Construction of Enterprise Marketing Management System in Digital Economic Environment from the Perspective of Green Ecology

Abstract

In the context of the digital economy, the speed of enterprise development is becoming faster and faster. To understand and establish the environment of network marketing, we must first understand the obstacles. Successful establishment depends on its promotion in society and the active participation of the government and enterprises. Only with the active cooperation of all parties can network marketing be rapidly established. At present, the marketing methods adopted by many enterprises are very traditional. These management methods are not only inefficient, but also present problems such as poor real-time information, a low degree of informatization, information asymmetry and closed marketing data. In order to address these problems, this paper proposes to use digital economic means to scientifically manage enterprise marketing. Here we examine the marketing effect of enterprises and determine the needs of consumers by constructing an enterprise marketing management system, so that enterprises can observe trends in consumer needs and consumer markets at any time, and make timely responses to markets and technological changes. We use a B/S model to build an enterprise marketing management system that can develop steadily in a green ecology. This paper mainly uses the mean clustering algorithm to analyze the performance of the enterprise marketing management system. Through the experiment, it is found that a good marketing management system can help enterprises improve economic efficiency and data processing ability. During the operation, it was found that the transaction success rate of the system reached 100%. Most enterprises responded within 1.5 s. The functional changes of the enterprise before and after the B/S architecture enterprise marketing management system were analyzed and it was found that the real-time information acquisition by enterprise employees, the sharing of marketing data, the degree of informatization of the system, the security of system information and the data processing capabilities had all been improved. Among these, the degree of informatization of enterprises improved the fastest and went from 48% to 94%. Under the enterprise marketing management system, the annual economic income of the enterprise increased by 1.35 million yuan, which was a year-on-year increase of 7.2%. The experimental data showed that under the green ecological perspective, the enterprise marketing management system based on the digital economic environment has certain practicality, which can promote the healthy development of enterprise marketing management.

1. Introduction

1.1. Research Background

To grow economically, enterprises must quickly establish a competitive platform conducive to current industry competition. The number and types of production enterprises continue to increase, and the business and scale of each enterprise are also increasing. However, a traditional marketing management system is still used by many enterprises. Management system equipment and facilities are backward and the management mechanism is relatively rigid. The management method is outdated and the degree of informatization is low. These have caused a continuous decline in the competitiveness of many enterprises. Against the background of a digital economy, the purpose of this paper is to analyze the marketing effect and economic benefits of enterprises by building a marketing management system, and to study the impact of green development on enterprise marketing in line with enterprise changes. The research objectives of this paper are to establish a more efficient enterprise marketing management system for enterprises to deal with the problems of low level of enterprise informatization, backward technology, and inability to grasp the needs of enterprise consumers. The research hypothesis of this paper is from the perspective of a green economy. Building an enterprise marketing management system in the digital economy environment has practical value and significance for the sustainable development of enterprises. We start with a background analysis of the digital economy environment. Then, combined with a B/S model, data mining technology was used to analyze the data in the management system. The system database was constructed and the system's functional requirements and non-functional requirements were analyzed. Finally, a marketing management system that suited the needs of enterprise development was constructed. The K-means clustering algorithm was used to analyze the functional models in the system, which made the feedback information of the system more comprehensive. Using this management system, the level of enterprise marketing management could be improved, information sharing could be achieved, enterprise management efficiency could be improved, the cost of obtaining information can be greatly reduced, and the time the enterprise took to respond to staff marketing questions was shortened. Human resources were retained, and the enterprise management costs were reduced; the higher the sales management level of the enterprise, the more products the enterprise sold. Resources were properly allocated. The targeting and effectiveness of marketing services activities were improved.

1.2. Literature Review

With the development of a social economy, it is important to build a marketing management system that is allows enterprises to develop. Li believed that large supermarkets were the strategic commanding heights of liquor marketing channels. Taking the high-end liquor brand A as an example, he analyzed the consumption data of a large supermarket from 1 January to 31 December 2015. Combined with the current situation of liquor marketing, a customer base was constructed. The algorithm used the AHP to calculate the weight of each index and identified the key customer groups of liquor enterprise marketing, which helped to optimize and integrate the marketing resources of liquor enterprises [1]. Mohamed et al. presented a new framework and a set of systematic approaches to construct a computer-based design system to allow homebuyers to participate in housing design in the United Arab Emirates housing market. To build this framework, they first outlined the current applications of mass customization in construction, highlighting the North American model of success. A new role for digital design and information management technologies was identified to enable participatory design. Furthermore, a new business model that requires extensive research on the market and buyer and developer behavior was proposed [2]. Huang analyzed the construction of an innovative intelligent decision support system for a private enterprise marketing model using the Internet under government policy. The development of a market economy required modern enterprises to pay full attention to effective cost control in the marketing process. Business costs associated with marketing were effectively reduced and competitive marketing was achieved in the enterprise. An enterprise’s marketing management mechanism, marketing innovation and advanced nature must be established and perfected. Marketing management must be perfected, and profit maximization of business management objectives must be achieved. Only in these ways could enterprises achieve stable development [3]. Harbar et al. discussed the role and place of economic security in the management of corporate strategic marketing activities. The use of marketing as a tool for the development of business innovation activities helped to maintain economic security so that the threats to the business economy in the market were tracked. Using SWOT analysis, the influence of development conditions and innovation activities on providing economic security was considered. The interrelationship and intersection of strategic management, marketing and innovation purposes should also be considered. A business strategy marketing purpose map was constructed. These determined the direction of further improvement [4]. Voronin et al. focused on solving a series of problems related to the operation of enterprises (firms) in a competitive market environment. The stability of a company under market conditions depended on the ability to optimally manage available resources and the effective planning of new products being rolled out in a timely manner, as well as the adequately addressing challenges posed by sudden changes in market conditions. An important factor in achieving economic stability of an enterprise was the existence of an internal concept of self-development. It combined the two main functions of marketing and a management system (administration). A model that fits the paradigm of economic synergy was proposed, which was a system of two nonlinear ordinary differential formulas. In this case, the classical linear superposition principle lost its relevance, and the application of traditional econometric analysis tools was not allowed. Therefore, the most important thing to implement the practice of economic forecasting was to build a regionally stable enterprise equilibrium. According to the findings, the focus was on the need for standards to be developed during the transition period when the economic system catastrophically changed its dynamic pattern, which brought system parameters close to the dangerous limit where the firm lost its stability. The key to this work was to determine the parameters of the cyclic process to indicate the amplitude, frequency, and stability of the periodic trajectory. The proposed numerical simulation results had practical value and could be used to analyze and predict the parameters of different stages of the corresponding cycle operation mode of enterprises with various types of stability [5]. At present, the research on marketing management system has made some achievements, but with the in-depth development of various industries, marketing competitiveness is growing, and the current marketing management system has been unable to match the speed of enterprise development.

The development of the digital economy had brought vigor and vitality to convenience marketing. Wrigley et al. believed that the impact of the global economic crisis, coupled with the continued growing “digital” storm of online retailing and its complex substitution and modification effects, has had a significant impact on UK town centers and high streets. Against a backdrop of profound changes in technology and consumer culture, the dramatic increase in vacancy rates within town centers has focused the policy debate on the drivers of town center vibrancy and viability. The convenience store industry had changed significantly and grown rapidly due to the entry of businesses, as consumers moved from the “big basket” of weekly one-stop shops in large off-center stores to “rarely and often” using fragmented alternatives. However, surprisingly little empirical evidence existed about the impact of these new generation of corporate convenience stores on town centers and communities. It highlighted the spatial and temporal contingency of findings in dynamic technological and regulatory contexts [6]. Kazakov and Valijonov believed that the competitiveness of products in the context of economic globalization and digitalization was crucial. Improving product quality was a necessary condition to ensure competitiveness. Producing quality products and ensuring competitiveness was discussed. The main directions for ensuring product competitiveness were identified, and methods for improving product quality management were proposed [7]. These studies give a detailed introduction to the importance of enterprise marketing management system to enterprises and the impact of digital economy on global economic development, which had guiding significance for the description of this article. However, most of these studies focus on theoretical significance, and practical significance has not been effectively explored. In contrast, this paper conducts in-depth research on the construction of enterprise marketing management system under the digital economy environment from the perspective of green ecology, improves the system construction on the basis of theory, and verifies and analyzes it from the practical use level.

1.3. Research Meaning

The study made an experimental analysis on the performance of the enterprise marketing management system, the system function modules and the economic benefits brought by enterprise marketing management systems. Experiments showed that the average accuracy of the B/S structure was 11.32% higher than that of the traditional C/S mode. Under the system data center point, the transaction function of the system reached 100% and the system response time was fast, which showed that the performance of the system was high; under the analysis of K-means clustering algorithm, the accuracy of potential customers, customer value, customer retention, customer churn, and customer satisfaction increased by 8%, 12%, 11%, 6%, and 32%, respectively. By constructing the enterprise marketing management system under the B/S mode, the informatization degree of the enterprise had increased by 46% and the economic benefit of the enterprise had increased by 7.2% within one year. This showed that building a comprehensive enterprise marketing management system was an important task to promote the economic development of enterprises.

2. Method of Constructing Enterprise Marketing Management System in Digital Economic Environment from the Perspective of Green Ecology

2.1. Digital Economy

• Basic concepts of the digital economy

The digital economy developed as an advanced stage from the information economy [8]. In the context of wider penetration, application and integration of digital technology in the economic sector, the digital economy will bring more profound changes to economic and social development in a broader, deeper and more advanced way [9]. The advancement of digital technology has led to the rise of the information and communication industry, which has rapidly developed into the most active and fastest-growing strategic emerging sector in the economy and society. Therefore, it is necessary to expand the understanding of the digital economy. The rise of the digital economy has led to the emergence of some new business models. Different from the traditional business operation model, the new business model under the digital economy relies on the continuous development of ICT technology. The constraints of distance on commercial development are broken. These reduce transaction costs to a certain extent and enable the further development of the globalized market [10].

From the perspective of econometrics, the establishment of an enterprise marketing management system also includes the control of enterprise benefit and value creation process. Under the understanding of econometrics, the fundamental purpose of implementing comprehensive marketing management is to maximize the benefit creation of enterprises. Every link of enterprise marketing management is a process of increasing enterprise benefits. The system takes marketing management as a means to ensure that every link of the enterprise’s marketing management activities can achieve the most reasonable value-added activities, or reduce non-value-added ones, so as to maximize the enterprise’s benefit creation through program control, authorization control, real-time monitoring, feedback control and other methods.

2.

Characteristics of the digital economy

The first characteristic is data dependency. Data play an important role in the development of the digital economy. The rapid development of ICT technology has increased computing power and storage capacity and decreased data storage costs, which has driven the widespread use of data. The increase in storage capacity and the decrease in storage cost greatly facilitate the use of data, which also broadens the scope of user groups; the rise in computing power facilitates comprehensive and targeted analysis of data [11,12]. Digital technology has greatly improved the ability of companies to convert the use of business chain data information into commercial benefits. Enterprises can manage performance based on data or provide tailored products or services based on data information. Enterprises can also use the operational analysis of data to support or improve management’s decision-making to optimize and upgrade products or services [13]. The data dependence of the digital economy is not only reflected in emerging industries such as the Internet, but also in the dependence of traditional processing, manufacturing and other industries and government decision-making on data.

The second is the mobility of the digital economy. In the context of the digital economy, both intangible assets and user groups are mobile. The mobility of intangible assets is shown as follows: based on the characteristics of intangible assets that are not constrained by space and do not depend on actual physical existence, enterprises can easily split all intangible assets into the affiliated enterprises [14]. With the help of ICT technology, enterprises can realize centralized management of business in various places, which enhances the flexibility of enterprise management and facilitates the expansion of business activities around the world. However, this must be limited to a certain place. In this context, the business operation of the enterprise has mobility.

The digital economy has strong network effects. Network effects refer to the fact that a user’s decisions may have a direct impact on the benefits other users receive.

The third is that the digital economy is a multi-layered business model. A multi-layered business model refers to the fact that different user groups in the same market conduct activities through a unified intermediary or platform, and the activities of each group will have an impact on the results of the other group [15,16]. In the digital economy, the multi-level business model has the characteristics of flexibility and remote accessibility, which facilitates enterprises to deploy the multi-level business model globally.

Finally, the digital economy has an oligopolistic trend and volatility. In some immature markets, it is easy for leading players to dominate the market in a relatively short period of time due to advantages such as network effects or patents. User groups tend to use these single platforms, which can easily form an oligopoly. At the same time, the development of ICT technology has lowered the entry barriers for new business models in the digital economy. Later companies can take advantage of factors such as stronger technology and more attractive value positioning to seize market share, while leading companies can only rely on continuous innovation to maintain market position.

2.2. Construction of Marketing Management System

• System requirements

System requirements are mainly divided into functional requirements analysis and non-functional requirements analysis [17]. Functional requirements analysis mainly includes commodity management business functions, customer business management functions, sales management business functions, decision management business functions, etc.

Non-functional requirements analysis requires data processing capabilities. For a sales management system, much order information may be processed. Sometimes a company has many commodities and much commodity information needs to be processed [18]. In this case, the operation of the system should first have an excellent hardware configuration. Secondly, the system software should optimize the processing to ensure that the response of the system cannot be too slow. The speed of data query and statistics and the management efficiency of the enterprise should be maximized. In the early stage of system design, for key node equipment, the network topology and reliability are calculated for key points of the equipment and security risk assessment is carried out. Routing should be based on the principle of redundancy backup. If the equipment or line does not meet the requirements of high reliability, the core server of high availability should be backed up by dual-processor backup. For systems that cannot guarantee the reliability of equipment, the system should be backed up at the same level to reduce the probability of a single node failure. When the hardware and network system performance is in a normal operating state, users need corresponding software that responds quickly to operational requirements. The response time of a single-user data query should be less than 5 s. Response time and complex queries should be controlled within 30 s.

2.

B/S Mode

In the context of the digital economy, more and more companies are changing traditional thinking to keep up with the trend of the times and respond to the national call for green ecology. For the rational development of enterprise law, a marketing management system that is more in line with enterprise development is constructed. At present, the main mode often used in the enterprise marketing management system is the B/S mode. Figure 1 is a schematic diagram of the B/S structure.

The B/S structure is an improvement on the traditional two-layer C/S structure. Compared with the C/S processing mode, B/S mainly simplifies the system and client use is simplified, as long as the client can access the Internet. The C/S requires client software to ensure the normal operation of the system [19]. The current B/S structure mainly includes three layers: user layer, business layer, and database layer. The user layer is mainly responsible for receiving the user’s request and passing the request to the server. Finally, the server’s feedback is accepted; the business layer is mainly used to handle the business logic of sales management. After receiving the request sent by the user layer, the relevant method is used by the salesperson; the database layer mainly stores the data information used by the system. In the database, operations such as query, modification, addition, deletion, etc. are performed, and feedback results are sent to the user and displayed on the user interface. Figure 2 is the working principle of the server.

Compared with the C/S structure, the three-layer B/S structure has very prominent advantages. The first is that B/S does not need to deploy clients. If the client needs to be updated, it is only necessary to modify the software structure of the server [20]; the second is that all the functions of the B/S structure system are concentrated on the server. If maintenance is required, only the system on the server needs to be maintained, which greatly simplifies the maintenance work.

3.

Construction method

When integrating marketing tools, we should make full use of various communication methods to help the company establish an ideal development goal and a good image, maintain the consistency of information sharing content, occupy a higher position in the minds of consumers, and make full use of marketing effects to provide more reliable cooperation. These marketing measures can promote nonhuman marketing measures, attract consumers and meet the needs of the industry. Taking the B/S structure as the main body, according to the needs of users, the functional modules of the system mainly include the system management module, commodity management module, customer management module, sales management module and query statistics module [21].

The customer management module includes a number of small modules, which mainly perform pre-sales customer tracking, analyze and record the consumption activities of target consumers, and do a good job of after-sales return visits [22]. Using K-means clustering algorithm to segment customer value is another way to improve the system that cannot be achieved by data mining technology. Similar customer samples are clustered in the K-means clustering algorithm. The methods used to measure the similarity of samples mainly include cosine similarity, Euclidean distance, Manhattan distance, Minkowski distance, etc. [23].

The formula expression of Euclidean distance is:

$$h\left(k_i,k_j\right)=\sqrt{{\sum }_x^n{\left(k_{ix}-k_{jx}\right)}^2}$$

(1)

The formula expression for the Manhattan distance is:

$$h\left(i,j\right)=\left|k_{i1}-k_{j2}\right|+\left|k_{i2}-k_{j2}\right|+\cdots +\left|k_{in}-k_{jn}\right|$$

(2)

The Minkowski distance formula can be expressed as:

$$h\left(k_i,k_j\right)={\left({\sum }_{x=1}^S{\left|k_{ix}-k_{jx}\right|}^g\right)}^{1/g}$$

(3)

The value of cosine similarity ranges from 0 to 1. Its characteristic is that the calculated similarity value has nothing to do with length and distance and is only related to the difference in the direction of the two samples.

$$sim\left(m,n\right)=cos\beta =\frac{\overline{m},\overline{n}}{\Vert m\Vert ·\Vert n\Vert }$$

(4)

In the calculation of cosine similarity, the sample individual is regarded as a point in the n-dimensional coordinate system and then the coordinate origin and the sample point form a vector [24].

In the process of customer segmentation, if better clustering effects and coupling effects are to be achieved, the error sum of squares criterion can be set. Assuming that H in the customer data is the objective function of the main subdivision, and there are S such data sample sets in the whole system, the formula can be obtained:

$$H_s={\sum }_{j=1}^s{\sum }_{x=1}^{g_j}\Vert k_x-e_j\Vert {}^2$$

(5)

Among them, $e_j$ represents the mean value of the sample, and its definition formula is:

$$e_j=\frac{1}{g}{\sum }_{j=1}^{g_j}{k}_j$$

(6)

In the formula, the larger the value of s, the larger the value of the objective function. This shows that the less ideal the clustering effect is in customer analysis, the greater the error in the segmentation of customer value. Therefore, it is necessary to find a clustering partition that can minimize the value of the objective function [25].

Using the weighted average squared distance and the criterion to subdivide the target information, the formula can be obtained:

$$H_i={\sum }_{j=1}^s{G}_j\times Y_j^{*}$$

(7)

Among them, it represents the average squared distance between samples within a class, and it is defined as follows:

$$Y_j^{*}=\frac{2}{g_j\left(g_j-1\right)}{\sum }_{k\in K_j}{\sum }_{k^{\prime }\in K_j}\Vert k-k^{\prime }\Vert {}^2$$

(8)

The criterion function is mainly used to represent the overall distribution of the clustering results among the classes. Generally, there are two definitions of interclass distance and weighted interclass distance. The interclass distance sum can be expressed as:

$$H_{b1}={\sum }_{j=1}^s{\left(e_j-e\right)}^R\left(e_j-e\right)$$

(9)

The weighted interclass distance sum can be formulated as:

$$H_{b2}={\sum }_{j=1}^s{G}_j{\left(e_j-e\right)}^R\left(e_j-e\right)$$

(10)

The value of 11 directly reflects the separation between the two divided classes. The larger the value, the more separated the two division classes are, which means that the clustering effect is better. On the contrary, the smaller the value is, the more similar the two categories are, which means that the clustering effect is worse.

4.

System Database

A basic database system consists of four parts, namely the database, hardware, software and personnel. A database is a must for all management systems. The database is mainly used to store the data used or generated by the system in the process of managing information [26]. Hardware is a variety of hardware products included in the system, including storage devices, external devices, and the like. Software refers to the software used to operate the data in the database [27,28]. It completes certain business functions by adding, deleting, modifying and querying the database data by the software. There are four categories of personnel, namely system analysts, application personnel, end users, and database administrators. Figure 3 is a diagram showing the composition of the companion database.

First, the data are collected and integrated, the goals of analysis and prediction are determined, and then the data are selected and prepared [29]. Two conditions must be met when using data mining technology for segmentation: completeness, that is, each customer in the database must belong to a segment; mutual exclusivity, that is, any customer in the database cannot belong to two or more segments at the same time. Figure 4 shows the model diagram of the data.

The potential customers, customer value, customer retention, customer churn and customer satisfaction of a perfect enterprise marketing management system are all in a very perfect state [30]. With a large amount of user data, finding out the common ground and similar behavior patterns of customers and effectively understanding potential customers will help marketers to carry out targeted marketing, thereby improving the work efficiency of enterprise staff; in many cases, the shopping habits of customers tend to be similar. Analyzing consumers’ shopping habits can help merchants better understand customers’ needs for better marketing strategies. Clustering algorithms are used to classify customer value. According to the different consumption habits of customers, tiered services are provided, which maximizes the potential consumption potential of customers; both churn and customer retention are linked [31,32]. Customer churn analysis is an important sub-link in customer relationship management; the essence of customer satisfaction is the comparison of customers’ expectations for products and actual needs. The size of this difference is called customer satisfaction, that is, customer satisfaction refers to the relationship between the quality of an enterprise’s products or services and the customer’s prior expectations. The better a product or service performs, the better the customer satisfaction [33].

3. Experiment on the Construction of Enterprise Marketing Management System in Digital Economic Environment from the Perspective of Green Ecology

3.1. Performance Experiment of Enterprise Marketing Management System

In order to verify the effectiveness of the enterprise marketing management system in the digital economy environment from the perspective of green ecology, this paper uses the enterprise management database to conduct performance experiments. This paper analyzes under the traditional C/S mode and B/S mode, investigates the performance value of an enterprise’s management system through the enterprise management database, and randomly extracts the initial data center points under different modes, and analyzes the accuracy of these points. The accuracy of dataset analysis at different initial points in the enterprise marketing management system can be seen from the data results in

Table 1. Data accuracy results of traditional C/S and B/S pairs.

Structure	Initial Data Center Point	Accuracy (%)	Average Value (%)
C/S structure	117,423	83.32	74.97
	38,562,311	82.12
	103,145	82.67
	232,566	57.33
	40,567,822	83.33
	1,312,456	82.76
	2,325,468	57.33
	102,563,123	76.73
	2,356,897	61.44
	105,136,101	82.67
B/S structure	35,621	86.29	86.29

.

It can be seen from Table 1 that the accuracy of the dataset is analyzed using the initial center of random data. The average accuracy of the C/S structure is 74.97%. The average accuracy of the B/S structure is 86.29%. Compared with the traditional C/S structure, the average accuracy of the B/S structure is 11.32% higher.

Test system performance. Using the background data of the enterprise management system, we investigated the success rate of different users’ concurrent operations, tested the concurrency of the system and the response time of the system, and investigated a total of 200 virtual users. See

Table 2. Test results of system concurrent users and response time.

User Concurrency	Success Rate	Response Time (s)
40	100%	0.54
60	100%	0.76
80	100%	1.17
100	100%	1.43

for test results.

As can be seen from Table 2, the test found that the transaction success rate of the system reached 100% during the concurrent operation of 200 virtual users at the same time. Among them, 90% of the transaction response time was within 1.5 s. It has a fast response time and can meet the requirements of the system.

3.2. Effect of K-Means Clustering Algorithm on System Functional Modules

After the establishment of the enterprise marketing management system, the clustering algorithm was used to analyze the factors that affect the development of enterprise marketing management. This paper takes a listed company as an example and the enterprise marketing management system established by B/S structure is applied to the company. A K-means clustering algorithm was used with the support of a digital environment to segment the company’s potential customers, customer value, customer retention, customer churn, and customer satisfaction. Before and after the K-means clustering algorithm was analyzed, the accuracy of the segmentation of potential customers, customer value, customer retention, customer churn, and customer satisfaction in this system was compared. Figure 5 shows the results of the before and after comparison.

It can be seen from Figure 5A,B that the precision analysis results of potential customers, customer value, customer retention, customer churn and customer satisfaction without the K-means clustering algorithm were 76%, 74%, 68%, 73% and 56%, respectively. After segmentation by the K-means clustering algorithm, the accuracy of potential customers, customer value, customer retention, customer churn, and customer satisfaction were 84%, 86%, 82%, 79%, and 88%, respectively. After clustering and segmentation, the segmentation accuracy of potential customers increased by 8%. Customer value segment increased by 12% and customer retention increased by 11%. The accuracy of customer churn increased by 6% and customer satisfaction increased by 32%. Among these results, the accuracy improvement range of customer satisfaction was the highest. From the experimental results, after using the K-means clustering algorithm, the clustering effect in all aspects was better and obvious, the accuracy was higher, and the clustering was more stable. After comparing the enterprise marketing management system with B/S architecture, the changes in the real-time information acquisition by enterprise employees, the sharing of marketing data, the degree of informatization of the system, the security of system information, and data processing capabilities are shown in Figure 6.

As can be seen from Figure 6A,B, analyzing the functional changes of the enterprise before the B/S architecture enterprise marketing management system, the functional performance was not very strong, except that the system was more secure. After using the B/S model to build the enterprise marketing management system, the informatization degree of the enterprise improved the most. It went from 48% to 94%, an increase of 46%. It can be seen that the degree of informatization in the improved enterprise marketing system has been unprecedentedly improved. Through data analysis, it can be seen that after the construction of the enterprise marketing management system, the functionality of the enterprise has been improved.

3.3. Economic Benefits Brought by Enterprise Marketing Management System under the Influence of Digital Economy

Under the new economic conditions, the consumption level of customers has been continuously increased, and the consumption demand has gradually diversified. When choosing products, many people not only pay attention to the quality and price of products, but also to the added value and personalized demand. Therefore, value-added products that do not meet the needs of consumers are gradually eliminated. Under the background of diversified market demand, enterprises should constantly improve the production structure, improve the value of goods, meet consumer demand and improve market competitiveness. Therefore, optimizing the industrial structure of the company provides sufficient impetus. In the new economic environment, the innovation economy is also in a leading position. In this case, the company can only keep up with the development of the market by constantly updating its products and processes. At the same time, the scientific and technological progress under the new economic conditions has created favorable conditions for the company’s product and technological innovation, promoted the company’s long-term development, and also bring greater challenges to the company. The new trends in the digital economy have vertically integrated new business models at all levels. In this environment, the established enterprise marketing management system combines with the digital economy and information technology, and the different businesses of the enterprise continue to expand, which brings new possibilities for the development of the enterprise. Taking the enterprises mentioned above as the experimental objects, the changes in the economic benefits of the enterprises within one year before and after the establishment of the enterprise marketing management system are analyzed, as shown in Figure 7.

As shown in Figure 7A,B, under the digital economy, the experiment compares the changes in the economic benefits of the enterprise before and after the enterprise uses the marketing management system within 12 months. Before using the enterprise marketing management system established in this paper, the company’s annual income was 8.68 million yuan. After using the enterprise marketing management system established in this paper, the enterprise’s annual revenue is 10.03 million yuan. Before the system, the annual economic income increased by 1.35 million yuan, a year-on-year increase of 7.2%. After the company uses the enterprise marketing management system, the company’s income rises every month. Therefore, it can be judged that the use of a sound enterprise marketing management system can effectively improve the economic efficiency of the enterprise and improve the competitiveness of the enterprise.

4. Discussion

This paper discussed the construction of enterprise marketing management system in digital economy environment from the perspective of green ecology. The article first analyzed the digital economy. Then, according to data mining, a large amount of customer data in the system was analyzed, and the obtained data were sorted into the database. Finally, the system database was obtained. For the system requirements of the system, customer requirements were analyzed and perfected. Targeted marketing plans were developed. A marketing management system suitable for enterprise development needs was constructed. In the experimental part of the article, combined with the K-means clustering algorithm, the effectiveness of the system functionality was analyzed and the accuracy of the system in customer segmentation was explored. It was found that the accuracy of the system’s analysis of potential customers, customer value, customer retention, customer churn, and customer satisfaction was higher than before. After analyzing the functional changes of the enterprise before and after the enterprise marketing management system under the B/S model, it was found that the informatization degree in the improved enterprise marketing system was unprecedentedly improved. In the context of the digital economy, the economic benefits brought by the enterprise marketing management system to the enterprise were improved. After the establishment of the enterprise’s marketing system, publicity and online advertising should be increased, so that more users can understand the enterprise’s marketing methods, understand the enterprise’s products through these marketing methods, and use the system to collect customers' product needs and psychological conditions, so as to better improve the marketing system and improve the economic efficiency of the enterprise. In the experiment, the performance of enterprise marketing management system, the effect of K-means clustering algorithm on system function modules, and the economic benefits brought by enterprise marketing management system to enterprise development under the influence of digital economy were analyzed.

5. Conclusions

This study was a process in building a corporate marketing management system for the promotion of corporate development under the background of the digital economic environment from the perspective of green ecology. In the methods section, the background of the digital economy was first analyzed in detail. Then the B/S mode was added during the system building process. System servers were optimized, and system development efficiency was improved. According to the data characteristics of the system, the data was mined and the database was perfected. System requirements were analyzed. Finally, the enterprise marketing management system was completed. In the experiment, the traditional C/S mode and the B/S mode were compared, and it was found that the accuracy of the dataset analysis of the B/S mode was higher than that of the B/S mode at different initial points of the enterprise marketing management system. The concurrency of the system and the response time of the system were tested, and it was found that the system transaction success rate was high, and the response time was fast. After analyzing the accuracy of customer segmentation before and after using the K-means clustering algorithm, it was found that the segmentation accuracy after using the K-means clustering algorithm was higher than before using the algorithm. From the experimental data, from the perspective of green ecology, under the background of digital economy, the enterprise marketing management system constructed in this paper has certain practical value and significance. There were many factors that needed to be considered in applying the enterprise marketing management system to practical applications, which also required detailed analysis. In future, this research will improve and supplement this point, and constantly improve the quality of research to promote the scientific development of enterprise marketing management.