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 Coclusion: 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.