# Network Traffic Monitoring and Marketing Strategy of Online Music Products in Multimedia Environment

Authors: Cheng Chen; Ming Hung Shu; Hsiao-Chun Huang; Tung-Chun Chen; Jui-Chan Huang
Journal: Security and Communication Networks
 (2022)

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## Abstract

The network traffic monitoring and marketing strategy of online music products in the multimedia environment refers to the use of existing multimedia technologies to analyze the environment of the online music industry, accurately locate the needs of product users, and formulate more practical and effective network traffic monitoring and marketing strategies. This paper firstly gives a comprehensive overview of the concept and characteristics of online music and then analyzes the development status of the online music industry. Then, according to the current situation and marketing direction of the industry, three network traffic monitoring and marketing strategies for online music products are proposed. They are marketing strategy based on big data mining, marketing strategy based on new media platform, and marketing strategy based on O2O model. These three network traffic monitoring and marketing strategies all need to rely on the existing multimedia technology. Finally, the effectiveness and practicability of the proposed network traffic monitoring and marketing strategies are analyzed by comparing the network traffic monitoring and marketing strategies of online music products with the traditional marketing strategies in the multimedia environment. The experimental results show that the number of visits to music products under the multimedia environment marketing strategy is 1.2 times that of the traditional marketing strategy, and the download volume is 1.5 times that of the traditional marketing strategy. This has a very good effect on the marketing of online music products.


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## Body

## 1. Introduction

With the development of science and technology and the progress of society, the Internet has been widely popularized. Marketing methods based on online platforms are also rapidly emerging, and the marketing environment of major industries is facing unprecedented changes. According to statistics, the number of Internet users is increasing year by year in the form of double-speed development, and so far the number of Internet users has reached 210 million. Network marketing is occupying an increasingly important position in the overall marketing strategy of enterprises.The rapid improvement of the level of economic development and the acceleration of the pace of life has caused people’s consumption structure to shift from the material level to the spiritual level. This is particularly reflected in the traditional music industry in the tertiary industry. Music creators are no longer limited by political, commercial, and other conditions and can freely develop their own musical ideas; different ideas can be communicated instantly and effectively between musicians and listeners from different countries and regions. Collision will enrich the language of music; the inclusiveness of the Internet makes music creation no longer the patent of a few people, and anyone can publish their own music works online. The marketing model of the traditional music industry market is relatively single and fixed, there are individual differences in the entire consumer group, and the needs and orientations of music are also different, so the traditional music industry market is increasingly unable to meet the diverse music consumption needs of consumers. In order to meet the music needs of consumers, diversified and rich music products have appeared in the online music market. Under the brand-new multimedia environment, how does the music industry integrate the internal and external development situation and the long-term development strategy of the industry itself? It is necessary to accurately position itself to make up for the shortcomings of the traditional music marketing model. Its improved service level allows consumers to enjoy more personalized services while achieving profitability. In addition, how to formulate appropriate sales and marketing strategies for these music products has become a problem that the entire music market must think about.At present, many scholars have conducted research on how to formulate reasonable music marketing. Barretta thought deeply about the American music market. By analyzing the archives of the Performing Arts Department of the New York Public Library, he found that culture and economy have promoted the further development of the music market. His research means that the American music market has the opportunity to achieve diversified development, and focusing on the symbolic use of music will give marketers the opportunity to allow consumers to accept diversity. He hopes to reverse the trajectory of music color lines and promote economic gains without harming cultural interests. His research considers skin color boundaries in the American music market more deeply than previous literature. The factors he considered include a variety of forces, and he has realized the transition from paying attention to profit to paying attention to culture [1]. Park conducted exploratory research on multimedia applications in popular music. On the basis of multimedia narrative strategies, popular music cases and content were analyzed, and new popular music marketing strategies were created. It was believed that the current music industry is reorganizing around the source of digital music. With the rapid changes in the era of smart media, a variety of business models have emerged. Therefore, in the multimedia narrative strategy that has become a hot spot in the music industry, the market expansion of the core content of the artist’s music system needs to be focused on [2]. Bengtsson L R believed that in the past decade, the music industry adjusted its marketing strategy. Marketing is now mainly based on mobile, modern, platform-based cross-media strategies. For researchers in contemporary culture, the diversity of propaganda activities has brought a lot of methodological challenges. Inspired by numbers and innovative methods and under the guidance of the Association of Internet Researchers (AOIR) Code of Ethics, two data collection strategies were developed: reverse engineering and real-time capture. Moreover, two analysis methods were applied, namely, visual mapping and time layering. Two studies of contemporary music marketing activities were used as illustrative cases to introduce and discuss such methods [3]. Saragih H learned about the applicability of cocreation in music marketing by studying previous literature. He believes that music is a communication channel for cultural products, and the cocreation platform used is helpful to the marketing of music products and the development of the entire music industry. Through his research, he found that music practitioners are also music marketers in a sense. He also found that before performing joint music creation activities, it is necessary to plan and understand the key points, channels, and platforms of the marketing strategy [4]. Panwar S proposed a machine learning music perception model to recognize the emotional content of a given audio stream. This model can determine the emotional state of the area of use, and can be also used in music marketing. A radio-induced emotion data set (RIED) was compiled based on songs played on the radio. A linear regression model was used to map the acoustic features of music to the corresponding arousal and compound emotion indexes to perform this emotion recognition task. Then, it was used as the input of the perceptual model to observe the emotional tendency of regional music [5]. Aguiar L used the growth of streaming media usage to measure its impact on the free consumption of music and the marketing of recorded music. Streaming media music services have been very popular in the past few years, which have triggered people to think about the factors affecting their marketing revenue to varying degrees. It was believed that streaming media allows sellers to participate in bundling sales and promises to increase revenue and profits, and consumer surplus will indeed increase the overall revenue of music marketing [6]. Many scholars have provided a lot of valuable research on the analysis of the marketing strategy of music products. However, relatively speaking, the academic circle’s discussion on the marketing strategy of online music products is a weak link in academic research.This article attempts to analyze and study the marketing strategies of online music products under the background of the multimedia environment. Based on the way of online consumption, analysis, summarization, and sorting are the new features of online music product marketing. This is a reliable way to explore the success of marketing in the music industry. Looking for the advantages of marketing in the Internet era, we find that it provides suggestions with certain reference value for the further development of the music industry.


## 2. Marketing Strategy of Online Music Products

### 2.1. Overview of Online Music

#### 2.1.1. Network Music Concept

Online music is an emerging music industry reformed and upgraded on the basis of the development of the times and the popularization of Internet technology in the traditional music industry [7]. It generally exists in the form of digitization and relies on the continuous development of communication technology and computer technology [8]. The concept of digitalization is divided into narrow digitalization and broad digitalization. Digitization in a narrow sense mainly refers to the use of digital technology to digitally transform specific businesses and scenarios and pay more attention to the cost reduction and efficiency enhancement of digital technology itself. Digitization in a broad sense refers to the use of digital technology to systematically and holistically change the business models and operation methods of various organizations such as enterprises and governments. The main types of music products include popular songs, traditional music, and MV with video images. Simply put, it is a music product that uses wireless or wired music as a medium and modern science and technology as a means to digitally process, create, disseminate, and sell it [9]. Unlike the traditional music industry that requires certain material carriers, online music products do not need to rely on actual material carriers for their spread and sale, which are done through digital means. To a certain extent, traditional music is equivalent to national music. From ancient times to the present, it has been widely spread as opera and quyi. In the traditional period, the transmission method of music was relatively ancient and single, mostly oral transmission and recording in written form and musical notation became the mainstream means of communication at that time. The main body of online music is divided into online music and mobile music. In online music, users use a computer or mobile client to listen to and download music online through the Internet or mobile Internet, and mobile music is the music that has been downloaded and saved on the mobile phone. The details are shown in Figure 1.Figure 1
Network music main picture.


#### 2.1.2. Features of Online Music

(1) Repeatability. Once an online music product is released, it will spread quickly on major online music platforms. It is not limited by time and space. As long as the users pay, they can listen to the music to feel the charm, and enjoy the rights of unlimited playback and download. It will be continuously shared and commented on by a large number of users even on social networking platforms.(2) Rapidity. With the continuous maturity of network communication technology, the speed of information dissemination has achieved unprecedented leaps. Online music has exerted its greatest spread effect and speed on the Internet by virtue of its advanced communication technology. Music product information can be spread around the world within a few seconds and be listened to and downloaded by users. This is also an absolute advantage that traditional music dissemination does not have. Online music is mainly composed of two parts: one is Internet online music that can be downloaded or played on computer terminals through the telecommunication Internet; the other is music that can only be played online but cannot be downloaded [10].(3) Openness. The openness of online music is mainly based on the background of the Internet. The network environment is a public environment, and users can express their opinions and suggestions in the network environment. The same is true for music; users can find their own satisfactory music types or products in music services. They make personal comments on music works or instantly share them on social platforms to meet their own individual needs. The time and type of listening are also unlimited, and users can listen to their favorite songs as long as they operate their fingers on the terminal. The music type can be changed anytime and anywhere according to their own preferences, which is very different from traditional music [11]. Traditional music can only be changed through manual searches or more complex means.(4) Diversity. The diversity of online music is mainly manifested in the diversity of music forms, styles, and types. Under the guidance of a professional music production company, singers compose songs suitable for their own musical style. This kind of song can be original, improvised, or cover song. The music style and type can be classical music, rock music, or lyrical music [12].


### 2.2. Development Status of Online Music

According to surveys and studies, so far, the number of users of online music among netizens ranks first. The number of users has grown from 650 million to 770 million, a growth rate of 5.5%, which is about two points higher than the overall growth rate of netizens. Online music users account for 70% of the total number of Internet users, and the market size of online music has reached more than 10 billion [13]. The sales of online music products mainly rely on music platforms for offline marketing and online sales. The music platform mainly includes six categories, namely, QQ Music, NetEase Cloud, Kugou Music, Kuwo Music, Migu Music, and QianQian Music, and its business development is shown in Figure 2.Figure 2
The development of online music business.With the popularization of the Internet and the development of the network economy, online music has achieved rapid development. The business volume and user growth of the six platforms are shown in Figure3. As an important means of channel introduction of users, online music has been continuously increasing in industrial value, attracting many traditional music companies. Internet companies are vying to enter the online music market, and numerous online music marketing companies and platforms have emerged [14]. According to expert predictions, the future development model of the online music market will form diversified integration and multiterminal cooperation. The fan economy will become an important support for the profitability of online music, and the competition for copyright of music works will promote the gradual legalization and enrichment of music content. With a large number of users and high activity, online music will still be an important user introduction channel for a long period of time. Nowadays, the amount of music available for users to listen to and download on the Internet is still increasing rapidly. Through the network platform, ordinary people can listen to the latest released songs in the first time. It is not difficult to see that Chinese online music is developing rapidly, the situation is good, and the prospects are very impressive.Figure 3
Business volume and user growth.


### 2.3. Online Music Marketing Strategy

The gradual growth of Internet users and the rise of the emerging music industry have continuously promoted and developed online music marketing. The marketing environment of the online music industry market has also undergone tremendous changes, and the competition for market share in the online music market has become increasingly fierce. If online music platform service providers want to achieve the sustainable development of the music industry, they must innovate existing marketing models. For example, the platform needs to think and develop more from the perspective of consumers and obtain more music copyrights and clearer quality. This kind of innovation is not only limited to product and service updates in the traditional sense or to meeting consumer needs. It should also include other kinds of activities, as well as resource sharing between enterprises [15]. Therefore, online music marketing means that music companies use online music as the medium and music products as the carrier to carry out creative marketing activities around the music platform. This makes music companies more efficient in marketing and consumer satisfaction and loyalty. The marketing model is shown in Figure 4.Figure 4
Online music product marketing model.First of all, compared with the traditional music marketing model, the online music marketing model appears to have more advantages. Specific performance such as online music marketing is not restricted by time, place, and personnel. It can trade with people who want to buy products around the world 24 hours a day. Secondly, online music marketing can analyze users’ consumption behavior and psychological expectations according to users’ differentiated needs and to provide consumers with the opportunity to directly experience products and other services. It implements differentiated strategies and personalized strategies to develop users into loyal consumers under its umbrella. It has mastered the marketing skills of the Internet economy era and perfected the Internet music marketing plan. It also focuses on music brand building, continuously expanding brand awareness and influence, and increasing product cultural connotation. The specific marketing strategies are mainly as follows.


#### 2.3.1. Marketing Strategy Based on Big Data Mining

In the new media era, massive amounts of data and information are generated every day. How to find the information that best matches the target user in the massive data, so as to push the music service with the highest relevance to the user, is the challenge faced by major online music platforms. Big data mining technology can just solve such problems [16].Data mining is a method of extracting useful knowledge and information from a large amount of noisy and incomplete data. It can be understood that the data must be real and huge, the content under investigation is useful knowledge for users, and mining knowledge must be useful, understandable, and applicable.At present, there are 5 most common big data mining and analysis algorithms, as shown in Table1.Table 1
Big data mining analysis algorithm.
AppellationThe main purposeExpectation–maximization (EM) algorithmFinding the most likely parameter estimatesFast Fourier transformDigital signal processingBranch and bound algorithmLooking for a specific optimal solutionCluster searchOptimizing the best first search algorithmClustering ensemble algorithmFinding data clustersThis article mainly introduces the clustering ensemble algorithm. It mainly uses a special form to divide all group sample data into small parts with the same number. Each of these small parts is called a class in the algorithm clustering process. The most important thing in the clustering algorithm is the distance measurement function of the group sample data and its similarity measurement. For thei-th sample, xi=xi1,xi2,…,xid of the set of n data samples; for the j-th sample, xj=xj1,xj2,…,xjd, xj=xj1,xj2,…,xjd; d is the number of their attributes [17]. The distance Distxi,xj between samples is generally used to measure the similarity between samples. The larger the Distxi,xj, the smaller the similarity between samples i and j, and the smaller the Distxi,xj, the greater the similarity between samples i, i and j, j. Common methods for calculating Distxi,xj are roughly divided into the following categories [18]:(1)
Euclidean distance(1)Distxi,xj=xi1−xj12+xi2−xj22+⋯+xid−xjd2.(2)
Manhattan distance(2)Distxi,xj=xi1−xj1+xi2−xj2+⋯+xid−xjd.(3)
Minkowski distance(3)Distxi,xj=xi1−xj1h+xi2−xj2h+⋯+xid−xjdh1/h.(4)
The angle cosine distance(4)Distxi,xj=1−simxi,xj=1−∑k=1dxik⋅xjk∑k=1dxik2⋅∑k=1dxjk2.In the current research field of big data mining, clustering ensemble algorithms have attracted the attention and focus of a large number of scholars. When researching the algorithm, most of the main research directions are in the consensus function of the algorithm. The effective formula function is designed to maximize the clustering value of the sample data without affecting the inherent characteristics of the sample data. It then combines the components of the cluster that have been passed in through the mapping function to form a new sample collection and passes it out again. The general design method is shown in Figure5.Figure 5
Common research methods of consensus function.(1) Cojoining Matrix Method. The cojoining matrix method uses a matrix with a scale of n×n to characterize all cluster components (n represents the number of samples in the data set). The frequency of each component of the matrix in n×n belongs to the same cluster. This is also equivalent to the similarity measurement matrix of the original sample group data, and each part in it represents the similarity strength of each sample data set. The matrix expression formulas are as follows [19]:(5)Co−associationxi,xj=1H∑h=1Hδπhxi,πhxj,(6)δx,y=0,otherwise,1,ifx=y.(2) Mutual Information Method. Mutual information is called a measure of the degree of association between two different sets of events in the methods of probability theory and mathematical statistics. In the clustering ensemble algorithm, mutual information can also be defined as parameters that are used to detect specific information in different distributions and are symmetrical to each other. Assuming that they represent two cluster members, the normalized mutual information of these two cluster members is defined as follows [20]:(7)NMIπa,πb=∑h=1ka∑l=1kbnh,llogn⋅nh,l/nhanlb∑h=1kanhalognha/n∑l=1kbnlblognlb/n.The meaning of each parameter of formula (7) is shown in Table 2.Table 2
Formula parameter meaning.
ParameterMeaningkaNumber of clusters ofπakbNumber of clusters ofπbnhaThe number of samples belonging to classCha in cluster member πanlbThe number of samples belonging to classClb in cluster member πbnh,lThe number of samples belonging to classCha in cluster member πa and class Clb in cluster member πb at the same timeNMIπa,πbThe degree of shared information in the two clustering resultsThe design goal of the consensus function based on the mutual information method is to find a cluster partition, and the mutual information between the cluster partition and all cluster members is the largest. The definition of the target cluster can be expressed as follows [21]:(8)πopt=argmaxπ∑h=1HNMIπ,πh.(3) Hypergraph. The clustering ensemble algorithm based on hypergraph partition is mainly divided into CSPA, HGPA, and MCLA. The definition similarity matrix of CSPA is as follows:(9)S=1hHHT,where S( ) is the matrix in the cluster ensemble algorithm. Self-organizing feature map is a neural network model commonly used in cluster analysis. The network topology is shown in Figure 6.Figure 6
Self-organizing feature map network topology.The SOM algorithm needs to calculate the distance between the current input vector and the neurons in the competition layer during the formation of the self-organizing map and select the neuron with the closest distance as the winning neuronqt:(10)qt=argminjxkt−wjt.The adjustment of the weight vector of the winning neuron and the neuron in its neighborhood is as follows [22]:(11)wjt+1=wjt+ηtxkt−wjtj∈Nqt,wjtj∉Nqt,where η is the learning rate parameter, 0<η<1, and Nqt is the current topological neighborhood radius of the winning neuron q. They all belong to the distance calculation of the SOM algorithm.There are several common functional forms of learning rateη [23]:(1)
Linear function(12)ηt=η01−tT.(2)
Inverse function(13)ηt=η0t.(3)
Power function(14)ηt=η0e−1/T.Among them,η is generally a larger positive number less than 1, and T is the total number of iterations of the algorithm.The size of the topological neighborhood radiusNqt also decreases with the increase of time t, and its general function form is as follows [24]:(15)Nqt=Nq0e−t/t1.The initial domain radiusNq0 is generally the network width, and t1 is the time constant, which is generally taken as follows [25]:(16)t1=TlogNq0.Cluster comprehensive qualityOcq is a cluster relative evaluation method that combines cluster density and cluster separation. The index of Ocq is defined as follows:(17)Ocqβ=1−β⋅Cmp+1−β⋅Sep,where Cmp means clustering density, Sep means cluster separation, and β means balance coefficient.For a given data setX=x1,x2,…,xN, its intracluster variance is defined as follows [26]:(18)Devx=1N∑i=1Nd2xi,x¯,where N is the number of samples in the data set X, X; dxi,x¯ represents the distance between xi and x¯, x¯; and x¯ is the mean of the data set X, namely,(19)x¯=1N∑i=1Nxi.The smaller the varianceDevx within the cluster, the denser the internal distribution structure of the entire data set, and so the higher the sample identity.If the clustering result divides the data set intoC=c1,c2,…,cc, the clustering density is defined as follows [27]:(20)Cmp=1C∑i=1CDevciDevX.Among them,C is the number of clusters, and Devci is the variance of class ci [28]. The cluster density is chosen because it can better reflect the structure of the data set within the cluster.The definition of cluster separation is as follows:(21)Sep=1CC−1∑i=1C∑j=1,j≠1Cexp−d2xci,xcj2δ2.Among them,δ is the Gaussian constant. For the convenience of calculation, usually 2δ2=xci, 2, and xcj are the cluster centers of the classes ci and classes cj. According to the principle that the clustering results should ensure the minimum similarity between clusters, in order to separate the data between clusters as much as possible, the cluster separation should be as small as possible. From the definitions of Cmp and Sep, we can know that the larger the comprehensive quality value of clustering Ocq is, the better the clustering effect is.This clustering algorithm can help online music platforms accurately analyze user information big data. It establishes the direction for the formulation of marketing strategies for online music products and lays the foundation for the precise marketing of products.


#### 2.3.2. Marketing Strategy Based on New Media Platform

In an era where new media technology shines, various online media platforms have emerged, and platform users can subscribe to information services according to their own interests. To a certain extent, this has also revolutionized the online music marketing model. This model does not have the limitations of time and space. After the user logs in to the media platform, the platform server will contact the user in the form of a friend and send information to the user. According to the individual differences of users, it pushes online music products related to their interests, so as to achieve point-to-point precision marketing. There are several specific methods: One is a new media platform represented by Weibo. It attracts target users by publishing microblogs of music products. Users have implicitly accepted the promotion of online music information when they read Weibo. Once the value of the music product meets the user’s expectations and needs, the user will share, comment on, and forward his own. This is also a form of promotion by others at no cost. The second is a new media platform represented by WeChat and QQ. The music platform can promptly push music product information to subscribed users in the form of service accounts or official accounts on QQ and WeChat for the purpose of marketing music products. This method is roughly the same as the marketing method on Weibo, and both can promote the dissemination of online music information and achieve precise marketing of music products.


#### 2.3.3. Marketing Strategy Based on O2O Model

Online-to-offline (O2O) mode refers to the combination of offline business opportunities with the Internet, making the Internet a platform for offline transactions. Through online and offline information and experience links, offline consumers avoid price deception due to information asymmetry. It also realizes the “presales experience” of online consumers, which is different from the traditional business model. It is shown in Table3.Table 3
The difference between O2O business model and traditional business model.
Traditional business modelO2O business modelOffline marketingOnline and offline marketingRelying on physical storefrontNot relying solely on physicalstoresRealistic goods salesRealistic field serviceNo technology dependenceMobile Internet technologyPhysical storeOnline trading platformHigh costModerate costThe marketing model of online music can not only be limited to the marketing of the network platform, but also completely realize multidimensional and diversified marketing. The O2O model can provide multidimensional perspectives for online music products and carry out multilevel classification according to the individual needs of consumers. It fully integrates traditional media and new media and utilizes the sensory information of audience users. Moreover, by carrying out extensive and accurate marketing coverage, it can create multichannel, diversified, multiangle, multilevel, and all-round three-dimensional marketing for online music products.


### 2.4. Network Traffic Monitoring

What is traffic monitoring? As we all know, network communication is done through data packets, and all information is contained in network communication data packets. Two computers “communicate” over a network by sending and receiving packets. The so-called traffic monitoring is actually to manage and control these network communication packets and to optimize and limit them at the same time. The purpose of flow monitoring is to allow and ensure the efficient transmission of useful data packets, and prohibit or restrict the transmission of illegal data packets. One guarantee and one limit are the essence of flow monitoring.Traffic monitoring refers to the monitoring of data flow, usually including the speed of outgoing data, incoming data, and total traffic.Traffic monitoring sometimes also generally refers to monitoring and filtering the user’s data traffic and effectively grasping the bad information within the monitoring scope. It is a professional term commonly used in network security.


## 2.1. Overview of Online Music

### 2.1.1. Network Music Concept

Online music is an emerging music industry reformed and upgraded on the basis of the development of the times and the popularization of Internet technology in the traditional music industry [7]. It generally exists in the form of digitization and relies on the continuous development of communication technology and computer technology [8]. The concept of digitalization is divided into narrow digitalization and broad digitalization. Digitization in a narrow sense mainly refers to the use of digital technology to digitally transform specific businesses and scenarios and pay more attention to the cost reduction and efficiency enhancement of digital technology itself. Digitization in a broad sense refers to the use of digital technology to systematically and holistically change the business models and operation methods of various organizations such as enterprises and governments. The main types of music products include popular songs, traditional music, and MV with video images. Simply put, it is a music product that uses wireless or wired music as a medium and modern science and technology as a means to digitally process, create, disseminate, and sell it [9]. Unlike the traditional music industry that requires certain material carriers, online music products do not need to rely on actual material carriers for their spread and sale, which are done through digital means. To a certain extent, traditional music is equivalent to national music. From ancient times to the present, it has been widely spread as opera and quyi. In the traditional period, the transmission method of music was relatively ancient and single, mostly oral transmission and recording in written form and musical notation became the mainstream means of communication at that time. The main body of online music is divided into online music and mobile music. In online music, users use a computer or mobile client to listen to and download music online through the Internet or mobile Internet, and mobile music is the music that has been downloaded and saved on the mobile phone. The details are shown in Figure 1.Figure 1
Network music main picture.


### 2.1.2. Features of Online Music

(1) Repeatability. Once an online music product is released, it will spread quickly on major online music platforms. It is not limited by time and space. As long as the users pay, they can listen to the music to feel the charm, and enjoy the rights of unlimited playback and download. It will be continuously shared and commented on by a large number of users even on social networking platforms.(2) Rapidity. With the continuous maturity of network communication technology, the speed of information dissemination has achieved unprecedented leaps. Online music has exerted its greatest spread effect and speed on the Internet by virtue of its advanced communication technology. Music product information can be spread around the world within a few seconds and be listened to and downloaded by users. This is also an absolute advantage that traditional music dissemination does not have. Online music is mainly composed of two parts: one is Internet online music that can be downloaded or played on computer terminals through the telecommunication Internet; the other is music that can only be played online but cannot be downloaded [10].(3) Openness. The openness of online music is mainly based on the background of the Internet. The network environment is a public environment, and users can express their opinions and suggestions in the network environment. The same is true for music; users can find their own satisfactory music types or products in music services. They make personal comments on music works or instantly share them on social platforms to meet their own individual needs. The time and type of listening are also unlimited, and users can listen to their favorite songs as long as they operate their fingers on the terminal. The music type can be changed anytime and anywhere according to their own preferences, which is very different from traditional music [11]. Traditional music can only be changed through manual searches or more complex means.(4) Diversity. The diversity of online music is mainly manifested in the diversity of music forms, styles, and types. Under the guidance of a professional music production company, singers compose songs suitable for their own musical style. This kind of song can be original, improvised, or cover song. The music style and type can be classical music, rock music, or lyrical music [12].


## 2.1.1. Network Music Concept

Online music is an emerging music industry reformed and upgraded on the basis of the development of the times and the popularization of Internet technology in the traditional music industry [7]. It generally exists in the form of digitization and relies on the continuous development of communication technology and computer technology [8]. The concept of digitalization is divided into narrow digitalization and broad digitalization. Digitization in a narrow sense mainly refers to the use of digital technology to digitally transform specific businesses and scenarios and pay more attention to the cost reduction and efficiency enhancement of digital technology itself. Digitization in a broad sense refers to the use of digital technology to systematically and holistically change the business models and operation methods of various organizations such as enterprises and governments. The main types of music products include popular songs, traditional music, and MV with video images. Simply put, it is a music product that uses wireless or wired music as a medium and modern science and technology as a means to digitally process, create, disseminate, and sell it [9]. Unlike the traditional music industry that requires certain material carriers, online music products do not need to rely on actual material carriers for their spread and sale, which are done through digital means. To a certain extent, traditional music is equivalent to national music. From ancient times to the present, it has been widely spread as opera and quyi. In the traditional period, the transmission method of music was relatively ancient and single, mostly oral transmission and recording in written form and musical notation became the mainstream means of communication at that time. The main body of online music is divided into online music and mobile music. In online music, users use a computer or mobile client to listen to and download music online through the Internet or mobile Internet, and mobile music is the music that has been downloaded and saved on the mobile phone. The details are shown in Figure 1.Figure 1
Network music main picture.


## 2.1.2. Features of Online Music

(1) Repeatability. Once an online music product is released, it will spread quickly on major online music platforms. It is not limited by time and space. As long as the users pay, they can listen to the music to feel the charm, and enjoy the rights of unlimited playback and download. It will be continuously shared and commented on by a large number of users even on social networking platforms.(2) Rapidity. With the continuous maturity of network communication technology, the speed of information dissemination has achieved unprecedented leaps. Online music has exerted its greatest spread effect and speed on the Internet by virtue of its advanced communication technology. Music product information can be spread around the world within a few seconds and be listened to and downloaded by users. This is also an absolute advantage that traditional music dissemination does not have. Online music is mainly composed of two parts: one is Internet online music that can be downloaded or played on computer terminals through the telecommunication Internet; the other is music that can only be played online but cannot be downloaded [10].(3) Openness. The openness of online music is mainly based on the background of the Internet. The network environment is a public environment, and users can express their opinions and suggestions in the network environment. The same is true for music; users can find their own satisfactory music types or products in music services. They make personal comments on music works or instantly share them on social platforms to meet their own individual needs. The time and type of listening are also unlimited, and users can listen to their favorite songs as long as they operate their fingers on the terminal. The music type can be changed anytime and anywhere according to their own preferences, which is very different from traditional music [11]. Traditional music can only be changed through manual searches or more complex means.(4) Diversity. The diversity of online music is mainly manifested in the diversity of music forms, styles, and types. Under the guidance of a professional music production company, singers compose songs suitable for their own musical style. This kind of song can be original, improvised, or cover song. The music style and type can be classical music, rock music, or lyrical music [12].


## 2.2. Development Status of Online Music

According to surveys and studies, so far, the number of users of online music among netizens ranks first. The number of users has grown from 650 million to 770 million, a growth rate of 5.5%, which is about two points higher than the overall growth rate of netizens. Online music users account for 70% of the total number of Internet users, and the market size of online music has reached more than 10 billion [13]. The sales of online music products mainly rely on music platforms for offline marketing and online sales. The music platform mainly includes six categories, namely, QQ Music, NetEase Cloud, Kugou Music, Kuwo Music, Migu Music, and QianQian Music, and its business development is shown in Figure 2.Figure 2
The development of online music business.With the popularization of the Internet and the development of the network economy, online music has achieved rapid development. The business volume and user growth of the six platforms are shown in Figure3. As an important means of channel introduction of users, online music has been continuously increasing in industrial value, attracting many traditional music companies. Internet companies are vying to enter the online music market, and numerous online music marketing companies and platforms have emerged [14]. According to expert predictions, the future development model of the online music market will form diversified integration and multiterminal cooperation. The fan economy will become an important support for the profitability of online music, and the competition for copyright of music works will promote the gradual legalization and enrichment of music content. With a large number of users and high activity, online music will still be an important user introduction channel for a long period of time. Nowadays, the amount of music available for users to listen to and download on the Internet is still increasing rapidly. Through the network platform, ordinary people can listen to the latest released songs in the first time. It is not difficult to see that Chinese online music is developing rapidly, the situation is good, and the prospects are very impressive.Figure 3
Business volume and user growth.


## 2.3. Online Music Marketing Strategy

The gradual growth of Internet users and the rise of the emerging music industry have continuously promoted and developed online music marketing. The marketing environment of the online music industry market has also undergone tremendous changes, and the competition for market share in the online music market has become increasingly fierce. If online music platform service providers want to achieve the sustainable development of the music industry, they must innovate existing marketing models. For example, the platform needs to think and develop more from the perspective of consumers and obtain more music copyrights and clearer quality. This kind of innovation is not only limited to product and service updates in the traditional sense or to meeting consumer needs. It should also include other kinds of activities, as well as resource sharing between enterprises [15]. Therefore, online music marketing means that music companies use online music as the medium and music products as the carrier to carry out creative marketing activities around the music platform. This makes music companies more efficient in marketing and consumer satisfaction and loyalty. The marketing model is shown in Figure 4.Figure 4
Online music product marketing model.First of all, compared with the traditional music marketing model, the online music marketing model appears to have more advantages. Specific performance such as online music marketing is not restricted by time, place, and personnel. It can trade with people who want to buy products around the world 24 hours a day. Secondly, online music marketing can analyze users’ consumption behavior and psychological expectations according to users’ differentiated needs and to provide consumers with the opportunity to directly experience products and other services. It implements differentiated strategies and personalized strategies to develop users into loyal consumers under its umbrella. It has mastered the marketing skills of the Internet economy era and perfected the Internet music marketing plan. It also focuses on music brand building, continuously expanding brand awareness and influence, and increasing product cultural connotation. The specific marketing strategies are mainly as follows.


### 2.3.1. Marketing Strategy Based on Big Data Mining

In the new media era, massive amounts of data and information are generated every day. How to find the information that best matches the target user in the massive data, so as to push the music service with the highest relevance to the user, is the challenge faced by major online music platforms. Big data mining technology can just solve such problems [16].Data mining is a method of extracting useful knowledge and information from a large amount of noisy and incomplete data. It can be understood that the data must be real and huge, the content under investigation is useful knowledge for users, and mining knowledge must be useful, understandable, and applicable.At present, there are 5 most common big data mining and analysis algorithms, as shown in Table1.Table 1
Big data mining analysis algorithm.
AppellationThe main purposeExpectation–maximization (EM) algorithmFinding the most likely parameter estimatesFast Fourier transformDigital signal processingBranch and bound algorithmLooking for a specific optimal solutionCluster searchOptimizing the best first search algorithmClustering ensemble algorithmFinding data clustersThis article mainly introduces the clustering ensemble algorithm. It mainly uses a special form to divide all group sample data into small parts with the same number. Each of these small parts is called a class in the algorithm clustering process. The most important thing in the clustering algorithm is the distance measurement function of the group sample data and its similarity measurement. For thei-th sample, xi=xi1,xi2,…,xid of the set of n data samples; for the j-th sample, xj=xj1,xj2,…,xjd, xj=xj1,xj2,…,xjd; d is the number of their attributes [17]. The distance Distxi,xj between samples is generally used to measure the similarity between samples. The larger the Distxi,xj, the smaller the similarity between samples i and j, and the smaller the Distxi,xj, the greater the similarity between samples i, i and j, j. Common methods for calculating Distxi,xj are roughly divided into the following categories [18]:(1)
Euclidean distance(1)Distxi,xj=xi1−xj12+xi2−xj22+⋯+xid−xjd2.(2)
Manhattan distance(2)Distxi,xj=xi1−xj1+xi2−xj2+⋯+xid−xjd.(3)
Minkowski distance(3)Distxi,xj=xi1−xj1h+xi2−xj2h+⋯+xid−xjdh1/h.(4)
The angle cosine distance(4)Distxi,xj=1−simxi,xj=1−∑k=1dxik⋅xjk∑k=1dxik2⋅∑k=1dxjk2.In the current research field of big data mining, clustering ensemble algorithms have attracted the attention and focus of a large number of scholars. When researching the algorithm, most of the main research directions are in the consensus function of the algorithm. The effective formula function is designed to maximize the clustering value of the sample data without affecting the inherent characteristics of the sample data. It then combines the components of the cluster that have been passed in through the mapping function to form a new sample collection and passes it out again. The general design method is shown in Figure5.Figure 5
Common research methods of consensus function.(1) Cojoining Matrix Method. The cojoining matrix method uses a matrix with a scale of n×n to characterize all cluster components (n represents the number of samples in the data set). The frequency of each component of the matrix in n×n belongs to the same cluster. This is also equivalent to the similarity measurement matrix of the original sample group data, and each part in it represents the similarity strength of each sample data set. The matrix expression formulas are as follows [19]:(5)Co−associationxi,xj=1H∑h=1Hδπhxi,πhxj,(6)δx,y=0,otherwise,1,ifx=y.(2) Mutual Information Method. Mutual information is called a measure of the degree of association between two different sets of events in the methods of probability theory and mathematical statistics. In the clustering ensemble algorithm, mutual information can also be defined as parameters that are used to detect specific information in different distributions and are symmetrical to each other. Assuming that they represent two cluster members, the normalized mutual information of these two cluster members is defined as follows [20]:(7)NMIπa,πb=∑h=1ka∑l=1kbnh,llogn⋅nh,l/nhanlb∑h=1kanhalognha/n∑l=1kbnlblognlb/n.The meaning of each parameter of formula (7) is shown in Table 2.Table 2
Formula parameter meaning.
ParameterMeaningkaNumber of clusters ofπakbNumber of clusters ofπbnhaThe number of samples belonging to classCha in cluster member πanlbThe number of samples belonging to classClb in cluster member πbnh,lThe number of samples belonging to classCha in cluster member πa and class Clb in cluster member πb at the same timeNMIπa,πbThe degree of shared information in the two clustering resultsThe design goal of the consensus function based on the mutual information method is to find a cluster partition, and the mutual information between the cluster partition and all cluster members is the largest. The definition of the target cluster can be expressed as follows [21]:(8)πopt=argmaxπ∑h=1HNMIπ,πh.(3) Hypergraph. The clustering ensemble algorithm based on hypergraph partition is mainly divided into CSPA, HGPA, and MCLA. The definition similarity matrix of CSPA is as follows:(9)S=1hHHT,where S( ) is the matrix in the cluster ensemble algorithm. Self-organizing feature map is a neural network model commonly used in cluster analysis. The network topology is shown in Figure 6.Figure 6
Self-organizing feature map network topology.The SOM algorithm needs to calculate the distance between the current input vector and the neurons in the competition layer during the formation of the self-organizing map and select the neuron with the closest distance as the winning neuronqt:(10)qt=argminjxkt−wjt.The adjustment of the weight vector of the winning neuron and the neuron in its neighborhood is as follows [22]:(11)wjt+1=wjt+ηtxkt−wjtj∈Nqt,wjtj∉Nqt,where η is the learning rate parameter, 0<η<1, and Nqt is the current topological neighborhood radius of the winning neuron q. They all belong to the distance calculation of the SOM algorithm.There are several common functional forms of learning rateη [23]:(1)
Linear function(12)ηt=η01−tT.(2)
Inverse function(13)ηt=η0t.(3)
Power function(14)ηt=η0e−1/T.Among them,η is generally a larger positive number less than 1, and T is the total number of iterations of the algorithm.The size of the topological neighborhood radiusNqt also decreases with the increase of time t, and its general function form is as follows [24]:(15)Nqt=Nq0e−t/t1.The initial domain radiusNq0 is generally the network width, and t1 is the time constant, which is generally taken as follows [25]:(16)t1=TlogNq0.Cluster comprehensive qualityOcq is a cluster relative evaluation method that combines cluster density and cluster separation. The index of Ocq is defined as follows:(17)Ocqβ=1−β⋅Cmp+1−β⋅Sep,where Cmp means clustering density, Sep means cluster separation, and β means balance coefficient.For a given data setX=x1,x2,…,xN, its intracluster variance is defined as follows [26]:(18)Devx=1N∑i=1Nd2xi,x¯,where N is the number of samples in the data set X, X; dxi,x¯ represents the distance between xi and x¯, x¯; and x¯ is the mean of the data set X, namely,(19)x¯=1N∑i=1Nxi.The smaller the varianceDevx within the cluster, the denser the internal distribution structure of the entire data set, and so the higher the sample identity.If the clustering result divides the data set intoC=c1,c2,…,cc, the clustering density is defined as follows [27]:(20)Cmp=1C∑i=1CDevciDevX.Among them,C is the number of clusters, and Devci is the variance of class ci [28]. The cluster density is chosen because it can better reflect the structure of the data set within the cluster.The definition of cluster separation is as follows:(21)Sep=1CC−1∑i=1C∑j=1,j≠1Cexp−d2xci,xcj2δ2.Among them,δ is the Gaussian constant. For the convenience of calculation, usually 2δ2=xci, 2, and xcj are the cluster centers of the classes ci and classes cj. According to the principle that the clustering results should ensure the minimum similarity between clusters, in order to separate the data between clusters as much as possible, the cluster separation should be as small as possible. From the definitions of Cmp and Sep, we can know that the larger the comprehensive quality value of clustering Ocq is, the better the clustering effect is.This clustering algorithm can help online music platforms accurately analyze user information big data. It establishes the direction for the formulation of marketing strategies for online music products and lays the foundation for the precise marketing of products.


### 2.3.2. Marketing Strategy Based on New Media Platform

In an era where new media technology shines, various online media platforms have emerged, and platform users can subscribe to information services according to their own interests. To a certain extent, this has also revolutionized the online music marketing model. This model does not have the limitations of time and space. After the user logs in to the media platform, the platform server will contact the user in the form of a friend and send information to the user. According to the individual differences of users, it pushes online music products related to their interests, so as to achieve point-to-point precision marketing. There are several specific methods: One is a new media platform represented by Weibo. It attracts target users by publishing microblogs of music products. Users have implicitly accepted the promotion of online music information when they read Weibo. Once the value of the music product meets the user’s expectations and needs, the user will share, comment on, and forward his own. This is also a form of promotion by others at no cost. The second is a new media platform represented by WeChat and QQ. The music platform can promptly push music product information to subscribed users in the form of service accounts or official accounts on QQ and WeChat for the purpose of marketing music products. This method is roughly the same as the marketing method on Weibo, and both can promote the dissemination of online music information and achieve precise marketing of music products.


### 2.3.3. Marketing Strategy Based on O2O Model

Online-to-offline (O2O) mode refers to the combination of offline business opportunities with the Internet, making the Internet a platform for offline transactions. Through online and offline information and experience links, offline consumers avoid price deception due to information asymmetry. It also realizes the “presales experience” of online consumers, which is different from the traditional business model. It is shown in Table3.Table 3
The difference between O2O business model and traditional business model.
Traditional business modelO2O business modelOffline marketingOnline and offline marketingRelying on physical storefrontNot relying solely on physicalstoresRealistic goods salesRealistic field serviceNo technology dependenceMobile Internet technologyPhysical storeOnline trading platformHigh costModerate costThe marketing model of online music can not only be limited to the marketing of the network platform, but also completely realize multidimensional and diversified marketing. The O2O model can provide multidimensional perspectives for online music products and carry out multilevel classification according to the individual needs of consumers. It fully integrates traditional media and new media and utilizes the sensory information of audience users. Moreover, by carrying out extensive and accurate marketing coverage, it can create multichannel, diversified, multiangle, multilevel, and all-round three-dimensional marketing for online music products.


## 2.3.1. Marketing Strategy Based on Big Data Mining

In the new media era, massive amounts of data and information are generated every day. How to find the information that best matches the target user in the massive data, so as to push the music service with the highest relevance to the user, is the challenge faced by major online music platforms. Big data mining technology can just solve such problems [16].Data mining is a method of extracting useful knowledge and information from a large amount of noisy and incomplete data. It can be understood that the data must be real and huge, the content under investigation is useful knowledge for users, and mining knowledge must be useful, understandable, and applicable.At present, there are 5 most common big data mining and analysis algorithms, as shown in Table1.Table 1
Big data mining analysis algorithm.
AppellationThe main purposeExpectation–maximization (EM) algorithmFinding the most likely parameter estimatesFast Fourier transformDigital signal processingBranch and bound algorithmLooking for a specific optimal solutionCluster searchOptimizing the best first search algorithmClustering ensemble algorithmFinding data clustersThis article mainly introduces the clustering ensemble algorithm. It mainly uses a special form to divide all group sample data into small parts with the same number. Each of these small parts is called a class in the algorithm clustering process. The most important thing in the clustering algorithm is the distance measurement function of the group sample data and its similarity measurement. For thei-th sample, xi=xi1,xi2,…,xid of the set of n data samples; for the j-th sample, xj=xj1,xj2,…,xjd, xj=xj1,xj2,…,xjd; d is the number of their attributes [17]. The distance Distxi,xj between samples is generally used to measure the similarity between samples. The larger the Distxi,xj, the smaller the similarity between samples i and j, and the smaller the Distxi,xj, the greater the similarity between samples i, i and j, j. Common methods for calculating Distxi,xj are roughly divided into the following categories [18]:(1)
Euclidean distance(1)Distxi,xj=xi1−xj12+xi2−xj22+⋯+xid−xjd2.(2)
Manhattan distance(2)Distxi,xj=xi1−xj1+xi2−xj2+⋯+xid−xjd.(3)
Minkowski distance(3)Distxi,xj=xi1−xj1h+xi2−xj2h+⋯+xid−xjdh1/h.(4)
The angle cosine distance(4)Distxi,xj=1−simxi,xj=1−∑k=1dxik⋅xjk∑k=1dxik2⋅∑k=1dxjk2.In the current research field of big data mining, clustering ensemble algorithms have attracted the attention and focus of a large number of scholars. When researching the algorithm, most of the main research directions are in the consensus function of the algorithm. The effective formula function is designed to maximize the clustering value of the sample data without affecting the inherent characteristics of the sample data. It then combines the components of the cluster that have been passed in through the mapping function to form a new sample collection and passes it out again. The general design method is shown in Figure5.Figure 5
Common research methods of consensus function.(1) Cojoining Matrix Method. The cojoining matrix method uses a matrix with a scale of n×n to characterize all cluster components (n represents the number of samples in the data set). The frequency of each component of the matrix in n×n belongs to the same cluster. This is also equivalent to the similarity measurement matrix of the original sample group data, and each part in it represents the similarity strength of each sample data set. The matrix expression formulas are as follows [19]:(5)Co−associationxi,xj=1H∑h=1Hδπhxi,πhxj,(6)δx,y=0,otherwise,1,ifx=y.(2) Mutual Information Method. Mutual information is called a measure of the degree of association between two different sets of events in the methods of probability theory and mathematical statistics. In the clustering ensemble algorithm, mutual information can also be defined as parameters that are used to detect specific information in different distributions and are symmetrical to each other. Assuming that they represent two cluster members, the normalized mutual information of these two cluster members is defined as follows [20]:(7)NMIπa,πb=∑h=1ka∑l=1kbnh,llogn⋅nh,l/nhanlb∑h=1kanhalognha/n∑l=1kbnlblognlb/n.The meaning of each parameter of formula (7) is shown in Table 2.Table 2
Formula parameter meaning.
ParameterMeaningkaNumber of clusters ofπakbNumber of clusters ofπbnhaThe number of samples belonging to classCha in cluster member πanlbThe number of samples belonging to classClb in cluster member πbnh,lThe number of samples belonging to classCha in cluster member πa and class Clb in cluster member πb at the same timeNMIπa,πbThe degree of shared information in the two clustering resultsThe design goal of the consensus function based on the mutual information method is to find a cluster partition, and the mutual information between the cluster partition and all cluster members is the largest. The definition of the target cluster can be expressed as follows [21]:(8)πopt=argmaxπ∑h=1HNMIπ,πh.(3) Hypergraph. The clustering ensemble algorithm based on hypergraph partition is mainly divided into CSPA, HGPA, and MCLA. The definition similarity matrix of CSPA is as follows:(9)S=1hHHT,where S( ) is the matrix in the cluster ensemble algorithm. Self-organizing feature map is a neural network model commonly used in cluster analysis. The network topology is shown in Figure 6.Figure 6
Self-organizing feature map network topology.The SOM algorithm needs to calculate the distance between the current input vector and the neurons in the competition layer during the formation of the self-organizing map and select the neuron with the closest distance as the winning neuronqt:(10)qt=argminjxkt−wjt.The adjustment of the weight vector of the winning neuron and the neuron in its neighborhood is as follows [22]:(11)wjt+1=wjt+ηtxkt−wjtj∈Nqt,wjtj∉Nqt,where η is the learning rate parameter, 0<η<1, and Nqt is the current topological neighborhood radius of the winning neuron q. They all belong to the distance calculation of the SOM algorithm.There are several common functional forms of learning rateη [23]:(1)
Linear function(12)ηt=η01−tT.(2)
Inverse function(13)ηt=η0t.(3)
Power function(14)ηt=η0e−1/T.Among them,η is generally a larger positive number less than 1, and T is the total number of iterations of the algorithm.The size of the topological neighborhood radiusNqt also decreases with the increase of time t, and its general function form is as follows [24]:(15)Nqt=Nq0e−t/t1.The initial domain radiusNq0 is generally the network width, and t1 is the time constant, which is generally taken as follows [25]:(16)t1=TlogNq0.Cluster comprehensive qualityOcq is a cluster relative evaluation method that combines cluster density and cluster separation. The index of Ocq is defined as follows:(17)Ocqβ=1−β⋅Cmp+1−β⋅Sep,where Cmp means clustering density, Sep means cluster separation, and β means balance coefficient.For a given data setX=x1,x2,…,xN, its intracluster variance is defined as follows [26]:(18)Devx=1N∑i=1Nd2xi,x¯,where N is the number of samples in the data set X, X; dxi,x¯ represents the distance between xi and x¯, x¯; and x¯ is the mean of the data set X, namely,(19)x¯=1N∑i=1Nxi.The smaller the varianceDevx within the cluster, the denser the internal distribution structure of the entire data set, and so the higher the sample identity.If the clustering result divides the data set intoC=c1,c2,…,cc, the clustering density is defined as follows [27]:(20)Cmp=1C∑i=1CDevciDevX.Among them,C is the number of clusters, and Devci is the variance of class ci [28]. The cluster density is chosen because it can better reflect the structure of the data set within the cluster.The definition of cluster separation is as follows:(21)Sep=1CC−1∑i=1C∑j=1,j≠1Cexp−d2xci,xcj2δ2.Among them,δ is the Gaussian constant. For the convenience of calculation, usually 2δ2=xci, 2, and xcj are the cluster centers of the classes ci and classes cj. According to the principle that the clustering results should ensure the minimum similarity between clusters, in order to separate the data between clusters as much as possible, the cluster separation should be as small as possible. From the definitions of Cmp and Sep, we can know that the larger the comprehensive quality value of clustering Ocq is, the better the clustering effect is.This clustering algorithm can help online music platforms accurately analyze user information big data. It establishes the direction for the formulation of marketing strategies for online music products and lays the foundation for the precise marketing of products.


## 2.3.2. Marketing Strategy Based on New Media Platform

In an era where new media technology shines, various online media platforms have emerged, and platform users can subscribe to information services according to their own interests. To a certain extent, this has also revolutionized the online music marketing model. This model does not have the limitations of time and space. After the user logs in to the media platform, the platform server will contact the user in the form of a friend and send information to the user. According to the individual differences of users, it pushes online music products related to their interests, so as to achieve point-to-point precision marketing. There are several specific methods: One is a new media platform represented by Weibo. It attracts target users by publishing microblogs of music products. Users have implicitly accepted the promotion of online music information when they read Weibo. Once the value of the music product meets the user’s expectations and needs, the user will share, comment on, and forward his own. This is also a form of promotion by others at no cost. The second is a new media platform represented by WeChat and QQ. The music platform can promptly push music product information to subscribed users in the form of service accounts or official accounts on QQ and WeChat for the purpose of marketing music products. This method is roughly the same as the marketing method on Weibo, and both can promote the dissemination of online music information and achieve precise marketing of music products.


## 2.3.3. Marketing Strategy Based on O2O Model

Online-to-offline (O2O) mode refers to the combination of offline business opportunities with the Internet, making the Internet a platform for offline transactions. Through online and offline information and experience links, offline consumers avoid price deception due to information asymmetry. It also realizes the “presales experience” of online consumers, which is different from the traditional business model. It is shown in Table3.Table 3
The difference between O2O business model and traditional business model.
Traditional business modelO2O business modelOffline marketingOnline and offline marketingRelying on physical storefrontNot relying solely on physicalstoresRealistic goods salesRealistic field serviceNo technology dependenceMobile Internet technologyPhysical storeOnline trading platformHigh costModerate costThe marketing model of online music can not only be limited to the marketing of the network platform, but also completely realize multidimensional and diversified marketing. The O2O model can provide multidimensional perspectives for online music products and carry out multilevel classification according to the individual needs of consumers. It fully integrates traditional media and new media and utilizes the sensory information of audience users. Moreover, by carrying out extensive and accurate marketing coverage, it can create multichannel, diversified, multiangle, multilevel, and all-round three-dimensional marketing for online music products.


## 2.4. Network Traffic Monitoring

What is traffic monitoring? As we all know, network communication is done through data packets, and all information is contained in network communication data packets. Two computers “communicate” over a network by sending and receiving packets. The so-called traffic monitoring is actually to manage and control these network communication packets and to optimize and limit them at the same time. The purpose of flow monitoring is to allow and ensure the efficient transmission of useful data packets, and prohibit or restrict the transmission of illegal data packets. One guarantee and one limit are the essence of flow monitoring.Traffic monitoring refers to the monitoring of data flow, usually including the speed of outgoing data, incoming data, and total traffic.Traffic monitoring sometimes also generally refers to monitoring and filtering the user’s data traffic and effectively grasping the bad information within the monitoring scope. It is a professional term commonly used in network security.


## 3. Marketing Testing of Online Music Products in a Multimedia Environment

This article analyzes the characteristics of online music products purchased by users in a multimedia environment and formulates corresponding product marketing strategies. Based on this, this article will verify the effectiveness and practicality of the multimedia environment marketing strategy by comparing it with the traditional marketing strategy.Based on the marketing strategy of big data mining, this section uses the marketing strategy based on the multimedia environment and traditional marketing methods. It separately developed visit records and purchase record tracking tests for 600 users of an online music platform in the age range of 20–40 years. The scheme is shown in Table4.Table 4
Marketing test plan.
Target user group600 users of an online music platform between the ages of 20 and 40Test methodSending online music product promotion information to target usersTesting time8 am12 pm4 pm8 pmAfter sending online music product information and related links to users in different time periods, statistics use users’ return visits and downloads. The statistical results are shown in Figures7 and 8.Figure 7
User visits tracking statistics results. (a). The number of user visits under the marketing strategy of the multimedia environment. (b) User visits under traditional marketing strategies.
(a)(b)Figure 8
User download tracking statistics results. (a) The downloads of users under the marketing strategy of the multimedia environment. (b) User downloads under traditional marketing strategies.
(a)(b)It can be seen from Figure7 that under the experimental base of 600 online music platform users, the total number of cumulative visits to online music products in different time periods under the multimedia environment marketing strategy reached 406, with a return visit rate of about 67.67%. Under the traditional marketing strategy, the total number of user visits is 337, and the return visit rate is about 56.17%.It can be seen from Figure8 that under the marketing strategy of the multimedia environment, 600 users have cumulatively downloaded 238 songs in different time periods, and the song download rate is about 39.67%. Under the traditional marketing strategy, 600 users have downloaded a total of 158 songs in different time periods, and the song download rate is about 26.33%.To verify whether the marketing strategy of online music products is effective or practical, it is also necessary to test the degree of preference and discussion of the product among users. This is because the strategy needs to be considered from the consumer’s point of view. By testing the user’s preference and discussion of the product, the marketing effect can be seen intuitively [29]. This article also selects 600 users of an online music platform between the ages of 20 and 40 and counts their love and discussion of different types of music under the two marketing strategies to verify whether the marketing strategy is practical and effective. The scheme is shown in Table 5.Table 5
Marketing test plan.
Target user group600 users of an online music platform between the ages of 20 and 40Test methodSending online music product promotion information to target usersTest the type of musicPop musicClassical musicRock musicFolk musicIn this paper, the number of favorite songs and the number of comments by users are selected as parameters to characterize the user’s love and discussion. The statistical results are shown in Figures9 and 10.Figure 9
User favorite tracking results. (a) The collection of songs under the marketing strategy of the multimedia environment. (b) The collection of songs under traditional marketing strategies.
(a)(b)Figure 10
Discussing and tracking statistical results. (a) The volume of song comments under the marketing strategy of the multimedia environment. (b) Song comment volume under traditional marketing strategy.
(a)(b)It can be seen from Figure9 that under the marketing strategy of the multimedia environment, the total number of collections of songs of four different styles by 600 users has reached 208, and the collection of songs accounted for about 34.67%. Under the traditional marketing strategy, 600 users have collected 110 songs with different styles in total, and the collection of songs accounted for about 18.33%.It can be seen from Figure10 that, under the multimedia environment marketing strategy, the total number of comments on the four types of songs by 600 users has reached 244, and the comments accounted for about 40.67%. Under the traditional marketing strategy, 600 users had a total of 194 comments on the four types of songs with different styles, and the comments accounted for about 32.33%.


## 4. Discussion

The following conclusions can be drawn from the comparative experimental data of multimedia environment marketing strategy and traditional marketing strategy.(1)
In the case of consistent experimental conditions, adopt the online network music product marketing strategy developed in the context of the multimedia environment. It is found that the number of visits for users to click on product information and links sent by the system is 69 times more than that under the traditional marketing strategy, and the visit rate is 11.50% higher.(2)
After adopting the marketing strategy developed in the context of the multimedia environment, the number of users downloading online music products is 80 times more than the number of downloads under the traditional marketing strategy, and the download rate is 13.34% higher.(3)
The number of collections of songs of four different styles by 600 users under the multimedia environment marketing strategy is 98 times more than the number of collections of songs under the traditional marketing strategy, and the collection ratio is 16.34% higher.(4)
The number of comments on the four types of songs by 600 users under the multimedia environment marketing strategy is 50 times more than the number of song comments under the traditional marketing strategy, and the proportion of comments is 8.34% higher. The entire comparative test data shows that the marketing strategy based on the multimedia environment surpasses the traditional marketing strategy to a large extent in terms of publicity influence and final purchase situation. This shows that this kind of marketing strategy can more accurately target the orientation of target users, so as to meet the consumer expectations and consumer needs of users, and is more conducive to promoting the upgrade and progress of the online music industry.


## 5. Conclusion

With the rapid development of multimedia technology and the continuous maturity of mobile terminal technology, the pace of development of the online music industry has begun to accelerate. In addition to the expansion of the market, the number of users is also increasing. Changes in the development direction of the entire market and innovations in business models have attracted much attention. The marketing of online music products is constantly catering to the characteristics of the times, changing the original model in terms of production, dissemination, and sustainable management. Using multimedia technology to accurately analyze consumer demand and expand product promotion and consumer purchases can effectively enhance the value connotation and competitiveness of network traffic monitoring and marketing strategies. This is of great significance to the sales of online music products and the development of the entire industry. In short, with the continuous development of computer technology, the use of multimedia technology in music has grown from scratch, from less to more and from simple to comprehensive, and the development process is very rapid. It greatly enriches the means and resources of music and improves the development of network music. Apply multimedia technology to online music and combine it with other marketing strategies to continuously optimize the online music industry and maximize industrial efficiency, so as to continuously deepen the development of online music products and develop and improve the quality and level of music products.



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*Source: 7119064-2022-03-21.xml*