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Exploring the Brand Competition Patterns of China’s Auto Market with Massive Sales Leads

机译:大量销售线索探索中国汽车市场的品牌竞争格局

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Understanding the brand competition pattern is extremely important for automakers and dealers to identify emerging competitors and respond agilely to market changes. However, the traditional methods are subject to the analysts’ experiences and analytical dimensions, which lack a reliable methodology and ignore the value of user behaviors. Therefore, in this paper, we incorporate the complex network into our model to explore and analyze the brand competition patterns in China’s auto market with users’ sales leads in detail. Our investigation involves 188,085,987 sales leads of 213 brands from a vertical auto website, and it has three important contributions. First, we propose an innovative visualization approach to help automakers quickly identify competitors and market positioning. Second, we analyze both the static and dynamic characteristics of the auto brand competition network, and reveal characteristics including the small-world phenomenon, disassortativeness, and preferential attachment. Finally, we discover the core-periphery structure in the network, and introduce a novel method to identify the core/periphery characteristics of brands with centrality metrics and K-Means clustering. Besides, features such as the number of sales leads, the number of models of the brand, and the sales price range are found to accurately predict the core/periphery characteristics of the brand.
机译:对于汽车制造商和经销商来说,了解品牌竞争模式对于识别新兴竞争对手并敏捷地应对市场变化至关重要。但是,传统方法受制于分析人员的经验和分析范围,这些方法缺乏可靠的方法论,并且忽略了用户行为的价值。因此,在本文中,我们将复杂的网络整合到我们的模型中,以详细分析和分析用户的销售线索来分析和分析中国汽车市场中的品牌竞争模式。我们的调查涉及垂直汽车网站上213个品牌的188,085,987个销售线索,该调查有三个重要贡献。首先,我们提出一种创新的可视化方法,以帮助汽车制造商快速识别竞争对手和市场定位。其次,我们分析了汽车品牌竞争网络的静态和动态特征,并揭示了包括小世界现象,分散性和偏好依附性在内的特征。最后,我们发现了网络中的核心-外围结构,并引入了一种新的方法来通过集中度指标和K-Means聚类来识别品牌的核心/外围特征。此外,还发现了诸如销售线索数量,品牌型号数量和销售价格范围之类的特征,可以准确地预测品牌的核心/外围特征。

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