# Construction and Risk Analysis of Marketing System Based on Deep Neural Network

Authors: Xuelian Yang; Jin Bai; Xiaolin Wang
Journal: Security and Communication Networks
 (2022)

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## Abstract

Enterprises needs to realize the unity of economic and social benefits. From the marketing theory, we know that the economic and social benefits form a symbiotic effect, which determines that enterprises need to pay attention to their own service quality. The current risks are in the areas of “customer relationship management” and “customer technical support,” specifically, the inability to effectively meet the needs of customers in terms of corporate infrastructure and product details, as well as the increase in costs for other customer services. The article firstly completes the construction of marketing risk early warning system, analyzes marketing risk types and formation factors, constructs indicators and mathematical modeling, then proposes the ANN combined with Petri algorithm, which is used to realize marketing risk early warning, completes sample selection and indicator quantification, sets up the early warning model, and conducts false alarm and false alarm degree analysis.


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## Body

## 1. Introduction

In the market economy-centered marketing system, all activities of the enterprise are market and customer-oriented, and the core and key of marketing is to build goods and services that satisfy customers on this basis. Marketing risks are unpredictable changes in the business operation process based on changes in internal and external conditions. The actual and expected marketing performance of the company deviates from each other and puts the company in an unusual position to suffer losses [1]. In addition, marketing is a thematic part of the whole business operation and is in a high risk module of the business.With the end of the system’s corporatization reform, companies are increasingly under pressure to sell their products and services. Although the enterprise still exists in the market monopoly structure, the power products are directly related to the livelihood issues of the region where they are located, thus having to bear greater pressure of social opinion and political pressure from the officials in the implementation of the business [2, 3]. Therefore, enterprises need to achieve the unity of economic and social benefits. From marketing theory, it is clear that economic and social benefits form a symbiotic effect, which determines the need for enterprises to constantly pay attention to the quality of their own services. For this reason, the analysis of risks in electricity marketing and construction of control measures become the starting point of this paper.It should be noted here that unlike the marketing purposes of ordinary commodities, regional consumer groups have the characteristics of rigid demand for electric power products, so the marketing of enterprises needs to pay more attention to achieving social benefit goals; however, due to the overcapacity dilemma that China is generally facing, effective marketing activities will also optimize the capacity structure of enterprises [4, 5].Though more complicated risk-averse modeling papers exist, this paper has its unique research perspective and practical considerations. Compared to the existing literature regarding end-of-life product acquisition and closed-loop supply chain coordination, this paper, under the market fluctuation realistic consideration, focuses on the risk-averse contracting mechanisms issue (in the field of remanufacturing supply chain coordination) with multiple sources of uncertainties. Moreover, the proposed risk-averse marketing strategy and the further developed coordination mechanism cover multiple practical issues at the same time, including the remarketing issue regarding shortage and lost sale consideration, the supply chain coordination issue between an end-of-life product collector (retailer) and a remanufacturer regarding lack of contract regulation, and the uncertainty issue regarding the fluctuating demand and the remanufacturability rate. Finally, based on the proposed risk-averse marketing strategy, the scientific value of this study lies in providing a feasible coordination mechanism under risk-averse consideration and identifying conditions for inspiring remanufacturers and retailers to participate in coordination of contract regulation, which helps to reduce the risk of remanufacturing shortage/inventory under heavy remanufacturing market fluctuation.We believe that the current risks are manifested in the positioning of customer relationship management and customer technical support through the control model, on the basis of which the control measures should be built around four aspects: deepening the understanding of marketing risks, optimizing the internal risk control mechanism, refining the external risk control mechanism, and establishing a risk control evaluation system [6, 7].Specifically, enterprise management and marketing department personnel need to deepen their understanding of marketing risks; they need to combine the current situation of product sales and consult with experts to develop training programs and hire training experts; for the management of VIP customers [8], the enterprise marketing department should set up a special function to give management, and they also need to establish a risk control evaluation system. This paper uses ANN combined with the Petri method to complete the design of marketing risk early warning program, which is important to effectively realize the marketing risk early warning.


## 2. Risk Analysis in Marketing

Combined with the authors’ work experience, the risks in marketing can be analyzed from the following two aspects.


### 2.1. Risks in Customer Relationship Management

Because of the rigidity of use and homogeneity of the product, in the marketing faced with customer relationship management, this activity is directly related to the social reputation of the enterprise degree and the size of external political pressure [9]. However, in the specific customer relationship management, because of the poor communication channels between enterprises and customers or due to the inertial thinking of enterprises themselves, there are still defects in the refinement of services to customers, thus failing to effectively meet the needs of customers in the details of power infrastructure and power products.


### 2.2. Risks in Customer Technical Support

Although customer relationship management constitutes the main focus of corporate marketing, there is a loss of asset exclusivity in this management. In other words, as a company implements technical support activities for a customer [10], it also leads to an increase in the cost of servicing other customers because of the exclusivity of the technology equipment that locks the company into the relationship management for that customer, for example, the lack of learning effect for other customers or the switching cost due to technology specificity [11]. This risk is rarely considered in existing discussions of the problem.


## 2.1. Risks in Customer Relationship Management

Because of the rigidity of use and homogeneity of the product, in the marketing faced with customer relationship management, this activity is directly related to the social reputation of the enterprise degree and the size of external political pressure [9]. However, in the specific customer relationship management, because of the poor communication channels between enterprises and customers or due to the inertial thinking of enterprises themselves, there are still defects in the refinement of services to customers, thus failing to effectively meet the needs of customers in the details of power infrastructure and power products.


## 2.2. Risks in Customer Technical Support

Although customer relationship management constitutes the main focus of corporate marketing, there is a loss of asset exclusivity in this management. In other words, as a company implements technical support activities for a customer [10], it also leads to an increase in the cost of servicing other customers because of the exclusivity of the technology equipment that locks the company into the relationship management for that customer, for example, the lack of learning effect for other customers or the switching cost due to technology specificity [11]. This risk is rarely considered in existing discussions of the problem.


## 3. Marketing Risk Warning System Construction

### 3.1. Field Marketing Risk Profile Classification

Marketing risk can only be achieved by adopting appropriate early warning strategies for each type of marketing risk. The marketing risks of a company mainly originate from product, pricing, distribution, and promotion [12]. Product risk includes design risk, functional quality risk, time-to-market risk, market positioning, and branding risk; pricing risk includes low price, high price, and price change risk; distribution includes commercial, storage, and lending risk; promotion includes advertising, marketing, and sales risk.


### 3.2. Traditional Marketing Risk Warning Indicator Model

The marketing risk estimation and evaluation early warning model refers to the unified body that can provide timely feedback on the marketing status of the enterprise; therefore, when constructing the marketing risk early warning indicator model, it is necessary to meet the criteria of flexibility, expensiveness, measurement, independence, and foresight [13]. Based on this, marketing risk early warning submodel is as follows.Enterprise marketing warning = {commodity warning, collaboration warning, public warning, competition warning} [14].


### 3.3. Mathematical Modeling of Traditional Marketing Risk Warning Indicators

According to the average value of covariates, the data regularization is shown in the following formula:(1)gi=ki−k′n,i=1,2,…,nk′=1n∑i=1nki.The covariance array of each initial criterion after the standardization is measured,o=oijnnn; then, the covariance measurement model is(2)oij=1n∑j=1Nkij−ki′kij−kj,i,j=1,2,…,n.Based on the initial information after calibrating the parameters, the correlation of the relevant parameter array is shown in the following formula:(3)uij=∑i,j=1nkinkjn∑i,j=1Nkin2∑i,j=1Nkjn2,i,j=1,2,…,n.The principal components are(4)gi=∑i=1npiki,i=1,2,…,n.


### 3.4. Traditional Marketing Risk Warning Level

Traditional marketing risk warning focuses on the design of marketing classification device, which is used in this paper to achieve enterprise marketing risk level discrimination and give relevant marketing risk warning alerts according to the risk type [15, 16]. The data diffusion gives the part that is the expected risk level, and the classification combination of the warning level is R, R = {r1,r2,r3,r4,r5}, where r1,r2,r3,r4,r5 indicate the unusual condition, low risk mode, medium risk mode, higher risk mode, and high risk mode.


## 3.1. Field Marketing Risk Profile Classification

Marketing risk can only be achieved by adopting appropriate early warning strategies for each type of marketing risk. The marketing risks of a company mainly originate from product, pricing, distribution, and promotion [12]. Product risk includes design risk, functional quality risk, time-to-market risk, market positioning, and branding risk; pricing risk includes low price, high price, and price change risk; distribution includes commercial, storage, and lending risk; promotion includes advertising, marketing, and sales risk.


## 3.2. Traditional Marketing Risk Warning Indicator Model

The marketing risk estimation and evaluation early warning model refers to the unified body that can provide timely feedback on the marketing status of the enterprise; therefore, when constructing the marketing risk early warning indicator model, it is necessary to meet the criteria of flexibility, expensiveness, measurement, independence, and foresight [13]. Based on this, marketing risk early warning submodel is as follows.Enterprise marketing warning = {commodity warning, collaboration warning, public warning, competition warning} [14].


## 3.3. Mathematical Modeling of Traditional Marketing Risk Warning Indicators

According to the average value of covariates, the data regularization is shown in the following formula:(1)gi=ki−k′n,i=1,2,…,nk′=1n∑i=1nki.The covariance array of each initial criterion after the standardization is measured,o=oijnnn; then, the covariance measurement model is(2)oij=1n∑j=1Nkij−ki′kij−kj,i,j=1,2,…,n.Based on the initial information after calibrating the parameters, the correlation of the relevant parameter array is shown in the following formula:(3)uij=∑i,j=1nkinkjn∑i,j=1Nkin2∑i,j=1Nkjn2,i,j=1,2,…,n.The principal components are(4)gi=∑i=1npiki,i=1,2,…,n.


## 3.4. Traditional Marketing Risk Warning Level

Traditional marketing risk warning focuses on the design of marketing classification device, which is used in this paper to achieve enterprise marketing risk level discrimination and give relevant marketing risk warning alerts according to the risk type [15, 16]. The data diffusion gives the part that is the expected risk level, and the classification combination of the warning level is R, R = {r1,r2,r3,r4,r5}, where r1,r2,r3,r4,r5 indicate the unusual condition, low risk mode, medium risk mode, higher risk mode, and high risk mode.


## 4. ANN Combined with Petri Algorithm

### 4.1. ANN Algorithm

The artificial neural network (ANN) model refers to the use of many simple neural units to fit human brain thinking patterns [17, 18].This method can make full use of various data, realize the correlation between given input and data, and realize the forward and backward correction of neural network. The weight change in the fitting process is achieved by cutting the bars ofU and H simple, and the network simulation is used to make the deviation between the given and the actual results smaller.Set the incoming part toO, and the incoming layer contains z neural units and e1 neural units implicitly, and the activation parametrization is Q1. The output layer contains e2 neural units and the associated activation parametrization is Q2, giving the result as I. The target covariates are X, D1, F1, D2, F2 weight and threshold covariates, and the forward transmission of data is(1)
Thejth neural unit in the hidden layer gives the result as(5)i1j=q1∑j=1nd1j−f1j,j=1,2,…,n.(2)
The result of thejth neural unit in the output hierarchy is given as [13](6)i2j=q2∑j=1nd2j−f2j,j=1,2,…,n.(3)
The analytical formula for the given deviation is(7)YD,F=12∑j=1nxj−ij,j=1,2,…,n.The gradient reduction scheme is used to obtain the weight covariance transformation and deviation inverse transmission.(1)
The weights of the output modules are transformed as follows, and the status of the output weights from thejth to the nth output is(8)Δd2nj=−η∂Y∂d2nj=−η∂Y∂i2n·∂i2n∂i2=ηi1.(2)
The weight of the intermediate structure of the neural network model will change from the j-th neuron to the n-th neuron(9)Δd1nj=−η∂Y∂d1nj=−η∂Y∂i1n·∂i2n∂i1nj·∂i1nj∂1j=η∑j=1ndn−i2n·d2·f2njd1.This paper gives the neural network model as a five-dimensional construct unit: ANN = (S, W, α, β, θ), where S is the combination of the neural network given data [19]; W is the combination of the neural network given data; α is the combination of the neural network implied units; the model given data and given data are combined in some way; β is the combination of the articulated weights of the neural network, the value of which gives the degree of association between each node; and θ is the threshold combination of neural units, as shown in Figure 1.Figure 1
ANN algorithm network model.


### 4.2. Petri Network Modeling

The Petri network is a graphical combination applied to a computer system to represent and analyze the concurrent processing of the marketing model. The entire model is divided into six units:(10)Petri=pp,tt,ff,ww,kk,mm.Pp in equation (10) is present the initial dataset, set to, expressed as the parts of criteria, given in information, given-in data, needed resources, etc., and expressed with “0,” and expressed as “Φ” if all data are available in the database; tt is the initial variation combination, which can be expressed as factors such as event, measurement step, data operation, task, and so on, and is chosen to be expressed as “δ,” and equation (11) then expresses the flow relationship of poetry network [20]:(11)ff=pp×tt∪tt×pp,where ww is the weight parser of the network; wwx,y expresses the weight parameter from x to y; kk is the capacity parser of the network; kk (p) is the maximum capacity of the p value of the dataset; and mm is the number of tokens that can be obtained for p in the dataset, given the identity [21].If (pp, tt, ff, ww, kk, mm) in a Petri network is set, then given the static mechanism in the network, the whole Petri network first uses model migration to obtain the evolutionary parameters and then shows the Petri network dynamics.


### 4.3. ANN Combined with Petri Algorithm

The ANN method actually uses a black-box training approach driven by information to accomplish the nonlinear association between the given incoming information and the given information and obtains the relevant states from the network training to combine in the neural unit. However, the ANN approach cannot give a real physical definition and cannot be compared with the human brain constructs that have the function of learning and memory and can complement the reasoning model [22].The Petri algorithm analyzes the graphs mathematically and fits the changes of the whole system, and its causal association of the system can complement the reasoning of the ANN method. The integration of the ANN method and Petri algorithm can complement the two methods in dealing with marketing risks separately [23].The overall model is shown in Figure2.Figure 2
ANN combined with Petri algorithm model.


## 4.1. ANN Algorithm

The artificial neural network (ANN) model refers to the use of many simple neural units to fit human brain thinking patterns [17, 18].This method can make full use of various data, realize the correlation between given input and data, and realize the forward and backward correction of neural network. The weight change in the fitting process is achieved by cutting the bars ofU and H simple, and the network simulation is used to make the deviation between the given and the actual results smaller.Set the incoming part toO, and the incoming layer contains z neural units and e1 neural units implicitly, and the activation parametrization is Q1. The output layer contains e2 neural units and the associated activation parametrization is Q2, giving the result as I. The target covariates are X, D1, F1, D2, F2 weight and threshold covariates, and the forward transmission of data is(1)
Thejth neural unit in the hidden layer gives the result as(5)i1j=q1∑j=1nd1j−f1j,j=1,2,…,n.(2)
The result of thejth neural unit in the output hierarchy is given as [13](6)i2j=q2∑j=1nd2j−f2j,j=1,2,…,n.(3)
The analytical formula for the given deviation is(7)YD,F=12∑j=1nxj−ij,j=1,2,…,n.The gradient reduction scheme is used to obtain the weight covariance transformation and deviation inverse transmission.(1)
The weights of the output modules are transformed as follows, and the status of the output weights from thejth to the nth output is(8)Δd2nj=−η∂Y∂d2nj=−η∂Y∂i2n·∂i2n∂i2=ηi1.(2)
The weight of the intermediate structure of the neural network model will change from the j-th neuron to the n-th neuron(9)Δd1nj=−η∂Y∂d1nj=−η∂Y∂i1n·∂i2n∂i1nj·∂i1nj∂1j=η∑j=1ndn−i2n·d2·f2njd1.This paper gives the neural network model as a five-dimensional construct unit: ANN = (S, W, α, β, θ), where S is the combination of the neural network given data [19]; W is the combination of the neural network given data; α is the combination of the neural network implied units; the model given data and given data are combined in some way; β is the combination of the articulated weights of the neural network, the value of which gives the degree of association between each node; and θ is the threshold combination of neural units, as shown in Figure 1.Figure 1
ANN algorithm network model.


## 4.2. Petri Network Modeling

The Petri network is a graphical combination applied to a computer system to represent and analyze the concurrent processing of the marketing model. The entire model is divided into six units:(10)Petri=pp,tt,ff,ww,kk,mm.Pp in equation (10) is present the initial dataset, set to, expressed as the parts of criteria, given in information, given-in data, needed resources, etc., and expressed with “0,” and expressed as “Φ” if all data are available in the database; tt is the initial variation combination, which can be expressed as factors such as event, measurement step, data operation, task, and so on, and is chosen to be expressed as “δ,” and equation (11) then expresses the flow relationship of poetry network [20]:(11)ff=pp×tt∪tt×pp,where ww is the weight parser of the network; wwx,y expresses the weight parameter from x to y; kk is the capacity parser of the network; kk (p) is the maximum capacity of the p value of the dataset; and mm is the number of tokens that can be obtained for p in the dataset, given the identity [21].If (pp, tt, ff, ww, kk, mm) in a Petri network is set, then given the static mechanism in the network, the whole Petri network first uses model migration to obtain the evolutionary parameters and then shows the Petri network dynamics.


## 4.3. ANN Combined with Petri Algorithm

The ANN method actually uses a black-box training approach driven by information to accomplish the nonlinear association between the given incoming information and the given information and obtains the relevant states from the network training to combine in the neural unit. However, the ANN approach cannot give a real physical definition and cannot be compared with the human brain constructs that have the function of learning and memory and can complement the reasoning model [22].The Petri algorithm analyzes the graphs mathematically and fits the changes of the whole system, and its causal association of the system can complement the reasoning of the ANN method. The integration of the ANN method and Petri algorithm can complement the two methods in dealing with marketing risks separately [23].The overall model is shown in Figure2.Figure 2
ANN combined with Petri algorithm model.


## 5. ANN Combined with Petri Algorithm for Marketing Risk Warning Scheme

### 5.1. Sample Selection

The data of this paper select the financial statistical information of major listed companies published on the Chinese enterprise database, the Chinese enterprise coordination Yearbook, and the website of the National Bureau of statistics. In this paper, based on the statistical information of 28 listed enterprises as a case study, the factor score covariance array is used to complete the analysis of 25 indicators under the commodity warning subsystem, collaboration warning subsystem, public warning subsystem, and competition warning subsystem given above [24, 25].Further obtaining the deviation contribution rates, six common factors were obtained with a cumulative degree of variance contribution of 0.91683. Table1 gives the results of the overall deviation of the sample data.Table 1
Results of overall deviation in interpretation of these data (unit: %).
Characteristic resultsDeviation contribution degreeCumulative deviation contribution15.81126.3194.92326.80918.32156.32232.40123.22368.82243.54126.19875.30158.93627.59880.81965.3128.23381.312


### 5.2. ANN Combined with Petri Algorithm for Quantifying Marketing Warning Indicators

The marketing warning criteria can be classified into four types: overhead type, benefit type, moderate type, and interval type. Among them, UU1 is the overhead warning subset; UU2 is the benefit warning subset; UU3 is the moderate warning subset; and UU4 is the interval warning subset. Set the definition domain interval as [ρ, τ], which represents the minimal and maximal results of the warning criteria, and get the satisfaction of the warning criteria of sample pp xxppi with the interval [0, 1].The mapping resolution of commodity cost type is shown in the following formula:(12)xxppi=1,xxppi<ρ,τ−xxppiτ−ρ,0<xxppi<1,0,xxppi>τ.


### 5.3. ANN Combined with Petri Algorithm for Marketing Alert Model

In this paper, we combine sample information and ANN with Petri algorithm to give a risk estimation model for corporate marketing. The information given into the whole model can be input into the multivariate influencing factors to complete the analysis, while avoiding the cross and overlap of data and interference conditions; in addition, it will also increase the speed of the whole algorithm training [26], as shown in Figure 3.Figure 3
ANN combined with Petri algorithm for marketing alert model.


### 5.4. ANN Combined with Petri Algorithm for Marketing Alert Steps

Assuming that a company has completed the marketing risk assessment and early warning, the ANN combined with Petri algorithm early warning system is constructed according to the above method, the marketing early warning characteristics are analyzed [27], and the warning criteria are set in the interval, and the marketing risk assessment scheme is given as follows:(1)
Set the given warning vector interval: [ρ, τ].(2)
Giveε warning sample attribute arrays λ and the desired output parameter ξ.(3)
Complete the linear transformation of the early warning sample attribute array intoµ according to the standard pattern in the early warning vector interval.(4)
The ANN combined with Petri algorithm is used to complete the learning, and a nonlinear correspondence is achieved between ∫ andµ. The whole model is(13)ξ=Fμj,j=1,2,…,n.(5)
Then, the ANN combined with Petri algorithm is used to evaluate the marketing risk of the enterprise, and when the risk warning is completed, it is only necessary to set the weight attribute parameter of the warning criterion to obtain the comprehensive judgment index parameter of the enterprise’s marketing risk warning, and then the related warning data are given to realize the enterprise’s marketing risk evaluation.


## 5.1. Sample Selection

The data of this paper select the financial statistical information of major listed companies published on the Chinese enterprise database, the Chinese enterprise coordination Yearbook, and the website of the National Bureau of statistics. In this paper, based on the statistical information of 28 listed enterprises as a case study, the factor score covariance array is used to complete the analysis of 25 indicators under the commodity warning subsystem, collaboration warning subsystem, public warning subsystem, and competition warning subsystem given above [24, 25].Further obtaining the deviation contribution rates, six common factors were obtained with a cumulative degree of variance contribution of 0.91683. Table1 gives the results of the overall deviation of the sample data.Table 1
Results of overall deviation in interpretation of these data (unit: %).
Characteristic resultsDeviation contribution degreeCumulative deviation contribution15.81126.3194.92326.80918.32156.32232.40123.22368.82243.54126.19875.30158.93627.59880.81965.3128.23381.312


## 5.2. ANN Combined with Petri Algorithm for Quantifying Marketing Warning Indicators

The marketing warning criteria can be classified into four types: overhead type, benefit type, moderate type, and interval type. Among them, UU1 is the overhead warning subset; UU2 is the benefit warning subset; UU3 is the moderate warning subset; and UU4 is the interval warning subset. Set the definition domain interval as [ρ, τ], which represents the minimal and maximal results of the warning criteria, and get the satisfaction of the warning criteria of sample pp xxppi with the interval [0, 1].The mapping resolution of commodity cost type is shown in the following formula:(12)xxppi=1,xxppi<ρ,τ−xxppiτ−ρ,0<xxppi<1,0,xxppi>τ.


## 5.3. ANN Combined with Petri Algorithm for Marketing Alert Model

In this paper, we combine sample information and ANN with Petri algorithm to give a risk estimation model for corporate marketing. The information given into the whole model can be input into the multivariate influencing factors to complete the analysis, while avoiding the cross and overlap of data and interference conditions; in addition, it will also increase the speed of the whole algorithm training [26], as shown in Figure 3.Figure 3
ANN combined with Petri algorithm for marketing alert model.


## 5.4. ANN Combined with Petri Algorithm for Marketing Alert Steps

Assuming that a company has completed the marketing risk assessment and early warning, the ANN combined with Petri algorithm early warning system is constructed according to the above method, the marketing early warning characteristics are analyzed [27], and the warning criteria are set in the interval, and the marketing risk assessment scheme is given as follows:(1)
Set the given warning vector interval: [ρ, τ].(2)
Giveε warning sample attribute arrays λ and the desired output parameter ξ.(3)
Complete the linear transformation of the early warning sample attribute array intoµ according to the standard pattern in the early warning vector interval.(4)
The ANN combined with Petri algorithm is used to complete the learning, and a nonlinear correspondence is achieved between ∫ andµ. The whole model is(13)ξ=Fμj,j=1,2,…,n.(5)
Then, the ANN combined with Petri algorithm is used to evaluate the marketing risk of the enterprise, and when the risk warning is completed, it is only necessary to set the weight attribute parameter of the warning criterion to obtain the comprehensive judgment index parameter of the enterprise’s marketing risk warning, and then the related warning data are given to realize the enterprise’s marketing risk evaluation.


## 6. Experiment

### 6.1. ANN Analysis of False Alarm and False Alarm Levels for Marketing Alerts Combined with Petri Algorithm

ObtainN samples of marketing risk prediction based on the marketing risk information of the company and test the above marketing warning model based on this [28].The number of samples to obtain false positives and false alarms ishh1 and hh2, and the probability of false positives and false alarms is(14)kk1=hh1N,kk2=hh2N.If the a priori probability is given asKK (Ψ), from the alert category Ψ, obtain early warning samples of n’ marketing models, i.e.,(15)N′=kkΨN.Furthermore, the optimal likelihood estimation results of misjudgment and false alarm are as follows:(16)kk1=∑hh1−1kkΨN,kk2=∑hh2−1kkΨN.This is shown in Figure4. The marketing landscape is complex and volatile, and the decisions and actions of the participants in marketing activities can have an impact on the market economy [29]. The study of marketing risk management programs, which anticipate the risks associated with the activities, can reduce losses and create greater returns. This paper firstly introduces the construction of a marketing risk early warning system, gives the classification of marketing risk conditions and formation factors, constructs an early warning indicator model, and then designs an ANN combined with Petri algorithm. Based on this algorithm, a marketing risk early warning scheme is given, sample selection is set, quantification of indicators is completed, early warning model is built and early warning steps are given, and finally the analysis of early warning misjudgment and false alarm degree is completed.Figure 4
Risk factors.As shown in the simulation results in Figure5, we can know that the enterprise management is based on the product marketing mode; Enterprises need to establish benchmarking management, performance evaluation and other methods to enhance the core values embedded in the enterprise; Enterprises should increase the marketing concept in the content of on-the-job training for employees. Imagine if the absence in this area leads to the region’s attraction projects not being successfully put into production, which will not only damage the regional economic development situation but also deteriorate the relationship with the government [30].Figure 5
Marketing efficiency.Therefore, the target market for marketing management is naturally focussed on urban areas.As shown in Figure6, corporate management and marketing staff need to deepen their understanding of marketing risks, and the logical path of understanding should be as follows: the purpose of product marketing, the current marketing risks, the root cause of the risks, and how to solve the shortcomings. This is the only way to distinguish it from ordinary commodity marketing and to provide feasible ideas for risk control. From Figure 6, we can see that the budget loss of this paper’s program is the least.Figure 6
Wind control comparison.


## 6.1. ANN Analysis of False Alarm and False Alarm Levels for Marketing Alerts Combined with Petri Algorithm

ObtainN samples of marketing risk prediction based on the marketing risk information of the company and test the above marketing warning model based on this [28].The number of samples to obtain false positives and false alarms ishh1 and hh2, and the probability of false positives and false alarms is(14)kk1=hh1N,kk2=hh2N.If the a priori probability is given asKK (Ψ), from the alert category Ψ, obtain early warning samples of n’ marketing models, i.e.,(15)N′=kkΨN.Furthermore, the optimal likelihood estimation results of misjudgment and false alarm are as follows:(16)kk1=∑hh1−1kkΨN,kk2=∑hh2−1kkΨN.This is shown in Figure4. The marketing landscape is complex and volatile, and the decisions and actions of the participants in marketing activities can have an impact on the market economy [29]. The study of marketing risk management programs, which anticipate the risks associated with the activities, can reduce losses and create greater returns. This paper firstly introduces the construction of a marketing risk early warning system, gives the classification of marketing risk conditions and formation factors, constructs an early warning indicator model, and then designs an ANN combined with Petri algorithm. Based on this algorithm, a marketing risk early warning scheme is given, sample selection is set, quantification of indicators is completed, early warning model is built and early warning steps are given, and finally the analysis of early warning misjudgment and false alarm degree is completed.Figure 4
Risk factors.As shown in the simulation results in Figure5, we can know that the enterprise management is based on the product marketing mode; Enterprises need to establish benchmarking management, performance evaluation and other methods to enhance the core values embedded in the enterprise; Enterprises should increase the marketing concept in the content of on-the-job training for employees. Imagine if the absence in this area leads to the region’s attraction projects not being successfully put into production, which will not only damage the regional economic development situation but also deteriorate the relationship with the government [30].Figure 5
Marketing efficiency.Therefore, the target market for marketing management is naturally focussed on urban areas.As shown in Figure6, corporate management and marketing staff need to deepen their understanding of marketing risks, and the logical path of understanding should be as follows: the purpose of product marketing, the current marketing risks, the root cause of the risks, and how to solve the shortcomings. This is the only way to distinguish it from ordinary commodity marketing and to provide feasible ideas for risk control. From Figure 6, we can see that the budget loss of this paper’s program is the least.Figure 6
Wind control comparison.


## 7. Conclusions

From marketing theory, it is known that a symbiotic effect is formed between economic and social benefits, which determine the need for enterprises to constantly pay attention to their own service quality. The article firstly completes the construction of marketing risk early warning system, analyzes the types of marketing risks and formation factors, constructs indicators and mathematical modeling, then proposes the ANN combined with Petri algorithm, uses the algorithm to realize marketing risk early warning, completes the sample selection, quantifies the indicators, sets the early warning model, and conducts false alarm and false alarm degree analysis.



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*Source: 4454283-2022-02-27.xml*