{"id":14066,"date":"2023-03-01T11:46:42","date_gmt":"2023-03-01T11:46:42","guid":{"rendered":"https:\/\/datama.io\/use-case-5-forecast-analysis-vs-actual-performance\/"},"modified":"2026-01-29T15:50:01","modified_gmt":"2026-01-29T15:50:01","slug":"use-case-5-forecast-analysis-vs-actual-performance","status":"publish","type":"post","link":"https:\/\/datama.io\/fr\/use-case-5-forecast-analysis-vs-actual-performance\/","title":{"rendered":"Cas d&rsquo;usage n\u00b0 5 : Analyse des pr\u00e9visions par rapport aux performances r\u00e9elles"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"14066\" class=\"elementor elementor-14066 elementor-7664\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-648e1b0b e-flex e-con-boxed e-con e-parent\" data-id=\"648e1b0b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-56eba7b elementor-widget elementor-widget-text-editor\" data-id=\"56eba7b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Category: Performance | Solution: Datama Compare, Analytical Solution | Type: Recurring | Client: Travel &amp; Leisure | Extension: Tableau<\/span><\/p>\n<p><strong style=\"font-size: 14px;\">Tags<\/strong><span style=\"font-size: 14px;\">: <em><span style=\"font-weight: 400;\">#Controlling #Performance #Budget#Waterfall<\/span><\/em><\/span><\/p>\n<div class=\"et_pb_testimonial_description_inner\">\n<div class=\"et_pb_testimonial_content\">\n<blockquote>\n<p><span style=\"font-weight: 400;\"><em>\u00ab\u00a0Datama helps us to manage and understand on a monthly basis the drivers of our growth vs what was forecasted. It enables us to make quick and adequate decisions regarding Marketing actions or operational effort.\u00a0\u00bb<\/em><br><strong>Victor Debray &#8211; VP Data &#8211; Click &amp; Boat<\/strong><br><\/span><\/p>\n<\/blockquote>\n<\/div>\n<\/div>\n<h1>&nbsp;<\/h1>\n<h2><strong>Context<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.clickandboat.com\/\">Click &amp; Boat<\/a> is a major web C2C marketplace matching private boat supply with customer rental demand.&nbsp;&nbsp;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The data team helps all business teams find insights and make decisions with first party data. Among other tools, they are regularly working with Tableau, based on a Snowflake database. They have been using Datama within that stack for more than a year.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Among their tasks, one key challenge is to define and monitor the forecast of income, and regularly explain gaps between actual and forecasted revenues throughout the year. This analysis is critical to discuss with investors and make business decisions. <\/span><\/p>\n<h2><strong>Approach<\/strong><\/h2>\n<h3><strong>Market Equation<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">One of the complexities of the C2C marketplace is that the company income consists of the demand performance on one side, and the supply performance on the other side.&nbsp;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The match between supply and demand is not always the same, and for our client, some important demands are \u201cFollowed\u201d, which means that they are handled by phone through a customer agent, and some other demands are \u201cUnfollowed\u201d which means that the owner alone is responsible for accepting the demand through the web platform. Obviously, the share of demand followed by customer agents is critical to explaining performance.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><img fetchpriority=\"high\" decoding=\"async\" class=\"wp-image-7675 aligncenter size-full\" src=\"https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/1.png\" alt=\"\" width=\"992\" height=\"379\" srcset=\"https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/1.png 992w, https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/1-300x115.png 300w, https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/1-768x293.png 768w\" sizes=\"(max-width: 992px) 100vw, 992px\" \/><\/span><\/p>\n<p><span style=\"font-weight: 400;\">As such, they ended up with the following market equation:<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><img decoding=\"async\" class=\"wp-image-7676 aligncenter size-full\" src=\"https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/2.png\" alt=\"\" width=\"998\" height=\"370\" srcset=\"https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/2.png 998w, https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/2-300x111.png 300w, https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/2-768x285.png 768w\" sizes=\"(max-width: 998px) 100vw, 998px\" \/><\/span><\/p>\n<p><span style=\"font-weight: 400;\">This equation, which seems slightly more complex than the usual analysis in Datama, has the important advantage of making the share of followed demand appear explicitly, which will allow the team to clearly monitor the impact of that KPI on the total revenue vs. forecasted.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Below is the technical translation in Datama\u2019s interface for that market equation:<\/span><\/p>\n<p><img decoding=\"async\" class=\"wp-image-7677 aligncenter size-full\" src=\"https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/3.png\" alt=\"\" width=\"973\" height=\"391\" srcset=\"https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/3.png 973w, https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/3-300x121.png 300w, https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/3-768x309.png 768w\" sizes=\"(max-width: 973px) 100vw, 973px\" \/><\/p>\n<h6><i><span style=\"font-weight: 400;\">In Datama, you can set up the market equation using the \u00ab\u00a0=[1]*[2]*([3]*[4]+(1-[3])*[5])*[6]\u00a0\u00bb annotation<\/span><\/i><\/h6>\n<h2>&nbsp;<\/h2>\n<h2>&nbsp;<\/h2>\n<h3><strong>Dataset<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">Forecast\/budget data is usually in an Excel spreadsheet which is used to build models and play with hypotheses. Once this exercise has been done for the year, the client brings it to Snowflake to join with the actual numbers for reporting purposes.&nbsp;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this case, the data is then accessible directly in Tableau, and then Datama is added as a Tableau extension, so that final users can have access to Datama\u2019s insights directly in their usual Tableau dashboard.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Obviously, the type of data (forecast version and actual) is the main dimension, which will be used for comparison. The data is also broken down by market and brand.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here is an example (anonymized) <\/span><a href=\"https:\/\/docs.google.com\/spreadsheets\/d\/16X-vl1aSHAmuzkWNIA6S5SFiUOMERKc7CbGiYDW9ZoI\/edit#gid=1549138389\"><span style=\"font-weight: 400;\">dataset<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-7678 aligncenter size-full\" src=\"https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/4.png\" alt=\"\" width=\"1027\" height=\"271\" srcset=\"https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/4.png 1027w, https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/4-300x79.png 300w, https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/4-1024x270.png 1024w, https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/4-768x203.png 768w\" sizes=\"(max-width: 1027px) 100vw, 1027px\" \/><\/span><\/p>\n<p><span style=\"font-weight: 400;\">We ingest the data using the <\/span><span style=\"font-weight: 400;\"><a href=\"https:\/\/datama-solutions.github.io\/\/docs\/extensions\/how-to-use\/tableau_viz.html\">Datama Tableau dashboard extension<\/a><\/span><\/p>\n<h2><strong>Takeaways<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">In Datama Compare, the use case provides a quick view of the key drivers between actual and forecast date, and between baseline and target data to explain why the client is straying from the target, and to understand the value of each initiative and where to focus.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-7679 aligncenter size-full\" src=\"https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/5.png\" alt=\"\" width=\"1251\" height=\"469\" srcset=\"https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/5.png 1251w, https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/5-300x112.png 300w, https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/5-1024x384.png 1024w, https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/5-768x288.png 768w\" sizes=\"(max-width: 1251px) 100vw, 1251px\" \/><\/span><\/p>\n<h6><span style=\"font-weight: 400;\"><i>The whole use case is set up in Tableau and published in Tableau server so that users have seamless access and can interact with filters and hypotheses<\/i><\/span><\/h6>\n<p><span style=\"font-weight: 400;\">In the example above, we see that the actual revenue is below the forecast (or targeted revenue), mainly due to the lack of traffic, particularly in the US, likely due to the pandemic. As a side effect, the Online Conversion (CVR) is down, because US users convert well, but they have decreased in the mix.&nbsp;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Thankfully, this decrease of traffic allowed the customer service to handle a larger share of demand than expected, which had a positive impact on revenue, and the acceptance rate of \u201cUnfollowed\u201d has also been higher than expected, limiting the impact on total revenue.<\/span><\/p>\n<h2><strong>Outcomes<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">The report has been published and shared on Click &amp; Boat\u2019s Tableau server, so that final users can access Datama\u2019s waterfall easily and interact with filters and hypotheses.&nbsp;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The fact that Datama was used in Tableau made adoption and regular updates easy and seamless.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Datama\u2019s waterfall allowed Click &amp; Boat to align all stakeholders on the main focus required for performance improvement and clearly explained the financial impact of complex effects such as mix effects or changes in customer service efforts.<\/span><\/p>\n<p>To learn more about <a href=\"https:\/\/www.clickandboat.com\/\">Click &amp; Boat<\/a>:<\/p>\n<p>To test <a href=\"https:\/\/app.datama.io\/demo\">Datama solution:<\/a><\/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":"<p>Cat\u00e9gorie : Performance | Solution : DataMa Compare, solution analytique | Type : R\u00e9current | Client : Travel &amp; Leisure | Extension : Tableau Tags: #Contr\u00f4leDeGestion #Performance #Budget#Waterfall \u00ab DataMa nous aide \u00e0 g\u00e9rer et comprendre mensuellement les drivers de notre croissance par rapport \u00e0 ce qui \u00e9tait pr\u00e9vu. Cela nous permet de prendre des [&hellip;]<\/p>\n","protected":false},"author":18,"featured_media":15485,"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":[127],"tags":[153,143],"class_list":["post-14066","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-use-case-fr","tag-datama-fr-2","tag-datama"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Cas d&#039;usage n\u00b0 5 : Analyse des pr\u00e9visions par rapport aux performances r\u00e9elles - Datama<\/title>\n<meta name=\"description\" content=\"Analysez les \u00e9carts entre vos pr\u00e9visions et vos performances r\u00e9elles pour ajuster votre strat\u00e9gie en temps r\u00e9el.\" \/>\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\/use-case-5-forecast-analysis-vs-actual-performance\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cas d&#039;usage n\u00b0 5 : Analyse des pr\u00e9visions par rapport aux performances r\u00e9elles - Datama\" \/>\n<meta property=\"og:description\" content=\"Analysez les \u00e9carts entre vos pr\u00e9visions et vos performances r\u00e9elles pour ajuster votre strat\u00e9gie en temps r\u00e9el.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datama.io\/fr\/use-case-5-forecast-analysis-vs-actual-performance\/\" \/>\n<meta property=\"og:site_name\" content=\"Datama\" \/>\n<meta property=\"article:published_time\" content=\"2023-03-01T11:46:42+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-01-29T15:50:01+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datama.io\/wp-content\/uploads\/2023\/03\/5-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1080\" \/>\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=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/datama.io\\\/fr\\\/use-case-5-forecast-analysis-vs-actual-performance\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/datama.io\\\/fr\\\/use-case-5-forecast-analysis-vs-actual-performance\\\/\"},\"author\":{\"name\":\"Guillaume\",\"@id\":\"https:\\\/\\\/datama.io\\\/fr\\\/#\\\/schema\\\/person\\\/cc38a71c241805e3b74a28a05f63f667\"},\"headline\":\"Cas d&rsquo;usage n\u00b0 5 : Analyse des pr\u00e9visions par rapport aux performances r\u00e9elles\",\"datePublished\":\"2023-03-01T11:46:42+00:00\",\"dateModified\":\"2026-01-29T15:50:01+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/datama.io\\\/fr\\\/use-case-5-forecast-analysis-vs-actual-performance\\\/\"},\"wordCount\":969,\"publisher\":{\"@id\":\"https:\\\/\\\/datama.io\\\/fr\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/datama.io\\\/fr\\\/use-case-5-forecast-analysis-vs-actual-performance\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/datama.io\\\/wp-content\\\/uploads\\\/2023\\\/03\\\/5-1.png\",\"keywords\":[\"DataMa\",\"DataMa\"],\"articleSection\":[\"Use case\"],\"inLanguage\":\"fr-FR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/datama.io\\\/fr\\\/use-case-5-forecast-analysis-vs-actual-performance\\\/\",\"url\":\"https:\\\/\\\/datama.io\\\/fr\\\/use-case-5-forecast-analysis-vs-actual-performance\\\/\",\"name\":\"Cas d'usage n\u00b0 5 : Analyse des pr\u00e9visions par rapport aux performances r\u00e9elles - 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