Use Amazon.com personalization on your WooCommerce store.
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This document summarizes a webinar about using Amazon Personalize for recommendations on WooCommerce stores. It introduces Amazon Personalize and how it works, demonstrating how to set it up through the AWS for WordPress plugin. It provides examples of how businesses have benefited from personalization. It also discusses considerations for getting started and pricing. Finally, it explores ways to extend personalization through custom plugins, such as integrating recommendations into a chat experience.
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Use Amazon.com personalization on your WooCommerce store.
- 1. #wpewebinar James Jory & Igor Krtolica, Amazon Web Services Anthony Burchell, WP Engine Use Amazon.com personalization on your WooCommerce store. New Amazon for WordPress plugin features, co-authored with WP Engine.
- 2. #wpewebinar What You’ll Learn ● Why personalization is important ● What AWS Personalize for WooCommerce is ● How AWS Personalize works - demo and next steps
- 3. #wpewebinar Ask questions as we go. We’ll answer as many questions as we can after the presentation Slides and recording will be made available shortly after the webinar Use the “Questions” pane throughout the webinar
- 4. #wpewebinar Igor Krtolica Solutions Architect, Applied AI Amazon Web Services James Jory ● Dabbled in winemaking and growing grapes ● Simulation racer in spare time ● Wannabe BBQ pitmaster ● Worked with Personalize during beta ● Loves good coffee ● Misses in-person presentations Partner Solutions Architect Amazon Web Services Anthony Burchell ● WordPress core committer ● VR/AR Enthusiast ● Makes music on a Gameboy WordPress Developer WP Engine Labs
- 6. 6 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | https://www.business2community.com/ marketing/30-amazing-personalization-statistics-02289 044 63% of customers see PERSONALIZATION AS THE STANDARD LEVEL OF SERVICE Market leaders are investing in personalization to meet customer expectations THE CURRENT LANDSCAPE
- 7. 7 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | IMPROVING BUSINESS OUTCOMES The power of personalization Understanding, measuring, and improving user experiences across digital channels Increasing time spent engaging with products and content ENGAGEMENT Attracting new customers Retaining customers in a crowded digital environment ACQUISITION AND RETENTION Improving digital marketing efficiencies Increasing average revenue per user EFFICIENCIES AND REVENUE Helping customers easily and quickly discover products and content they want Highlight new products, content, and promotion offerings DISCOVERABILITY
- 8. 8 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | 8 Delivering sophisticated, unique experiences to customers across channels and devices using machine learning NOW First feature launched for recommendations in 1998 THEN PIONEERING PERSONALIZATION AT AMAZON The evolution over 20+ years
- 9. 9 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Deliver high quality recommendations Personalize every customer touchpoint Easily implement an ML solution at scale Data privacy and security Leveraging ML to improve business metrics The benefits of Amazon Personalize
- 10. 10 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Personalization in Retail Deliver unique homepage experience Personalize users’ homepage with product recommendations based on their shopping history Refine product recommendations Recommend similar items on product detail pages to help users’ easily find what they are looking for Improve discoverability Help users’ quickly find relevant new products, deals, and promotions Relevant product rankings Easily re-rank relevant product recommendations to drive tangible business outcomes Enhance marketing communication Personalize push notifications and marketing emails with individualized product recommendations Boost upsell and cross-sell Combine Amazon Personalize with business logic to create high quality cart upsell and cross-sell recommendations 50% increase in customer engagement on ”recommended for you” product row Common Use Cases
- 11. 11 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | HOW IT WORKS Amazon Personalize Customized personalization API Item metadata (details of articles, products, videos, etc.) User metadata (age, location, etc.) User events / interactions (views, signups, conversions, etc.) Inspect data Identify features Select hyper-parame ters Train models Optimize models Host models Real-time feature store Amazon Personalize Fully managed by Amazon Personalize
- 12. 12 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | User impressions Modeling impression data, or items that are seen but not clicked Item exploration Balance between exploring for new items and items a user is likely to find relevant Filtering for events Exclude or include items to recommend based on event criteria Filtering based on metadata Exclude or include items to recommend based on item or user criteria Cold start Include recommendations for new users and new items Key features of Amazon Personalize
- 13. 13 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Personalized online shopping experiences MECCA uses Amazon Personalize to extend its very high touch and personal in store shopping experience to its online channels such as email and web. Amazon Personalize has helped to increase customer engagement by 65% in e-mail click-through rates and a corresponding increase in email revenue from personalized product recommendations. Since integrating Personalize, we are seeing our customers respond positively to the new recommendations with a 65% increase in e-mail click-through rates and a corresponding increase in email revenue relating to the products recommended by Personalize. To personalize our customer experience further, we are now extending the use of Personalize to additional areas, including our website. - MECCA e-Commerce & CRM Director “ ”
- 14. 14 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Providing Personalization at scale Pomelo Fashion used Amazon Personalize to build a recommendation engine to personalize the shopping experience to each customer. It now reflects user preferences on product pages in minutes, driving sales. As a result, click-through rates from category to product pages have increased by up to 18%, gross revenue from category pages have gone up by 15%, and Pomelo increased their return on investment by 400% within 1 month. “It’s so fast that you can even see your recommendations changing during the same session. ”
- 15. 15 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Amazon Personalize for Magento Many small retailers use Magento, an open-source ecommerce platform, to create websites or mobile applications to sell their products online. Personalization is key to creating high-quality ecommerce experiences, but small businesses often lack access to resources required to implement a scalable, sophisticated personalization solution—especially one that is powered by machine learning (ML). Hoopologie, a small hula hoop supply company, has seen an increase of 40% in sales and AOV increase of $50 per order since integrating Amazon Personalize. “Creating personalized recommendations for every type of user on the site would take us hours and hours every month. It’s not affordable, and I’d rather have our limited staff help our customers in other ways. - Melina Rider, Founder, Hoopologie ”
- 16. #wpewebinar How Amazon Personalize works with WooCommerce
- 17. #wpewebinar Amazon Personalize Architecture Amazon Personalize Real-time Data Historical Data Web App Server-Side App Mobile App Historical Activity User Attributes Item Catalog S3 Bucket
- 18. #wpewebinar Amazon Personalize Components User / Item Interactions Catalog Data Interactions Dataset Solution Version(s) Recipe Event Tracker Campaign Personalized Recommendation s Dataset Group Solution User Data Users Dataset Items Dataset Filters User Behavior
- 19. #wpewebinar WooCommerce and Personalize Architecture AWS Cloud AWS for Wordpress Plugin Amazon Personalize Datasets Train Models Recommendation Function User Events Event Tracker WooCommerce User Interactions Product Recommendations
- 20. #wpewebinar • 25 unique users • 1,000 interactions • 100 products • Users must be known / logged in Things to consider before getting started Recommended Minimum Requirements Deploying Amazon Personalize
- 21. #wpewebinar $0.05 / Gigabyte uploaded to Amazon Personalize. This includes data sent in real time via trackers. Data Ingestion $0.24 / training hour for the training hours consumed to train a custom model with your data. // First 20K TPS-hour per month $0.20 per TPS-hour for real-time recommendations // Next 180K TPS-hour per month $0.10 per TPS-hour for real-time recommendations // Over 200K TPS-hour per month $0.05 per TPS-hour for real-time recommendations Training Time Real-Time Recommendations Amazon Personalize Pricing See https://aws.amazon.com/personalize/pricing/ for more details.
- 22. #wpewebinar AWS for WordPress Personalize How to use Amazon Personalize with your WooCommerce Shop
- 23. #wpewebinar Don’t...if you are already using WooCommerce, you can add machine-learning powered personalization using the AWS WordPress plugin. Setting up recommended products the hard way? Install the AWS WordPress Plugin Enter your AWS account ID (this is required) Enable the Amazon Personalize integration Train an Amazon Personalize solution Deploy recommendations to your WooCommerce site Set up AWS services directly from the Admin console Personalization AWS Wordpress Plugin Setup
- 24. #wpewebinar AWS for WordPress Plugin • Set your AWS credentials and you’re ready! • Details on setting up AWS for WordPress can be found here.
- 25. #wpewebinar Setup • “New Products in Recommendations” ▪ for when new products are added after training is complete. • Manual cycle for training is recommended for lower traffic shops.
- 26. #wpewebinar New Products in Recommendations Start Training createPersonalizeFullAccessPolicy createPersonalizeS3BucketAccessPolicy createPersonalizeS3BucketAccessPolicyBatch createS3BucketAccessPolicy createS3BucketAccessPolicyBatch createRole createDatasetGroup createInteractionsSchema createItemsSchema createInteractionsDataset createItemsDataset uploadCustomersCSV uploadProductsCSV uploadInteractionsCSV createDatasetImportJob getDatasetImportJobStatus createSolution getSolutionStatus createSolutionVersion getSolutionVersionStatus createCampaign
- 27. #wpewebinar Setup process Start Training Now Collect interaction data And upload when enough is gathered
- 28. #wpewebinar When training completes, recommendations will start AWS Cloud AWS for Wordpress Plugin Amazon Personalize Datasets Train Models Recommendation Function User Events Event Tracker WooCommerce User Interactions Product Recommendations
- 30. #wpewebinar Recommendations Results • A simple way to track the success and details of your campaigns. • On submit of a purchase the total is added to the campaign total. • More to come as we refine tracking results!
- 31. #wpewebinar Extending Personalize in your own plugin
- 32. #wpewebinar Use Case: ML Assisted Shopping Experience
- 33. #wpewebinar Use Case: Use Case: ML Assisted Shopping Experience CRM Chat/Support Product Recommendations From userID Or hide ID! AWS Cloud AWS for Wordpress Plugin Amazon Personalize Datasets Train Models Recommendation Function User Events Event Tracker WooCommerce User Interactions Product Recommendations
- 34. #wpewebinar ML assisted customer experience • Easy to pass data from your WooCommerce shop to other apps. • In this example, a chat app is sending personalized data about the shopper to the agent while hiding the identity of the shopper. • Personalization can be a tool to closer engage with your customers. ML assisting Humans! Chat Agent Dashboard
- 35. #wpewebinar Use Case: Chat Embed Code
- 37. #wpewebinar Passing $recommended_products to Javascript PHP / Plugin Root PHP File ./js/chat-attributes.js wp_localize_script() to pass the response as `personalize_info`
- 39. #wpewebinar
- 40. #wpewebinar Timing and next steps! Target beta mid February. Want early access? Tell us about your shop at wptech@wpengine.com
- 41. #wpewebinar Slides and recording will be made available shortly after the webinar QUESTIONS AND ANSWERS
- 42. #wpewebinar Resources. Want to get involved? Join the beta! AWS for WordPress Plugin Download Setting up the AWS for WordPress Plugin