تحلیل پرسونای مشتریان سکوهای پخش آنلاین موسیقی با الگوریتم HDBSCAN: مورد مطالعه کاربران اسپاتیفای

نوع مقاله : مقاله پژوهشی

نویسندگان

استادیار، دانشکدگان مدیریت دانشگاه تهران، تهران، ایران.

10.22070/cs.2026.21227.1459

چکیده

تحلیل پرسونای مشتریان به‌عنوان یکی از ابزارهای کلیدی در طراحی تجربه کاربری و تدوین استراتژی‌های بازاریابی داده‌محور، نقش فزاینده‌ای در موفقیت سکوهای دیجیتال ایفا می‌کند. با گسترش پلتفرم‌های پخش آنلاین موسیقی و افزایش تنوع الگوهای مصرف کاربران، ضرورت بهره‌گیری از رویکردهای مقیاس‌پذیر و تجربی برای درک رفتار شنیداری مخاطبان بیش از پیش احساس می‌شود. هدف این پژوهش، شناسایی و تبیین پرسونای کاربران پلتفرم‌های پخش آنلاین موسیقی با استفاده از یک رویکرد کمی و داده‌محور است. بدین منظور، داده‌های رفتاری و جمعیت‌شناختی ۸۰۰۰ کاربر فعال پلتفرم اسپاتیفای شامل ده شاخص اصلی، از جمله سن، جنسیت، کشور، نوع اشتراک، زمان گوش دادن روزانه، تعداد آهنگ‌های پخش‌شده در روز، نرخ رد کردن آهنگ، نوع دستگاه، میزان مواجهه با تبلیغات هفتگی و سطح گوش دادن آفلاین، مورد تحلیل قرار گرفت. برای استخراج الگوهای رفتاری پایدار و تمایز کاربران با تراکم‌های متفاوت، از الگوریتم خوشه‌بندی مبتنی بر تراکم HDBSCAN استفاده شد که بدون نیاز به تعیین تعداد خوشه‌ها از پیش، امکان شناسایی ساختارهای پیچیده داده را فراهم می‌سازد. نتایج خوشه‌بندی به شناسایی شش پرسونای متمایز شامل مشتریان پُرمصرفِ پریمیوم، مشتریان رایگان پس‌زمینه‌ای، شکارچیان ترند موبایلی، سنت‌گراهای باکیفیت و آفلاین، جوانان سریع‌گذرِ تبلیغ‌پذیر و حرفه‌ای‌های چنددستگاهی جهانی انجامید. این پرسونـاها بیانگر تفاوت‌های معنادار در عمق توجه، سطح خدمت و بافت مصرف کاربران هستند. مقایسه یافته‌ها با پژوهش‌های پیشین نشان‌دهنده همخوانی مفهومی نسبی با برخی پرسونای شناسایی‌شده در مطالعات قبلی و در عین حال، ظهور الگوهای رفتاری جدید مبتنی بر مدل‌های اشتراک، تحرک‌پذیری و استفاده چنددستگاهی است. یافته‌های این پژوهش چارچوبی تجربی و قابل تکرار برای تحلیل پرسونای داده‌محور ارائه می‌دهد و می‌تواند در بخش‌بندی بازار، هدف‌گذاری مشتریان، طراحی تجربه کاربری و تدوین استراتژی‌های بازاریابی دیجیتال در سکوهای داخلی و بین‌المللی مورد استفاده قرار گیرد.

کلیدواژه‌ها

عنوان مقاله [English]

Customer Persona Analysis of Online Music Streaming Platforms with HDBSCAN Algorithm: The Case of Spotify Users

نویسندگان [English]

  • Ehsan Soltanifar
  • Navid Mohammadi
Assistant Professor, College of Management, University of Tehran, Tehran, Iran
چکیده [English]

Aim and Introduction: Persona, a concept originating in academic psychology, was introduced by psychiatrist Carl Gustav Jung and refers to the face or social mask an individual presents to the outside world. With advances in data and personalization technologies, Alan Cooper pioneered the modern use of personas in user experience (UX) design in the 1990s (Prutt & Grudin, 2003). The persona concept has been used in various fields, including software development, health, higher education, marketing, robotics, responsive systems, video games, and system security (Salminen et al., 2022). The persona concept in the field of marketing has been used more in the field of digital marketing and digital marketing strategy, as Wanga (2024) says, the emergence of persona marketing is closely related to the growth of popular social media platforms such as Instagram, TikTok, YouTube, and Douyin, and has also been widely used in studying users of music distribution platforms such as Spotify. Accordingly, the present study examined the personas of Spotify music platform users. Next, three overlapping waves in the field of persona studies were introduced, including (1) qualitative and research-based personas; (2) quantitative approaches and algorithmic clustering; and (3) hybrid or predictive approaches based on machine learning.
Methodology: This study adopted a quantitative, clustering-based approach in an observational-analytical, data-driven manner. It used the HDBSCAN density-based clustering algorithm to discover behavioral patterns and extract customer personas among 8,000 active Spotify users. The data included 10 behavioral and demographic indicators: gender, age, country, subscription type, daily listening time, number of songs played per day, song skip rate, device type, number of ads heard per week, and offline listening. This work was carried out in three stages: data description, data preprocessing, and HDBSCAN clustering.
Finding: By aggregating identified clusters and analyzing user behavior within each, six audience personas were identified on online music streaming platforms.

Premium consumers: listeners with high attention span, high daily listening time, high number of songs played, and stricter selection standards that can lead to a moderate to slightly high dropout rate, but this group of customers is accompanied by frequent returns to selected playlists.
Background-free customers: music is the “background” of other activities for this group. Low to moderate daily listening time and number of songs played, low dropout rate (due to semi-active listening), high ad exposure (free subscription type), and low offline listening. Their listening behavior is related to the context of their daily life (work, study, household chores).
Mobile Trend Hunters: The main characteristics of this customer group are young, mobile, and sensitive to trends; a high number of songs played, a high skip rate (fast discovery), and average daily listening time.
Quality and Offline Traditionalists: The main characteristics of this customer group are older age, high loyalty to established playlists and familiar genres, low skip rate, high daily listening time, high offline listening, and low exposure to advertising due to the tendency towards premium.
Fast-paced, ad-enabled youth: This customer group is free-based with a “slice-by-slice” model. This means they have a high number of songs played daily but relatively low daily listening time, a high skip rate, high advertising exposure, and low offline listening.
Global Multi-Device Professionals: This user group is characterized by moderate but profound consumption with significant switching between mobile and desktop. This user group has relatively high daily listening time, moderate skip rate, moderate to high offline listening, and high geographic diversity.

Discussion and Conclusion: As we mentioned in previous studies by Fuller et al. (2016), seven personas were identified: active curator, addict, guided listener, discerning listener, isolated user, distrustful, and wanderer or free music explorer. Kim (2016) also identified three personas among music professionals, including: 1- the listener who listens to high-resolution music recreationally as a new hobby, 2- the music seeker who listens to high-resolution music as a way to learn about music, and 3- the artist lover who is excited when his favorite artist releases a high-resolution version of music. Comparing the results of this study with previous studies shows relative correspondences, such that the premium consumer, background music for other tasks, mobile predator, fast-paced advertising, and global multi-device careers from the present study have complete or relative conceptual correspondence with the personas of the listener with a specific moment, music addict, wanderer or free music explorer, guided listener, and active curator from the study of Fuller et al. (2016), respectively. No correspondence was found for the persona of the traditionalist with online quality in this study. On the other hand, the distrustful and isolated user persona in the study by Fuller et al. (2016) was not found in the present study. In general, after examining and describing the personas identified in this study, it can be stated that the distinction of personas is mainly based on three underlying vectors: which include depth of attention (combination of daily listening time, number of songs per day, and song skip rate), level of service (covariation of subscription type with number of ads heard per week and offline listening rate), and consumption context (device type and country). Additional studies using quantitative data-based approaches across other platforms for selling goods and services can lead to the development of more effective marketing strategies. The researchers are ready to conduct similar research and collaborate with other researchers interested in this field.

کلیدواژه‌ها [English]

  • Marketing strategy
  • Online music streaming platforms
  • Persona
  • Platform business models
  • Clustering

مراجع

  • آقازاده, هاشم, جعفری, عماد, اسمعیلی, حسین. (1399). بررسی رابطه همخوانی شخصیت مصرف‌کننده با برند، هویت پذیری مصرف‌کننده از برند و نیات رفتاری پس از خرید. نشریه علمی راهبردهای بازرگانی. دورۀ 12، شمارۀ6، 17-40. https://cs.shahed.ac.ir/article_2293.html
  • یعقوبی چوبری, علی . (1396). زبان استعاری و بینش جامعه‏ شناختی اِروینگ گافمن. مطالعات جامعه شناختی (نامه علوم اجتماعی سابق), 24(1), 149-170 https://doi.org/10.22059/jsr.2017.63433
  • باقری, جواد, و یزدانی, ناصر. (1402). تأثیر خلق و به‌کارگیری هوش بازار صادراتی بر عملکرد از طریق استراتژی بخش‌بندی و اولویت‌بندی مشتریان و مدل‌های فروش. نشریه علمی راهبردهای بازرگانی, 20(22), 65-82. https://doi.org/10.22070/cs.2024.19239 .1386
  • Adlin, T., Jamesen, H., & Krebs, T. (2001). Fake people and sticky notes: Fostering communication for human-centered software design. Whitepaper, published under: http://www. jamesen. com/publications/FakePeople-G. pdf.
  • Aghazade, H., Jafari, E., & Esmaeily, H. (2020). Investigate the Relationship between Consumer Personality Congruence with the Brand, Brand Identity and Post-Purchase Behavioral Intentions. Commercial Strategies, 12(6), 17-40. https://cs.shahed.ac.ir/article_2293.html?lang=en [in Persian]
  • Akner-Koler, C., & Ranjbar, P. (2016). Integrating sensitizing labs in an educational design process for haptic interaction. FormAkademisk, 9(2). https://doi.org/10.7577/formakademisk.1269
  • Alves Gomes, M., & Meisen, T. (2023). A review on customer segmentation methods for personalized customer targeting in e-commerce use cases. Information Systems and e-Business Management, 21(3), 527-570. https://doi.org/10.1007/s12652-020-02015-w
  • Antle, Alissa Nicole. "Child-personas: fact or fiction?." Proceedings of the 6th conference on Designing Interactive systems. 2006. https://doi.org/10.1145/1142405.1142411
  • Atzeni, A., Cameroni, C., Faily, S., Lyle, J., & Fléchais, I. (2011, August). Here's Johnny: a methodology for developing attacker personas. In 2011 Sixth International Conference on Availability, Reliability and Security (pp. 722-727). IEEE . https://doi.org/10.1109/ARES.2011.115
  • Bagheri, J., & Yazdani, N. (2024). The Impact of the Creation and Application of Market Intelligence on Performance Through Strategy of Segmentation, Prioritization, and Sales Models. Commercial Strategies, 20(22), 65-82. https://doi.org/10.22070/cs.2024.19239.1386 [in Persian]
  • Barata, M. L., & Coelho, P. S. (2021). Music streaming services: understanding the drivers of customer purchase and intention to recommend. Heliyon, 7(8), e07783. [PMC8379453] https://doi.org/ 10.1016/j.heliyon.2021.e07783
  • Blomquist, Å., & Arvola, M. (2002, October). Personas in action: ethnography in an interaction design team. In Proceedings of the second Nordic conference on Human-computer interaction (pp. 197-200). https://doi.org/10.1145/572020.572044
  • Bonnardel, N., & Pichot, N. (2020). Enhancing collaborative creativity with virtual dynamic personas. Applied ergonomics, 82, 102949. https://doi.org/10.1016/j.apergo.2019.102949
  • Brooks, C., & Greer, J. (2014, March). Explaining predictive models to learning specialists using personas. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 26-30). https://doi.org/10.1145/2567574.2567612
  • Campello, R. J., Moulavi, D., & Sander, J. (2013, April). Density-based clustering based on hierarchical density estimates. In Pacific-Asia conference on knowledge discovery and data mining (pp. 160-172). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/1007/978-3-642-37456-2_14
  • Canossa, A., & Drachen, A. (2009, August). Play-personas: behaviours and belief systems in user-centred game design. In IFIP Conference on Human-Computer Interaction (pp. 510-523). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-03658-3_55
  • Chen, R. C., Nivala, W. C. Y., & Chen, C. B. (2011, July). Modeling the role of empathic design engaged personas: an emotional design approach. In International Conference on Universal Access in Human-Computer Interaction (pp. 22-31). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/1007/978-3-642-21663-3_3
  • Dharwada, P., Greenstein, J. S., Gramopadhye, A. K., & Davis, S. J. (2007, October). A case study on use of personas in design and development of an audit management system. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 51, No. 5, pp. 469-473). Sage CA: Los Angeles, CA: SAGE Publications. https://doi.org/10.1177/154193120705100509
  • Dos Santos, T. F., de Castro, D. G., Masiero, A. A., & Aquino Junior, P. T. (2014, June). Behavioral persona for human-robot interaction: a study based on pet robot. In International Conference on Human-Computer Interaction (pp. 687-696). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-07230-2_65
  • Ferreira, B. M., Barbosa, S. D., & Conte, T. (2016, June). PATHY: using empathy with personas to design applications that meet the users’ needs. In International Conference on Human-Computer Interaction (pp. 153-165). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-39510-4_15
  • Frauenberger, C., Good, J., Keay-Bright, W., & Pain, H. (2012, May). Interpreting input from children: a designerly approach. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 2377-2386). https://doi.org/10.1145/2207676.2208399
  • Fuller, J., Hubener, L., Kim, Y. S., & Lee, J. H. (2016, August). Elucidating User Behavior in Music Services Through Persona and Gender. In ISMIR (pp. 626-632). https://doi.org/10.5281/zenodo.1415928
  • Hendriks, N., Truyen, F., & Duval, E. (2013, September). Designing with dementia: Guidelines for participatory design together with persons with dementia. In IFIP Conference on Human-Computer Interaction (pp. 649-666). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-40483-2_46
  • Hensely-Schinkinger, Susanne,. Pinatti de Carvalho, Aparecido Fabiano,. Glanznig, Michael,. and Tellioğlu, Hilda,. 2015. The Definition and Use of Personas in the Design of Technologies for Informal Caregivers. In Human-Computer Interaction: Design and Evaluation, Masaaki Kurosu (ed.). Springer International Publishing,Cham, 202–213. https://doi.org/1007/978-3-319-20901-2_19
  • Högberg, D., Hanson, L., Lundström, D., Jönsson, M., & Lämkull, D. (2008, August). Representing the elderly in digital human modeling. In Proceedings of the 40th Annual Nordic Ergonomic Society Conference, Reykjavik, Iceland. https://www.researchgate.net/publication/247778784_Representing_the_elderly_in_digital_human_modelling
  • Hsu, P. F., Lu, Y. H., Chen, S. C., & Kuo, P. P. Y. (2024). Creating and validating predictive personas for target marketing. International Journal of Human-Computer Studies, 181, 103147. https://doi.org/10.1016/j.ijhcs.2023.103147
  • Im, H., & Kim, N. W. (2016, May). Three Personas of Potential High-Resolution Music Users. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2851-2856). https://doi.org/10.1007/978-3-031-63223-5
  • Jansen, B. J., Salminen, J. O., & Jung, S. G. (2020). Data-driven personas for enhanced user understanding: Combining empathy with rationality for better insights to analytics. Data and Information Management, 4(1), 1-17. https://doi.org/10.2478/dim-2020-0005
  • Jensen, M. B., Elverum, C. W., & Steinert, M. (2017). Eliciting unknown unknowns with prototypes: Introducing prototrials and prototrial-driven cultures. Design Studies, 49, 1-31. https://doi.org/10.1016/j.destud.2016.12.002
  • Kauppinen, S., Luojus, S., & Lahti, J. (2016, October). Involving citizens in open innovation process by means of gamification: The case of WeLive. In Proceedings of the 9th Nordic Conference on Human-Computer Interaction (pp. 1-4). https://doi.org/10.1145/2971485.2971526
  • Kim, E., Yoon, J., Kwon, J., Liaw, T., & Agogino, A. M. (2019, July). From innocent irene to parental patrick: Framing user characteristics and personas to design for cybersecurity. In Proceedings of the Design Society: International Conference on Engineering Design (Vol. 1, No. 1, pp. 1773-1782). Cambridge University Press. https://doi.org/10.1017/dsi.2019.183
  • Klepek, M. (2019). Buyer personas: its use and limitations in online marketing. Marketing Identity, 7(1), 886-896. https://www.ceeol.com/search/article-detail?id=871161
  • Laine, T. H., Normark, J., Lindvall, H., Lindqvist, A. K., & Rutberg, S. (2020). A distributed multiplayer game to promote active transport at workplaces: user-centered design, implementation, and lessons learned. IEEE Transactions on Games, 12(4), 386-397. https://doi.org/10.1109/TG.2020.3021728
  • Lee, B., Srivastava, S., Kumar, R., Brafman, R., & Klemmer, S. R. (2010, April). Designing with interactive example galleries. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 2257-2266). https://doi.org/10.1145/1753326.1753667
  • Lee, J. H., Price, R., et al. (2015). Understanding users of music streaming services through persona identification. https://visualizationdesign.wordpress.com/2015/06/05/understanding-users-of-music-streaming-services-through-persona-identification/
  • Maier, R., & Thalmann, S. (2010). Using personas for designing knowledge and learning services: results of an ethnographically informed study. International Journal of Technology Enhanced Learning, 2(1-2), 58-74. https://doi.org/10.1504/IJTEL.2010.031260
  • Malik, A. (2019). Persona based marketing strategies: creation of personas through data analytics (Master's thesis, A. Malik). https://urn.fi/URN:NBN:fi:oulu-201906052360
  • Park, D., & Kang, J. (2022). Constructing data-driven personas through an analysis of mobile application store data. Applied Sciences, 12(6), 2869. https://doi.org/10.3390/app12062869
  • Pruitt, J., & Grudin, J. (2003, June). Personas: practice and theory. In Proceedings of the 2003 conference on Designing for user experiences (pp. 1-15). https://doi.org/10.1145/997078.997089
  • Qyll, N. (2020). Persona as key component in (cultural) person branding. Persona studies, 6(1), 56-71. https://doi.org/10.21153/psj2020vol6no1art941
  • Rea, D. J., & Young, J. E. (2019, March). Backseat teleoperator: affective feedback with on-screen agents to influence teleoperation. In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 19-28). IEEE. https://dl.acm.org/doi/10.1145/3488242
  • Salminen, J., Guan, K., Jung, S. G., & Jansen, B. J. (2021). A survey of 15 years of data-driven persona development. International Journal of Human–Computer Interaction, 37(18), 1685-1708. https://doi.org/10.1080/10447318.2021.1908670
  • Salminen, J., Wenyun Guan, K., Jung, S. G., & Jansen, B. (2022, April). Use cases for design personas: A systematic review and new frontiers. In Proceedings of the 2022 CHI Conference on human factors in computing systems (pp. 1-21). https://doi.org/10.1145/3491102.3517589
  • Stoll, J., McColgin, D., Gregory, M., Crow, V., & Edwards, W. K. (2008, May). Adapting personas for use in security visualization design. In VizSEC 2007: Proceedings of the Workshop on Visualization for Computer Security (pp. 39-52). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-78243-8_3
  • Threatt, A. L., Merino, J., Brooks, J. O., Healy, S., Truesdail, C., Manganelli, J., ... & Green, K. E. (2017). The design, prototyping, and formative evaluation of an assistive robotic table (art) for stroke patients. HERD: Health Environments Research & Design Journal, 10(3), 152-169. https://doi.org/10.1177/1937586716687802
  • Tu, N., He, Q., Zhang, T., Zhang, H., Li, Y., Xu, H., & Xiang, Y. (2010, November). Combine qualitative and quantitative methods to create persona. In 2010 3rd International Conference on Information Management, Innovation Management and Industrial Engineering (Vol. 3, pp. 597-603). IEEE. https://doi.org/10.1109/ICIII.2010.463
  • Wagner, C., Hess, T., & Wlömert, N. (2014). Freemium to premium conversion: The role of perceived feature-fit. https://doi.org/10.1007/s12525-014-0168-4
  • Wanga, R. (2024). Persona Marketing on Social Media: The Evolution from Constructed Authenticity to Consumer Trust. Journal of Advanced Academic Research and Studies 高等学术研究期刊, 1(12), 1-16. http://www.nlbaeai.org/static/upload/file/20250804/1754277658828753.pdf#page=6
  • Yaghoobi, A. (2017). Metaphorical Language and Erving Goffman’s Sociological Imagination. Sociological Review, 24(1), 149-170. https://doi.org/10.22059/jsr.2017.63433 [in Persian]
  • Zhang, G. (2019, July). Creating Enhanced User Experience Through Persona and Interactive Design: A Case of Designing a Motion Sensing Game. In International Conference on Human-Computer Interaction (pp. 382-394). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-23570-3_28
  • Zhao, Z. (2024). From Free to Premium: A Persona-Based Approach to Understanding Spotify Subscription Upgrades. MA thesis at the University of Chicago. https://doi.org/6082/uchicago.11903
نشریه علمی راهبردهای بازرگانی

مقالات آماده انتشار، اصلاح شده برای چاپ
انتشار آنلاین از تاریخ 28 فروردین 1405

فایل ها

هم رسانی

ارجاع به این مقاله

آمار