Analysis of the recommended algorithm whichspotify has applied

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<b> VIET NAM NATIONAL UNIVERSITY, HO CHI MINH CITYUNIVERSITY OF ECONOMICS AND LAW</b>

<b>FINAL PROJECT OF SEMESTER 1</b>

Course: NEW ICT Course’s ID: 231BIE105106

<i><b>ANALYSIS OF THE RECOMMENDED ALGORITHM WHICH</b></i>

<i><b>SPOTIFY HAS APPLIED</b></i>

<b>Instructor guides:</b> PhD. Nguyễn Thôn Dã

<b>Member list:</b>

1. K234040511 - Nguyễn Thị Bích Thủy 2. K234040497 - Vũ Nguyễn Yến Nhi 3. K234111350 - Nguyễn Vũ Gia Ngân 4. K234060700 - Nguyễn Như Khải 5. K234060689 - Nguyễn Tơ Hồng Gia

<i>Hồ Chí Minh City, 20th December, 2023</i>

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<b>BẢNG PHÂN CHIA NHIỆM VỤ</b>

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<b>LỜI CẢM ƠN</b>

Trước khi hoàn thành bài báo cáo, em xin được dành lời cảm ơn chân thành đến thầy phụ trách hướng dẫn của bộ môn Công nghệ và truyền thông mới - NEW ICT, thầy Nguyễn Thôn Dã. Cảm ơn vì sự tận tình chỉ bảo của thầy, kiến thức thầy dạy đóng góp một phần rất lớn đối với sự hoàn thiện của bản báo cáo của chúng em.

Bên cạnh đó, em xin gửi lời cảm ơn tới Trường đại học Kinh tế Luật vì trường đã tạo ra một môi trường học tập đầy sáng tạo và thực tế, giúp chúng em cải thiện hơn về những kỹ năng mềm cần thiết cho cuộc sống và cơng việc sau này như kỹ năng thuyết trình và kỹ năng viết báo cáo, không những vậy chúng em còn được cọ sát, đến gần hơn với những kiến thức chun mơn.

Vì kiến thức cịn hạn chế, trong q trình học tập, hồn thiện báo cáo này chúng em khơng tránh khỏi những sai sót, kính mong nhận được những ý kiến đóng góp từ thầy cơ.

Chúng em xin chân thành cảm ơn !

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<b>LỜI CAM KẾT</b>

Nhóm chúng em cam đoan kết quả nghiên cứu này là của riêng nhóm, khơng sao chép kết quả nghiên cứu của những cá nhân hoặc nhóm nghiên cứu nào khác ! Chúng em xin chịu hoàn tồn trách nhiệm với bài báo cáo của nhóm mình

TP Hồ Chí Minh, ngày 20 tháng 12 năm 2023

<b> Tập thể thành viên nhóm:</b>

1. Nguyễn Thị Bích Thủy 2. Vũ Nguyễn Yến Nhi 3. Nguyễn Vũ Gia Ngân 4. Nguyễn Như Khải 5. Nguyễn Tơ Hồng Gia

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1.3. Current trends/ application in business...6

1.4. Challenges and impact to different aspect of business...7

<b>PART II: Technology implementation in business...11</b>

2.1. Business brief overview...11

2.3.2. Data synthesis and operation process...20

2.3.2.1. Collect user data:...20

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2.3.2.2. Using machine learning algorithms for data analysis:...22

2.3.2.3. Suggest songs, artists, and playlists:...22

2.3.3. Application, function...22

2.3.4. Detailed description of how to operate...24

2.3.4.1. Collaborative Filtering models...24

2.3.4.2. Natural Language Processing (NLP)...27

2.3.4.3. Raw Audio...27

2.3.4.4. Productivity and User feedback...29

2.3.4.5. Training, registration and legal of Recommender System of Spotify...31

<b>PART III. Assessment on the technological implementation...32</b>

3.1. Overall outcomes:...32

3.1.1. Personalized recommendation...32

3.1.2. Hybrid method for recommendation...32

3.1.3. The benefits that algorithms bring to users...33

3.2. Pros and cons:...35

3.3. Proposal Improvement:...37

3.4. Final conclusion:...39

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<b>TABLE OF BOARD AND IMAGE</b>

Figure 1: Principle Factors of Spotify, Apple and Amazon...8

Figure 2: Music Streaming Market Share on 2020 and Mergent Online Company Profiles on 2021...9

Figure 3: Percentage of Digital Share in Music Industry (IFPI 2017)...10

Figure 4: Proportion of Subscribers at Music’s Platforms...19

Figure 5: Math matrix and Python library...25

Figure 6: Recipe for complex math...25

Figure 7: The song user matrix...26

Figure 8: Recommending music on Spotify with deep learning...28

Figure 9: Illustrates time periods...29

Figure 10: Playlist of seasonal music...34

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SaaS Service as a software CTO Chief Technology Officer LTV Lifetime value

RS Recommender System AI Artificial Intelligence

BaRT Bandits for Recommendations as Treatment APIs Application Programming Interface ACM Association for Computing Machinery

BERT Bidirectional Encoder Representations from Transformers GPT Generative Pre-training Transformer

XML eXtensible Markup Language

mBERT Multilingual Bidirectional Encoder Representations from Transformers

MUSE Multilingual Universal Sentence Encoder

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<b>PART I. Overview of technology</b>

→ These algorithms work together to create a personalized music experience for each Spotify user, ensuring that users discover new music they love while enjoying their favorite artists and genres. Spotify's commitment to innovation and machine learning ensures that its algorithms continue to evolve and deliver even better recommendations in the future.

● Recommendation System is the algorithm deploying machine learning (ML) algorithms to recommend new titles for all their users. Spotify's recommendation algorithm uses a variety of algorithms to process this data and generate recommendations. Some of the key algorithms include:

+ Collaborative filtering: This algorithm identifies users with similar listening habits and recommends songs that those users have enjoyed. It's based on the principle that people with similar tastes tend to like the same music.

+ Content-based filtering: This algorithm analyzes the musical characteristics of songs to recommend songs that share similar traits with songs the user has already listened to. It's based on the idea that users prefer music with similar sonic elements.

+ Hybrid filtering: This algorithm combines collaborative filtering and content-based filtering to create a more comprehensive recommendation system. It takes into account both user preferences and song characteristics to provide a more personalized experience.

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● Natural language processing (NLP) is an algorithm that provides the ability to understand text and speech. Spotify uses NLP to classify their music. By searching the web for any text, Spotify can also find out about a specific song. ● Spotify’s NLP then categorized songs based on the language used to describe

them. Keywords will be picked out and assigned a weight, which can measure how much a song exhibits a particular emotion. This helps spotify’s algorithms identify which songs and artists belong in playlists together, which can then be more easily deployed to the recommendation system.

● Reinforcement learning (Rl) is a system produced based on ML methods to understand goals and respond to data through trial and error during interaction. Spotify uses the RL system to feature songs and artists. From there they will be accurate and meaningful to the home page of subscribers.

+ New content is first delivered to subscribers through additional filtering or NLP. Thereby, subscribers can easily interact with the song in many different ways (listen to the song once, play the song multiple times or listen to more songs from other artists) or stop playing by pressing Skip the song. In all cases, subscribers send information to the RL algorithm about their desired level of success.

+ RL offers users to explore other areas of music through knowing their preferences and typical listening history. Expanding the range of music subscribers are listening to will expand the range of music consumed within the Spotify app's catalog – benefiting both artists and the Spotify platform. + New content is first served to subscribers using collaborative filtering or NLP.

The subscriber will then engage with the song on varying levels (listen to the song once, on repeat, listen to more songs by the artist) or disengage by skipping the song. In either case, the user is sending information to the algorithm about how successful their prediction was.

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<b>1.2. Characteristics </b>

<b>1.2.1. Recommendation System</b>

Spotify's recommendation system relies on analyzing various characteristics to personalize your music experience. These characteristics can be broadly categorized into three main types:

● User Characteristics:

- Listening history: This includes the songs you have listened to, skipped, saved, and added to playlists. It provides valuable insight into your musical preferences and listening habits.

- Saved songs and artists: These represent songs and artists you explicitly show interest in, indicating a strong preference.

- Followed playlists: This reflects your interest in specific genres, themes, or curators.

- Demographic information: Although not directly used for recommendations, information like age, gender, and location can be incorporated for broader targeting.

● . Song Characteristics:

- Audio features: This includes musical attributes like tempo, key, energy level, danceability, and acousticness, allowing for categorization and suggestions based on similar sonic qualities.

- Genre and subgenre: Categorization based on genre and subgenre helps identify songs with similar musical styles and themes.

- Lyrics and artist information: Analyzing lyrics can reveal themes, emotions, and concepts, while artist information provides context about their musical style and influences.

- Release date and popularity: Newer releases and trending songs can be factored in for discovering fresh music or exploring popular trends.

● Contextual Information:

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- Time of day: Spotify considers the time of day to recommend music suitable for different activities and moods, like upbeat music for mornings or relaxing tracks for evenings.

- Location: This can be used to suggest music popular in your region or relevant to specific locations.

- Device: Depending on the device you use (phone, laptop, speaker), Spotify might suggest music suitable for different listening environments or activities.

<b>1.2.2. Natural Language Process</b>

- With all the data Spotify has collected, the NLP algorithm is capable of classifying songs based on the type of language used in their descriptions and similarity to other songs used for the same purpose. Artists and songs will be classified based on data and each term has a certain weight assigned to them. Similar to collaborative filtering, a vector representation of the song is created and used for the purpose of recommending other similar songs to the user.

- Search and Discovery:

+ Natural Language Search: Search for music using natural language queries instead of exact keywords, allowing for more flexibility and understanding of user intent. Personalized Recommendations: Generate "Discover Weekly" and "Release Radar" playlists using NLP to analyze listening history, saved songs, and followed artists.

+ Understanding Lyric and Artist Descriptions: Analyze lyrics and artist descriptions to categorize music by genre, mood, and theme, improving search results and music discovery.

- User Interface and Interaction:

+ Voice Search and Assistant: Interact with Spotify using natural language commands to search for music, control playback, and access features. + Context-Aware Recommendations: Recommend music based on the time of

day, location, and activity level using NLP to understand user context.

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+ Generating Captions and Transcripts: Generate captions and transcripts for podcasts and other audio content, making them more accessible and engaging. - Content Analysis and Organization:

+ Automatic Song Metadata Generation: Generate song metadata like genre tags, mood labels, and descriptions based on audio features and lyrics analyzed using NLP.

+ Music Categorization and Classification: Categorize music by genre, subgenre, mood, and theme based on lyrics, artist descriptions, and audio features analyzed with NLP.

+ Identifying Named Entities: Identify and classify named entities like artists, instruments, and locations mentioned in lyrics, enhancing search and organization.

- NLP technologies are continually advancing, driven by machine learning and deep learning techniques. These advancements enable computers to handle language-related tasks with increasing accuracy and sophistication.

<b>1.2.3. Reinforcement Learning (RL)</b>

- The limitation of these collaborative filtering methods is that they rely on explicit or implicit feedback signals to know whether a user likes a playlist or not. As a result, they will have difficulty considering other important factors (e.g., the coherence of the song's sound, the context of the music listening session, and the optimal presence of musical item sequences). ). This leads to a mismatch between offline metrics and user satisfaction metrics (which we want to optimize).

- For example, collaborative filtering has the ability to recommend playlists with high ratings but does not classify suitability for users as containing a mixture of adult and children's music. This leads to user dissatisfaction. Therefore creating a suitable and impressive playlist is a difficult task.

- The field of Reinforcement Learning (RL) can be enhanced without explicit feedback signals. Instead, RL can learn through interacting with users on Spotify. Therefore, RL agents will interact and learn to increase user satisfaction in creating their own playlists.

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<b>1.3. Current trends/ application in business</b>

Spotify has applied the power of data to personalize customer experience. Spotify uses data analysis, user profile mining, and customer experience personalization to launch new features for the paid version.

Specifically, with current user data and analysis technology, Spotify implements a strategy including the following 4 main activities:

- Reorganize the music store and optimize the user experience of music playlists - Personalize playlists

- Localization

- Build campaigns connecting local artists with users.

● Reorganize the music store and optimize the user experience of music playlists: To suggest the next song to users and play music automatically, Spotify's algorithm is created based on machine learning: This feature analyzes the songs in a certain playlist and tries to predict what music will come next. Spotify's AI has studied millions of user-created playlists to understand what a good music playlist is, then provides suggestions that are most similar to user intent.

Optimize the UX-UI of the music playback menu: Is an application service on a technology platform (Service as a software - SaaS), as well as other SaaS software. Design that ensures optimal UX/UI is one of Spotify's priorities. Therefore, the location of menus, buttons, control bars, tabs, pop-ups, etc. are designed to be reasonable and most user-friendly for the user's experience.

Creates a limited skip feature and only listens to music in random mode in the free version

● Personalize playlists

The company has created algorithms to optimize the music suggestions that pop up from your home menu to curated playlists like Discover Weekly.

While competitors like Apple Music, Amazon Prime Music and Google Music rely on a combination of paid users and community-generated playlists,

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Spotify's key differentiator is its degree of customization and expansion of musical knowledge offered to customers.

Spotify just launched a collection of personalized playlists called Spotify Mixes. Built around users' listening preferences, Spotify Mixes starts with each person's favorite songs and continually updates with recommended songs Spotify thinks you'll love.

● Localization

Spotify has playlists tailored to your area. To promote this feature, Spotify uses code on playlists and their ads are designed based on the playlists that have been shared in each region.

To create a difference and attract users to use the paid version, Spotify offers a policy: when traveling abroad (not your home country), Spotify free version will no longer be available. You will need to purchase a 14-day music package for your trip.

● Build campaigns connecting local artists with users.

They developed a new strategy: supporting and uplifting local artists. This strategy has created a connection not only in territory but also in musical thinking between listeners and musicians. Local music ads are listed on public transit, airports, or near popular tourist destinations to increase local music awareness as well as Spotify awareness in that location. Additionally, advertisements are also posted around local bars or cafes to help promote the artists to their public.

<b>1.4. Challenges and impact to different aspect of business</b>

- The music streaming industry is dominated by large, multinational companies that account for the majority of the market share in this field. Specifically, Spotify, Apple Music, Amazon Music, YouTube and Pandora are the five big companies. They are rivals and there is always great competition in the global music streaming market. According to the survey, they hold more than 74% of the global music streaming market share as of April 2020. The companies do not compete on price because they all offer basic services at mid-range prices. average about 9.99 USD/month (see

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Picture 1). A framework of strategies is shown for the three companies Apple, Spotify and Amazon to illustrate how the three companies compete with each other (see Picture 2).

Companies generally compete to gain users through several key factors:

1. Size of the music catalog offered by the music streaming service creates value by appealing to a wide range of listeners;

2. Podcasts provided by the music streaming service on the same app as music create value by added convenience;

3. Personalization of listening experience through personalized playlists; 4. Complimentary products offered by the streaming company to work with their

music streaming service;

5. Original content and other exclusive offerings to subscribers; 6. Price for a standard subscription

<i>Figure 1: Principle Factors of Spotify, Apple and Amazon</i>

Spotify uses a more widespread, popular differentiation strategy. Spotify's goal is to bring a completely different product to its large customer base through personalized

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playlists and exclusive podcasts only available on Spotify. Therefore, Spotify has the market share lead in the industry (see Picture 1).

<i>Figure 2: Music Streaming Market Share on 2020 and Mergent Online CompanyProfiles on 2021</i>

● Challenges:

The biggest potential threat to Spotify's long-term competitive advantage is a legal change to its data collection policies from users. Spotify is heavily dependent on collecting data from its customer groups. In addition, recently, Spotify has started collecting data on users' speech to analyze guest metadata to improve more value such as emotional state, gender and voice, mood... ( according to Hendler, released 2021). The change in laws restricting user data collection and policies allowing data usage will severely limit Spotify's ability to grow in providing new, more personalized listening experiences for users and would take away this key source of Spotify's competitive advantage.

● Impacts:

Businesses are using new applications to rethink their business models and — in some cases — disrupting their industries.

<i>Figure 3: Percentage of Digital Share in Music Industry (IFPI 2017)</i>

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We have reviewed the potential impact of these technologys on the music streaming app. The music industry had rapid changes from the physical market to the digital market in the past decades. The consumers download and stream music online and mobile during the digital dominant market. These technologys can increase revenue, attract more consumers. The positive impact to different aspect on Spotify only be possible if there are detailed consideration of the industry and careful understanding

- Improved music discovery: By combining these technologies, Spotify recommends music that goes beyond user listening history, introducing them to new artists and genres they might enjoy.

- Personalized experience: Recommendations are tailored to individual preferences, taking into account user history, cultural context, and even mood. - Increased engagement: Users are more likely to discover and enjoy music they

hadn't heard before, leading to longer listening sessions and higher retention rates.

- Enhanced music understanding: NLP and RL help Spotify understand how music is perceived and consumed by its users, contributing to a deeper understanding of music preference and culture.

- Content Management: Recommendation: Identifies trending artists and genres, helping curators create relevant playlists and editorial content. NLP anh RL : Analyzes music metadata and user feedback to categorize and label music accurately, enhancing optimize, search and discovery features, ensuring maximum visibility and engagement.

- Marketing and Advertising: Recommendation: Personalized recommendations drive advertising campaigns, NLP and RL: Analyzes user data and cultural trends to identify potential target audiences, maximize reach

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Overall, in terms of using recommendation algorithms, NLP and RL will have lasting effects on various aspects of the Spotify app. Together, these technologies will create a diverse system that will help Spotify deliver personalized, flexible, and high-quality music services to users around the world. These technologies create a more personalized, engaging and efficient platform, benefiting users, artists and the music industry as a whole. These technologies create a more personalized, engaging and efficient platform, benefiting users, artists and the music industry as a whole.

<b>PART II: Technology implementation in business2.1. Business brief overview </b>

<b>2.1.1. History</b>

On April 23, 2006, Daniel Ek, the former CTO of Stardoll and Martin Lorentzon, a co-founder of Tradedoubler established Spotify as a Swedish audio streaming and music service provider.<b>[1]</b> According to Ek, the name "Spotify" was mispronounced as a name that Lorentzon had yelled out. Eventually, they combined the terms "Spot" and "Identify" to form their company's title. Spotify was developed to address the issue of music piracy.Before music streaming services gained popularity, a lot of people downloaded music files illegally. This was an increasing challenge for the whole music industry and served as the foundation for Spotify's establishment. Daniel and Martin founded Spotify after realizing the enormous potential of music streaming

With its principal office in Stockholm, Sweden, Spotify offers a vast collection of over 100 million songs and five million podcasts from a wide range of record labels and media businesses. On October 7, 2008, Spotify's services were made available to the general public (by invitation only) in Scandinavia, the UK, France, and Spain. In

<b>the UK, Spotify began providing free, restricted access to its services in 2009. [2]</b>

For the year 2019, Spotify made a profit for the first time ever. Currently, Spotify is accessible in the majority of Europe, Africa, the Americas, Asia, and

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Oceania. It boasts over 574 million users, with 226 million of those being subscribers across 184 regions

<b>2.1.2. VISION</b>

“We envision a cultural platform where professional creators can break free of their medium’s constraints and where everyone can enjoy an immersive artistic experience that enables us to empathize with each other and to feel part of a greater whole.”

<b>2.1.3. MISSION</b>

“Our mission is to unlock the potential of human creativity – by giving a million creative artists the opportunity to live off their art and billions of fans the opportunity to enjoy and be inspired by it.”

<b>2.1.4. OPERATIONS</b>

Spotify operates on a "freemium" strategy, similar to other music streaming services. This model states that it makes use of a freemium business model, providing an unlimited premium service for a membership price and a basic, limited, ad-supported service for free. The service includes a number of features, including social sharing, offline playback, and high-quality audio. It is accessible on multiple platforms, including desktop, mobile, and web.

1. Draw a sizable user base by offering a free service.

Users of Spotify's free music streaming service can choose from millions of songs in its catalog. Users of the free service must listen to advertisements that help to partially fund the service, and it only offers basic functionality.

2. Convert complimentary users to a high value offering

Spotify has had great success turning free users into paying customers. In addition to more features, its premium subscription does away with advertisements. 2018 saw 46% of Spotify's users become premium subscribers, who account for 90% of the service's earnings..

3. Control churn and retention

The longer Spotify can keep users, the more money it can get from them over time— the user's lifetime value, or LTV—increases, similar to any other subscription model.

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We refer to this as managing customer turnover. Spotify's premium customer turnover rate dropped to a record-low 4.6% in the first half of 2019.

4. Equalize the cost of premium and free

Record labels receive almost fifty-two percent of Spotify's revenue from each stream. Sony, Universal, Warner, and Merlin are the four record labels that own more than 85% of the music that is streamed on Spotify. In 2018, Spotify paid out €0.5 billion in royalties to free users and €3.5 billion to premium users, or 74% of total expenses. 5. Use your premium revenue stream to finance the entire amount.

The unique aspect of the freemium business model is that you have to be able to pay for both free and paid users. In 2019, Spotify's user base reaches over 248 million, for which royalties are required. Of those, 54% listen to music for free, albeit in

Spotify's advanced algorithmic approach, which examines a tonne of user data to comprehend unique preferences and behaviors, is what powers its personalized recommendations. The algorithm can provide highly personalized song recommendations and carefully curated playlists based on each user's taste by analyzing listening habits, playlists, and user-generated content.

2. Social media integration that is seamless

Spotify creates a lively environment for music discovery and sharing by enabling users to share their favorite songs, playlists, and recently played tracks with friends and followers. This not only makes it easier for users to discover new music through social networks, but it also promotes a sense of musical community as users converse, trade recommendations, and find out new artists together.

3. Insights based on data

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Spotify's data-driven insights offer artists a detailed and insightful understanding of their fan base. Through demographic analysis, musicians can learn more about the age, gender, and interests of their fan base and adjust their music and marketing tactics accordingly.

4. Synchronization throughout platforms

The cross-platform synchronization feature of Spotify improves user flexibility and convenience. When a user switches between devices during the day or moves from a smartphone to a laptop, Spotify picks up where they left off, saving them the trouble of looking for the last track or playlist they played.

5.Upgraded Features for Accessibility

Spotify has demonstrated its dedication to accessibility by offering improved accessibility features. By providing support for screen readers, people with vision impairments can use assistive technologies which speak the text on the screen to navigate and interact with the platform.

Additionally, text resizing selections enable users with vision impairments to change the font size for improved legibility.

<b>● WEAKS</b>

1 Inadequate payment to artists

Both artists and industry insiders have criticized Spotify's artist compensation model, raising issues with the comparatively low royalties that are given to artists. Discussions regarding the equitable allocation of earnings in the music industry and the financial viability of musicians have been sparked by this pay gap.

2 Insufficiency of Live Content

Users may have to rely on other platforms or services in order to stream live concerts and events or listen to live radio broadcasts due to Spotify's lack of live content. Some users may find this fragmentation of content inconvenient if they are used to a single, comprehensive music streaming service.

3 Restricted user individualization options

It is a disadvantage if they want more control over how they listen to music. Even though Spotify's algorithms are made to offer tailored recommendations, some users

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might want more precise control—like the capacity to adjust their preferences or bar particular musicians or genres.

4 Platform compatibility

Any technical problems can make it difficult for users to stream music or access their music library, such as server outages or app crashes. Because of this dependence, users' overall experience may be impacted by outages or service disruptions.Additionally, if Spotify stops supporting particular devices or operating systems, compatibility problems could occur, limiting users' options and requiring them to upgrade or find other music streaming services.

<b>● OPPORTUNITIES</b>

1 Application of voice assistants and smart devices

Spotify can take advantage of the expanding market for smart home technology and satisfy users who want voice-activated interactions by integrating with well-known smart devices. Through this integration, users can conveniently and intuitively listen to their favorite tracks, playlists, or podcasts by simply using voice commands. 2. Experiences with Augmented Reality

Spotify has the potential to completely change the way people interact with music by utilizing augmented reality technology. Users can explore more multimedia content related to the album and gain a deeper understanding of the musicians' artistic vision by utilizing interactive album covers.

3. Improved collaboration among artists

Spotify has an exciting opportunity to promote creativity and create original content that connects with users through enhanced artist collaboration. As a result, Spotify can present unique and inventive musical collaborations that blur boundaries and combine genres, establishing the company as a platform that actively promotes and fosters artistic cooperation.

4 Sharing music on social media

The social features of Spotify can be strengthened and user engagement increased with the aid of social music sharing. As a result, users can collaborate to curate and add to a shared musical experience, encouraging a sense of community and teamwork.

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<b>● THREATS</b>

1 Increasing license fees

Growing licensing fees pose a serious threat to Spotify's revenue and financial stability. The price of obtaining licenses from record labels and other rights holders has gone up in tandem with the growth in popularity of music streaming.

2. Data Privacy Issues

As Spotify grows and enters new markets, it may encounter various data privacy laws and compliance issues. To ensure that user data is protected everywhere, it will need to have strong systems and procedures in place. Spotify needs to make sure users have control over their private information and continue to be open and honest about its data handling policies in light of the growing scrutiny surrounding the data practices of tech companies.

3 Illegal streaming and piracy

Another concern is illegal streaming and piracy. Spotify's ability to make money from its services is hampered and the value proposition of paid subscriptions is undermined if users can readily obtain copyrighted music for free through illegal channels. 4 Royalty rate discussions

Since Spotify depends on licensing deals with record labels and other rights holders, a large rise in royalties could have a big effect on the streaming service's bottom line. 5. Fierce rivalry

Spotify's market position is seriously threatened by the fierce competition in the music streaming space. With their large user bases and financial resources, rivals like Apple Music, Amazon Music, and YouTube Music are able to invest in marketing, original

<b>content, and technology improvements. [4]</b>

<b>2.2.2. Market analysis</b>

<b>1. Determining customer segments: </b>

The target market for Spotify is global, with the largest concentration of users in the USA and Europe. Young adults in their Millennial and Gen Z years are the typical Spotify users, but there is also a sizable population of older adults (55 and over) who

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appreciate the music on the app.The average Spotify user is devoted, spending about 118 minutes a day listening to the service. The platform has a higher female user base than male user base (56% female to 44% male).

<b>2. Segmenting the Target Market for Spotify</b>

Spotify analyzes segmentation data based on demographics, geography, behavior, and psychographics. It will be able to target your audience more precisely as a result of its improved understanding and insights.

- Demographic Segmentation

Even though the brand is well-liked by people of all ages, its appeal is greatest to younger audiences. 29% of Spotify's user base consists of millennials, with 26% of them being younger than 24.

In fact, among Americans between the ages of 12 and 34, Spotify was the most widely used online music service in 2020. On the other hand, just 19% of Spotify users are over 55. 71% of Spotify's free audiences are under 35 years old, indicating that younger people are big fans of the platform's freemium subscription.

- Geographic Segmentation

With over 159 million users in Europe compared to 111 million in the North America, 116 million in Latin America, and 551 million worldwide, Spotify's customer segmentation is highest in Europe. The target market in the US consists of those who use the music streaming service frequently. Over the past ten years, the percentage of US users who use the music app has increased to 28% weekly and 30% monthly.

- Behavioral Segmentation

Most Spotify users find pleasure in the platform's customized experience. Listening to playlists takes up over one-third of time on Spotify, with 36% of those playlists being made and shared with other users. The success of Spotify extends beyond music streaming.

- Psychographic Segmentation

The Millennial generation's attitude toward media consumption in general is reflected in Spotify's popularity: 60% of them believe that audio is the most immersive form of media. For younger listeners, 14 to 35 years old, Spotify's enormous audio library with 70 million song titles and almost 3 million podcasts is extremely appealing. This

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