[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118332-en":3,"doc-seo-118332-105":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},118332,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Leveraging Machine Learning Insights to Optimize Marketing Strategies - Portfolio-based thesis - Bachelor of Engineering","This thesis applies machine learning, specifically logistic regression, to optimize marketing strategies in the Business-to-Consumer (B2C) sector. Using a dataset from a Portuguese banking institution’s direct marketing campaigns, it analyzes customer responses to financial product offers to support segmentation, campaign timing, and personalized messaging. A logistic regression model predicts whether clients will subscribe to a bank term deposit, emphasizing proper categorical handling with OneHotEncoder. Results show partial predictive success while highlighting multicollinearity and accuracy challenges, with recommendations for more advanced methods.","Leveraging Machine Learning Insights to Optimize Marketing Strategies  \nPortfolio-based thesis  \nEmiliia Zemskova  \nThesis  \nDegree Programme in Machine Learning and Data Engineering Bachelor of Engineering  \nYEAR 2024  \nAbstract of Thesis  \nName of Degree Programme Degree  \nAuthor Supervisor Commissioned by Title of Thesis  \nNumber of pages  \nEmiliia Zemskova Year  \nKenneth Karlsson  \nName of the Commissioner Leveraging Machine Learning Insights to Optimize Marketing Strategies 44  \n2024  \nThis thesis focused on the use of machine learning techniques, specifically logistic regression, to optimize marketing strategies for the Business-to-Consumer (B2C) sector. The study utilized a dataset from a Portuguese banking institution's direct marketing campaigns, which provided insights into customer responses to financial product offers. The main objective was to apply data-driven methods to enhance marketing outcomes, including customer segmentation, campaign timing, and personalized communication.  \nA logistic regression model was developed to classify customer behavior, with a focus on predicting whether clients would subscribe to a bank term deposit. Through this analysis, the thesis explored how machine learning can help marketers better understand customer preferences and tailor their strategies accordingly. One key finding was the importance of using OneHotEncoder to handle categorical data effectively, avoiding bias in model predictions. Additionally, the thesis examined the potential for optimizing marketing efforts by identifying the best times to contact customers based on historical engagement data.  \nThe results of the logistic regression model indicated some success in predicting customer behavior, though challenges such as multicollinearity and model accuracy were noted. The conclusion suggests that further improvements could be made by exploring more advanced machine learning techniques. Overall, the thesis demonstrates the value of integrating machine learning insights into marketing strategies to drive better customer engagement and conversion rates.  \nKey words machine learning, logistic regression, marketing strategies,  \nB2C, customer segmentation, data-driven marketing  \nCONTENTS  \n1 INTRODUCTION 4  \n2 BACKGROUND OF RESEARCH 5  \n2.1 Origins of the dataset 5  \n2.2 Tools and environments 8  \n3 DETAILED DESCRIPTION OF THE LOGISTIC REGRESSION MODEL DEVELOPMENT 8  \n3.1 Data Engineering 9  \n3.2 Balancing of the data 19  \n3.3 Split-train data, multicollinearity and scaling the values 27  \n3.4 The development of the model 31  \n4 ANALYSIS OF OUTCOMES OF THE LOGISTIC REGRESSION 32  \n4.1 Classification error metrics 32  \n5 POTENTIAL MARKETING STRATEGIES 37  \n5.1 Marketing background of the thesis author 37  \n5.1 Marketing strategies 38  \n5.2.1 Visual marketing 38  \n5.2.2 Personalized campaigns 40  \n6 DISCUSSION 43  \n7 REFERENCE NOTATION 45  \n7.1 Bibliography 45  \n4  \n1 INTRODUCTION  \nIn the modern business landscape, data-driven decision-making has become a cornerstone of successful marketing strategies. With the increasing availability of customer data and advances in machine learning, businesses now have the opportunity to better understand consumer behaviour and tailor their marketing efforts to maximize engagement and conversion. This thesis explores the application of machine learning, specifically logistic regression, to optimize marketing strategies using customer data.  \nThe goal of this thesis is to analyze how machine learning techniques can be leveraged to provide deeper insights into customer behavior, which in turn can inform more effective and personalized marketing strategies. In particular, the focus is on the Business-to-Consumer (B2C) sector, where personalization and targeting are key factors in building strong relationships with customers. The logistic regression model is employed to classify customer responses to marketing campaigns, allowing for the identification of patterns and trends that can be us","cbCaipoQk96U3Gdr","https://ap.wps.com/l/cbCaipoQk96U3Gdr","pdf",15093693,1,46,"English","en",105,"# Introduction\n## Goal and focus of the thesis\n# Background of Research\n## Origins of the dataset\n# Detailed Description of the Logistic Regression Model Development\n## Data engineering\n## Balancing of the data\n## Split-train data, multicollinearity and scaling\n## Development of the model\n# Analysis of Outcomes of the Logistic Regression\n## Classification error metrics\n# Potential Marketing Strategies\n## Marketing strategies\n## Visual marketing\n## Personalized campaigns\n# Discussion\n# Reference Notation\n## Bibliography","[{\"question\":\"What machine learning method does the thesis use to optimize marketing?\",\"answer\":\"The thesis uses logistic regression to model and predict customer behavior in B2C marketing campaigns.\"},{\"question\":\"What dataset is used for the analysis?\",\"answer\":\"The study relies on a dataset from a Portuguese banking institution’s direct marketing campaigns, including customer responses to financial product offers.\"},{\"question\":\"What key data-processing step is emphasized for the model?\",\"answer\":\"It emphasizes using OneHotEncoder to handle categorical data effectively and avoid bias in model predictions.\"}]","Leveraging Machine Learning Insights to Optimize Marketing Strategies - 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