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Developing technology and changing lifestyles have made online grocerydelivery applications an indispensable part of urban life. Since the beginningof the COVID-19 pandemic, the demand for such applications has dramaticallyincreased, creating new competitors that disrupt the market. An increasinglevel of competition might prompt companies to frequently restructure theirmarketing and product pricing strategies. Therefore, identifying the changepatterns in product prices and sales volumes would provide a competitiveadvantage for the companies in the marketplace. In this paper, we investigatealternative clustering methodologies to group the products based on the pricepatterns and sales volumes. We propose a novel distance metric that takes intoaccount how product prices and sales move together rather than calculating thedistance using numerical values. We compare our approach with traditionalclustering algorithms, which typically rely on generic distance metrics such asEuclidean distance, and image clustering approaches that aim to group data bycapturing its visual patterns. We evaluate the performances of differentclustering algorithms using our custom evaluation metric as well as CalinskiHarabasz and Davies Bouldin indices, which are commonly used internal validitymetrics. We conduct our numerical study using a propriety price dataset from anonline food and grocery delivery company, and the publicly available Favoritasales dataset. We find that our proposed clustering approach and imageclustering both perform well for finding the products with similar price andsales patterns within large datasets.

Time Series Clustering for Grouping Products Based on Price and Sales Patterns