Keywords

1 Introduction

The theory of customer relationship management is developed based on the theory of relationship marketing [1]. Its core lies in taking the customer as the centre, fully doing a good job in the collection, exploration, utilization of customer information, implementing customer service and obtaining customer satisfaction [2]. In the actual application process, the most important link is to collect customer information and perform data processing [3]. When performing data processing, customer information data can be classified from different perspectives [4].

2 Edit Data According to Customer Value

Customer value refers to the total benefits that the customer’s entire life cycle can bring to the company. It includes two major parts: potential value and current value. On the basis of fully collecting customer information, companies can conduct customer development analysis based on customer business indicators and then determine Value customers. Potential value customers and other customer types provide corresponding levels of service based on value to achieve customer satisfaction, customer loyalty and customer retention, and optimize the input and output efficiency [5, 6].

The evaluation of customer value can be analysed and considered through current value and potential value, and the current value evaluation and potential value evaluation of customers can be decomposed into corresponding indicators, and the category of customer value can be obtained by coordinated analysis [7]. This model is shown as Fig. 1:

Fig. 1.
figure 1

Customer value evaluation mode diagram 1.

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Generally speaking, customers with low contribution to the company also have less expectation of service demand. The higher the value, the higher the demand for service. Therefore, the company can combine customer value evaluation and provide corresponding services referring to high, medium, low and potential distribution, so as to improve the comprehensive satisfaction of all levels of value customers and achieve continuous optimization of customer relationships with limited resources.

3 Edit Data According to Customer Satisfaction Evaluation

Customer satisfaction is the basis of keeping customers and realizing customer loyalty, and it is also one of the purposes of customer relationship management and customer service. Grasping customer satisfaction, improving customer satisfaction in a targeted manner, and developing customer service are key tasks for customer relationship maintenance. Customer satisfaction evaluation model is a more effective satisfaction evaluation tool [8]. This model is shown as Fig. 2:

The customer satisfaction evaluation model is developed from the five aspects of reliability, security, tangibility, responsiveness, and personalised care. Based on the actual situation, corresponding indicators are designed and scored, to evaluate the customer’s satisfaction with the service and follow the satisfaction degree to edit customer information.

Fig. 2.
figure 2

Customer value evaluation mode diagram 2.

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4 Edit Data Information According to the Service Blueprint

Kahneman [9], winner of the Nobel Prize in Economics in 2002, after a long period of in-depth research, he proposed the famous peak-end law, that is, people’s evaluation of experience mainly depends on the memory of the peak and the end. Comparing with the peak-end law, it can be concluded that doing a good service must pay attention to the customer needs at the peak moment and the end-point experience. If we want to use this theory to build a service system, it is required to describe the whole processes of the horizontal service, find the peak-end moment, explore the customer response demand and do a good job of service design.

The service blueprint is a deepening of the peak-end law, which includes not only the horizontal external customer service processes, but also the vertical internal company service activities. When an enterprise is editing customer information, it can record customer demand information from multiple contents such as customer behaviour, employee service behaviour, tangible display, background service and supportive activities.

5 Edit Data from the Perspective of Data Quality Evaluation Dimensions

According to specific actual conditions, various international organizations have proposed their own data quality evaluation dimensions [10]. The requirements of US national accounting for data quality are from the three dimensions of accuracy, comparability, and applicability. The British statistics department conducts quality assessment in terms of timeliness, effectiveness, accuracy, and objectivity. The statistical departments of Europe, Canada, Australia, and other countries, as well as international organizations such as the IMF, have also proposed their own corresponding data quality analysis and evaluation dimensions. In general, the analysis dimensions proposed by various scholars and countries are basically similar. Chinese companies can also learn from three dimensions of accuracy, comparability and applicability which are used for data recording.

6 Optimize Data in Terms of Data Quality Factors

When companies edit customer information data, they should also pay attention to some factors affecting data quality, including inherent data quality problems, quality problems during data acquisition, quality problems during data analysis and processing, errors caused by insufficient preparation, errors during data collection, data processing errors and errors caused by personal subjective factors [11, 12].

7 Conclusion

Through the above explanation, I believe that companies can edit customer information data from the aspects of customer value, customer satisfaction evaluation, service blueprint, data quality evaluation dimensions and optimize data from the aspects of data quality influencing factors. When faced with so many angles of data classification, it is often difficult to choose. To solve such problems, the American mathematician Sadie proposed the famous analytical hierarchy process (AHP) [13], which can decompose the core problem into several impact factors and factor supporting indicators. In AHP, the upper and lower logical hierarchical structure between the impact factors and its indicators is constructed and the judgment matrix is established. Through the expert evaluation method, the importance of each factor indicator is determined in pairs comparative judgment. Based on the judgment matrix, the feature vector is calculated, and the corresponding weight of each indicator is obtained, and then the influence degree of the lowest level indicator on the overall goal is obtained. This method can provide a good way for how to select data [14, 15].