{"id":1731,"date":"2026-03-17T15:26:11","date_gmt":"2026-03-17T20:26:11","guid":{"rendered":"https:\/\/strata-analytics.us\/?post_type=customer-story&p=1731"},"modified":"2026-04-29T16:27:47","modified_gmt":"2026-04-29T21:27:47","slug":"using-a-tailor-made-version-of-the-recommendation-service-to-improve-retention-campaigns-and-customer-satisfaction","status":"publish","type":"customer-story","link":"https:\/\/strata-analytics.us\/es\/customer-story\/using-a-tailor-made-version-of-the-recommendation-service-to-improve-retention-campaigns-and-customer-satisfaction\/","title":{"rendered":"Utilizar una versi\u00f3n personalizada del Servicio de Recomendaciones para mejorar las campa\u00f1as de retenci\u00f3n y la satisfacci\u00f3n del cliente."},"content":{"rendered":"

Desaf\u00edo<\/h3>\n\n\n\n

La empresa necesitaba lograr una mayor tasa de captaci\u00f3n de clientes entre aquellos con mayor probabilidad de cancelar su servicio m\u00f3vil prepago. Anteriormente, se les hac\u00edan ofertas espec\u00edficas en funci\u00f3n de su nivel de propensi\u00f3n a la cancelaci\u00f3n (Alto, Medio y Bajo).<\/p>\n\n\n\n

En la soluci\u00f3n presentada, no hab\u00eda forma de determinar si la oferta era la m\u00e1s adecuada para cada cliente. En otras palabras, a los clientes con alta propensi\u00f3n a la deserci\u00f3n se les ofrec\u00eda la oferta designada para ese grupo. \u00bfEra esa la mejor oferta para ese grupo? Tampoco hab\u00eda forma de saber si estas ofertas eran realmente las m\u00e1s apropiadas para ese cliente en particular; es decir, \u00bfel cliente del grupo deseaba esa oferta o alguna otra? Por \u00faltimo, \u00bfqu\u00e9 oferta desear\u00eda el cliente y ser\u00eda rentable para la empresa?<\/p>\n\n\n\n

El principal obst\u00e1culo para lograrlo era c\u00f3mo crear un modelo que pudiera predecir eficazmente lo que el cliente podr\u00eda elegir bas\u00e1ndose en decisiones anteriores y dentro de un contexto espec\u00edfico (como el saldo disponible, el momento de la oferta, el comportamiento, etc.).<\/p>\n\n\n\n

Otro desaf\u00edo fue c\u00f3mo aumentar la monetizaci\u00f3n de los clientes. Esto significaba que, si bien la primera oferta personalizada ten\u00eda la mayor probabilidad de ser seleccionada, podr\u00eda no ser la m\u00e1s rentable. Quiz\u00e1s la segunda oferta personalizada, con una mayor probabilidad, podr\u00eda ser m\u00e1s rentable.<\/p>\n\n\n\n

<\/p>\n\n\n\n

\u00bfPor qu\u00e9 AWS?<\/h3>\n\n\n\n

Durante varios a\u00f1os, CWP ha utilizado los servicios de AWS para almacenar sus datos en lagos de datos, as\u00ed como para realizar an\u00e1lisis, modelado predictivo y visualizaciones. La experiencia del cliente y de Strata con los servicios de AWS gener\u00f3 confianza en su rendimiento, velocidad de implementaci\u00f3n y conveniencia. Implementar SRE en esta plataforma fue ideal, ya que no requiri\u00f3 migrar a ning\u00fan servicio externo a AWS, puesto que todo el entorno de servicio SRE-MLOps y el desarrollo se ejecutan en la plataforma de AWS.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Por qu\u00e9 el cliente eligi\u00f3 al socio<\/h3>\n\n\n\n

Strata ha colaborado con CWP en diversos proyectos, incluyendo el desarrollo de diferentes modelos predictivos para los segmentos fijo y m\u00f3vil, as\u00ed como an\u00e1lisis para generar informaci\u00f3n valiosa para el negocio. Adem\u00e1s, se crearon varios paneles de control con QuickSight para visualizar la efectividad de las campa\u00f1as y otras soluciones. En otras palabras, Strata posee un amplio conocimiento del potencial y el desarrollo del entorno AWS, y el cliente tambi\u00e9n conf\u00eda en Strata y dispone de datos en AWS.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Soluci\u00f3n de socio <\/h3>\n\n\n\n

La soluci\u00f3n implementada por Strata consisti\u00f3 en crear un marco que recopila datos de tres fuentes: interacciones, datos de usuario y datos de ofertas. Con esta informaci\u00f3n, se entren\u00f3 un modelo con AWS Personalize, seguido de una clasificaci\u00f3n ponderada para priorizar la monetizaci\u00f3n. A partir de estos resultados, se desarrollaron campa\u00f1as personalizadas para visualizar los resultados de conversi\u00f3n. Todo este proceso se ejecuta autom\u00e1ticamente dos veces por semana, utilizando los datos m\u00e1s recientes para proporcionar informaci\u00f3n de entrenamiento pr\u00e1cticamente en tiempo real.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Arquitectura HL<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n

La arquitectura parte de datos de diversas fuentes, categorizados en tres dominios: interacciones, informaci\u00f3n del usuario y detalles del producto. Estos datos se preprocesan y utilizan en SageMaker, aprovechando tambi\u00e9n los servicios de AWS Personalize, y las inferencias se almacenan en un bucket de S3. Posteriormente, las funciones Lambda acceden a estas inferencias para activar campa\u00f1as dirigidas a los usuarios. Asimismo, se emplean servicios de registro para supervisar cada paso del proceso.<\/p>\n\n\n\n

<\/p>\n\n\n\n

\n

Resultados y beneficios<\/h3>\n\n\n\n

La soluci\u00f3n es totalmente automatizada y no requiere intervenci\u00f3n humana. Adem\u00e1s, es f\u00e1cil de replicar para otros mercados o campa\u00f1as, con un tiempo de desarrollo muy corto. Al ser una soluci\u00f3n parametrizada, se puede ajustar f\u00e1cilmente si es necesario modificar par\u00e1metros como el n\u00famero de ofertas o el periodo de entrenamiento. El proceso completo tiene una duraci\u00f3n aproximada de 2 horas.<\/p>\n<\/div>\n\n\n\n

<\/p>\n\n\n\n

Pr\u00f3ximos pasos<\/h3>\n\n\n\n

Los siguientes pasos implican la mejora continua del modelo a medida que se entrenan m\u00e1s casos con interacciones adicionales proporcionadas por SRE, ya que se trata de un modelo que aprende de sus recomendaciones anteriores. Tambi\u00e9n se est\u00e1 avanzando en la implementaci\u00f3n de esta soluci\u00f3n en otras campa\u00f1as de clientes.<\/p>","protected":false},"excerpt":{"rendered":"

Cable & Wireless Panama, also known as “+m\u00f3vil”, is the leading provider of mobile and fixed internet services in Panama. Like other prepaid mobile service providers, they run campaigns targeting customers with a high likelihood of stopping the service, commonly called churn. It was decided to implement a Strata Recommendation Engine (SRE) to personalize offers for these customers and increase the conversion rate by 5% above the current level. The solution was built on a scalable MLOps platform using AWS services. 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