Consumption patterns and prediction with neural networks in e-commerce from a star product: case of United Kingdom
DOI:
https://doi.org/10.54167/tch.v19iEspecial.1988Keywords:
data analysis, e-commerce, forecasting, neural networksAbstract
E-commerce has transformed the relationship between businesses and consumers, generating large volumes of data that require advanced tools for analysis. Artificial Neural Networks (ANNs) are valuable for predicting trends and consumption patterns in dynamic markets. This study focuses on the United Kingdom, Europe’s e-commerce leader, to analyze consumer behavior and the best-selling flagship product: Popcorn Holder or Popcorn Bouquet. The objective was to predict the price of the Popcorn Holder in the United Kingdom using an artificial neural network, identifying consumption patterns and providing useful insights for decision-making in e-commerce. A total of 536,350 global sales records were analyzed, filtering 1,352 data points corresponding to the selected product and country. Data cleaning and exploratory analysis were performed using Python in Google Colab®, employing libraries such as pandas, numpy, and tensorflow. A sequential neural network was built and trained with the Adam optimizer, mean squared error loss function, and 3,000 epochs. The loss during training was evaluated, and predictions were compared with actual prices. The United Kingdom was identified as the largest consumer of the product, which was also the best-selling item globally. The ANN generated a linear equation that captured the overall downward trend in prices, despite short-term fluctuations. Loss decreased rapidly during the initial training phase and stabilized, indicating model convergence. Customer number 14646 stood out as the top buyer, a valuable insight for customer loyalty strategies. Artificial neural networks proved to be effective tools for predicting prices in e-commerce and supporting inventory management and market strategies. The results confirm the United Kingdom’s position as Europe’s e-commerce leader and highlight the usefulness of ANN-based analysis for identifying consumption patterns and business opportunities.
DOI: https://doi.org/10.54167/tch.v19iEspecial.1988
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