Application of an Improved Neural Network Algorithm in E- commerce Customer Satisfaction Evaluation

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1 Application of an Improved Neural Network Algorithm in E- commerce Customer Satisfaction Evaluation Lei Yang 1,2 *, Ying Ma 3 1 Science and Technology Key Laboratory of Hebei Province, Baoding , China 2 Collaborative Innovation Center of Science and Technology Finance of Hebei Finance University, Baoding , China 3 Hebei College of Science and Technology, Baoding , China Abstract With the development of network technology, more and more people begin to use network trading to online shopping. Therefore, in this environment, customer satisfaction is particularly important for the development of enterprises, decision-making, e-commerce development. Aiming at the shortcomings of current e-commerce customer satisfaction evaluation methods, this paper proposes an evaluation model based on BP neural network. The verification results show that BP neural network method has a good effect on customer satisfaction evaluation of e-commerce enterprises in B2C mode. This marks that only need to input the standardized index data of the evaluation sample, we can get accurate evaluation results in the evaluation of e-commerce business customer satisfaction. Keywords: E-commerce, B2C, BP neural network, optimization algorithm, customer satisfaction. 1. INTRODUCTION With the development of network technology, more and more people begin to use network trading to online shopping. Therefore, in this environment, customer satisfaction is particularly important for the development of enterprises, decision-making, e-commerce development. The research on e-commerce customer satisfaction has been the focus of scholars, in the past few decades there have been many ideas, these ideas promote the development of e-commerce theory. Wolfinbarger and Gilly (2002) believe that factors affecting network service quality include the reliability, security, and direct customer service. These factors have a significant impact on customer satisfaction, customer attitude loyalty and behavior loyalty. Based on the field trips of nearly a hundred travel agencies and network CD retailers in Taiwan, Gu and Lin (2003) believe that the network customer trust, customer satisfaction, and customer perceived service quality have a significant impact on network loyalty. Dina et al., (2004) point out that electronic trust and electronic satisfaction are the main factors affecting electronic loyalty. Shergill et al., (2004) point out that customer satisfaction, brand reputation, customer trust and switching cost have significant impact on electronic loyalty. According to different tasks of recommendation system, Jiang (2015) introduces different accuracy metrics and their advantages and disadvantages. Based on the particularity of network marketing, Wei and Wang (2006) divide the factors that affect the electronic loyalty into network security, customer trust and customer satisfaction. By referring to the more mature American customer satisfaction index model, Bao (2007) points out that the factors influencing customer loyalty in virtual environment include customer perceived value, customer expectation, customer satisfaction and customer trust in virtual environment. Taking shipping company as the research obect, Wang (2012) analyzes the main factors of customer satisfaction. Through various methods, he finds out the main factors affecting customer satisfaction in e-commerce environment. Through the empirical investigation of online shopping behavior of college students, Zhang et al., (2007) obtain 5 factors that affecting electronic loyalty. They are customer perceived value, customer trust, website construction, network technology and customer service. Du (2009) concludes that the factors affecting e-commerce customer loyalty include the product quality and price, 344

2 service provided by enterprise, image of enterprise, switching cost, customer trust and customer satisfaction. Liu (2014) analyzes the relationship between customer perceived value, satisfaction and loyalty under the electricity supplier environment, and uses structural equation model to test the theoretical model. In conclusion, although some aspects of customer satisfaction in electronic commerce are studied, and achieved certain results, but there is no research on the complete B2C e-commerce system structure in the Internet, the study of customer satisfaction system is still in the exploratory stage. Aiming at the shortcomings of current e- commerce customer satisfaction evaluation methods, this paper proposes an evaluation model based on BP neural network. First of all, this paper introduces the characteristics of several consumer behavior of online shopping in e-commerce, and establishes the framework of e-commerce customer satisfaction which is based on these characteristics. Secondly, because of many factors affecting customer satisfaction, it brings inconvenience to the accurate evaluation. This paper establishes the evaluation model based on simulated annealing optimization neural network. The simulated annealing algorithm with global search ability can avoid the disadvantage of BP neural network with slow convergence speed and easy to fall into local minimum. By optimizing the weights of the neural network, the model increases the memory capacity of the current optimal solution. Therefore, it avoids the loss of the current optimal solution and improves the search efficiency of the algorithm. Third, this paper establishes the evaluation index system of e-commerce enterprise customer satisfaction in B2C mode. We take the previously collected e-commerce customer satisfaction evaluation results as training samples, and then the stable neural network model is trained by the algorithm established in this paper, it makes the error of the model reach a predetermined range. After that, five companies are introduced in the same way, and the satisfaction evaluation results which are calculated by this method are compared with the actual results. 2. ELECTRONIC COMMERCE CUSTOMER SATISFACTION There is a huge difference between B2C electronic retail mode and the traditional model, so in the B2C electronic retail mode, online shopping consumer behavior is more complex than that in the traditional mode. Specifically, the consumer behavior characteristics of online shopping customers mainly in the following areas: (1) Personalization of consumer demand For personalized consumption requirements, online shopping model is easier to achieve. Personalized customers can interact with online shopping website at any time, can directly participate in the design, production and packaging of enterprise products. Customers actively express their ideas and desires to suppliers, so as to make their own personalized needs to be met. (2) Differences in customer demand Because of E-commerce customer's environment and economic conditions are different, their needs are also different. Moreover, different e-commerce customers, even at the same level of demand, their needs will be different. Therefore, for the B2C e-commerce enterprises engaged in online retail, only seriously consider the differences between customer needs, enterprises can be successful. (3) Customer loyalty Because online customers can get more information and choice opportunity through the Internet, customer online shopping activities will be more realistic. Customers can pay more attention to the utility value of the products that they will buy, and constantly enhance their pursuit of new products and fashion. Moreover, with the help of search engines and other technologies, the cost of using the Internet is getting lower and lower. This makes it easier, more convenient, and less costly for customers to buy, compare, and turn to other online retailers. Therefore, customer conversion cost gradually reduced, resulting in customer loyalty continues to decline. Based on the above analysis, the author constructs the B2C e-commerce satisfaction driven model, as shown in Figure

3 Quality Price Product quality Convenient Service Service quality Customer satisfaction Interflow of goods and materials Security System and process Customer service Figure 1. B2C e-commerce satisfaction driven model Next, we use section 3-6 to introduce the evaluation methods and modeling knowledge of B2C e-commerce customer satisfaction. 3. SIMULATED ANNEALING (1) Basic concepts of simulated annealing algorithm The idea of simulated annealing algorithm is derived from the similarity between solid material annealing process and general combinatorial optimization problem. Simulated annealing algorithm is a general optimization algorithm, which has been widely used in optimal control, machine learning, neural networks and other optimization problems. According to the Metropolis criterion, the probability of particles tending to equilibrium at temperature T is p. Among them, e - E/KT is the internal energy at temperature T, E is its change quantity, and K is Boltzmann constant. If the state of the material is defined by the particle energy, the Metropolis algorithm can describe the annealing process with a simple numerical model. Assuming the energy of material in the state i energy is E(i), then the material at temperature T from the state i into the state follow the following rules: If E(i) E(), receive the state is converted; If E(i) E(), the state transition is received as follows: p e ( E( i) E( )) KT (1) In the formula (1), K is the Boltzmann constant in physics and T is the temperature of the material. (2) Steps of simulated annealing algorithm Step 1: initialization, given the initial temperature T 0 and the initial solution, calculate the obective function value f( ) of corresponding solution. 346

4 Step 2: use the model to generate the new and its corresponding obective function value f( ). Step 3: calculation function difference f=f( )-f( ) and udge the size of f and 0 If f 0, the receiving solution as the current solution; If f 0, calculated with probability P as a receiving solution with the formula (1). Step 4: reduce the current temperature, and then use Step 2 ~ Step 3 iteration N times. Step 5: if the algorithm satisfies the termination condition, the output current solution is the optimal solution and the end algorithm. Otherwise reduce the temperature, ump to Step 2 continue iterative calculation. 4. NEURAL NETWORK BP neural network, which is multilayer error feedback neural network, belongs to error back propagation algorithm. It consists of input layer, output layer and several hidden layers. Each layer has a plurality of nodes, each node represents a neuron, and the upper node and the lower node are connected by weight. The nodes between the layer and the layer adopt a fully interconnected connection mode, and there is no connection between nodes in each layer. The topology of neural network with multiple hidden layers is shown in figure 2. Figure 2. Topology of the multi hidden layer neural network The input layer has M input signals, and any input signal is represented by m; First hidden layer has I neurons, any of the neurons is represented by i; Second hidden layer has J neurons, any of the neurons expressed in ; The output layer has P neurons, either of which is represented by p. The weight input layer and first hidden layer is represented by W mi, and the weight of the first hidden layer and the hidden layer is represented by W i, and the weight of the second hidden and output layers is represented by W p. The input of neuron is expressed by u, and the excitation output is represented by v. The BP algorithm usually uses the sigmoid function as the excitation function. Next, we give several commonly used excitation functions. (1) Threshold excitation function: 1, x 0 f( x) 0, x 0 (2) (2) Sigmoid excitation function: 1 f( x) x 1 e (3) 347

5 (3) Linear excitation function: f ( x) kx (4) (4) Hyperbolic tangent excitation function: x f( x) tan( ) T (5) The BP neural network uses differentiable functions of formula 3. It is strict incremental, and can show good balance between the linear and nonlinear, so the input and output can be achieved between any nonlinear mapping. It has the advantages of good approximation effect, fast calculation speed and high precision. At the same time, its theoretical basis is solid, the derivation process is rigorous, the formula is symmetrical and graceful, and has strong nonlinear fitting ability, suitable for small data processing. 5. IMPROVED SIMULATED ANNEALING OPTIMIZATION NEURAL NETWORK ALGORITHM This method takes the BP algorithm as the main framework, and updates the weights by gradient descent method. Let W be the weight matrix of the neural network, Q is the minimum error of the precision of the system, n is the number of iterations, N is the number of samples in the training set, E(n) is the nth network learning error square, SA(W) is the function of the simulated annealing algorithm. BP algorithm based on BP algorithm and simulated annealing algorithm is described as follows: Step 1: establish BP network, clear learning accuracy Q and other parameters, set a counter Local Count and initialize it to 0; Step 2: if the network error is less than the preset constant, the counter increases by 1; Step 3: to determine whether the counter is greater than, if greater than that the learning has been caught in a local minimum, record the value of the error Q at this time. Then, the algorithm goes to step 4. If not greater than that the algorithm continues iterative learning; Step 4: call simulated annealing algorithm; E( n) SA( W ) Step 5: to udge whether E(n) Q, if less than that it has completely umped out of local minima, while a new round of iterative learning. Otherwise go to step 4 and continue to call simulated annealing algorithm for network training. 6. SIMULATION EXPERIMENT AND RESULT ANALYSIS 6.1 Electronic commerce customer satisfaction index system The evaluation of customer satisfaction is a process of comprehensive evaluation, and the choice of evaluation index is the key point. To scientifically implement customer satisfaction evaluation for B2C e-commerce enterprises, we must have a set of customer satisfaction evaluation index system in line with B2C service model. Based on the basic principles mentioned in the section 2 and other scholars' theories, we draw up the following 18 influential factors. They include quality, price, convenience, service, security, the logistics and customer service etc. Then, we give the B2C e-commerce customer satisfaction evaluation index system shown in Table 1: 348

6 Table 1 B2C e-commerce customer satisfaction evaluation index system First level index Second level index satisfaction index Variable Types of products X 1 Quality Product quality X 2 Description of product X 3 Commodity price X 4 Price Member points X 5 Discount X 6 Round the clock service X 7 Facilitate The convenience of Web Design service X 8 Customer service attitude X Electronic commerce 9 Customer service Corresponding speed of customer service X customer satisfaction 10 Problem solving skills of customer service X 11 Security Online payment security X 12 Personal privacy protection X 13 The way of logistics distribution X 14 Logistics The speed of logistics distribution X 15 The cost of logistics distribution X 16 Customer service Overall satisfaction after shopping on the site X 17 The next shopping will come back to this site X Data preprocessing of network satisfaction index Table 1 reflects the electronic commerce customer satisfaction from different angles. As the dimensions of the various indicators are different, so we cannot make a direct comparison. In order to make the index have comparability, and to speed up the convergence rate of the neural network, this paper has carried on the normalized processing to each index: (1) For the normalization of quantitative indicators, the methods are as follows: When the target is bigger, the better the evaluation, x min x y max x min x (6) When the target is smaller, the better the evaluation, x min x y 1 max x min x (7) Among them, y is the standardized value of the index x ; x is the minimum value of the th indicators which is determined in advance, maxx is the maximum value of the th indicators which is determined in advance, and N is the number of evaluation indicators. (2) For qualitative indicators, we use expert scoring method. In order to maintain comparability with quantitative indicators, it also normalized. 6.3 Simulation experiment Firstly, we collect the actual data and results of customer satisfaction evaluation of 10 e-commerce companies 349

7 operated by B2C model in advance. Then we normalize the data into the sample data of training and inspection, as shown in table 2. Table 2 The sample data of customer satisfaction evaluation sample data of 10 companies X1 X2 X3 X4 X5 X6 X7 X8 X9 Enterprise Enterprise Enterprise Enterprise Enterprise Enterprise Enterprise Enterprise Enterprise Enterprise X10 X11 X12 X13 X14 X15 X16 X17 Enterprise Enterprise Enterprise Enterprise Enterprise Enterprise Enterprise Enterprise Enterprise Enterprise In this paper, the electronic commerce customer satisfaction data of an enterprise is selected. The result of the scoring is the input value of the improved BP neural network. Because the neural network model of this paper is a 18-X-1 model. We carry out the training of the sample according to principle. According to the Fibonacci method, the number of input layer node is k, the number of output layer node is l, and the number of hidden layer node is m. Where the m [ a, b], and it satisfies the following formula: a ( k l) 2 m ( k l) 5 b (8) Next, we test the neural network containing 10, 13, 16, and 20 hidden layers, and find out the number of hidden layers when the minimum fitting error exists. As shown in Figure 3-7. Figure 3. The number of hidden layer node is 10 in neural network training 350

8 Figure 4. The number of hidden layer node is 13 in neural network training Figure 5. The number of hidden layer node is 16 in neural network training Figure 6. The number of hidden layer node is 20 in neural network training Figure 7. The number of hidden layer node is 23 in neural network training Above all, when number of hidden layer node of the BP network is 16, the mean square error reaches the minimum, and it is Thus, a neural network trained for 16 hidden layer nodes is completed. From the above five graphs, we can see that after 1000 iterations, the convergence rate is different. Among them, the fastest 351

9 convergence rate is when there are 16 hidden layer nodes, iteration about 90 times it reached the error convergence, while the convergence times of the other four states is about between 400 to Setting of electronic commerce customer satisfaction level According to the comprehensive score of the index, we can evaluate the electronic commerce customer satisfaction. According to the relevant research, the electronic commerce customer satisfaction level is divided into 5 levels. They include very satisfactory (A), satisfaction (B), general (C), dissatisfaction (D), and dissatisfaction (E). We set the total score of satisfaction level as 1, then the corresponding satisfaction level and the corresponding score is shown in table 3. Table 3 Electronic commerce customer satisfaction level level A B C D E Score Next, we compare the experimental results from two aspects. First, we introduce five new companies in the same way, The input value is introduced into the improved neural network model which has been trained, then the satisfaction evaluation results calculated by this method are compared with the actual results, as shown in table 4. Table 4 Comparison of e-commerce satisfaction evaluation results Output result Output level Actual level Consistent? Enterprise A A Y Enterprise B B Y Enterprise B B Y Enterprise B B Y Enterprise A A Y Then, we use this algorithm to compare with other methods which are artificial neural network prediction algorithm and gray prediction model. We repeat the experiment 5 times, the correct rate of each algorithm is shown in Figure 8. Figure 8. Comparison chart of the assessment result It is not difficult to see from table 4 and figure 8, whether in the newly introduced company perspective or in contrast to other algorithms, the experimental results show that the prediction results are more close to the expected output value of the enterprise. 7. CONCLUSION Aiming at the shortcomings of current e-commerce customer satisfaction evaluation methods, this paper proposes an evaluation model based on BP neural network. Then, this paper establishes the evaluation model based on 352

10 simulated annealing optimization neural network. The simulated annealing algorithm with global search ability can avoid the disadvantage of BP neural network with slow convergence speed and easy to fall into local minimum. By optimizing the weights of the neural network, the model increases the memory capacity of the current optimal solution. Therefore, it avoids the loss of the current optimal solution and improves the search efficiency of the algorithm. So we can use the method established in this paper to calculate the satisfaction evaluation results. The verification results show that BP neural network method has a good effect on customer satisfaction evaluation of e-commerce enterprises in B2C mode. This marks that only need to input the standardized index data of the evaluation sample, we can get accurate evaluation results in the evaluation of e-commerce business customer satisfaction. ACKNOWLEDGMENTS The translation of this paper was funded by Open fund of Collaborative Innovation Center of Science and Technology Finance of Hebei province (STFCIC201715); 2017 Social Science Foundation of Hebei province (HB17YJ052); 2016 Social Science Foundation of Hebei province (HB16YJ035) Research proect supported by Hebei Talents Training Fund (A ); 2016 National Social Science Foundation Proect (16BJY169); Key proect of Humanities and social science research of Hebei Education Department (ZD201617). REFERENCES Bao J. (2007). B2C website customer loyalty influencing factors empirical analysis, East China economic management, 9, 132. Du X. (2009). Research on influencing factors and Cultivation Strategies of customer loyalty in e-commerce website, Reform and strategy. 7, Jiang S. (2015). Effects of diversity strategies on e-commerce recommendation system, e-commerce, (03), Liu L. (2014). Research on the relationship among perceived value, satisfaction and loyalty in e-commerce environment, Chinese Management Modernization Research Association, Luarn, Lin. (2003). A Customer Loyalty Model for E-Service Context, Journal of Economic Commerce Research. 4, Ribbink D., Allard C.R. (2004). Comfort your Online Customer: Quality, Trust, and Loyalty on the Internet, Marketing Service Quality. 6, Shergill, Bill, Mgt. (2009). Managing What Customers Learn from Experience. Journal of Marketing. 4, Wang Y. (2012). Research on customer satisfaction of shipping companies under e-commerce environment: a case study of an international shipping company, East China University of Science and Technology, Wei Y.F., Wang C. (2006). B2C e-commerce customer loyalty path analysis, Business modernization. 10, Wolfinbarger M., Gilly M.C. (2003). Dimensionalizing, Measuring and Predicting E-tail Quality, Journal of Retailing, 3, Zhang Y., Wang F. (2007). An empirical study on factors influencing E- loyalty, Marketing Management Herald, 3,