[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124099-en":3,"doc-seo-124099-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124099,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Identifying User Groups - A Machine Learning Framework for Classifying Job Roles Based on Clickstream Data","Clickstream data, a digital record of online browsing activity, provides a focused view of user interests and intent. This work presents a machine learning framework that classifies website visitors by job function using features engineered from six months of server-side clickstream data. The predicted job roles support targeted communications for end users of a low-code B2B service to increase engagement. Results show strong separation of developers from other job roles, improving job-role identification for personalization.","A Work Project, presented as part ofthe requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics.  \nIdentifying User Groups: A Machine Learning Framework for Classifying Job Roles Based  \non Clickstream Data  \nWilliam Esary  \nWork project carried out under the supervision of:  \nQiwei Han  \nBruno Silva  \nMiguel Almas  \n15/12/2023  \nAbstract  \nClickstream data, the digital footprint of a user’s online browsing activity, offers a unique window into an individual’s interests and intentions. This work showcases a machine learning framework designed to classify website visitors by job function using features crafted from 6 months’ worth of server-side clickstream data. These predicted job functions can be used to send targeted communications to end users of a low-code B2B service in order to boost engagement. The study finds success at differentiating developers, the key users, from other job roles based on their use of the company website.  \nKeywords: User Segmentation; End Users; Machine Learning; Targeted Advertising; OutSystems; Clickstream Data; Web Behavior  \nThis work used infrastructure and resources funded by Fundação para a Ciência e a Tecnologia (UID/ECO/00124/2013, UID/ECO/00124/2019 and Social Sciences DataLab, Project 22209), POR Lisboa (LISBOA-01-0145-FEDER-007722 and Social Sciences DataLab, Project 22209) and POR Norte (Social Sciences DataLab, Project 22209) .  \nIntroduction  \nThe global B2B (Business to Business) market was valued at over 7 trillion USD in 2022 with an expected 18% compounded annual growth rate through the year 2030 (Vantage Market Research 2022) . The booming industry has attracted much attention from researchers examining the B2B buying process, specifically the interaction between the supplier and the key decision-makers of the buying firm. One group often left out of this research is the end user. End users are the people actually using the product in question on a day-to-day basis without necessarily playing arole in its purchasing (Vivek 2012). Many B2B companies operate under a recurring revenue model, relying on customer firms to continue their subscriptions to stay afloat. As end users are the ones using the product, keeping them engaged is crucial to ensuring this happens (Amy Greiner Fehl 2023) . To take steps to boost engagement among end users, it is first crucial to get an idea of who they are.  \nIn the era where data is the new currency, understanding user behavior online has become a cornerstone for businesses to get to know their customer base. Clickstream data, the electronic record of a user’s journey across a website, offers a wealth of information that can provide a profound window into a user’s intent and preferences (Randolph E. Bucklin 2002) . When analyzed effectively, it can yield insights that allow for effective user targeting, capitalizing on discovered interests. Clickstreams have been used widely as a tool for predicting customer actions (will a browsing session lead to a purchase conversion), but their use in grouping users into well-defined demographic segments like job roles has been minimal. B2B products are designed to be used in the workplace, so knowledge of an end user's job role could significantly boost the effectiveness of promotional content for features pertaining to a specific function.  \nThis thesis, conducted on behalf of OutSystems, utilizes clickstream data to classify  \nvisitors of the OutSystems website according to their job function, with the specific intent of recognizing developers. The website holds content for both stakeholders with purchasing power and end users, acting as a one-stop shop for all parties involved, aligning a user’s website usage with their usage of the product. Knowledge of a user’s job function will not only give OutSystems a better idea of who is using their product, who is the end user, but will also provide the opportunity for personalized promotions to help re","cbCaiejlUmki6HG1","https://ap.wps.com/l/cbCaiejlUmki6HG1","pdf",1957891,1,35,"English","en",105,"# Introduction\n## End user engagement in B2B\n## Role of clickstream data\n## Project aim and scope\n# OutSystems\n## Platform overview\n## Website content and visitor types\n# Literature Review\n## End user engagement","[{\"question\":\"What does the project classify using clickstream data?\",\"answer\":\"It classifies website visitors by job function, with a specific focus on identifying developers.\"},{\"question\":\"How is clickstream data used in the framework?\",\"answer\":\"The study engineers features from six months of server-side clickstream data and feeds them into a machine learning pipeline for job-role prediction.\"},{\"question\":\"How can the results be used in practice?\",\"answer\":\"Predicted job functions enable targeted communications and personalized promotions to increase engagement for end users of a low-code B2B service.\"}]","Identifying User Groups - 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