[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118861-en":3,"doc-seo-118861-105":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},118861,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Customer Segmentation and Business Sales Forecasting using Machine Learning for Business Development","This study explores how machine learning can support business development by improving sales forecasting and customer segmentation using a Walmart dataset. Model quality is evaluated with Mean Absolute Error (MAE) and R2 scores. A hybrid strategy is proposed, combining the BIRCH algorithm with time-lagged machine learning (TL-ML). Findings show that customer segmentation boosts performance across all metrics. Among tested approaches, models integrating customer segmentation (CS-RFR and CSTL-ML) outperform standard Random Forest regression.","Customer Segmentation and Business Sales Forecasting using Machine Learning for Business  \nDevelopment  \nPravin Malviya1, Dr. Vijay Bhandari2 Pankaj Singh Sisodiya3, Saurabh Suman4  \n1,3,4Research Scholar Department of Computer Science & Engineering , MPU Bhopal, India.  \n2Associate Professor Department of Computer Science & Engineering , Bhopal, India.  \nAbstract: This study explores the application of machine learning techniques for business development, focusing on sales prediction and customer segmentation, using a Walmart dataset. Performance metrics include Mean Absolute Error (MAE) and R2 scores. Our hybrid approach combines the BIRCH algorithm with time-lagged machine learning (TL-ML) . The results reveal that customer segmentation significantly improves model performance across all metrics. Among the techniques tested, models incorporating customer segmentation (CS-RFR and CSTL-ML) outperform standard Random Forest Regressor models. Specifically, CS-TL-ML shows a slight advantage in terms of both lower MAE and higher R2 scores, confirming its efficacy for sales prediction and customer segmentation tasks.  \nKeywords: Customer Segmentation, Sales Prediction, BIRCH Algorithm, Time-Lagged Machine Learning, Business Development.  \nI. Introduction  \nIn the era of digital transformation, Machine Learning (ML) has emerged as a disruptive technology that is profoundly altering the landscape of business development. As companies across industries strive to stay ahead of the curve, the integration of machine learning technologies offers unprecedented advantages. From making business processes more streamlined to enhancing decision-making through data-driven insights, machine learning is rapidly becoming an essential tool in the contemporary business toolkit. Beyond the broad applications in fields like healthcare, finance, and autonomous vehicles, ML's influence has become increasingly pervasive in business development, enabling companies to adopt more strategic and data-centric approaches to growth [1] . Businesses today amass enormous volumes of customer data, from purchasing habits to engagement metrics. Traditional methods of data analysis often fall short in the task of interpreting this wealth of information in meaningful ways. Machine learning algorithms, however, can rapidly and efficiently segment customers based on an array of metrics such as behaviour, purchasing patterns, and other key performance indicators. This segmentation not only allows for more effective targeted marketing but also enhances customer engagement by delivering more personalized experiences  \n[2][3] . In the fiercely competitive market, the ability to predict future sales trends is indispensable for sustainable growth. Machine learning leverages historical data to make accurate predictions about which leads are most likely to convert into customers. This predictive analytics capability empowers businesses to allocate their resources more efficiently and focus  \ntheir efforts on the most promising opportunities, thus maximizing returns on investment [4][5] . Product recommendation systems are another vital aspect where machine learning shows its prowess. By analyzing customer browsing history, purchase behaviour, and preferences, ML algorithms can provide highly tailored product suggestions that are likely to resonate with the individual consumer. This feature not only enhances the user experience but also increases the chances of successful sales conversions and customer retention [6] . By weaving these features into a cohesive machine learning-based business development module, this paper aims to provide a detailed exploration of each, while also discussing the limitations, ethical considerations, and future directions for the research. Through empirical analysis and case studies, we aspire to demonstrate how the adoption of machine learning technologies can significantly augment business development efforts, leading to more strategic and informe","cbCaitt7n4AZzK1w","https://ap.wps.com/l/cbCaitt7n4AZzK1w","pdf",1010297,1,9,"English","en",105,"# I. Introduction\n## Machine learning for business development\n## Customer segmentation and targeted marketing\n## Sales forecasting and predictive analytics\n## Product recommendation and personalization\n# II. Related Work\n## Business intelligence and visualization in finance\n## Machine learning in telecommunications\n## Supply chain collaboration\n## Discount impact on buying behavior","[{\"question\":\"Which dataset and evaluation metrics are used to assess the proposed approach?\",\"answer\":\"The study uses a Walmart dataset and evaluates performance using Mean Absolute Error (MAE) and R2 scores.\"},{\"question\":\"What hybrid method is used for customer segmentation and sales prediction?\",\"answer\":\"The approach combines the BIRCH algorithm with time-lagged machine learning (TL-ML) to better model relationships over time.\"},{\"question\":\"How does customer segmentation affect forecasting performance compared with standard models?\",\"answer\":\"Customer segmentation significantly improves model performance across all metrics; segmentation-based models outperform standard Random Forest Regressor models, with CS-TL-ML showing a slight advantage.\"}]","Customer Segmentation and Business Sales Forecasting using Machine Learning for Business Development | PDF",1785720666,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"customer-segmentation-and-business-sales-forecasting-using-machine-learning-for-business-development","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/customer-segmentation-and-business-sales-forecasting-using-machine-learning-for-business-development/118861/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which dataset and evaluation metrics are used to assess the proposed approach?","Question",{"text":76,"@type":77},"The study uses a Walmart dataset and evaluates performance using Mean Absolute Error (MAE) and R2 scores.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What hybrid method is used for customer segmentation and sales prediction?",{"text":81,"@type":77},"The approach combines the BIRCH algorithm with time-lagged machine learning (TL-ML) to better model relationships over time.",{"name":83,"@type":74,"acceptedAnswer":84},"How does customer segmentation affect forecasting performance compared with standard models?",{"text":85,"@type":77},"Customer segmentation significantly improves model performance across all metrics; segmentation-based models outperform standard Random Forest Regressor models, with CS-TL-ML showing a slight advantage.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]