第三方医学检验机构消费者满意度分析数据

企业在面对客户定制某些运营、营销策略时，希望能够针对从众多影响绩效的工作内容中甄选出最重要的内容，从而精准把握客户需求、优化服务体验、提升满意度。
精准把握客户需求、优化服务体验、提升满意度的前提是明确工作内容中客户对于该产品或服务各属性的重视程度，以及对各属性绩效表现程度的评价。
通过识别关键服务属性，评估、筛选出客户最看重的服务属性，指导企业将有限的资源集中投入到最能提升客户满意度的服务改进上，从而实现资源优化配置、提高顾客的整体满意度和忠诚度。并为同行业企业提供参考。1、单项满意度得分最低为1分，最高为3分。2、数据处理：对数据进行脱敏。 3、数据加工：运用修正IPA模型， ①计算单个客户17个题项的整体满意度均值：单个客户17个题项的满意度评分总和/17；②计算所有客户对17个题项单项的满意度均值：所有客户某一题项的满意度评分总和/客户总数；③根据②，计算所有客户对17个题项的整体满意度均值：所有客户对17个题项单项的满意度均值的总和/17；④计算单个客户对每个题项的满意度的自然对数、形成新变量，计算公式：ln(单个客户对某一题项的满意度评分)；⑤以新变量为自变量，以①为因变量，通过线性回归分析，分别计算17个题项的偏相关性系数作为引申重要性；⑥计算引申重要性的平均值；⑦以引申重要性（⑤）为纵坐标，满意度均值（②）为横坐标，以③的数值作为x轴参考线、以⑥的数值作为y轴参考线、形成四象限，将数据在IPA矩阵上制成散点图，得到各服务项目的归属的象限，其中左上代表重点问题区，重要性高，满意度低，首位改善的弱势属性；右上代表表现良好区，重要性高，满意度高，产品/服务的强势属性；右下为额外资源区，重要性低，满意度高；左下为低优先级区，重要性低，满意度低。

When enterprises customize certain operation and marketing strategies for customers, they aim to select the most critical content from numerous performance-influencing work contents, so as to accurately grasp customer needs, optimize service experience and improve customer satisfaction.
The premise of accurately grasping customer needs, optimizing service experience and improving customer satisfaction is to clarify the degree of importance that customers attach to each attribute of the product or service in the work content, as well as their evaluation of the performance level of each attribute.
By identifying key service attributes, evaluating and screening the service attributes that customers value most, enterprises can guide their limited resources to be invested in service improvements that best enhance customer satisfaction, thereby optimizing resource allocation, improving overall customer satisfaction and loyalty, and providing references for enterprises in the same industry.
1. The single-item satisfaction score ranges from a minimum of 1 point to a maximum of 3 points.
2. Data processing: Perform data desensitization.
3. Data processing using the Modified IPA Model:
① Calculate the overall satisfaction mean of 17 items for a single customer: Sum of the satisfaction scores of the 17 items for a single customer divided by 17;
② Calculate the overall satisfaction mean of each of the 17 items across all customers: Sum of the satisfaction scores of a specific item from all customers divided by the total number of customers;
③ Calculate the overall satisfaction mean of the 17 items across all customers based on the result of step ②: Sum of the overall satisfaction means of the 17 items across all customers divided by 17;
④ Calculate the natural logarithm of the satisfaction score of each item for a single customer to form a new variable, with the formula: ln(satisfaction score of a specific item for a single customer);
⑤ Take the new variable as the independent variable and the result of step ① as the dependent variable, and calculate the partial correlation coefficients of the 17 items as the derived importance through linear regression analysis respectively;
⑥ Calculate the average value of the derived importance;
⑦ Take the derived importance (result of step ⑤) as the ordinate and the satisfaction mean (result of step ②) as the abscissa, use the value of step ③ as the x-axis reference line and the value of step ⑥ as the y-axis reference line to form a four-quadrant matrix. Plot the data as scatter points on the IPA matrix to obtain the quadrant each service item belongs to:
- Upper-left quadrant: Key problem area, with high importance and low satisfaction, which are the weak attributes that need to be improved first;
- Upper-right quadrant: Good performance area, with high importance and high satisfaction, which are the strong attributes of the product or service;
- Lower-right quadrant: Excess resource area, with low importance and high satisfaction;
- Lower-left quadrant: Low-priority area, with low importance and low satisfaction.

搜集汇总

数据集介绍

特点

该数据集提供了第三方医学检验机构的消费者满意度分析数据，包含17个服务属性的满意度评分和评价分类，旨在帮助企业识别关键服务属性并优化资源配置。数据规模为695条，每季度更新一次，应用场景包括客户需求分析和满意度提升。