男士净爽云感洁面慕斯用户消费能力分层数据
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通过对男士净爽云感洁面慕斯用户历史下单数据的收集分析，了解该商品用户消费情况，对消费者进行标签制定，定位消费级别，了解消费者的偏好，从而了解该产品是否畅销，为所有批发、零售行业制定采购、销售策略，更好地为消费者提供个性化的商品和服务，从而帮助企业更准确地掌握市场动态，提升消费者满意度和竞争力。如A类消费：建立专门的客户关系管理团队，与他们保持密切的一对一沟通，及时了解他们的需求变化并满足。设立专属的忠诚度奖励计划，例如每消费一定金额就给予额外的高额积分，积分可兑换高价值的商品、服务或者现金折扣。B类消费：虽然不能像A类客户那样提供高度定制化服务，但可以根据他们的消费历史提供一定程度的个性化推荐。例如，推荐与他们之前购买产品相匹配的升级产品或者配套产品。提供有针对性的优惠券和折扣券，例如针对他们经常购买的产品类别提供较大幅度的折扣。C类消费：根据他们的消费偏好，推荐性价比高的产品组合。设立会员成长计划，向他们展示从C类消费升级到B类消费可以获得的更多权益，如更多的折扣、优先购买权等，激励他们提高消费占比等等。1、数据收集：从数据库中根据用户消费能力梳理数据，筛选出用户ID、付款时间、消费金额（元）字段； 2、数据加工：a.先针对消费金额字段用SUM函数求和，得到总销售金额（元）；b.消费占比=消费金额（元）/总销售金额（元）*100%；c.用户消费占总消费的比例按从大到小进行排名，消费分类运用ABCDEF分类法，对占比大于等于1%以上，给予“A类消费”分层；占比在小于1%到大于等于0.8%区间，则给予“B类消费”分层；占比在小于0.8%到大于等于0.6%区间，则给予“C类消费”分层；占比在小于0.6%到大于等于0.4%区间，则给予“D类消费”分层；占比在小于0.4%到大于等于0.2%区间，则给予“E类消费”;占比在小于0.2%以下，则给予“F类消费”分层。 3、数据应用：通过这样的分析流程，企业不仅能够更准确地把握市场动态，还能够有效提升客户满意度和市场竞争力。
This dataset is constructed by collecting and analyzing the historical order data of users who purchased Men's Refreshing Cloud-like Cleansing Mousse, with the objectives of understanding users' consumption status, developing user tags, identifying their consumption tiers and preferences, evaluating the product's market popularity, formulating procurement and sales strategies for both wholesale and retail industries, delivering personalized products and services to consumers, enabling enterprises to accurately grasp market dynamics, and enhancing consumer satisfaction and corporate competitiveness. For Class A consumers: Establish a dedicated Customer Relationship Management (CRM) team to maintain close one-on-one communication with them, timely understand their evolving needs and address them accordingly. Set up an exclusive loyalty reward program, for example, granting additional high-value points for every fixed amount of consumption, where the points can be redeemed for high-value goods, services or cash discounts. For Class B consumers: While highly customized services cannot be provided as for Class A customers, personalized recommendations can be made based on their consumption history. For example, recommend upgraded products or complementary matching products aligned with their prior purchases. Provide targeted coupons and discount vouchers, such as offering significant discounts for the product categories they frequently purchase. For Class C consumers: Recommend cost-effective product bundles based on their consumption preferences. Launch a member growth program to showcase the additional benefits they can gain by upgrading their consumption tier from Class C to Class B, such as enhanced discounts and priority purchase rights, to motivate them to increase their consumption share, etc. 1. Data Collection: Extract and organize data from the database based on users' consumption capabilities, and retain the fields including User ID, Payment Time, and Consumption Amount (CNY). 2. Data Processing: a. Calculate the total sales amount (CNY) using the SUM function on the consumption amount field; b. Compute the consumption proportion as (Consumption Amount / Total Sales Amount) * 100%; c. Rank users by their total consumption proportion in descending order, and classify them using the ABCDEF classification method: Class A consumption for proportions ≥ 1%; Class B consumption for proportions in the range [0.8%, 1%); Class C consumption for [0.6%, 0.8%); Class D consumption for [0.4%, 0.6%); Class E consumption for [0.2%, 0.4%); Class F consumption for proportions < 0.2%. 3. Data Application: Through this standardized analysis workflow, enterprises can not only accurately grasp market dynamics, but also effectively improve customer satisfaction and market competitiveness.
