用户是企业发展的核心资源,尤其在当前互联网与企业信息化快速发展的背景下,用户可以通过大量信息深入了解企业的具体产品和服务,进而做出最佳选择。随着信息技术的进步,用户拥有更多选择,离开平台的成本也变得极低。相比于过去以交易...用户是企业发展的核心资源,尤其在当前互联网与企业信息化快速发展的背景下,用户可以通过大量信息深入了解企业的具体产品和服务,进而做出最佳选择。随着信息技术的进步,用户拥有更多选择,离开平台的成本也变得极低。相比于过去以交易量为核心的营销模式,现今企业更加关注用户体验及其多样化需求,通过企业信息化手段不断优化产品和服务以提升用户粘性。因此,用户留存已成为互联网科技企业发展的关键。在此背景下,企业应深度思考如何通过企业信息化与智能化手段增强用户体验,提升用户留存,并通过老用户带动新增用户的增长。基于增长黑客理论,本文采用AARRR模型,深入探讨了Chat-GPT的用户增长策略,结合企业信息化的优势,为企业实现可持续增长提供新的思路与解决方案。Users are the core resource for enterprise development, especially in the context of rapid internet growth and enterprise informatization. Users can now gain a deep understanding of a company’s specific products and services through abundant information, enabling them to make optimal choices. With advancements in information technology, users have more options, and the cost of leaving a platform has become extremely low. Compared to past marketing models focused on transaction volume, today’s enterprises place greater emphasis on user experience and diverse needs, continuously optimizing products and services through informatization to enhance user engagement. Consequently, user retention has become a critical factor in the development of internet technology companies. In this context, enterprises should deeply consider how to leverage informatization and intelligent tools to enhance user experience, improve user retention, and drive new user growth through existing users. Based on growth hacking theory, this study adopts the AARRR model to explore Chat-GPT’s user growth strategy, integrating the advantages of enterprise informatization to provide new insights and solutions for sustainable growth.展开更多
BACKGROUND Small intestinal bacterial overgrowth(SIBO)poses diagnostic and treatment challenges due to its complex management and evolving guidelines.Patients often seek online information related to their health,prom...BACKGROUND Small intestinal bacterial overgrowth(SIBO)poses diagnostic and treatment challenges due to its complex management and evolving guidelines.Patients often seek online information related to their health,prompting interest in large language models,like GPT-4,as potential sources of patient education.AIM To investigate ChatGPT-4's accuracy and reproducibility in responding to patient questions related to SIBO.METHODS A total of 27 patient questions related to SIBO were curated from professional societies,Facebook groups,and Reddit threads.Each question was entered into GPT-4 twice on separate days to examine reproducibility of accuracy on separate occasions.GPT-4 generated responses were independently evaluated for accuracy and reproducibility by two motility fellowship-trained gastroenterologists.A third senior fellowship-trained gastroenterologist resolved disagreements.Accuracy of responses were graded using the scale:(1)Comprehensive;(2)Correct but inadequate;(3)Some correct and some incorrect;or(4)Completely incorrect.Two responses were generated for every question to evaluate reproducibility in accuracy.RESULTS In evaluating GPT-4's effectiveness at answering SIBO-related questions,it provided responses with correct information to 18/27(66.7%)of questions,with 16/27(59.3%)of responses graded as comprehensive and 2/27(7.4%)responses graded as correct but inadequate.The model provided responses with incorrect information to 9/27(33.3%)of questions,with 4/27(14.8%)of responses graded as completely incorrect and 5/27(18.5%)of responses graded as mixed correct and incorrect data.Accuracy varied by question category,with questions related to“basic knowledge”achieving the highest proportion of comprehensive responses(90%)and no incorrect responses.On the other hand,the“treatment”related questions yielded the lowest proportion of comprehensive responses(33.3%)and highest percent of completely incorrect responses(33.3%).A total of 77.8%of questions yielded reproducible responses.CONCLUSION Though GPT-4 shows promise as a supplementary tool for SIBO-related patient education,the model requires further refinement and validation in subsequent iterations prior to its integration into patient care.展开更多
文摘用户是企业发展的核心资源,尤其在当前互联网与企业信息化快速发展的背景下,用户可以通过大量信息深入了解企业的具体产品和服务,进而做出最佳选择。随着信息技术的进步,用户拥有更多选择,离开平台的成本也变得极低。相比于过去以交易量为核心的营销模式,现今企业更加关注用户体验及其多样化需求,通过企业信息化手段不断优化产品和服务以提升用户粘性。因此,用户留存已成为互联网科技企业发展的关键。在此背景下,企业应深度思考如何通过企业信息化与智能化手段增强用户体验,提升用户留存,并通过老用户带动新增用户的增长。基于增长黑客理论,本文采用AARRR模型,深入探讨了Chat-GPT的用户增长策略,结合企业信息化的优势,为企业实现可持续增长提供新的思路与解决方案。Users are the core resource for enterprise development, especially in the context of rapid internet growth and enterprise informatization. Users can now gain a deep understanding of a company’s specific products and services through abundant information, enabling them to make optimal choices. With advancements in information technology, users have more options, and the cost of leaving a platform has become extremely low. Compared to past marketing models focused on transaction volume, today’s enterprises place greater emphasis on user experience and diverse needs, continuously optimizing products and services through informatization to enhance user engagement. Consequently, user retention has become a critical factor in the development of internet technology companies. In this context, enterprises should deeply consider how to leverage informatization and intelligent tools to enhance user experience, improve user retention, and drive new user growth through existing users. Based on growth hacking theory, this study adopts the AARRR model to explore Chat-GPT’s user growth strategy, integrating the advantages of enterprise informatization to provide new insights and solutions for sustainable growth.
文摘BACKGROUND Small intestinal bacterial overgrowth(SIBO)poses diagnostic and treatment challenges due to its complex management and evolving guidelines.Patients often seek online information related to their health,prompting interest in large language models,like GPT-4,as potential sources of patient education.AIM To investigate ChatGPT-4's accuracy and reproducibility in responding to patient questions related to SIBO.METHODS A total of 27 patient questions related to SIBO were curated from professional societies,Facebook groups,and Reddit threads.Each question was entered into GPT-4 twice on separate days to examine reproducibility of accuracy on separate occasions.GPT-4 generated responses were independently evaluated for accuracy and reproducibility by two motility fellowship-trained gastroenterologists.A third senior fellowship-trained gastroenterologist resolved disagreements.Accuracy of responses were graded using the scale:(1)Comprehensive;(2)Correct but inadequate;(3)Some correct and some incorrect;or(4)Completely incorrect.Two responses were generated for every question to evaluate reproducibility in accuracy.RESULTS In evaluating GPT-4's effectiveness at answering SIBO-related questions,it provided responses with correct information to 18/27(66.7%)of questions,with 16/27(59.3%)of responses graded as comprehensive and 2/27(7.4%)responses graded as correct but inadequate.The model provided responses with incorrect information to 9/27(33.3%)of questions,with 4/27(14.8%)of responses graded as completely incorrect and 5/27(18.5%)of responses graded as mixed correct and incorrect data.Accuracy varied by question category,with questions related to“basic knowledge”achieving the highest proportion of comprehensive responses(90%)and no incorrect responses.On the other hand,the“treatment”related questions yielded the lowest proportion of comprehensive responses(33.3%)and highest percent of completely incorrect responses(33.3%).A total of 77.8%of questions yielded reproducible responses.CONCLUSION Though GPT-4 shows promise as a supplementary tool for SIBO-related patient education,the model requires further refinement and validation in subsequent iterations prior to its integration into patient care.
