Product recommendations
Product recommendations are a powerful marketing tool for merchants to increase conversions, and stimulate shopper engagement. Marketing managers can quickly create, manage, and deploy recommendations across their store views directly from Adobe Commerce Admin panel.
Because these suggestions are backed by a deep analysis of aggregated visitor data, they result in highly engaging, relevant, and personalized experiences for the shopper.
Because these suggestions are backed by a deep analysis of aggregated visitor data, they result in highly engaging, relevant, and personalized experiences for the shopper.
Example of a product recommendations widget on a storefront
My role
Working in collaboration with another UX designer I was leading the design for major product enhancements, Working with a PM and a team of developers on a daily cadence.
Competitors research An analysis of preview patterns across Adobe products and external solutions
Ideation with a product manager and a developer
User testing Collected user feedback in user testing sessions.
Wireframes I Created low-fidelity to high-fidelity mockups based on the Adobe Spectrum design system and Adobe Commerce's unique needs.
UI Design specs for developers
Interactions and behaviors spec
The Problem
For a Merchandiser, the only way to test the recommendation settings was to deploy it in a test environment or a live store, a jarring process that had to be repeated multiple times until the optimal settings were achieved.
By Adding a preview, the merchandiser can see a live simulation of how every selection yields different results, it boosts the merchandiser's confidence in the settings choices and make it clear how each strategy is affecting the products that are offered.
User testing
We tested 3 strategies for the preview display, we initially did internal testing, and later with a group of early adopters. we heard about how the product impacts their user engagements and what they would like to see in upcoming builds.
Two of the tested solutions
In interviews, users appreciated the live preview better than the more robust preview.
We validated our assumptions and focused our message by identifying where users showed less confidence. We saw a boost in the merchandiser's confidence and we were able to spotlight our Adobe Sensei AI-powered abilities and demonstrate the different product recommendations strategies.
By selecting the products that the shopper has in his cart I’m getting a real-time simulation of the products that are recommended to the shopper. The rich preview dropdown that I introduced here, together with the product card was added to the Spectrum design system and was implemented in new commerce designs.
Filtering
This upgrade enables users to quickly activate inclusion and exclusion filters without the need for complex rule building. This feature provides a user-friendly, no-code solution catered to merchandisers and marketers.
The filtering functionality was evaluated by a group of early adopters and received positive feedback.
Outcome
Enabling merchants and merchandisers to have a comprehensive understanding of the items shoppers will encounter, along with a user-friendly option to pin or exclude specific items, significantly increased confidence in the Product Recommendations tool. This led to a notable increase in the number of product recommendations incorporated into stores. Additionally, there was a substantial uptick in the overall number of sessions and impressions generated.