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汽车车辆使用总成本数据

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广东省数据知识产权存证登记平台2023-12-07 更新2024-05-08 收录
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该数据是用于衡量车辆在使用期间的总成本,是反映车型竞争力的重要参考指标。该数据根据汽车主流网站的新车报价数据、二手车报价数据、车型属性数据、保养数据、油价、电价、税费政策等,结合数鼎的残值预测模型,综合处理得出。其中,车型属性数据、新车报价数据、二手车报价数据均需要经过数据清洗和处理。数据清洗包括去除无效数据、异常报价等;数据处理包括各网站的车型匹配、数据整合等;数鼎的残值预测模型是为了预测车辆在使用期末的价值,从而得到折旧成本数据。该数据可以计算各种维度的成本,例如品牌维度、细分维度、车系维度等,也可以监测成本随时间的走势,从而为汽车厂商提供销售策略支持以及产品改进方向,也可以为经销商提供销售话术支持等。因此,其应用场景包括销售策略制定、品牌价值提升,以及消费者购买决策。

This dataset is designed to measure the total cost of vehicle ownership (TCO), a critical reference indicator for evaluating the competitiveness of vehicle models. It is derived through comprehensive processing by integrating data such as new vehicle quotation data, second-hand vehicle quotation data, vehicle model attribute data, maintenance records, oil and electricity prices, and tax policies from mainstream automotive online platforms, combined with Shuding's residual value prediction model. Specifically, the vehicle model attribute data, new vehicle quotation data and second-hand vehicle quotation data require preliminary data cleaning and processing. Data cleaning covers the removal of invalid entries and abnormal quotations, while data processing includes model matching across different platforms and data consolidation. Shuding's residual value prediction model is developed to forecast the vehicle's value at the end of its service life, thereby deriving depreciation cost data. This dataset supports cost calculations across multiple dimensions, including brand, market segment and vehicle series, and enables real-time monitoring of cost trends over time. It can provide automotive manufacturers with support for sales strategy formulation and product improvement directions, as well as offer sales script assistance for dealers and other relevant parties. Therefore, its application scenarios include sales strategy development, brand value enhancement and consumer purchase decision-making.
提供机构:
广东数鼎科技有限公司
创建时间:
2023-12-07
搜集汇总
数据集介绍
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背景与挑战
背景概述
该数据集通过整合新车报价、二手车报价、车型属性、保养数据、油价、电价和税费政策等多源信息,结合残值预测模型,计算车辆在使用期间的总成本,以评估车型竞争力。数据经过清洗和处理,确保准确性,可支持品牌、细分和车系等多维度成本分析,并监测成本随时间变化。其应用场景包括为汽车厂商提供销售策略和产品改进依据,为经销商优化销售话术,以及辅助消费者做出购买决策。
以上内容由遇见数据集搜集并总结生成
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