技术
- 应用基础设施与中间件 - 数据可视化
- 基础设施即服务 (IaaS) - 私有云
适用行业
- 水泥
- 零售
适用功能
- 采购
用例
- 零售店自动化
- 时间敏感网络
关于客户
本案例研究中的客户是美国证券公司,一家私募股权公司。该公司的商业模式包括购买私营企业,管理它们三到五年,然后出售它们以获取利润。该公司参与竞标过程来收购业务。在这种情况下,该公司正在考虑收购一家在美国特定地区拥有多家商店的零售连锁店。该公司需要了解零售连锁店是否可以通过建立更多商店来继续盈利,并使用空间数据分析来深入了解零售连锁店扩张策略的潜在盈利能力。
挑战
该案例研究围绕一家私募股权公司美国证券公司进行,该公司正在考虑收购一家零售连锁店。在做出投资决定之前需要回答的首要问题是零售连锁店是否可以通过开设更多商店来继续盈利。该公司对两种扩张战略感兴趣:空白,零售连锁店将扩展到目前没有品牌存在的新市场;以及填充,即在零售连锁店现有的市场上开设更多商店。鉴于投标过程的竞争性和时间密集性,面临的挑战是在短时间内了解这些策略的潜在盈利能力。该公司还需要了解可能影响商店业绩的因素,例如距核心市场距离的影响以及其他潜在的混杂因素。
解决方案
该公司利用空间数据分析来深入了解零售连锁店扩张战略的潜在盈利能力。他们收集了大约 500 个空间变量,包括人口统计、社会人口统计和竞争存在数据,并将这些变量与每家商店周围 10 或 15 分钟的车程半径进行匹配。他们使用等时线技术来汇总数据并确定与商店收入的相关性。该公司还使用分类和回归树 (CART) 分析来了解新商店与旧商店的特征以及高于平均水平的商店与低于平均水平的商店的特征。 CART 分析显示,较新的商店平均距离核心市场较远,且没有卡车停车场,而面积较大的商店即使远离核心市场也表现良好。该公司还使用 CART 根据预测收入对现有市场周围的人口普查区进行评分,这有助于他们确定新商店的潜在位置。
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