An\'alisis de Canasta de mercado en supermercados mediante mapas auto-organizados

TL;DR

This paper presents a method using self-organizing maps to analyze supermarket shopping baskets, revealing product relationships for better marketing strategies based on real transaction data.

Contribution

It introduces a neural network-based approach for market basket analysis that visualizes product relationships in a topological map, aiding decision-making.

Findings

Identified product groupings based on purchase patterns

Enabled visualization of product relationships for marketing

Provided actionable recommendations for promotions

Abstract

Introduction: An important chain of supermarkets in the western zone of the capital of Chile, needs to obtain key information to make decisions, this information is available in the databases but needs to be processed due to the complexity and quantity of information which becomes difficult to visualiz,. Method: For this purpose, an algorithm was developed using artificial neural networks applying Kohonen's SOM method. To carry it out, certain key procedures must be followed to develop it, such as data mining that will be responsible for filtering and then use only the relevant data for market basket analysis. After filtering the information, the data must be prepared. After data preparation, we prepared the Python programming environment to adapt it to the sample data, then proceed to train the SOM with its parameters set after test results. Result: the result of the SOM obtains the relationship between the products that were most purchased by positioning them topologically close, to form promotions, packs and bundles for the retail manager to take into consideration, because these relationships were obtained as a result of the SOM training with the real transactions of the clients. Conclusion: Based on this, recommendations on frequent shopping baskets have been made to the supermarket chain that provided the data used in the research