[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122056-en":3,"doc-seo-122056-105":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":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},122056,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Predictive Modelling on Competitor Analysis Performance - Generalised Linear Models and Machine Learning Approach","Competitive analysis in digital and technology services is complex and requires identifying drivers that reflect company performance and the significant services offered to business users. This research develops a predictive modelling approach combining generalised linear models (GLM) and machine learning using a telecommunications case study with a data science life cycle. Because parts of the original data are confidential, a synthetic dataset is generated via Gamma, Gaussian and Poisson distributions, then validated using RMSE, MAE and R-squared.","|  | Journal of Advanced Research in Applied Sciences and Engineering Technology\u003Cbr>Journal homepage:\u003Cbr>[https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/index](https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/index)\u003Cbr>ISSN: 2462-1943 |  |  |\n| --- | --- | --- | --- |\n| Predictive Modelling on Competitor Analysis Performance by using Generalised Linear Models and Machine Learning Approach\u003Cbr>Noryanti Muhammad1,*, Mohamad Nadzman Mohd Amin2, Rose Adzreen Adnan3, Orasa Nunkaw4\u003Cbr>1 Centre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuhraya Persiaran Tun Khalil Yaakob, 26300 Kuantan, Pahang, Malaysia\u003Cbr>2 Centre for Artificial Intelligence & Data Science, Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuhraya Persiaran Tun Khalil Yaakob, 26300 Kuantan, Pahang, Malaysia\u003Cbr>3 Credence 1, Jalan Damansara, Damansara Kim, 60000 Kuala Lumpur, Malaysia\u003Cbr>4 Department of Mathematics and Statistics, Faculty of Science and Digital Innovation Thaksin University, 222 Moo 2, Ban Phrao Subdistrict, Pa Phayom District, Phatthalung Province 93210, Thailand |  |  |  |\n| ARTICLE INFO |  | ABSTRACT |  |\n| Article history:\u003Cbr>Received 25 September 2023\u003Cbr>Received in revised form 8 November 2023 Accepted 11 April 2024\u003Cbr>Available online 12 May 2024 |  | Competitive analysis in digital and technology is trending in the business field. However, the field of digital and technology in the business world is vast and challenging to analyse. The purpose of this research is first to identify the success factor which represent the company performance. Second, is to identify the significant services provided by the company to their business user. Then, based on the first and second objectives, a predictive modelling is developed to produce the best solution to their business user. The research is implementing a case study from Telecommunication Company and using data science life cycle methodology. The statistical modelling that is used to develop the competitor’s analysis model is generalised linear model (GLM) which integrated with machine learning approach. Furthermore, the synthetic data set is created by using Gamma Distribution, Gaussian Distribution and Poisson Distribution due to some data from the case study is confidential. The synthetic data set is based on existing real data which are from Telecommunication Company sentiment analysis data, were used to investigate the performance of the proposed model. The machine learning technique is used to get the accuracy of the significant GLM which has been developed. The accuracy is tested by using the error rates ofthe machine learning technique which are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared. This research discovered that the business solution to the significant service for the business user and discovered the best statistical model to be used for the business solution. The results show that the Gumbel distribution is the best fit model for the synthetic dataset where the values of |  |\n| Keywords: |  | RMSE is 1.0574, MAE is 0.9168 and R-squared is 0.3994, and the significant success factor that has been identified by using the GLM is advertising success factor. The |  |\n| Competitive analysis; Generalised linear model; Machine learning |  | model developed can be improved with another type of data set and different sizes of data. Hence, further studies and real-world data are required for better validation. |  |\n|  |  |  |  |\n\n* Corresponding author.  \n[E-mail address: noryanti@umpsa.edu.my](E-mail address: noryanti@umpsa.edu.my)  \n[https://doi.org/10.37934/araset.45.1.5159](https://doi.org/10.37934/araset.45.1.5159)  \n1. Introduction  \nIn digital technology services, Each Telecommunication Company has their own specialty that can compete to the other service provider. However, the difference from one to another is the business user that uses their service in their company. Often, a busi","cbCaio7Qv5X1F1Et","https://ap.wps.com/l/cbCaio7Qv5X1F1Et","pdf",394794,1,9,"English","en",105,"# Introduction\n## Business user and competition context\n## Competitor analysis and modelling approach\n# Methodology\n## Case study and data science life cycle\n## GLM integrated with machine learning\n## Synthetic data generation\n# Results and Evaluation\n## Model accuracy metrics (RMSE, MAE, R-squared)\n## Best-fit distribution selection\n# Conclusion","[{\"question\":\"What is the main goal of the research on competitor analysis?\",\"answer\":\"To identify success factors representing company performance and to determine significant services provided to business users, then build a predictive modelling solution.\"},{\"question\":\"How is the predictive model constructed in this study?\",\"answer\":\"The study integrates generalised linear models (GLM) with machine learning techniques, then uses the resulting model to predict the best solution for business users.\"},{\"question\":\"Why is synthetic data used, and how is it evaluated?\",\"answer\":\"Some case-study data are confidential, so a synthetic dataset is generated using Gamma, Gaussian and Poisson distributions. The proposed model is evaluated using RMSE, MAE and R-squared, and further validated by selecting the best-fit distribution for the synthetic dataset.\"}]","Predictive Modelling on Competitor Analysis Performance - Generalised Linear Models and Machine Learning Approach | PDF",1785808595,23,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predictive-modelling-on-competitor-analysis-performance-generalised-linear-models-and-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@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/predictive-modelling-on-competitor-analysis-performance-generalised-linear-models-and-machine-learning-approach/122056/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the research on competitor analysis?","Question",{"text":75,"@type":76},"To identify success factors representing company performance and to determine significant services provided to business users, then build a predictive modelling solution.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the predictive model constructed in this study?",{"text":80,"@type":76},"The study integrates generalised linear models (GLM) with machine learning techniques, then uses the resulting model to predict the best solution for business users.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is synthetic data used, and how is it evaluated?",{"text":84,"@type":76},"Some case-study data are confidential, so a synthetic dataset is generated using Gamma, Gaussian and Poisson distributions. 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