[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127649-en":3,"doc-seo-127649-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127649,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Optimizing Sales Performance in Creative as a Service (CaaS) Companies - A Machine Learning Approach to Opportunity Time - Series Forecasting","This thesis addresses the gap in empirical studies on the sales generation process of Creative as a Service (CaaS) companies across various channels. The study uses predictive analytics, including feature selection and hyperparameter optimization, to build a supervised machine learning model for nontraditional sales approaches with distinctive parameters and time-series data. The work identifies key attributes in the sales funnel that drive forecasting in the B2B CaaS model, using a customized model-stacking method under CRISP-DM with careful handling of missing and categorical data.","MDDM  \nMaster’s degree Program in  \nData-Driven Marketing  \nOptimizing Sales Performance in Creative as a Service (CaaS) Companies: A Machine Learning Approach to Opportunity Time  \nSeries Forecasting  \nSuha San  \nDissertation  \npresented as partial requirement for obtaining the Master Degree Program in Data-Driven Marketing  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nOptimizing Sales Performance in Creative as a Service (CaaS) Companies: A Machine Learning Approach to Opportunity Time  \nSeries Forecasting  \nSuha San  \nMaster Thesis / Project Work presented as partial requirement for obtaining the Master’s degree in Data-Driven Marketing, with a specialization in Data science for Marketing  \nSupervisor/Orientador(a): Prof. Marlon Dalmoro  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledge the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \n[student signature]  \n[place, date]  \nDEDICATION  \nI would like to dedicate this thesis to the following individuals and entities who have played a significant role in my academic journey:  \nFirst and foremost, I am deeply grateful to my family for their unwavering support, encouragement, and belief in me throughout this entire endeavor. Their love and guidance have been invaluable, and I am truly fortunate to have them by my side.  \nI would also like to express my heartfelt appreciation to Raissa Oliveira Viana de Barros, for her continuous support and belief in my abilities. Her encouragement and presence have been a source of inspiration and motivation throughout my academic pursuit. I am grateful for her guidance and for always being there for me.  \nFurthermore, I extend my sincere thanks to my manager and mentor Andreas Drakos for his invaluable support and guidance throughout this research journey. His expertise in the field, insightful discussions, and the encouragement have greatly contributed to shaping my understanding and enhancing my knowledge. I am grateful for the trust he placed in me and for the resources made available, including the invaluable dataset provided by the company, which has served as the foundation for this research. I also would like to acknowledge and express my gratitude to my teammates for their support and collaboration throughout this research journey. Their insights, discussions, and assistance have greatly enriched my work, and I am thankful for their contributions.  \nLastly, I am deeply grateful to my thesis advisor, Professor Marlon Dalmoro, for his invaluable guidance and unwavering support. His expertise, meticulous attention to detail, and constructive feedback have been instrumental in shaping this thesis. Without his mentorship, I would not have achieved this significant milestone. Thank you, Professor Dalmoro, for your dedication to academic excellence and for being an integral part of my academic journey.  \nThis dedication is a reflection of the immense support, guidance, and belief bestowed upon me by my family, Raissa Oliveira Vienna de Barros, Andreas Drakos, my teammates, my company and my thesis advisor Professor Marlon Dalmoro. Their unwavering presence and encouragement have been instrumental in my academic success, and I am deeply grateful for their contributions.  \nABSTRACT  \nThis thesis addresses the gap in empirical studies on the sales generation process of Creative as a Service (CaaS) companies across various channels. The study employs predictive analysis techniques, including feature selection and hyperparameter optimization, to develop a supervised ma","cbCailiM61ncL6Er","https://ap.wps.com/l/cbCailiM61ncL6Er","pdf",2796507,1,73,"English","en",105,"# Introduction\n## Background and problem statement\n## Research objective\n## Methodological approach and scope\n## Organization of the thesis","[{\"question\":\"What gap does the thesis address in CaaS companies?\",\"answer\":\"It targets the lack of empirical studies on how CaaS companies generate sales across different channels and how that process affects forecasting.\"},{\"question\":\"What machine learning techniques are used to support the study?\",\"answer\":\"The thesis develops a supervised machine learning model using feature selection and hyperparameter optimization, with a customized stacking approach that incorporates boosting, tree-based methods, random forests, and neural networks.\"},{\"question\":\"How does the thesis structure its methodology?\",\"answer\":\"It follows CRISP-DM, covering data preparation, cleaning, transformation, and modeling, and it examines the challenges of missing and categorical data along with feature selection and encoding for B2B sales forecasting.\"}]","Optimizing Sales Performance in Creative as a Service (CaaS) Companies - 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