员工卡订单消费分析数据

通过分析金华行平台的集团员工卡订单消费数据，根据订单所属集团子公司、消费类型和消费时段等信息，掌握不同订单的有效价值评分情况，基于订单的评分信息，筛选高价值的订单研究用户的消费行为偏好（如不同消费商品的高消费时间段），进而识别出消费的高峰时段和热门商品，采用个性化、精准化的营销手段挖掘潜在的消费需求，提升金华行APP的整体消费体验。1、数据采集：收集金华行平台中员工卡消费的订单数据，包含：交易号、外部订单号、公司名称、交易原价、交易金额、消费类型、消费说明、消费商户名称等字段；
2、数据处理：对采集到的数据进行清洗、去除无效数据，对公司名称进行脱敏处理；
3、数据加工：（1）使用SUMIFS函数分别计算消费总金额、该消费类型消费金额、该公司消费金额、本时段消费金额；（2）计算消费占比：该消费类型消费占比=该消费类型消费金额/消费总金额；该公司消费占比=该公司消费金额/消费总金额；本时段消费占比=本时段消费金额/消费总金额；（3）设置评分标准：消费类型评分=该消费类型消费占比*10；所属公司评分=该公司消费占比*10；消费时段评分=本时段消费占比*10；消费金额（M）评分：M>100，计为100；100≥M>50，计为50；50≥M>20，计为20；20≥M>1，计为1；M>0，计为0；评分=消费类型评分+所属公司评分+消费时段评分+消费金额（M）评分；
4、数据应用：使用可视化图表观察评分分布情况，评分越高，这条消费订单数据的价值越高。

This dataset is constructed by analyzing the consumption data of group employee card orders from the Jinhua Xing Platform. Based on information such as the group subsidiary to which each order belongs, consumption type and consumption time period, we obtain the effective value score of individual orders. By screening high-value orders based on the score information, we study users' consumption behavior preferences (e.g., peak consumption time periods for different goods), identify peak consumption periods and popular commodities, adopt personalized and precise marketing strategies to tap potential consumption demands, and improve the overall consumption experience of the Jinhua Xing APP.
1. Data Collection: Collect order data of employee card consumption on the Jinhua Xing Platform, which includes fields such as transaction number, external order number, company name, original transaction price, transaction amount, consumption type, consumption description, and merchant name for consumption.
2. Data Preprocessing: Clean the collected data, remove invalid records, and perform desensitization processing on company names.
3. Data Enrichment: (1) Use the SUMIFS function to calculate the total consumption amount, consumption amount of the current consumption type, consumption amount of the current company, and consumption amount of the current time period respectively; (2) Calculate consumption proportions: Consumption proportion of the current consumption type = Consumption amount of the current consumption type / Total consumption amount; Consumption proportion of the current company = Consumption amount of the current company / Total consumption amount; Consumption proportion of the current time period = Consumption amount of the current time period / Total consumption amount; (3) Set scoring criteria: Consumption type score = Consumption proportion of the current consumption type * 10; Affiliated company score = Consumption proportion of the current company * 10; Consumption time period score = Consumption proportion of the current time period * 10; Consumption amount (M) score: assign a score of 100 if M>100; 50 if 100≥M>50; 20 if 50≥M>20; 1 if 20≥M>1; 0 if M>0; Total score = Consumption type score + Affiliated company score + Consumption time period score + Consumption amount (M) score.
4. Data Application: Use visual charts to observe the score distribution. The higher the score, the higher the value of the corresponding consumption order data.

搜集汇总

数据集介绍

特点

该数据集包含613条员工卡订单消费记录，每日更新，用于分析消费行为偏好和制定精准营销策略。数据结构涵盖交易号、消费金额、消费类型等18个字段，通过评分机制识别高价值订单。