Muy buena introducción Daniel 😃
Retail Store en Google Cloud Platform
Lo que aprenderás sobre GCP para ecommerce
Etapas clave y MLOps
Arquitectura de alto nivel
Tour de la aplicación de retail
Backend as a Service y modelo de seguridad
Introducción al proyecto
Medición de interacciones
Setup de Google Tag Manager
Etiquetando con Google Tag Manager
Etiquetas relevantes para CLV
Integración con servicios
Exposición de servicios con Apigee
Servicios expuestos con APIs
¿Qué son las APIs?
Apigee
Creación de tu primer API Proxy en Apigee
Administración de APIs con Apigee
Creando un portal de desarrolladores
Interactuando con el portal de desarrolladores
Insights to Actions
Generación de modelos AI/ML
Machine Learning con datos estructurados
BigQuery para modelos de Forecasting y LTV
Bigquery ML - Manos a la Obra
Auto ML vs. Bigquery ML
Consideraciones para entrenar un modelo en BigQueryML
Entrenamiento del modelo en BigQuery ML
Cómo exportar modelos hechos en BQML
Exportando un modelo hecho con BQML
Consumo de servicios de AI/ML
Cómputo Serverless y Contenedores
¿Qué es Kubernetes?
Consumo de modelos ML mediante BigQuery API
Almacenamiento de predicciones
Ejecución de predicciones y persistencia
Despliegue continuo con Cloud Run
Ejecución de despliegue con Cloud Run
Escalamiento de servicios en Cloud Run
AuthN y AuthZ con Cloud Run
Google Marketing Platform
Análisis de las predicciones
Segmentamos nuestras Predicciones
Caso práctico para definir tu estrategia de activación
Generemos nuestros modelos en la plataforma
Segmentamos nuestras audiencias en BigQuery
Carga tus audiencias y conecta tu medio de activación
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The world of digital marketing is vast, and with powerful tools like Google Cloud, it is possible to optimize every strategy we elaborate. The purpose is to unite disparate data, analyze predictions and activate effective audiences based on these observations. This is explained by instructor Daniel Sanchez, Cloud Customer Engineer for Google Cloud, offering a detailed perspective on how to use these tools for marketing purposes.
The first step, fundamental for every digital marketer, is analyzing predictions and understanding the purpose of creating machine learning models. In essence, this process begins:
The process ends with the implementation of the predictions made to optimize marketing strategies based on real and relevant data.
For data analysis to be effective, it is crucial to connect Google Analytics with BigQuery, enabling continuous data export. Some benefits of this connection include:
Google provides detailed documentation to guide users in making this connection, ensuring constant access to accurate and up-to-date data.
Data cleansing is one of the most critical steps in data analytics. By providing quality data to a model, the resulting predictions will also be more accurate and valuable. An example mentioned is the calculation of customer lifetime value, essential for any targeted marketing campaign.
There are several ways to tailor marketing campaigns, using tools such as:
These tools optimize the efficiency and targeting of campaigns, ensuring they reach the right audience with the right message.
Once data has been collected and analyzed, it is critical to present the results in an understandable way. This is where Google Data Studio comes in, allowing you to create dashboards from:
Tip: Platzi offers a specific Data Studio course for those interested in deepening its capabilities.
In summary, by integrating data across multiple platforms, optimizing using Google tools, and applying machine learning models, any business can become data-driven eCommerce. With a focus on the customer and data-driven strategies, not only marketing decisions are optimized, but the relationship with customers in the long run.
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Muy buena introducción Daniel 😃
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