Bibliographic citations
Villoslada, A., (2022). Incremento de cobertura y reducción de tiempos en el relevo del indicador share of shelf en consumo masivo aplicando inteligencia artificial [Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/5805
Villoslada, A., Incremento de cobertura y reducción de tiempos en el relevo del indicador share of shelf en consumo masivo aplicando inteligencia artificial []. PE: Universidad Nacional Agraria La Molina; 2022. https://hdl.handle.net/20.500.12996/5805
@misc{renati/246402,
title = "Incremento de cobertura y reducción de tiempos en el relevo del indicador share of shelf en consumo masivo aplicando inteligencia artificial",
author = "Villoslada Huamán, Alba Rocio",
publisher = "Universidad Nacional Agraria La Molina",
year = "2022"
}
In the commercial direction, for an agency that provides Trade Marketing services and market information in movement of the different commercial channels: traditional, ecommerce and modern through Point of Sale Management indicators, it is vital that the clients receive information with the greatest accuracy, frequency and coverage of their activity, so that decision-making and action plans on opportunities are accurate and achieve the results required by their organizations. Within these indicators, one of the most important for the modern channel is the Share of Shelf (SOS), which indicates the representativeness of your brand on the shelf versus the competition within a specific category. The present work based on resolution No. 119-2020- CU-UNALM, of June 08, 2020, which resolves in article 1 Approve the new Regulation of Titling for Work of Professional Sufficiency of the UNALM, had as objective to increase the coverage and delivery frequency of the Share of Shelf indicator in a modern channel for the mass consumption category, through Artificial Intelligence (AI). To achieve the stated objective, a series of actions were carried out that began with the definition of the client's problem, continuing with the specification of the requirements and needs, then with the approach of the possible tools for the solution, passing to the codification of the best tool chosen based on AI, ending with the verification of the tool in field tests. Once the positive and expected results of the tests were obtained, the new tool was deployed to measure the SOS indicator in all modern channels, thus replacing the previous tool with this one based on AI. The results of implementing this new tool in the measurement of the SOS indicator were an increase of more than 100% in category coverage, a reduction in relay times of more than 50% and an increase in the accuracy of the indicator, going from 91% to 97% average per category. Generating a positive impact on our client's decision-making, offering a completely new tool created especially for them, which saved them a 34% investment in field personnel if this tool was not created based on AI.
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