Developing technology and changing lifestyles have made online grocery delivery applications an indispensable part of urban life. Since the beginning of the COVID-19 pandemic, the demand for such applications has dramatically increased, creating new competitors that disrupt the market. An increasing level of competition might prompt companies to frequently restructure their marketing and product pricing strategies. Therefore, identifying the change patterns in product prices and sales volumes would provide a competitive advantage for the companies in the marketplace. In this paper, we investigate alternative clustering methodologies to group the products based on the price patterns and sales volumes. We propose a novel distance metric that takes into account how product prices and sales move together rather than calculating the distance using numerical values. We compare our approach with traditional clustering algorithms, which typically rely on generic distance metrics such as Euclidean distance, and image clustering approaches that aim to group data by capturing its visual patterns. We evaluate the performances of different clustering algorithms using our custom evaluation metric as well as Calinski Harabasz and Davies Bouldin indices, which are commonly used internal validity metrics. We conduct our numerical study using a propriety price dataset from an online food and grocery delivery company, and the publicly available Favorita sales dataset. We find that our proposed clustering approach and image clustering both perform well for finding the products with similar price and sales patterns within large datasets.
翻译:开发技术和改变生活方式使在线杂货交付应用成为城市生活不可或缺的组成部分。自COVID-19大流行以来,对此类应用的需求急剧增加,创造了破坏市场的新竞争者。竞争水平的提高可能促使公司经常调整其营销和产品定价战略。因此,确定产品价格和销售量的变化模式将为市场中的公司提供竞争优势。我们在本文件中调查基于价格模式和销售量将产品分组的替代集群方法。我们提出了新的距离指标,其中考虑到产品价格和销售如何共同发展,而不是使用数字值计算距离。我们比较了我们的方法与传统的集群算法,后者通常依赖通用的远程计量法,如Euclidean距离和图像组合法,其目的是通过捕捉其视觉模式来将数据分组。我们用我们的定制评价基准以及Calinski Harabasz和Davies Bouldin指数来评估不同的组合算法的绩效,这些指数通常使用内部有效性指标。我们利用一个适当的价格数据集进行数字研究,而不是使用数字值值计算。我们从一个在线食品和食品销售公司中找到一个适当的价格数据集,并用一个公开的销售模型进行。