Cluster Analysis and Forecasting on Local Shoe Products: Case study for Ventela in Indonesia

Abstract

Shoes are a secondary need that is in demand by all age groups. Each shoe brand has many models, which means a store must provide complete stock to meet consumer needs. The available models have different purchasing power or demand, which creates difficulties for stores in determining shoe products that are often sold and shoe products that are not in demand by customers. The data used in cluster formation includes three variables recorded and collected from e-commerce transactions, namely Number of Visitors, Number of Buyers, and Total Sales. Based on these variables, Ventela shoe models are grouped into three clusters, namely low-selling, normal and best-selling. Next, the variable number of transactions for Ventela shoe models in the best-selling cluster is taken to be predicted using the Exponential Smoothing method. The forecasts obtained are used to determine future demand to maximize profits. Based on the results of the clustering analysis, it was found that the number of shoe models included in the best-selling cluster was six, including (1) Ventela Ethnic Low All Black, (2) Ventela Ethnic Low Black Natural, (3) Ventela Public Low Black Natural, (4) Ventela Public Low Cream, (5) Ventela Republic Low Black Natural, and (6) Ventela Republic Low White. Referring to the sample of this study, which only spanned less than three years, several shoe models produced a forecast value of zero. It means, based on the forecast results for the next 12 months, there may be no sales.