Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/152086
|
| Title: | 影響App使用者從免費到付費的關鍵因素探討: 以財經App為例
What Drives App Users from Freemium to Premium: A Case of Financial Service App |
| Authors: | 李玟霖
Lee, Wen-Lin |
| Contributors: | 何乾瑋
Ho, Chien-Wei
李玟霖
Lee, Wen-Lin |
| Keywords: | 免費增值模式
財經 App
顧客感知價值
付費使用者的轉化率
Freemium model
financial App
customer perceived value
conversion rate of paying users |
| Date: | 2024 |
| Issue Date: | 2024-07-01 12:43:58 (UTC+8) |
| Abstract: | 隨著數位轉型的快速發展，免費增值模式在各個領域廣泛應用。這種模式吸引了大量使用者，並引導其成為付費會員，進而實現盈利。因此，如何增加使用者的黏著度，進而提升付費會員轉換率，成為免費增值營運模式的關鍵問題。
本研究以個案公司之財經App為研究對象，有別於以往常用的問卷調查或網路資料搜集方法，利用個案資料庫中的實際使用者行為數據資料，深入探討影響使用者從免費使用者轉變為付費會員的關鍵功能，從而提供精準且具參考價值的分析結果。
透過機器學習梯度提升決策樹及存活分析，本研究發現「普遍性」、「新內容發現」以及「價格價值」等功能對提升使用者付費轉換率有正向影響。因此，個案公司可以持續優化現有功能，或者規劃相關行銷活動，針對免費會員推廣相關功能，以提高使用率，進而提升付費會員轉換率。相對地，「社會連結」及「個人化功能」則無顯著的正向影響，個案公司可以思考是否優化相關功能，或創造更符合使用者需求的功能，從而提升使用體驗。最後，未來公司可根據分析結果預測有較高機率轉化為付費會員的免費使用者，進行精準行銷的活動規劃。
With digital transformation advancing rapidly, the freemium model has been widely applied across various industries. This model attracts a large user base, guiding them towards premium subscriptions that lead to profitability. Therefore, increasing user engagement and enhancing the conversion rate to paid membership become a critical issue under the freemium business model.
This research explores the key features that influence the transition from freemium to premium by studying a case of financial App, which is developed by a leading financial technology company in Taiwan. Different from traditional data collection methods, such as survey research or web data collection, this research extracted real user behavior data from the database in the case company to provide precise and valuable analytical results.
Using XGBoost techniques in machine learning and survival analysis, this research found that "ubiquity," "the discovery of new content," and "price value" have significant impacts on increasing users’ conversion from freemium to premium. Consequently, the case company can continue optimizing existing features or introduce additional functionalities to enhance the product's value proposition for paid subscriptions. Conversely, "social connectivity" and "personalization features" show no significant impact, indicating a potential reevaluation of these features or the development of new ones that better meet user needs. Finally, the company can use these insights to predict which free users are likely to convert and tailor their marketing strategies accordingly. |
| Reference: | 中文部分
參考文獻
段詩潔. (2020). 回顧台股 30 年 12682 的驚奇與瘋狂之旅。檢自 https://www.wealth.com.tw/articles/1d5f25d5-4add-40e6-a2d3-8b2b26aa97ca
潘智義. (2024). 台股熱 20 至 30 歲開戶數達 160 萬、占同齡人口逾 5 成。檢自 https://money.udn.com/money/story/5618/7681487 (2024/4/15)
App 的趨勢與臺灣 App 新創的發展方向. (2023)。檢自 https://www.koryu.or.jp/Portals/0/BokeibuJ-Startup/su2023tacc02tw.pdf
雲科技谷. (2019). 你知道機器學習(Machine Learning)，有幾種學習方式嗎? 檢自 https://www.ecloudvalley.com/tw/blog/machine-learning/
英文部分
一、 網路和書籍
2019 Personalization Development Study. (2019). Personalization Development study. Retrieved from https://info.monetate.com/rs/092-TQN- 434/images/2019_Personalization_Development_Study_US.pdf
Anand, K. (2020, August 30). As lockdown blues hit, most brokerages see mobile trading doubling in 2020. Moneycontrol. Retrieved from https://www.moneycontrol.com/news/business/markets/as-lockdown-blues-hit-most- brokerages-see-mobile-trading-doubling-in-2020-5758791.html
Business of Apps. (2023). Retrieved from https://www.businessofapps.com/data/robinhood-statistics/
DIGITAL 2024: TAIWAN. (2024). Retrieved from https://datareportal.com/reports/digital-2024-taiwan
Fumo, J. (2017). Types of Machine Learning Algorithms You Should Know. Towards Data Science. Retrieved from https://towardsdatascience.com/types-of-machine- learning-algorithms-you-should-know-953a08248861
Griffin, T. (2017). The Rise of Freemium Models. Retrieved from https://www.weareones.com/blog/7260064d69c
Heaton, K. (2022). 15 Ways Machine Learning Will Impact Your Everyday Life. Elite Data Science. Retrieved from https://elitedatascience.com/machine-learning- impact
IFPI. (2012). Digital Music Report 2012. International Federation of the Phonographic Industry.
Mansoor Iqbal. (2024). YouTube Revenue and Usage Statistics. Retrieved from https://www.businessofapps.com/data/youtube-statistics/
Mobile apps in Taiwan - statistics & facts. Statista. (2023). Retrieved from https://www.statista.com/study/164083/mobile-apps-in-taiwan/
MARIE BEEREN. (2022). Best Mobile Trading Platforms In IBD Best Online Brokers Survey. Retrieved from https://www.investors.com/news/best-mobile- trading-platforms-in-ibd-best-online-brokers-survey/
OpenView Advisors LLC. (2023). 2023 Product Benchmarks Report. Spotify. (2023). Spotify Reports Third Quarter 2023 Earnings.
YouTube. (2024). YouTube Music Premium surpasses 100 million subscribers. YouTube Official Blog. Retrieved from https://blog.youtube/news-and- events/youtube-music-premium-100-million-subscribers/
Vrountas, T. (n.d.). 68 personalization statistics every digital advertiser must keep in mind. Retrieved from https://instapage.com/blog/personalization-statistics/
二、 文獻參考
Balapour, A., Nikkhah, H. R., & Sabherwal, R. (2020). Mobile application security: Role of perceived privacy as the predictor of security perceptions. International Journal of Information Management, 52(102063), 1-13.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370.
Breiman, L. (1996). Bagging Predictors. Machine-mediated Learning. Chen, T. Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal
Statistical Society. Series B (Methodological), 34(2), 187-220.
Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, Organizational Behavior and Human Decision, 50(2), 179-211.
C. L. Hsu and J. C. C. Lin. (2016). Effect of perceived value and social influences on mobile app stickiness and in-app purchase intention, Technol Forecast Soc Change, vol. 108, 42–53.
Chiu, C., Wang, E. T. G., Fang, Y., & Huang, H. (2014). Understanding customers' repeat purchase intentions in B2C e-com- merce: The roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal, 24(1), 85–114.
Dörr, J., Benlian, A., Vetter, J., & Hess, T. (2010). Pricing of Content Services – An Empirical Investigation of Music as a Service. Lecture Notes in Business Information Processing, 2, 36.
Farzin, M., Sadeghi, M., Yahyayi Kharkeshi, F., Ruholahpur, H., & Fattahi, M. (2021). Extending UTAUT2 in M-banking adoption and actual use behavior: Does WOM communication matter? Asian Journal of Economics and Banking, 5(2), 136- 157.
Friedman, J. H. (2002). Stochastic Gradient Boosting. Computational Statistics & Data Analysis, 38(4), 367-378.
Hermanto, J., & Napitupulu, T. A. (2023). Factors That Influence the Selection of a Mobile Stock Trading Application. Journal of Theoretical and Applied Information Technolog,. 101(8), 3197-3210.
Kim, H. & Gupta, S. (2009). A comparison of purchase decision calculus between potential and repeat customers of an online store. Decision Support Systems,47(4), 477-487.
Lemmens.A. & Croux.C. (2006). Bagging and boosting classification trees to predict churn. Journal of Marketing Research, 43(2).
Lu, J., Lu, C., Yu, C. S., & Yao, J. E. (2003). Exploring factors associated with wireless internet via mobile technology acceptance in Mainland China. Communications of the IIMA, 3(1), 101-120.
Mäntymäki, M., Islam, A.K.M.N., & Benbasat, I. (2020). What drives subscribing to premium in freemium services? A consumer value-based view of differences between upgrading to and staying with premium. Information Systems Journal, 30(2), 295- 333.
Mittal, B., & Lassar, W. M. (1996). The role of personalization in service encounters. Journal of Retailing, 72(1), 95–109.
Oestreicher-Singer, G., & Zalmanson, L. (2013). Content or community? A digital business strategy for content providers in the social age. MIS Quarterly, 37(2), 591– 616.
Okazaki, S., & Mendez, F. (2013). Perceived Ubiquity in Mobile Services. Journal of Interactive Marketing, 27(2), 98-111.
Reichheld, F. F., & Schefter, P. (2000). E-loyalty: Your secret weapon on the web. Harvard Business Review, 78(4), 105–113.
Sheth, J. N., Newman, B. I., & Gross, B. L. (1991). Why We Buy What We Buy: A Theory of Consumption Values. Journal of Business Research, 22(2), 159-170.
Tojib.D., & Tsarenko.Y. (2012). Post-adoption modeling of advanced mobile service use. Journal of Business Research, 65(7), 922-928.
Wagner, T. M., Benlian, A., & Hess, T. (2014). Converting freemium customers from free to premium—the role of the perceived premium fit in the case of music as a service. Electronic Markets, 24(4), 259-268.
Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137–209.
Zhang, W., Lu, Q., Chen, K., Zheng, Z., & Yu, Y. (2012). SVDFeature: A Toolkit for Feature-based Collaborative Filtering. Journal of Machine Learning Research, 13(1), 3619-3622. |
| Description: | 碩士
國立政治大學
國際經營與貿易學系
111351038 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111351038 |
| Data Type: | thesis |
| Appears in Collections: | [國際經營與貿易學系 ] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 103801.pdf | | 1753Kb | Adobe PDF | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
