门店销售分析数据

通过乐檬零售后台将门店销售数据导出，由于导出数据庞大，较为细碎，需要人工处理才可以得到有实用价值的销售数据。通过细致的客单分析，零食连锁品牌能够深入理解客户行为模式，了解消费者偏好零食类型以及他们的购买频率等关键信息。通过分析客单价和交易次数，可以判断门店是否有效地吸引了顾客，以及顾客的零食购买力如何，进行客户细分，可以更好得制定对应的营销计划和活动策略，以达到帮助门店提高盈利水平的目的。其次这些分析结果也可以帮助经营者更好地理解市场和消费者，从而优化门店的产品和服务，提高顾客满意度和忠诚度。利用乐檬零售后台导出门店销售数据，由于只能导出门店单次交易，无法直接使用，需要进行一定的计算后方可达到使用目的。采集商品订单数据，编写代码处理获取所需数据。计算每家门店营业额：收银销售单据应收金额 - 收银退货单据应退金额；营业额(消费券)：收银销售单单据应收金额汇总 + 消费券抵扣金额；金额占比;单个门店营业额/总门店营业额；客单量：收银销售单据计数；客单量占比：单个门店客单量/总门店客单量；客单价：营业额/客单量；会员商品销售金额：会员的营业额汇总；会员金额占比：会员商品销售金额/营业额；会员客单数;会员收银销售单据计数；会员客单占比：会员客单数/客单量；会员客单价：会员商品销售金额/会员客单数；
连带率：商品销售数量 / 客单数，。通过析对比各门店客单数据，制定更合理的定价策略，吸引更多客户。

Store sales data is exported via the Lemon Retail Backend. Given that the exported data is voluminous and highly fragmented, manual processing is required to obtain actionable sales data. Through meticulous customer ticket analysis, snack chain brands can gain in-depth insights into customer behavioral patterns, including key information such as consumers' preferred snack categories and their purchase frequencies. By analyzing average order value (AOV) and transaction counts, one can evaluate whether stores effectively attract customers and assess customers' snack purchasing power. Conducting customer segmentation allows for the development of targeted marketing plans and campaign strategies, which helps stores improve their profitability. Additionally, these analysis results can assist store operators in better understanding the market and consumers, thereby optimizing store products and services, and enhancing customer satisfaction and loyalty. When exporting store sales data via the Lemon Retail Backend, only individual store transaction records can be exported, which cannot be directly utilized. Thus, certain calculations must be performed before the data can serve its intended purposes. First, collect commodity order data and write code to process and obtain the required data. Calculate the turnover of each store as the accounts receivable amount of cash register sales documents minus the refundable amount of cash register return documents; calculate the voucher-included turnover as the total accounts receivable amount of cash register sales documents plus the voucher deduction amount; calculate the amount proportion as the turnover of a single store divided by the total turnover of all stores; calculate the customer ticket count as the count of cash register sales documents; calculate the customer ticket count proportion as the customer ticket count of a single store divided by the total customer ticket count of all stores; calculate the average order value (AOV) as the turnover divided by the customer ticket count; calculate the member product sales amount as the total turnover of member transactions; calculate the member amount proportion as the member product sales amount divided by the total turnover; calculate the member transaction count as the count of member cash register sales documents; calculate the member transaction proportion as the member transaction count divided by the customer ticket count; calculate the member average order value as the member product sales amount divided by the member transaction count; calculate the tie-in rate as the number of sold products divided by the number of customer tickets. By analyzing and comparing customer ticket data across various stores, more reasonable pricing strategies can be formulated to attract more customers.

搜集汇总

数据集介绍

特点

门店销售分析数据是一个包含685条记录的企业数据集，每月更新，涵盖16个字段如营业额、客单量等，用于分析客户行为和优化门店运营。