Release Summary - Jun 19, 2024 (24.12)
The following key features and improvements, along with bug fixes, have been released in Algonomy CXP products in the release version 24.12 during Jun 04, 2024 - Jun 19, 2024.
Enterprise Dashboard, Science
Ensemble AI (Composite Outfit): Model to Include Subcategory Products
The Ensemble AI (Composite Outfit) model now considers all products from a selected category, including those from its subcategories. This enhancement ensures that merchandisers no longer need to manually select each subcategory when defining a style.
Previously, only recommendable products from the main category were considered, excluding those from subcategories. With this update, the model now includes all recommendable products from both the main category and its subcategories at all levels. This change streamlines the style definition process and ensures comprehensive product coverage.
Jira: ENG-28123
Ensemble AI (Composite Outfit): Dynamic Experience Report - View/Click Based Performance Reporting
The Ensemble AI (Composite Outfit) feature now includes comprehensive view and click-based performance reporting. This enhancement allows merchandisers to track the effectiveness of the Ensemble AI experience by providing detailed metrics on shopper interactions.
Merchandisers can now access view-based metrics, which include:
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View CVR (Conversion Rate)
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View RPV (Revenue Per Visit)
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View Converted Visits
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View Based ATC (Add To Cart) Visits
Additionally, click-based metrics are also available, providing insights into:
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Click CVR (Conversion Rate)
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Click RPV (Revenue Per Visit)
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Click Converted Visits
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Click Based ATC (Add To Cart) Visits
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Click Sales
To enable dynamic experience reporting with click metrics, the system now captures clicks on the variations as part of the track experience API call. When a shopper clicks on a product from the outfits, an API call with eventType=click is triggered, allowing for accurate tracking and reporting of variation clicks.
Jira: ENG-28149
Ensemble AI (Composite Outfit): User ID Entry for Affinity Ranking Impact
Ensemble AI (Composite Outfit) now allows merchandisers to enter a user ID to see how user affinity ranking impacts the generated outfits. This feature helps merchandisers test and confirm their user affinity configuration settings.
A new entry field is provided within Ensemble AI, positioned above the seed product specification, where the merchandiser can input a user ID. The interface is designed similarly to the view results page for configurable strategies. As different seed products are selected, the system uses the entered user ID to adjust the outfits based on the user's affinity settings, allowing for a more personalized and accurate assessment.
Jira: ENG-28172
Configurable Strategies: Added Movers and Shakers Model
The Configurable Strategies UI now includes the "Movers and Shakers" model, enabling digital optimization managers to create strategies that incorporate this model sorted by User Affinity.
Key Features:
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Model Selection: You can now select "Movers and Shakers" and choose from various seed options including Context Category, Context Brand, Fixed Category, Fixed Brand, Category Affinity, and Brand Affinity.
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Fixed Seed Selection: When selecting Fixed Category or Fixed Brand, the existing category and brand input fields allow you to choose the desired category or brand.
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Sorting Options: The model supports sorting by User Affinity (with configuration selection) and Smart Shuffle.
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Filtering Options: Filters such as No Filter, Category Diversity, and Brand Diversity are available.
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Additional Options: All standard additional options are included.
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Preview Breadcrumbs: The preview feature now shows a breadcrumb trail indicating the selected model.
Enhance p13n.js to Support Single Page Applications
The p13n.js library has been enhanced to support Single Page Applications, improving functionality when the website is loaded in incognito mode or no-cookie mode. Previously, if a shopper accepted cookies after initially declining them, the p13n-generated calls would not function correctly, as no rcs value was exchanged.
Now, the p13n library effectively handles this scenario by setting ‘RR.noCookieMode=false’ and ‘RR.onloadCalled=false’ when the user accepts cookies. This ensures that p13n_generated.js is loaded correctly, enabling the appropriate context and information transfer to the RRserver. This update applies to all versions of p13n.js (1.2, 2.0, and 2.1), ensuring consistent functionality across different implementations.
Jira: ENG-28188
Show API Client Keys on Site Configurations Page
The Site Configurations page now displays API Client Keys to all users, making it easier for administrators to access these keys for personalization projects. Previously, this section was only visible to employees.
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The API Client Keys section is now populated for all users with access to the page.
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The /channels API is used to retrieve the keys, excluding default channels such as Desktop ("WEB"), Email ("EMAIL"), Phone ("WEB_PHONE"), and Tablet ("WEB_TABLET").
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Non-employee users will not see the "Add Key" button or the action columns (Active, Disable event logging, Actions) in the table.
Jira: ENG-28141
Primary Category Configuration
The Primary Category Configuration feature enables Digital Optimization managers to filter product recommendations by either keeping them within the same category or applying category diversity for cross-sell opportunities.
Designating a primary category for products with multiple categories ensures effective filtering, preventing repetitive recommendations and promoting variety. Configuration is done through the Omnichannel Personalization Cloud portal, where primary categories are assigned, starting from high-level nodes. For optimal performance, select high-level categories as Primary Category Nodes and use Non-Primary Nodes for exclusions.
This feature is accessible in the navigation under the Optimization section. For more information, refer to How to Set Primary Categories.
Jira: ENG-28130
Recommend Test Drive
The Recommend Test Drive feature allows Optimization managers to view recommendations for any configured placements, providing a clear understanding of the system’s setup.
This feature assists managers in:
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Reviewing the outcomes of various strategies for a placement.
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Observing the rules triggered for a placement.
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Understanding the differences between user-affinity and non-user-affinity sorting or personalization for a placement, with or without a user ID.
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Comparing the results of an email placement against a desktop placement.
This feature is accessible in the navigation under the Recommend section. For more information, refer to Recommend Test Drive.
Jira: ENG-28378
Find
Find Response Report: Add to Reporting Menu
The Find Response Report provides insight into query results and helps improve find configurations to enhance search results and their positions.
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View which products are returned for a given search query and the percentage of times a product appears in a specific position.
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See how many times a specific product is clicked in each position.
Jira: ENG-28280
Other Feature Enhancements
The following feature enhancements and upgrades have been made in the release version 24.12 during Jun 04, 2024 - Jun 19, 2024.
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Jira # |
Module/Title |
Summary |
General Availability |
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Social Proof: Social Proof Badging - Allow Deletion of Defined Badges |
Social Proof Badging has been enhanced to allow merchandisers to delete badges they have defined. This update provides greater flexibility and control, enabling users to remove badges that are no longer relevant or needed. |
19-Jun-2024 |
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Enterprise Dashboard: Change API Client Key Label to Channel in Recs Test Drive |
The label for the API Client Key has been updated to "Channel" in the Recs Test Drive. |
19-Jun-2024 |
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Data Engineering: Analytics/Reporting - Add "Apply" Button at the Top for Reports |
An "Apply" button has been added at the top of the reports page in addition to the existing one at the bottom. This floating button ensures that users can apply changes without having to scroll down, enhancing usability when multiple dimensions are involved. |
19-Jun-2024 |
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Enterprise Dashboard, Recommend: Add an Option to Use Affinity Sort in Advanced Merchandising |
We have enhanced Advanced Merchandising rules to include an option for affinity sorting. This allows digital merchandisers to make recommendations more relevant to each individual shopper by utilizing User Affinity sorting within their Advanced Merchandising rules. The affinity sorting can be applied at the Rec Group level and enabled for any of the existing sort options in Advanced Merchandising. When this option is enabled, products are re-ordered based on the user's affinities, combining the base strategy results with affinity-based ranks to provide highly personalized recommendations. |
19-Jun-2024 |
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Data Engineering: Find Report - Zero Search Results Detail Report |
We have enhanced the reporting capabilities to include a Zero Search Results Detail Report for Find. This report allows digital optimization managers to analyze sessions where users encountered zero search results, providing insights into search terms and filter selections that led to zero results. Key features include tracking each search term and filter selection in the session, capturing date/time, session ID, user ID, search terms, filters, number of results, and the number of facets/filters applied. This comprehensive view helps identify areas for optimization and improve the overall search experience. This enhancement is currently implemented for the backend (rollup) only. Further tickets will be required to expose it to the UI for clients and internal teams. |
19-Jun-2024 |
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Engage, Recs + Email: Support for Deduplication of Engage Content in rrmail |
We have added support for deduplication of Engage content in rrmail. This enhancement ensures that when multiple content placements are included in emails, the same content will not appear in both placements, providing a more diverse and engaging experience for recipients. |
19-Jun-2024 |
Bug and Support Fixes
The following issues have been fixed in the release version 24.12 during Jun 04, 2024 - Jun 19, 2024.
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Jira# |
Title |
Summary |
General Availability |
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Platform: Fix for Storing Brand Views from Email Open Requests |
We have resolved an issue where brand views from email open requests were not being stored in UPS. With this fix, brand view data is now correctly logged and visible in the UPS response. |
19-Jun-2024 |
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Enterprise Dashboard, Recommend: Advanced Merchandising Rules Category Selection Repeatable Issue |
We have resolved an issue in the Advanced Merchandising section where some categories were being repeated when creating "buy together" rules. Additionally, selected categories were not appearing correctly when viewed. This fix ensures that duplicate categories are no longer visible after the rule loads, and the category picker no longer shows duplicate selections. |
19-Jun-2024 |
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Enterprise Dashboard, Recommend: JsonCallback Function Issue in Version Upgrade |
We have resolved an issue with the JsonCallback function in version V2.1.0.20240408. Previously, this function only ran once on the first page load, causing fallback recommendations to appear instead of the correct Algonomy carousels/placementIDs on subsequent pages. With this fix, the JsonCallback function now triggers on every page load, ensuring the proper recommendations are displayed consistently across all pages. |
19-Jun-2024 |
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Enterprise Dashboard, Recommend: New Library Update for First Party Migration Issue |
We have resolved an issue related to the first-party migration where the new library setting was not working for multiple requests as described in the integration guide. The latest update ensures that the new library setting now functions correctly for both normal recommendations and multiple requests. |
19-Jun-2024 |
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Issues with JavaScript version 2.0 and Single Page Application |
We have resolved an issue with the 2.0 version of the JavaScript library affecting the rcs parameter in Single Page Applications. Previously, users accessing the site in incognito mode or after clearing cookies would not receive the rcs parameter in responses unless a full page reload was performed. This issue has been fixed to ensure consistent inclusion of the rcs parameter without requiring a full page reload. |
19-Jun-2024 |
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Catalog Update API: Content Update API: 401 Unauthorized Error Fix |
We have fixed the issue causing the Content Update API command to fail with a 401 - unauthorized error. The updates include removing reliance on the legacy header "X-Consumer-Custom-ID" and ensuring APIs are unsecured so Kong handles authentication. |
19-Jun-2024 |
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Enterprise Dashboard, product badging: Social Proof Badging: Badge is not showing on the client site |
We have resolved an issue with social proof badging where badges were not displaying on the client site despite appearing correctly in the preview. This fix ensures that badges such as "Sold as One" based on category attributes will now be visible to shoppers as intended. |
19-Jun-2024 |
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Enterprise Dashboard>Social Proof: Social Proof UX was not Working as Expected |
The multiple message option on cart pages were appearing intermittently. Sometimes the messages displayed as expected, while other times they did not display at all. For campaigns set up before the last release:
The same inconsistency was observed with newly created campaigns. The issue has been resolved now. |
19-Jun-2024 |