Funnel_Step et Number_Users en un ensemble de donn\u00e9es avec autant de colonnes qu’il y a de segments, disons que nous avons trois \u00e9tapes du funnel, nous aurions alors les colonnes suivantes : Date, Step1, Step2 et Step3 avec la valeur du nombre d’utilisateurs sur chacune des \u00e9tapes avec le code suivant :<\/span><\/p>\n

    <\/p>\n

    PIVOT (SUM(Number_Users) FOR Funnel_Step IN (\u2018Step1\u2019, \u2018Step2\u2019, \u2018Step3\u2019))<\/span><\/span><\/p>\n

    <\/p>\n

    Enfin, vous devrez choisir les dimensions et m\u00e9triques qui vous int\u00e9ressent, et vous devriez avoir une requ\u00eate qui ressemble \u00e0 :<\/span><\/p>\n

    <\/p>\n

    WITH Table_Unnest_GA4 AS (
    <\/span>SELECT
    <\/span>#native GA4 properties
    <\/span>DATE(TIMESTAMP_MICROS(event_timestamp)) as event_date,
    <\/span>device.category,
    <\/span>device.language as language,
    <\/span>geo.country as country,
    <\/span> traffic_source.source as source,<\/span><\/p>\n

    <\/p>\n

    #user properties unnest
    <\/span> MAX(CASE WHEN up.key=’user_id’ THEN COALESCE(up.value.string_value, CAST(up.value.int_value AS STRING),CAST(up.value.float_value AS STRING),CAST(up.value.double_value AS STRING)) END) as user_id,<\/span><\/p>\n

    <\/p>\n

    #event parameters unnest
    <\/span> MAX(CASE WHEN ep.key=’Funnel_Step’ THEN COALESCE(ep.value.string_value, CAST(ep.value.int_value AS STRING),CAST(ep.value.float_value AS STRING),CAST(ep.value.double_value AS STRING)) END) as Funnel_Step<\/span><\/p>\n

    <\/p>\n

    FROM <\/span>`ProjectName.analytics_XXXXXXXXX.events_*` ,
    <\/span>unnest(user_properties) as up,
    <\/span> unnest(event_params) as ep<\/span><\/p>\n

    <\/p>\n

    WHERE regexp_extract(_table_suffix, r'[0-9]+’) BETWEEN format_date(‘%Y%m%d’,date_sub(current_date(), interval 8 day)) #To extract over the 7 last days
    <\/span>AND FORMAT_DATE(‘%Y%m%d’,DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY))
    <\/span>GROUP BY 1,2,3,4,5,6,7,8,9,10<\/span>),<\/span><\/p>\n

    <\/p>\n

    Table_Aggregation AS (
    SELECT
    <\/span> event_date,
    <\/span> device_category,
    <\/span> language,
    <\/span> country,
    <\/span> source,
    <\/span> Funnel_Step,
    <\/span> COUNT(DISTINCT user_id) AS Number_Users
    <\/span>FROM Table_Unnest_GA4
    <\/span>GROUP BY 1,2,3,4,5,6<\/span>)<\/span><\/p>\n

    <\/p>\n

    SELECT *
    FROM Table_Aggregation WHERE
    <\/span>PIVOT(SUM(Number_Users) FOR Funnel_Step IN (\u2018Step1\u2019, \u2018Step2\u2019, \u2018Step3\u2019))<\/span><\/p>\n

    <\/p>\n

    On obtient ainsi le dataset suivant :<\/span><\/p>\n

    <\/p>\n

    \"\"<\/h1>\n

    <\/p>\n

     <\/h2>\n

    <\/p>\n

    Utilisation du connecteur Prep dans DataMa<\/strong><\/h2>\n

    <\/p>\n

    Pour ceux qui ne veulent pas passer du temps sur des requ\u00eates SQL, faire la m\u00eame requ\u00eate est possible en quelques clics dans DataMa. Dans DataMa, cr\u00e9ez un nouveau cas d\u2019u et cliquez sur le connecteur GA4 pour connecter votre compte GA4.<\/span><\/p>\n

    <\/p>\n

    \n

    <\/p>\n

    Remarque : Si vous n’avez pas de compte DataMa, vous pouvez acc\u00e9der \u00e0 app.datama.io\/demo pour tester l’ex\u00e9cution de la solution Contactez-nous \u00e0 (solutions@datama.io) pour acc\u00e9der gratuitement \u00e0 la solution et tester notre connecteur GA4.<\/span><\/p>\n

    <\/p>\n<\/blockquote>\n

    <\/p>\n

    \"\"<\/figure>\n

    <\/p>\n

    <\/span><\/p>\n

    <\/p>\n

    <\/p>\n

     <\/h1>\n

    <\/p>\n

    Ensuite, vous pouvez s\u00e9lectionner la propri\u00e9t\u00e9 \u00e0 partir de laquelle vous souhaitez extraire les donn\u00e9es, puis choisir vos dimensions et m\u00e9triques comme vous le feriez si vous construisiez votre propre requ\u00eate SQL. Ajoutez des filtres si n\u00e9cessaire et choisissez la plage de dates sur laquelle vous souhaitez extraire les donn\u00e9es. Il y a ensuite plusieurs \u00e9tapes facultatives :<\/span><\/span><\/p>\n

    <\/p>\n

      <\/p>\n

    • Nettoyer : pour ordonner ou renommer des colonnes, ou ajouter des champs calcul\u00e9s<\/span><\/li>\n

      <\/p>\n

    • Pivot : transformer les segments d’une colonne en autant de colonnes qu’il y a de segments.<\/span><\/li>\n

      <\/p>\n

    • Append : pour ajouter d’autres sources de donn\u00e9es \u00e0 vos donn\u00e9es GA4 Cela vous donnera un sch\u00e9ma similaire au suivant avec plus ou moins de blocs, en fonction de vos op\u00e9rations :<\/span><\/li>\n

      <\/p>\n<\/ul>\n

      <\/p>\n

      \"\"<\/figure>\n

      <\/p>\n

      Ensuite, il vous suffit d’ouvrir la solution DataMa que vous souhaitez utiliser (Compare, Assess, Detect ou Pivot).<\/span><\/p>\n

      <\/p>\n

      Conclusion<\/strong><\/h2>\n

      <\/p>\n

      Avec la r\u00e9cente migration vers GA4, il est essentiel de vous familiariser avec de nouveaux outils et techniques pour maximiser l’efficacit\u00e9 de vos analyses. Les deux approches ci-dessus peuvent obtenir des r\u00e9sultats similaires. Le faire directement via BigQuery vous donnera beaucoup plus de libert\u00e9 dans vos calculs mais cela n\u00e9cessite une certaine ma\u00eetrise de SQL. L’utilisation de DataMa Prep est plus rapide et beaucoup plus facile \u00e0 configurer, mais vous disposez de moins d’op\u00e9rations et de calculs que dans BigQuery. <\/span><\/span><\/p>\n

      <\/p>\n

      Vous pouvez cr\u00e9er votre premier cas d’utilisation en cr\u00e9ant un compte d\u00e9mo sur notre site.<\/span><\/p>\n

      <\/p>\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":"

      Avant de se lancer dans l\u2019analyse du funnel de performance d\u2019un site web, une \u00e9tape essentielle consiste \u00e0 optimiser les donn\u00e9es pour mener des analyses plus pertinentes. Avec la r\u00e9cente migration d’Universal Analytics vers Google Analytics 4 (GA4), la pr\u00e9paration des donn\u00e9es est plus importante que jamais, car les donn\u00e9es doivent \u00eatre configur\u00e9es d’une mani\u00e8re […]<\/p>\n","protected":false},"author":18,"featured_media":16810,"comment_status":"closed","ping_status":"open","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":[161,161],"class_list":["post-14051","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles-fr","tag-google-analytics-fr"],"yoast_head":"\nUtiliser Datama avec Google Analytics 4 : un guide pour les Web Analystes - 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\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Utiliser Datama avec Google Analytics 4 : un guide pour les Web Analystes - Datama\" \/>\n<meta property=\"og:description\" content=\"Avant de se lancer dans l\u2019analyse du funnel de performance d\u2019un site web, une \u00e9tape essentielle consiste \u00e0 optimiser les donn\u00e9es pour mener des analyses plus pertinentes. Avec la r\u00e9cente migration d’Universal Analytics vers Google Analytics 4 (GA4), la pr\u00e9paration des donn\u00e9es est plus importante que jamais, car les donn\u00e9es doivent \u00eatre configur\u00e9es d’une mani\u00e8re […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/\" \/>\n<meta property=\"og:site_name\" content=\"Datama\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-13T08:18:22+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-04-02T13:13:18+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datama.io\/wp-content\/uploads\/2023\/07\/Screenshot-2023-07-13-101243.png\" \/>\n\t<meta property=\"og:image:width\" content=\"292\" \/>\n\t<meta property=\"og:image:height\" content=\"172\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Guillaume\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@datama_solution\" \/>\n<meta name=\"twitter:site\" content=\"@datama_solution\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Guillaume\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"8 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/\"},\"author\":{\"name\":\"Guillaume\",\"@id\":\"https:\/\/datama.io\/fr\/#\/schema\/person\/cc38a71c241805e3b74a28a05f63f667\"},\"headline\":\"Utiliser Datama avec Google Analytics 4 : un guide pour les Web Analystes\",\"datePublished\":\"2023-07-13T08:18:22+00:00\",\"dateModified\":\"2025-04-02T13:13:18+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/\"},\"wordCount\":1607,\"publisher\":{\"@id\":\"https:\/\/datama.io\/fr\/#organization\"},\"image\":{\"@id\":\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/datama.io\/wp-content\/uploads\/2025\/02\/Article26.png\",\"keywords\":[\"Google Analytics\",\"Google Analytics\"],\"articleSection\":[\"Articles\"],\"inLanguage\":\"fr-FR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/\",\"url\":\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/\",\"name\":\"Utiliser Datama avec Google Analytics 4 : un guide pour les Web Analystes - Datama\",\"isPartOf\":{\"@id\":\"https:\/\/datama.io\/fr\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/datama.io\/wp-content\/uploads\/2025\/02\/Article26.png\",\"datePublished\":\"2023-07-13T08:18:22+00:00\",\"dateModified\":\"2025-04-02T13:13:18+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#primaryimage\",\"url\":\"https:\/\/datama.io\/wp-content\/uploads\/2025\/02\/Article26.png\",\"contentUrl\":\"https:\/\/datama.io\/wp-content\/uploads\/2025\/02\/Article26.png\",\"width\":3410,\"height\":2016},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Accueil\",\"item\":\"https:\/\/datama.io\/fr\/accueil\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Utiliser DataMa avec Google Analytics 4 : un guide pour les Web Analystes\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datama.io\/fr\/#website\",\"url\":\"https:\/\/datama.io\/fr\/\",\"name\":\"Datama\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/datama.io\/fr\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datama.io\/fr\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/datama.io\/fr\/#organization\",\"name\":\"Datama\",\"url\":\"https:\/\/datama.io\/fr\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datama.io\/fr\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/datama.io\/wp-content\/uploads\/2023\/12\/cropped-Logo-Datama.png\",\"contentUrl\":\"https:\/\/datama.io\/wp-content\/uploads\/2023\/12\/cropped-Logo-Datama.png\",\"width\":400,\"height\":100,\"caption\":\"Datama\"},\"image\":{\"@id\":\"https:\/\/datama.io\/fr\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/x.com\/datama_solution\",\"https:\/\/www.linkedin.com\/company\/datama\/\",\"https:\/\/www.youtube.com\/channel\/UCoxfesECCiubm0XLCXcN3TA\/featured\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/datama.io\/fr\/#\/schema\/person\/cc38a71c241805e3b74a28a05f63f667\",\"name\":\"Guillaume\",\"description\":\"Founder & CTO @DataMa, Decision Scientist For ten years I have acquired CRO expertise as an Analytics Manager at Expedia and a consultant at Bain & Company. Now at DataMa I create algorithms that optimize CRO analysis, customer journey, and digital performance.\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Utiliser Datama avec Google Analytics 4 : un guide pour les Web Analystes - Datama","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/","og_locale":"fr_FR","og_type":"article","og_title":"Utiliser Datama avec Google Analytics 4 : un guide pour les Web Analystes - Datama","og_description":"Avant de se lancer dans l\u2019analyse du funnel de performance d\u2019un site web, une \u00e9tape essentielle consiste \u00e0 optimiser les donn\u00e9es pour mener des analyses plus pertinentes. Avec la r\u00e9cente migration d’Universal Analytics vers Google Analytics 4 (GA4), la pr\u00e9paration des donn\u00e9es est plus importante que jamais, car les donn\u00e9es doivent \u00eatre configur\u00e9es d’une mani\u00e8re […]","og_url":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/","og_site_name":"Datama","article_published_time":"2023-07-13T08:18:22+00:00","article_modified_time":"2025-04-02T13:13:18+00:00","og_image":[{"width":292,"height":172,"url":"https:\/\/datama.io\/wp-content\/uploads\/2023\/07\/Screenshot-2023-07-13-101243.png","type":"image\/png"}],"author":"Guillaume","twitter_card":"summary_large_image","twitter_creator":"@datama_solution","twitter_site":"@datama_solution","twitter_misc":{"\u00c9crit par":"Guillaume","Dur\u00e9e de lecture estim\u00e9e":"8 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#article","isPartOf":{"@id":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/"},"author":{"name":"Guillaume","@id":"https:\/\/datama.io\/fr\/#\/schema\/person\/cc38a71c241805e3b74a28a05f63f667"},"headline":"Utiliser Datama avec Google Analytics 4 : un guide pour les Web Analystes","datePublished":"2023-07-13T08:18:22+00:00","dateModified":"2025-04-02T13:13:18+00:00","mainEntityOfPage":{"@id":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/"},"wordCount":1607,"publisher":{"@id":"https:\/\/datama.io\/fr\/#organization"},"image":{"@id":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#primaryimage"},"thumbnailUrl":"https:\/\/datama.io\/wp-content\/uploads\/2025\/02\/Article26.png","keywords":["Google Analytics","Google Analytics"],"articleSection":["Articles"],"inLanguage":"fr-FR"},{"@type":"WebPage","@id":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/","url":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/","name":"Utiliser Datama avec Google Analytics 4 : un guide pour les Web Analystes - Datama","isPartOf":{"@id":"https:\/\/datama.io\/fr\/#website"},"primaryImageOfPage":{"@id":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#primaryimage"},"image":{"@id":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#primaryimage"},"thumbnailUrl":"https:\/\/datama.io\/wp-content\/uploads\/2025\/02\/Article26.png","datePublished":"2023-07-13T08:18:22+00:00","dateModified":"2025-04-02T13:13:18+00:00","breadcrumb":{"@id":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#primaryimage","url":"https:\/\/datama.io\/wp-content\/uploads\/2025\/02\/Article26.png","contentUrl":"https:\/\/datama.io\/wp-content\/uploads\/2025\/02\/Article26.png","width":3410,"height":2016},{"@type":"BreadcrumbList","@id":"https:\/\/datama.io\/fr\/using-datama-with-google-analytics-4-a-guide-for-web-analysts\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Accueil","item":"https:\/\/datama.io\/fr\/accueil\/"},{"@type":"ListItem","position":2,"name":"Utiliser DataMa avec Google Analytics 4 : un guide pour les Web Analystes"}]},{"@type":"WebSite","@id":"https:\/\/datama.io\/fr\/#website","url":"https:\/\/datama.io\/fr\/","name":"Datama","description":"","publisher":{"@id":"https:\/\/datama.io\/fr\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datama.io\/fr\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"fr-FR"},{"@type":"Organization","@id":"https:\/\/datama.io\/fr\/#organization","name":"Datama","url":"https:\/\/datama.io\/fr\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/datama.io\/fr\/#\/schema\/logo\/image\/","url":"https:\/\/datama.io\/wp-content\/uploads\/2023\/12\/cropped-Logo-Datama.png","contentUrl":"https:\/\/datama.io\/wp-content\/uploads\/2023\/12\/cropped-Logo-Datama.png","width":400,"height":100,"caption":"Datama"},"image":{"@id":"https:\/\/datama.io\/fr\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/x.com\/datama_solution","https:\/\/www.linkedin.com\/company\/datama\/","https:\/\/www.youtube.com\/channel\/UCoxfesECCiubm0XLCXcN3TA\/featured"]},{"@type":"Person","@id":"https:\/\/datama.io\/fr\/#\/schema\/person\/cc38a71c241805e3b74a28a05f63f667","name":"Guillaume","description":"Founder & CTO @DataMa, Decision Scientist For ten years I have acquired CRO expertise as an Analytics Manager at Expedia and a consultant at Bain & Company. Now at DataMa I create algorithms that optimize CRO analysis, customer journey, and digital performance."}]}},"_links":{"self":[{"href":"https:\/\/datama.io\/fr\/wp-json\/wp\/v2\/posts\/14051","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datama.io\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datama.io\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datama.io\/fr\/wp-json\/wp\/v2\/users\/18"}],"replies":[{"embeddable":true,"href":"https:\/\/datama.io\/fr\/wp-json\/wp\/v2\/comments?post=14051"}],"version-history":[{"count":11,"href":"https:\/\/datama.io\/fr\/wp-json\/wp\/v2\/posts\/14051\/revisions"}],"predecessor-version":[{"id":17213,"href":"https:\/\/datama.io\/fr\/wp-json\/wp\/v2\/posts\/14051\/revisions\/17213"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datama.io\/fr\/wp-json\/wp\/v2\/media\/16810"}],"wp:attachment":[{"href":"https:\/\/datama.io\/fr\/wp-json\/wp\/v2\/media?parent=14051"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datama.io\/fr\/wp-json\/wp\/v2\/categories?post=14051"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datama.io\/fr\/wp-json\/wp\/v2\/tags?post=14051"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}