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The main objective of this work is to determine the characteristics of those customers who are more likely to contract electric energy and what differentiates them from those who do not contract it, in order to focus future advertising campaigns on those who are more likely to. To carry out this research, information about the company’s telephone customers who had received advertising campaigns for the contracting of energy was used, of which some ended up contracting it (2%) and many others did not (98%). To characterize the customers, association rules and frequent sets were used to compare the frequency with which customers who contract energy had certain characteristics compared to the frequency with which those who do not contract energy had them. As a result, it was found that those customers who have contracted internet (specifically fiber) and also have unlimited call minutes are the most likely to contract energy. In addition, those customers who meet these characteristics and are also between 30 and 50 years old and are new customers (less than 200 days with the company) are three times more likely to contract energy.<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.0″ _module_preset=”default” custom_margin=”20px||||false|false” custom_padding=”0px||||false|false” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”1_2″ _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_image src=”https:\/\/storage.googleapis.com\/wp-uploads.bucket.wp.uc3m.es\/wp-content\/uploads\/sites\/70\/2022\/10\/21104922\/Cris_foto.jpg” title_text=”Cris_foto” _builder_version=”4.18.0″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″]<\/p>\n
FINAL DEGREE PROJECT OF:<\/strong><\/span><\/p>\n CRISTINA PI\u00d1A MIGUELSANZ<\/strong><\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″]<\/p>\n Degree<\/strong><\/p>\n Graduated in Computer Engineering in 2022 from Universidad Carlos III de Madrid, with a major in Computer Science.<\/p>\n Currently, I work as a Data Engineer at MASMOVIL Group, where I use tools such as Big Query, Airflow, among others.<\/p>\n Previously, between September 2021 and May 2022, I participated as a researcher within the chair of the Applied Artificial Intelligence Group (GIAA) of the Carlos III University of Madrid.<\/p>\n
Work Experience
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