Bibliographic citations
Caycho, L., (2024). Segmentación de clientes digitales del ecommerce de una empresa del sector retail con algoritmos de análisis cluster [Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/6463
Caycho, L., Segmentación de clientes digitales del ecommerce de una empresa del sector retail con algoritmos de análisis cluster []. PE: Universidad Nacional Agraria La Molina; 2024. https://hdl.handle.net/20.500.12996/6463
@misc{renati/245901,
title = "Segmentación de clientes digitales del ecommerce de una empresa del sector retail con algoritmos de análisis cluster",
author = "Caycho Huamaní, Lucila Noemí",
publisher = "Universidad Nacional Agraria La Molina",
year = "2024"
}
Companies in the retail sector have not only physical sales channels but also digital sales channels that allow them to reach any internet-connected user easily, securely, and directly to fulfill various needs, whether they be related to consumption, education, entertainment, health, and more. The retail company that I am focusing on in this document was no exception. In the period following COVID-19, it had to swiftly enhance its digital sales channel (e-commerce) to continue being one of the most prominent players in the Peruvian market. In the Ecommerce management under the Marketing department, our primary goal is to provide an excellent omnichannel experience for our customers by leveraging information and continuous experimentation. We consolidate all the data we gather from advertising platforms (such as Facebook Ads, Google Ads, etc.), digital analytics platforms (like Google Analytics, Hotjar, Google Optimize), and digital asset management platforms (such as VTEX) to transform them into insights that aid in decision-making. This paper will present a project that aims to create a CRM strategy based on the identification of segments within the digital customers of fast-moving consumer goods, using statistical algorithms. In the initial phase, the entire ETL process was considered, which enables data to be made available in a clean and organized manner for subsequent cluster analysis using the k means algorithm in the free software R.Four customer segments were identified, to whom relevant benefits and offers were communicated based on their preferences, resulting in increased redemptions as well as interactions through communication channels.
This item is licensed under a Creative Commons License