The study has the purpose of examining in depth the socio-demographic characteristics and the listening habits of users of a music streaming service.
The outcome of the analysis will be able to respond to the following research questions:
1. How can the user base of a music streaming service be segmented? Which characteristics and listening habits characterize each segment?
2. Which improvements and additions can be implemented to Spotify in order to improve the listening experience of each segment?
3. Which characteristics and listening habits characterize a user subscribed to the free version of a music streaming service?
4. Which offers and features should a music streaming service implement to raise the percentage of premium users?
5. How can the Spotify interface be improved?
Before introducing the on-field analysis, a desk analysis was carried out with the aim of studying the current scenario of the music industry.
In relation to the music industry, the audio-streaming services currently account for a market share of 76% in terms of revenue. Despite paid subscriptions being the growth engine of music streaming services (making up 72% of the overall revenue), in 2022 just 46% of users claimed to use the premium version of a music streaming service.
Talking about Spotify, in the first trimester of 2023 it reached 515 million active users and 210 million paying subscribers (with an increase of 22% and 15% respectively from the year before). Although the Swedish company still dominates the streaming market, its market share (30,5%) has been in constant decline since 2018. Hence the need for identifying marketing strategies able to raise the percentage of premium users and increase the overall Spotify user base.
Then an on-field analysis was conducted, which was divided in 2 phases.
In the first phase a qualitative research with in-depth interviews was carried out using 3 different projective techniques:
· ZMET (Zaltman Metaphor Elicitation Technique): analyzing in depth the unconscious thoughts of the interviewees through the use of visual images (through symbolic and metaphorical associations).
· Mapping: Investigating the user perceptions on the various musical streaming services available on the market.
· Storytelling: Researching the personal experiences of the candidates with a musical streaming service’s interface through the narration of an autobiographic episode.
In the second phase a quantitative research was carried out, composed by the following analysis:
⁃ Multiple logistic regression
⁃ Decision tree
⁃ Factors analysis
⁃ Cluster analysis
In order to carry out the qualitative research 8 in-depth interviews were conducted by using a convenience sampling. In this context the informants were chosen based on their different methods of consumption, biological features and typologies of subscription.
Using the qualitative research results, a survey was realized for the quantitative research. Here too, a convenience sampling was used and it was taken into account the following target audience, composed of individuals:
older than 16 years old;
able to access the internet through at least one device;
subscribed to a music streaming service with a free or paid subscription.
251 responses were collected, 19 of which were from people not subscribed to any music streaming service and therefore deleted. And additional response was disqualified since the relative answers had been given under 200 seconds (1 speeder).
As a result, 231 responses were taken into account for the final sample.
After collecting all the data, it was then possible to carry out a multiple logistic regression with the aim of answering the following question: what habits and characteristics define a user subscribed to the free version of Spotify?
The dependent variable used for this specific analysis was the probability of subscribing a premium subscription on a music-streaming service. On the other hand gender, the level of education, age, the geographical area and the daily time spent on a music-streaming platform were taken into consideration as predictors (independent variables).
Based on the following analysis we can see how the probability of getting a premium subscription is lower for users with the following characteristics:
with a low level of education;
with less than an hour spent daily on the platform.
A decision tree was later created to better individuate the prototype of a user subscribed to a free version of a music streaming service.
Observing the decision tree graph obtained with the software “Orange”, it was possible to distinguish the characteristics of individuals with a higher probability of being subscribed with a free account to a music streaming platform, which are:
Users with early secondary school education;
Users not inclined to create their own playlists;
Users who are very likely to listen to music on the radio (on the platform or offline);
Users uninterested in the extension and the updating of the music catalogue of a music-streaming service.
A factor analysis was then carried out with the aim of reducing the dimension of the dataset used for the following cluster analysis.
Using a varimax rotation, the factors analysis managed to extract 11 factors which respect all of the optimality criteria.
The assigned label of each component was chosen based on the strongest correlations between the extracted factors and the input variables. These 11 factors are the following:
1. A desire to discover new music
2. Emotional awareness
3. Nostalgia for the past
4. Being energetic
5. Listening on multiple devices
6. Being a people person
7. Trusting only your own taste
8. Listening to music on your own
9. Attention to user- friendliness
10. Listening to music while commuting
11. Being monotonous with your music choice
Once the dataset was reduced, the final phase of the quantitative research consisted in a Cluster Analysis, which was conducted with the aim of answering the following question: how can the user base of a music streaming service be segmented?
The Cluster Analysis was carried out using the K-means algorithm (Centroid-Distance Based Clustering). In the context of our study, after several attempts the best clustering has led to the generation of 6 clusters:
1 - The raver: they are inclined to listen to music together with their peers and in social settings (like concerts or live events); they usually listen to upbeat and energetic music (mainly pop) and prefer a repetitive choice of music (monotonous music habits).
2 - The sentimental: they tend to listen to different artists and genres based on their mood and emotions; they are also nostalgic about past music and more inclined to trust playlists and suggestions made by the platform algorithms.
3 - The wary: they are suspicious towards listening to new music and suggestions made by the platform, therefore being more likely to listen to songs and playlists already saved on their digital library; they also tend to listen to music on various devices (both traditional – e.g., radio, CDs, vinyl etc. – and modern devices).
4 - The rational: they are young, careful to the cost-effectiveness and interested in an intuitive interface easy to use; they tend to listen to music on their own.
5 - The thoughtful: They tend to listen to music on their own and when they are commuting (usually by car); they are more likely to listen to music on radio stations.
6 - The curious: they have a strong desire to discover new music, keep up with the latest releases and listen to a wide range of artists (both popular and niche artists). .
After finalizing the on-field analysis, the last section of the study consisted on defining the marketing implications for the Spotify’s Platform.
Firstly, the aim was to identify which improvements or integration should be considered in order to improve the listening experience of each segment of Spotify users:
⁃ Improve the social features of the platform by introducing a chat and an interface section designed for the personal profile of each user;
⁃ Make the live events’ section more complete and updated.
⁃ Introduce thematic sections dedicated to specific moods, occasions of use and musical eras on the research page;
⁃ Insert some mixes dedicated to past music on the homepage;
⁃ Insert some visual elements (e.g., photos, videos etc.) related to a specific musical era within the relative section.
⁃ Suggest some customized mixes on the homepage which include exclusively tracks or artists saved in personal library of each user;
⁃ Increase the available features with the devices compatible with Spotify, prioritizing the latest generation devices (e.g., wearables and smartspeakers).
⁃ Extend the free trial period for the individual subscription (from 3 to 5 months);
⁃ Introduce a new subscription plan for under 26 at a reduced price (e.g., 4,99€);
⁃ Allow the reproduction of the desired track in less than 3 clicks (e.g., by introducing some customized mixes on the homepage based on the listening habits of each user).
⁃ Extend the private listening mode for an indefinite time;
⁃ Introduce a new interface section dedicated to the radio stations (containing both customized and on-air radios);
⁃ Insert some playlists and mixes on the homepage related to the experience of listening music on the go (e.g., traveling, at the gym, in the car etc.)
⁃ Extend the music library on the platform, including music from amateur and emerging artists (offering them the chance to upload their music on the platform without paying any fees – similarly to SoundCloud’s Business Model).
⁃ Insert playlists and mixes dedicated to new music right on the homepage;
⁃ If you look for a song on the search bar, add at the bottom of the search page songs that are similar for genre, rhythm, artist etc.
Secondly, the study tried to identify which offers and features should a music streaming service implement in order to raise the percentage of premium users. Considering the evidence obtained through the multiple logistic regression and the decision tree, two marketing strategies were defined in order to increase the premium users’ share:
1.Introduce a new subscription plan for over 50 at a reduced price (e.g., 4,99€)
2.Introduce a new interface section dedicated to the radio stations (containing both customized and on-air radios).
Lastly, once all the marketing implications were summarized, the following study defined how the Spotify interface could be improved compared to the current one.
With reference to the ideal interface established during the research, 6 main sections were identified:
Homepage: it includes a series of musical content customized on musical preferences and listening habits of each user;
Research bar: it allows users to explore new music on the base of specific genres, circumstances and themes (moods, musical eras etc.);
Radio: it includes personalized radio stations based on a specific song or artist listened recently and two live stations (a global conducted in English and a national one conducted in Italian);
Library: It allows users to access easily into their saved songs and albums, self-made playlists and followed artists;
Profile: it allows each user to keep track of their followings and followers, recently played songs and playlists and to post content such as comments and musical recommendations;
Chat: it allows users to monitor the listening activities of their followers and engage with them with musical recommendations and preferences.