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The company operates in an e-commerce model and serves multiple markets simultaneously, running advertising campaigns and analytics on dozens of different accounts and platforms.\n\nThe growing scale of operations meant that the existing data processing model required process automation.\n\n---\n\n## The Challenge\n\nUnitrailer's dynamic expansion into **25 European markets** generated immense data complexity.\n\nThe company had to manage a multidimensional matrix of marketing, analytical, and sales data. This included:\n\n* **- Analytical and advertising systems:** Google Analytics 4, Google Ads, Meta Ads, TikTok Ads, Bing Ads, Criteo, and Google Search Console accounts.\n* **- E-commerce and CRM systems:** The IdoSell platform, containing sales and product data from all markets and other marketplace platforms.\n* **- Marketplace Data:** Detailed data from Allegro, including information on auction costs and sales generated within them.\n\nIn total, this amounted to nearly **100 different data sources**.\n\n**The business bottleneck was analytical chaos, i.e., manual work:**\n\nThe team spent a huge amount of time manually combining data from various systems just to create simple reports (e.g., a summary of marketing expenses).\n\n\u003Cbr>\n\u003Cdiv style=\"border-radius: 25px; overflow: hidden; width: fit-content; margin: 0 auto;\">\n    \u003Cimg src=\"https://a.storyblok.com/f/296300/1024x718/77dc329b1f/unitrailer_example_mess.webp\" style=\"display: block; width: 100%; height: auto; margin: 0; padding: 0; border: none;\"/>\n\u003C/div>\n\n\n## 💡 The Solution: WitCloud Automated Data Platform\n\nUnitrailer needed to reduce the time required to prepare data for decision-making. To this end, they decided to implement WitCloud as a central system for automatic data integration, processing, and modeling.\n\n1. **Automating 100 sources without coding:** WitCloud automatically connected all 100 data sources. The entire process took place **without writing code**, thanks to ready-made connectors.\n2. **Automatic transformation of chaos into ready-to-use data:** WitCloud not only dumps 800 raw tables into one place, which is BigQuery, but in subsequent steps, it automatically processes the data, unifies it, and builds **lightweight, small, and aggregated** tables (datamarts) so that they meet business needs and are usable in visualizations. This approach also significantly lowers the costs of querying data from the database.\n3. **One Coherent Report:** Everything was made available in one coherent report located in Looker Studio, giving a full picture of the business.\n4. **Infrastructure \"on autopilot\":** The entire process runs fully automatically, 24/7, from data downloading, through error monitoring, to infrastructure maintenance.\n\n![](https://a.storyblok.com/f/296300/1666x707/a357a36d79/multicountry_reporting.png)\n\n\n## Results\n\n####\n\n#### 1\\. Reduction of analytics costs and business decisions in seconds, not days\n\nReports that previously required **hours of manual combination of xls sheets** (e.g., summary of marketing expenses, Google Analytics 4 data, or sales from CRM for all markets) are now executed in a few seconds. Data is always ready \"at hand\" for all markets.\n\n#### 2\\. Full Cross-Market Product Analysis\n\nThe company gained the instant ability to analyze **the entire product offer and sales** (from IdoSell, Allegro, and other systems) in one place. This allows for strategic decisions regarding the assortment in individual markets.\n\n#### 3\\. Team Focused on Growth, Not on \"Putting Out Fires\"\n\nWhen a company stops wasting time asking \"How much was it?\" and starts discussing \"What do we do next?\" – it is a sign that it has moved from chaos to strategy. Specialists and analysts stop being data \"gatekeepers\" and become strategic partners for the business.\n\n#### 4\\. Foundation for AI ready immediately\n\nInstead of heavy tables, **lightweight, optimized, and organized datasets** were created, which are ready to be used by AI models.\n\n## Client Opinion\n\n\n\u003Cdiv style=\"font-family: system-ui, -apple-system, sans-serif; padding: 60px 20px; text-align: center; background: linear-gradient(115deg, #ffffff 50%, #eff6ff 50.1%); color: #111827; width: 900px; max-width: 100%; margin: 30px 0;\">\n \u003Cdiv style=\"display: inline-flex; align-items: center; margin-bottom: 40px; color: #4f46e5; max-width: 300px;\">\n\u003Cimg src=\"https://a.storyblok.com/f/296300/1999x265/5f88a993bc/logo_unitrailer.png\">\n\n  \u003C/div>\n\n  \u003Cp style=\"font-weight: 600; font-size: 22px; color: #111827; line-height: 1.5; max-width: 800px; margin: 0 auto 40px auto;\">\n    “We can't live without this data”\n  \u003C/p>\n\n  \u003Cdiv style=\"font-size: 16px;\">\n    \u003Cspan style=\"font-weight: 700; color: #111827;\">Bartosz Nawrocki\u003C/span>\n    \u003Cspan style=\"color: #9ca3af; margin: 0 8px;\">•\u003C/span>\n    \u003Cspan style=\"color: #6b7280;\">Menadżer e-commerce w Unitrailer\u003C/span>\n  \u003C/div>\n\n\u003C/div>\n\n\n## Summary\n\nThanks to WitCloud, Unitrailer transitioned from a dispersed, expensive, and hard-to-maintain data ecosystem to a modern infrastructure:\n\n* **- fully automated reports available on demand, not on order**\n\n* **- cost-optimized processes**,\n\n* **- data ready for prompting with AI models**\n\nThis is not just ordinary \"reporting\" — it is a **data platform** supporting expansion and rapid business decisions.\n\n\u003Cdiv style=\"\n    background-color: #f9fafb; \n    border-radius: 16px; \n    padding: 32px 0; \n    font-family: system-ui, -apple-system, sans-serif;\n    display: flex; \n    flex-wrap: wrap; \n    align-items: center; \n    justify-content: center;\n    width: 100%;\nmargin-top: 40px;\n\">\n\n  \u003Cdiv style=\"flex: 1; min-width: 150px; text-align: center; border-right: 1px solid #e5e7eb; padding: 10px;\">\n    \u003Cdiv style=\"font-size: 24px; font-weight: 700; color: #111827; margin-bottom: 4px;\">25 \u003C/div>\n    \u003Cdiv style=\"font-size: 14px; font-weight: 500; color: #6b7280;\">European markets\u003C/div>\n  \u003C/div>\n\n  \u003Cdiv style=\"flex: 1; min-width: 150px; text-align: center; border-right: 1px solid #e5e7eb; padding: 10px;\">\n    \u003Cdiv style=\"font-size: 24px; font-weight: 700; color: #111827; margin-bottom: 4px;\">100\u003C/div>\n    \u003Cdiv style=\"font-size: 14px; font-weight: 500; color: #6b7280;\">different data sources\u003C/div>\n  \u003C/div>\n\n  \u003Cdiv style=\"flex: 1; min-width: 150px; text-align: center; border-right: 1px solid #e5e7eb; padding: 10px;\">\n    \u003Cdiv style=\"font-size: 24px; font-weight: 700; color: #111827; margin-bottom: 4px;\">800\u003C/div>\n    \u003Cdiv style=\"font-size: 14px; font-weight: 500; color: #6b7280;\">raw tables\u003C/div>\n  \u003C/div>\n\n  \u003Cdiv style=\"flex: 1; min-width: 150px; text-align: center; padding: 10px;\">\n    \u003Cdiv style=\"font-size: 24px; font-weight: 700; color: #111827; margin-bottom: 4px;\">1\u003C/div>\n    \u003Cdiv style=\"font-size: 14px; font-weight: 500; color: #6b7280;\">automated report\u003C/div>\n  \u003C/div>\n\n\u003C/div>\n\n\n\u003Ca href=\"https://unitrailer.pl/\" target=\"_blank\">We invite you to visit the Unitrailer website\u003C/a>","Case Study Unitrailer x WitCloud","case-study","The business bottleneck was analytical chaos, i.e., manual work:\n\nThe team spent a huge amount of time manually combining data from various systems just to create simple reports (e.g., a summary of marketing expenses).","content-page",{"id":269,"alt":19,"name":19,"focus":19,"title":19,"source":19,"filename":270,"copyright":19,"fieldtype":21,"meta_data":271,"is_external_url":32},190524925491044,"https://a.storyblok.com/f/296300/900x600/aa3fffa6ba/05-unitrailer-case-study.png",{},[273],{"_uid":274,"name":275,"content":276,"component":233},"f34609f1-8fcd-404a-aa30-9063d2f22e22","Meta Og Image","https://a.storyblok.com/f/296300/1200x628/1cc35d5e85/head_en.png",[],"2025-11-25 00:00",[],"case-study-unitrailer-x-witcloud","content/case-studies/case-study-unitrailer-x-witcloud",0,[],"a0fb85b3-7295-42e0-b7e8-37adf5dfc33d",[],{"data":287,"body":288,"excerpt":-1,"toc":298},{"title":19,"description":222},{"type":289,"children":290},"root",[291],{"type":292,"tag":293,"props":294,"children":295},"element","p",{},[296],{"type":297,"value":222},"text",{"title":19,"searchDepth":299,"depth":299,"links":300},2,[],{"data":302,"body":303,"excerpt":-1,"toc":309},{"title":19,"description":226},{"type":289,"children":304},[305],{"type":292,"tag":293,"props":306,"children":307},{},[308],{"type":297,"value":226},{"title":19,"searchDepth":299,"depth":299,"links":310},[],{"data":312,"body":313,"excerpt":-1,"toc":319},{"title":19,"description":262},{"type":289,"children":314},[315],{"type":292,"tag":293,"props":316,"children":317},{},[318],{"type":297,"value":262},{"title":19,"searchDepth":299,"depth":299,"links":320},[],{"data":322,"body":324,"excerpt":-1,"toc":335},{"title":19,"description":323},"The business bottleneck was analytical chaos, i.e., manual work:",{"type":289,"children":325},[326,330],{"type":292,"tag":293,"props":327,"children":328},{},[329],{"type":297,"value":323},{"type":292,"tag":293,"props":331,"children":332},{},[333],{"type":297,"value":334},"The team spent a huge amount of time manually combining data from various systems just to create simple reports (e.g., a summary of marketing expenses).",{"title":19,"searchDepth":299,"depth":299,"links":336},[],1787319094165]