Commercial Model to Increase Sales through Mixed Methodologies in an Automotive Retailer

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Resumen

In recent years, the digital channel of companies in the automotive retail industry has experienced considerable growth, reflected in the percentage of sales of this channel with respect to total sales. Therefore, companies that are not aligned with this growth, are those that have inefficiencies in their processes of attention to leads. Processes that consist of different procedures and essential operations that are not considered, nor are they applied efficiently, ignoring the magnitude of the impact on the company's sales. This research focuses on analyzing 4 tools to obtain a new commercial model that optimizes and improves the low conversion of leads into sales, due to inefficient lead handling processes, identified as the main problems within the company studied. The model makes use of tools such as: demand management based on Forecasting, optimization of the process as such through BPM, improvement of the CRM database through Poka Yoke, and improvement of salespeople skills under a profile standardization approach, resulting in a 15.73% improvement in the company's net profit, providing an effective commercial model with the capacity to be replicated in other companies.

Idioma originalInglés estadounidense
Título de la publicación alojadaProceedings of the 2023 10th International Conference on Industrial Engineering and Applications, ICIEA-EU 2023
EditorialAssociation for Computing Machinery
Páginas234-240
-7
ISBN (versión digital)9781450398527
DOI
EstadoIndizado - 9 ene. 2023
Publicado de forma externa
Evento10th International Conference on Industrial Engineering and Applications, ICIEA-EU 2023 - Rome, Italia
Duración: 9 ene. 202311 ene. 2023

Serie de la publicación

NombreACM International Conference Proceeding Series

Conferencia

Conferencia10th International Conference on Industrial Engineering and Applications, ICIEA-EU 2023
País/TerritorioItalia
CiudadRome
Período9/01/2311/01/23

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Publisher Copyright:
© 2023 ACM.

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