[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127156-en":3,"doc-seo-127156-105":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},127156,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Optimized Collaborative Approach for Client Structured Web Customer Segmentation Using Machine Learning Techniques - Research","An optimized customer segmentation workflow focuses on the business goal and aligns data selection, machine learning approaches, methodologies, and tooling to deliver efficient results. The proposed corporate-to-customer, client-structured web segmentation uses a collaborative process to address limitations of single-approach methods lacking robust data metrics and modern ML tool trends. Future work extends toward automation of customer segmentation within web information systems.","Optimized Collaborative Approach for Client Structured Web Customer Segmentation Using Machine Learning Techniques  \nAdlin Selva Golda.V1, Narayani.V2  \n1Research Scholar  \nDepartment of Computer Science  \nSt.Xavier’s College (Autonomous), Palayamkottai – 627 002  \n2Assistant Professor  \nDepartment of Computer Science  \nSt.Xavier’s College (Autonomous), Palayamkottai – 627 002  \nAffiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli – 627 012  \nAbstract  \nThe process of customer segmentation generally focuses on the customer domain rather than the customer segmentation claimant domain which actually represent the top down design of a business model. If the target or goal is known for the customer segmentation then the design and implementation process adopt the essential components for achieving the goal in an optimized way. Based on requirements the customer segmentation process concentrates on selecting the appropriate data, approaches, methodologies, and tools with prompt measures so as to avoid unnecessary data handling process which produces efficient results. The existing customer segmentation methods are single approach oriented without any proper implementation of data metrics and current trends of machine learning tools. This research article proposes a corporate to customer oriented approach for the implementation of client structured web customer segmentation with the optimized collaborative process based on their requirements for customer segmentation. In near future this research article incorporates the automation based customer segmentation in web information system.  \nKeywords: Machine learning, web data, segmentation, information system, customer data  \nI. INTRODUCTION Collaborative approach:  \nCollaborative approach includes processes, behaviors, and conversations that relate to the collaboration between two or multiple approaches. This approach specifically aims to increase the success of output as they engage in collaborative problem solving.  \nSegmentation:  \nSegmentation means to divide the marketplace into parts, or segments, which are definable, accessible, actionable, and profitable and have a growth potential. In other words, a company would find it impossible to target the entire market, because of time, cost, and effort restrictions [1] . It needs to have a 'definable' segment - a  \nmass of people who can be identified and targeted with reasonable effort, cost and time [2] .  \nCustomer Segmentation:  \nCustomer segmentation is the process of dividing a customer base into distinct groups of individuals that have similar characteristics [3] . This process makes it easier to target specific groups of customers with tailored products, services, and marketing strategies [4] . By segmenting customers into different classes, businesses can better understand their needs, preferences, and buying patterns, allowing them to create more personalized and effective marketing campaigns.  \nMachine Learning:  \nMachine Learning is the field of study that gives computers the capability to learn without being explicitly programmed [5] . ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn [6] .  \nII. METHODOLOGY  \nThe proposed methodology comprises 8 phases of implementation. They are  \na. Requirement Phase  \nThe corporate or an organization basic requirements for a customer segmentation is as follows,  \n❖ Data collection for analytics.  \n❖ Proper Approaches for customer data learning towards effective data access.  \n❖ Identify all possible segments based on real-time criteria.  \n❖ Collaboration of recent methodologies.  \n❖ Identification of interface tools to interact.  \n❖ Optimize the performance if required.  \n❖ Effective Presentation.  \nb. Data collection phase:  \nThe data collection from the web information system includes the data heterogeneous from diff","cbCaipC5J055QjyC","https://ap.wps.com/l/cbCaipC5J055QjyC","pdf",680177,1,13,"English","en",105,"# Introduction\n## Collaborative approach\n## Segmentation\n## Customer Segmentation\n## Machine Learning\n# Methodology\n## Requirement Phase\n## Data collection phase\n## Design Phase\n## Segmentation phase\n## Collaborative approach phase\n## Tools identification phase\n## Performance tuning phase\n## Presentation Phase","[{\"question\":\"What is the purpose of customer segmentation in this research?\",\"answer\":\"Customer segmentation divides customers into distinct groups with similar characteristics, enabling targeted products, services, and marketing strategies based on needs, preferences, and buying patterns.\"},{\"question\":\"How does the methodology handle web data for segmentation?\",\"answer\":\"Web data collection gathers heterogeneous data from different sources and formats, and the collection procedures depend on data cleansing to ensure usable inputs for the segmentation pipeline.\"},{\"question\":\"Which machine learning learning types are used in the design phase?\",\"answer\":\"The design phase uses supervised learning for overall customer examination, semi-supervised learning to understand each customer, unsupervised learning to explain segmentation possibilities, and reinforcement learning for customer segmentation analysis.\"}]","Optimized Collaborative Approach for Client Structured Web Customer Segmentation Using Machine Learning Techniques - 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