Retail Store en Google Cloud Platform

1

Lo que aprenderás sobre GCP para ecommerce

2

Etapas clave y MLOps

3

Arquitectura de alto nivel

4

Tour de la aplicación de retail

5

Backend as a Service y modelo de seguridad

6

Introducción al proyecto

7

Medición de interacciones

8

Setup de Google Tag Manager

9

Etiquetando con Google Tag Manager

10

Etiquetas relevantes para CLV

11

Integración con servicios

Exposición de servicios con Apigee

12

Servicios expuestos con APIs

13

¿Qué son las APIs?

14

Apigee

15

Creación de tu primer API Proxy en Apigee

16

Administración de APIs con Apigee

17

Creando un portal de desarrolladores

18

Interactuando con el portal de desarrolladores

19

Insights to Actions

Generación de modelos AI/ML

20

Machine Learning con datos estructurados

21

BigQuery para modelos de Forecasting y LTV

22

Bigquery ML - Manos a la Obra

23

Auto ML vs. Bigquery ML

24

Consideraciones para entrenar un modelo en BigQueryML

25

Entrenamiento del modelo en BigQuery ML

26

Cómo exportar modelos hechos en BQML

27

Exportando un modelo hecho con BQML

Consumo de servicios de AI/ML

28

Cómputo Serverless y Contenedores

29

¿Qué es Kubernetes?

30

Consumo de modelos ML mediante BigQuery API

31

Almacenamiento de predicciones

32

Ejecución de predicciones y persistencia

33

Despliegue continuo con Cloud Run

34

Ejecución de despliegue con Cloud Run

35

Escalamiento de servicios en Cloud Run

36

AuthN y AuthZ con Cloud Run

Google Marketing Platform

37

Análisis de las predicciones

38

Segmentamos nuestras Predicciones

39

Caso práctico para definir tu estrategia de activación

40

Generemos nuestros modelos en la plataforma

41

Segmentamos nuestras audiencias en BigQuery

42

Carga tus audiencias y conecta tu medio de activación

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Resources

How to activate audiences with Google Cloud for digital marketing?

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.

What is the starting point for using data in marketing?

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:

  • Collecting data from multiple sources, such as Google Analytics through Tag Manager or information from the company's CRM.
  • Bringing this data into analytics platforms such as BigQuery.
  • Analyzing and cleaning this data to ensure that the information used in machine learning models is of the highest quality.

The process ends with the implementation of the predictions made to optimize marketing strategies based on real and relevant data.

How to integrate Google Analytics with BigQuery?

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:

  • Ability to analyze internal website activity at a detailed level.
  • Creation of partitioned tables based on streaming data.

Google provides detailed documentation to guide users in making this connection, ensuring constant access to accurate and up-to-date data.

What is the importance of data cleansing?

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.

How to personalize marketing campaigns with Google tools?

There are several ways to tailor marketing campaigns, using tools such as:

  • DB360 and Google Marketing Platform: These platforms allow you to activate audiences based on similar behaviors of current consumers.
  • Google Ads: Offers options to develop bids using precise keywords that users search for.
  • YouTube Ads: Propositions based on user behavior, segmenting by interests identified through purchase and navigation data.

These tools optimize the efficiency and targeting of campaigns, ensuring they reach the right audience with the right message.

Why is Data Studio valuable in data analysis and visualization?

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:

  • Detailed analysis of data from Google Analytics and CRM.
  • Predictions from machine learning models.
  • Effective visualizations to establish the best data-driven strategy.

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 😃