Amazon Personalize for Magento 2

• Products result will show according to Personalize data.
• Personalized and recommended products will be shown to logged-in customers
• It also works in real-time and shows product recommendations immediately to customers.
• Integrates AWS Personalize Machine Learning algorithm to process customer data.
• Even it helps merchants to provide individualized recommendations and personal shopping experiences to each customer.

The installation is quite simple just like the standard Magento 2 extensions.

#Download Module

Firstly, you need to log in to the Webkul Store, go to My Account>My Purchased Products section, verify, and then download and extract the contents of this zip folder on the system.

#Upload Folder

Once the module zip extracts, follow path src>app and then copy the app folder into the Magento 2 root directory on the server as shown below:

# Run Commands

You need to run the following commands:

composer require aws/aws-sdk-php

php bin/magento setup:upgrade

php bin/magento setup:di:compile

php bin/magento setup:static-content:deploy

php bin/magento indexer:reindex

php bin/magento cache:flush

For Multilingual support, please navigate to Store>Configuration>General >Locale Options. And then select your desired language from the Locale option.

Even, if you need to do the module translation, please navigate to the following path in your system app/code/Webkul/Personalize/i18n.

Forthwith, Open the file named en_US.CSV for editing as shown in the below screenshot.

Once you have opened the file for editing. Replace the words after the†comma(,)†on the right with your translated words.

Then, after editing and translating the CSV file, you need to save the translated file name according to your region language and country code such as – de_DE.CSV.

Then upload the translated file to the same folder from where you have obtained it. Now your module translation is complete.

Amazon Personalize is a machine learning service helpful for customers because it makes the individualized recommendations for customers by using and observing their store activity.

Also, it drives 25-30% of total product orders comes from the recommendation created by the machine learning technique.

• AWS account required for login click here and for sign-up click here.
• To know more about pricing click here.
• Follow the path to get ARN Campaign – login to AWS account => Services Dropdown => S3 (Storage) => Copy ARN (if already created) or Create Bucket

• The users can click here to understand how to get started with Amazon S3

• To get the Access Key ID and Secret Key follow the path – login to AWS account => Account Owner Name Dropdown => My Security Credentials => Access keys (access key ID and secret access key) => Create New Access Key or Use Existing

Thus, this all was to collect the details required to integrate Amazon Personalize with Magento 2 Store. Forthwith merchants can provide customers with better user experience with personalized recommendations based on their activity.

After the successful installation of the module, the admin will navigate to Stores -> Configuration -> Webkul -> Personalize Settings

General Settings

Integrate Amazon Personalize with Magento Store

• Personalize Status – First, select “Yes” to enable Amazon Personalize for E-commerce Store
• Campaign ARN – Then, enter the campaign ARN created on Amazon AWS.
• Access Key ID – Next, copy and paste the Access Key ID from Amazon AWS.
• Secret key – Now, fill in the Secret key copied from Amazon AWS.
• Region – Lastly, select the region from the drop-down.
• Version – Enter the version of the web service to use.

Hence, Above defined details will integrate Amazon Personalize with the Magento e-commerce store. Moreover, Facilitate store customers to get better user experience and personalized product or service recommendations based on behavior observation of machine learning.

Export:

Here, the admin will set up the data to be used to communicate to AWS and populate the AWS Personalize’s data sets.

Here, the admin will –

• Enter the Bucket Name – The name of the Amazon S3 bucket where the data that you want to upload to your dataset is stored. For example the bucket name.
• Set the Filepath – The file path to the CSV file inside the S3 bucket. For example – folder1/folder2/training data.csv
• Enter the DatasetArn – The ARN of the data set that receives the imported data.
• Set the JobName – The name of the dataset import job.
• Enter the RoleArn – The ARN of the IAM role that has permissions to read from the Amazon S3 data source.

Products:

Here, the admin will set the configuration used for the product’s dataset.

Here, the admin will –

• Enter the Bucket Name – The name of the Amazon S3 bucket where the data that you want to upload to your dataset is stored. For example: bucket-name
• Set the Filepath – The file path to the CSV file inside the S3 bitbucket, for example folder1/folder2/training-data.csv
• Enter the DatasetArn – The ARN of the data set that receives the imported data.
• Set the JobName – The name of the dataset import job.
• Enter the RoleArn – The ARN of the IAM role that has permission to read from the Amazon S3 data source.

Interaction:

Here, the admin will set up the configuration used for the interaction data sets.

Here, the admin will –

• Enter the Bucket Name – The name of the Amazon S3 bucket where the data that you want to upload to your dataset is stored. For example: bucket-name
• Set the Filepath – The file path to the CSV file inside the S3 bitbucket, for example folder1/folder2/training-data.csv
• Enter the DatasetArn – The ARN of the data set that receives the imported data.
• Set the JobName – The name of the dataset import job.
• Enter the RoleArn – The ARN of the IAM role that has permission to read from the Amazon S3 data source.

After the above configurations, the admin will configure the events section.

Events:

Set up events that can be triggered according to customer actions.

For the same, configure the Interactions section.

Here, to populate the interactions model every time a customer places an order, the admin will –

• Enter the tracking ID of your event.

• Set the Tracking Event type name(set any suitable name for recognizing the same).

Lastly, the admin can download the Sample CSV files for datasets for the –

Customers:

Products:

Interactions:

Save the configuration settings by tapping the Save button.

Importing data to AWS Personalize

Once you have all the configuration set up we can start to export data from Magento 2 to Personalize. We have three ssh commands, each one responsible to export data to a profile.

php bin/magento aws:personalize:import:customers

php bin/magento aws:personalize:import:products

php bin/magento aws:personalize:import:interactions

Also, this extension facilitates the customers to have personalized recommended products on the home page after the customer login based on their store activity.

Before Login

Additionally, it works in real-time and shows the product suggestions instantly to customers.

After Login

After login to the store customer will be able to see personalize and recommended products. The advanced machine learning technique observes the activity of every single buyer and delivers them with the personalized suggestion of their interest.

Personalized Recommended Product for Customer

As you can check the below given personalized suggestion screenshot of another customer based on personal interest.

That’s all for the Amazon Personalize for Magento 2 (Adobe Commerce). If you have any queries regarding the plugin, please contact us at Webkul Support System.

You can also check our complete Magento 2 Modules.