This project is designed to provide you with an environment in which you can learn to use AWS services to modify the behavior of an ecommerce application, based on business requirements. This can be done in a group setting or as an individual using self-paced workbooks. Currently there are workshops for adding search, personalization, experimentation frameworks, a/b testing, analytics, customer data platforms (CDPs), messaging, and more.
In order to use the workshops, you will need to deploy the Retail Demo Store into an AWS account, using one of the methods described in the Getting Started or Developers sections below. This is necessary because the workshops run in SageMaker Jupyter notebooks, which provide an interactive Python environment where you can execute code in the Retail Demo Store environment.
| AWS Service | Workshops Overview | Workshop Links | Level | Duration |
|---|---|---|---|---|
 Amazon Personalize |
The Retail Demo Store uses Amazon Personalize to provide similar item recommendations, search re-ranking based on user preferences, and product recommendations based on user item interactions. The attached workshop is a throrough walk through of the major features of Amazon Personalize, and how it can be deployed in an ecommerce application like the Retail Demo Store. | Personalize Setup | 300 | 2-2.5 hours |
 Amazon Pinpoint |
In this workshop we will use Amazon Pinpoint to add the ability to dynamically send welcome messages, abandoned cart messages, and messages with personalized product recommendations to the customers of the Retail Demo Store. | Email Campaigns | 200 | 1 hour |
 Amazon Lex |
In this module we're going to implement a conversational chatbot using Amazon Lex and integrate it into the Retail Demo Store's web UI. We'll provide some basic functionality to our chatbot such as being able to provide a return policy to users as well as wiring up the chatbot to the Amazon Personalize ML models we created in the Personalization workshop to provide personalized product recommendations to our users. | Lex Chatbot | 200 | 30 minutes |
 Amazon OpenSearch |
In this workshop, you will create a new index using Amazon OpenSearch Service and then index the Retail Demo Store product data so that users can search for products. | Product Search | 200 | 20 minutes |
 Amazon Location Services |
Create a geofence for customers approaching your physical store and send them timely pickup notifications and offers. | Geofencing | 300 | 2 hours |
 Amazon Alexa |
Incorporating Location Service, Personalize and Retail Demo Store into a hands-free ordering experience. | Alexa skill deployment | 300 | 60 minutes |
| Experimentation | In this module we are going to add experimentation to the Retail Demo Store. This will allow us to experiment with different personalization approaches in the user interface. Through notebooks in this module we will demonstrate how to implement three experimentation techniques as well as how to use Amazon CloudWatch Evidently for A/B tests. | Overview A/B (400) Interleaving (400) Multi-Armed Bandit (400) CloudWatch Evidently (200) |
200/400 | 1.5 hours |