配送全国区域内的各个KTV层级数据

通过计算本公司与全国区域内的不同KTV的配送消费信息，对不同的KTV按照RFM模型进行分层，本行业所有配送企业可对不同层级的KTV采取不同的运营策略。其次，本数据还能为配送行业的相关如物流公司等提供整体性参考，从而有效洞察市场趋势，更好地做出科学的营销决策。1、数据采集：导出本公司与全国区域内的不同KTV的配送消费信息。2、数据处理：以KTV编号作为唯一标识，对数据进行清洗、去除无效数据和极限数据等操作。3、数据加工：通过LOOKUP函数计算出KTV最近一次配送距离当月月底的天数，COUNTIF函数计算当月配送次数，SUMIF函数计算当月配送总金额，最近一次配送距离当月月底的天数大于或者等于当月平均配送间隔天数则R定档值为1，反之R定档值为0，当月配送次数大于或者等于当月平均配送次数则F定档值为1，反之F定档值为0，当月配送总金额大于或者等于当月配送平均金额则M定档值为1，反之M定档值为0，再根据RFM模型分层规则按RFM值将KTV分为8个层级，即分为重要价值客户(RFM为111)、重要保持客户(RFM为101)、重要发展客户(RFM为011)、重要挽留客户(RFM为001)、一般价值客户(RFM为110)、一般保持客户(RFM为100)、一般发展客户(RFM为010)和一般挽留客户(RFM为000)。4、数据应用：通过对KTV进行分层管理，所有KTV可对不同层级的KTV定制不同的运营策略。

By analyzing the delivery and consumption data between our company and various KTVs across the country, we can stratify different KTVs using the RFM model. All delivery enterprises in this industry can then adopt differentiated operational strategies for KTVs at different tiers. Additionally, this dataset can provide holistic references for related stakeholders in the delivery industry such as logistics companies, enabling effective market trend insights and more scientific evidence-based marketing decision-making.
1. Data Collection: Export the delivery and consumption data between our company and various KTVs across the country.
2. Data Preprocessing: Use KTV IDs as unique identifiers to clean the data, remove invalid data and outlier values, etc.
3. Data Calculation and Stratification: Calculate the days between the last delivery and the end of the current month using the LOOKUP function, the number of monthly deliveries using the COUNTIF function, and the total monthly delivery amount using the SUMIF function. Set the R score to 1 if the days between the last delivery and the end of the current month are greater than or equal to the average delivery interval days of the current month, otherwise set to 0; set the F score to 1 if the monthly delivery count is greater than or equal to the average monthly delivery count, otherwise set to 0; set the M score to 1 if the total monthly delivery amount is greater than or equal to the average monthly delivery amount, otherwise set to 0. Then stratify KTVs into 8 tiers based on their RFM values according to the RFM model stratification rules: Important Value Customers (RFM=111), Important Retention Customers (RFM=101), Important Development Customers (RFM=011), Important Win-Back Customers (RFM=001), General Value Customers (RFM=110), General Retention Customers (RFM=100), General Development Customers (RFM=010), and General Win-Back Customers (RFM=000).
4. Data Application: Through stratified management of KTVs, all delivery enterprises can formulate tailored operational strategies for KTVs at different tiers to optimize their business operations.

搜集汇总

数据集介绍

特点

该数据集包含全国范围内KTV的配送消费信息，通过RFM模型对KTV进行分层，共1632条数据，每月更新。应用场景包括对不同层级的KTV采取不同的运营策略，以及为配送行业提供市场趋势洞察。