{"id":5315,"date":"2020-09-11T09:14:12","date_gmt":"2020-09-11T09:14:12","guid":{"rendered":"https:\/\/bluetab.net\/crm-con-inteligencia-artificial\/"},"modified":"2020-09-11T09:14:12","modified_gmt":"2020-09-11T09:14:12","slug":"crm-with-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/bluetab.org\/en\/casos-2\/crm-with-artificial-intelligence\/","title":{"rendered":"CRM with artificial intelligence"},"content":{"rendered":"<p>As a follow-up to the technological strategy of one of the leading financial institutions, a machine learning algorithm is implemented to predict and prioritize the best offering mix to propose to customers, with the aim of increasing cross-selling and linking of the installed customer base.<\/p>\n<p>By implementing ML, it is possible to enhance the commercial strategy of the entity, broadening the customer vision and integrating with the current CRM, which will increase the probability of success of the actions to be carried out, improving income and customer satisfaction and loyalty. For this, the algorithm prioritizes through existing campaigns crossing with alternative products such as loans, mortgages, funds, cards, pension plans, or different types and insurance coverage (home, health, car)<\/p>\n<p>As output, it is intended to show the improvement by measuring the increase in specific indicators such as:<\/p>\n<ul>\n<li>\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" id=\"Capa_1\" data-name=\"Capa 1\" viewBox=\"0 0 113.39 113.39\"><defs><\/defs><\/svg><\/p>\n<style>.cls-1{fill:#e76118;}<\/style>\n<p><title>barra-bluetab<\/title><path d=\"M73.6,4.18c-.08,0,0,0-.08,0a10.35,10.35,0,0,0-9.59,6.5L26.86,109.2H39.79c.07,0,0,0,.07,0a10.41,10.41,0,0,0,9-5.15L86.53,4.18Z\"><\/path>\nContracts<\/li>\n<li>\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" id=\"Capa_1\" data-name=\"Capa 1\" viewBox=\"0 0 113.39 113.39\"><defs><\/defs><\/svg><\/p>\n<style>.cls-1{fill:#e76118;}<\/style>\n<p><title>barra-bluetab<\/title><path d=\"M73.6,4.18c-.08,0,0,0-.08,0a10.35,10.35,0,0,0-9.59,6.5L26.86,109.2H39.79c.07,0,0,0,.07,0a10.41,10.41,0,0,0,9-5.15L86.53,4.18Z\"><\/path>\nvalue-customer ratio<\/li>\n<li>\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" id=\"Capa_1\" data-name=\"Capa 1\" viewBox=\"0 0 113.39 113.39\"><defs><\/defs><\/svg><\/p>\n<style>.cls-1{fill:#e76118;}<\/style>\n<p><title>barra-bluetab<\/title><path d=\"M73.6,4.18c-.08,0,0,0-.08,0a10.35,10.35,0,0,0-9.59,6.5L26.86,109.2H39.79c.07,0,0,0,.07,0a10.41,10.41,0,0,0,9-5.15L86.53,4.18Z\"><\/path>\n% digitization per customer<\/li>\n<li>\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" id=\"Capa_1\" data-name=\"Capa 1\" viewBox=\"0 0 113.39 113.39\"><defs><\/defs><\/svg><\/p>\n<style>.cls-1{fill:#e76118;}<\/style>\n<p><title>barra-bluetab<\/title><path d=\"M73.6,4.18c-.08,0,0,0-.08,0a10.35,10.35,0,0,0-9.59,6.5L26.86,109.2H39.79c.07,0,0,0,.07,0a10.41,10.41,0,0,0,9-5.15L86.53,4.18Z\"><\/path>\nClick-through rate<\/li>\n<li><\/li>\n<\/ul>\n<p>SUCCESS STORIES<br \/>\n<a href=\"\/en\/nps\/\"><br \/>\n<img decoding=\"async\" src=\"https:\/\/bluetab.net\/wp-content\/uploads\/2020\/09\/john-barkiple-l090ufwopai-unsplash-scaled.jpeg\" title=\"\" alt=\"Foto por John Barkiple en Unsplash\">\t\t\t\t<\/a><\/p>\n<h2><a href=\"\/en\/nps\/\">NPS<\/a><\/h2>\n<p>Project for the Customer Experience area of \u200b\u200ba Telco operator. Preparation and quantitative and qualitative analysis of customer surveys to determine the basic indicator of customer experience with the company and its services.The Bluetab Data Scientist team designed an automatism\u2026<\/p>\n<p><a href=\"\/en\/modelo-de-prediccion-de-fraude\/\"><br \/>\n<img decoding=\"async\" src=\"https:\/\/bluetab.net\/wp-content\/uploads\/2020\/09\/kaleidico-3v8xo5gbusk-unsplash-scaled.jpeg\" title=\"\" alt=\"Foto por Kaleidico en Unsplash\">\t\t\t\t<\/a><\/p>\n<h2><a href=\"\/en\/modelo-de-prediccion-de-fraude\/\">Fraud prediction model<\/a><\/h2>\n<p>Design of the predictive model of Web Frauds carried out by Bluetab for a leading financial institution in the Spanish marketIt consists of developing a predictive algorithm capable of classifying browsing sessions according to their similarity to sessions in which\u2026<\/p>\n<p><a href=\"\/en\/crm-con-inteligencia-artificial\/\"><br \/>\n<img decoding=\"async\" src=\"https:\/\/bluetab.net\/wp-content\/uploads\/2020\/09\/photos-hobby-jjgxjesmxoy-unsplash-scaled.jpeg\" title=\"\" alt=\"Foto por Photos Hobby en Unsplash\">\t\t\t\t<\/a><\/p>\n<h2><a href=\"\/en\/crm-con-inteligencia-artificial\/\">CRM with artificial intelligence<\/a><\/h2>\n<p>As a follow-up to the technological strategy of one of the leading financial institutions, a machine learning algorithm is implemented to predict and prioritize the best offering mix to propose to customers, with the aim of increasing cross-selling and linking\u2026<\/p>\n","protected":false},"excerpt":{"rendered":"<p>As a follow-up to the technological strategy of one of the leading financial institutions, a machine learning algorithm is implemented to predict and prioritize the best offering mix to propose to customers, with the aim of increasing cross-selling and linking of the installed customer base. By implementing ML, it is possible to enhance the commercial [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":4416,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[20],"tags":[23],"bt_technology":[],"class_list":["post-5315","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-casos-2","tag-augmented-analytics-2"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"As a follow-up to the technological strategy of one of the leading financial institutions, a machine learning algorithm is implemented to predict and prioritize the best offering mix to propose to customers, with the aim of increasing cross-selling and linking of the installed customer base. 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