Retailor — AI-Driven Social Shopping

Retailor is the studio's vision for the next generation of online shopping: AI-powered Reverse Image Search, a proprietary fashion social network, and AR try-before-you-buy — Pinterest meets Amazon, designed mobile-first to enhance product discovery and drive repeat visits.

Role: Design

Overview

In an era where online shopping is rapidly evolving, the studio has been exploring ways to innovate the retail customer experience through cutting-edge technologies. This vision for retail innovation began to take shape early in the studio's work. Projects for renowned online retailers, including Audi, Louis Vuitton, and Toyota, fueled a fascination with the next generation of e-commerce and prompted continual refinement of this vision.

This vision evolved during conversations with Macy's, developing a forward-thinking approach that continues to be refined. That approach was compiled into a concept named "Retailor," representing this vision for online shopping. The focus is on utilizing Artificial Intelligence (AI), particularly Reverse Image Search (RIS), alongside a proprietary social network dedicated to fashion. Additionally, technologies like Augmented Reality (AR) and other AI applications play a crucial role. The ultimate goal is to enhance product discovery, encourage repeat visits, and provide a user-friendly, mobile-first experience.

Objectives

Streamline product discovery: implement RIS to allow users to identify products effortlessly through images.
Foster community engagement: create a social network that encourages users to share and discover fashion.
Enhance user experience: develop a mobile-friendly platform that aligns with modern shopping habits.

Approach

Reverse Image Search: users take a photo of a desired item; the AI engine analyzes the image and matches it with products in the Retailor catalog. This intuitive method caters to the visual nature of today's shoppers — picture a user out with friends, snapping photos of interesting items, then later uploading those images to find matching products effortlessly.
Social shopping platform: users can opt to share their RIS searches and purchases publicly, creating a social feed where users can discover fashion inspiration from others. The platform combines elements from Instagram and Pinterest, allowing users to follow, like, and direct message one another — simply put, Pinterest meets Amazon.
Leveraging influencers: influencers could share their finds and connect with followers, while Retailor benefits from their reach, driving traffic and sales.
Augmented Reality — try before you buy: users upload a photo or video of themselves, and AR overlays items onto their AR persona, helping them visualize how products would look before purchasing — addressing the challenge of online returns.
Integrated support system: a dual support system combining a virtual assistant and live chat support ensures smooth adoption of this new shopping experience. Insights gathered by the support team are used to refine the RIS algorithms for improved accuracy.

Responsibilities

As the design partner for this project, the studio's responsibilities included:

Strategy development: crafting a comprehensive strategy to integrate AI and social features into the retail experience.
Final visual design: creating polished visual designs aligned with Retailor's branding and user experience goals.
Marketing and branding design: developing marketing collateral and branding designs to promote the new features and ensure a cohesive look and feel across the platform.

Product Leadership

The concept's product thinking was written as a five-document system: a strategy memo (Pinterest owns inspiration, Amazon owns checkout; Retailor owns the moment between), a go-to-market plan built on creators as the supply side, a zero-to-one roadmap of four phases separated by evidence gates rather than dates, an AI-investment case that gives every AI feature a job and a gate, and a measurement plan grounded in the moderated study.

Roadmap — gates, not dates: four phases (prove the utility, earn the habit, switch on the network, widen the moat), each opened only by named evidence; tripwires state in advance which findings would change the plan.
Research & evidence: six moderated sessions, 55 changes shipped live between sessions, mean task ease 4.1 of 5 — with AR try-on honestly reported as the hardest task (one clean success in six) and gated accordingly.
Heuristic benchmark: a 1–5 assessment of ASOS, Amazon Fashion, Pinterest, Instagram Shop, and Zalando across the concept's competitive axes, with Retailor's row a design target — an assessment, never a measurement.

Results & Anticipated Impact

Enhanced user engagement: the social shopping features created a vibrant community, driving user interaction and content sharing.
Streamlined shopping experience: RIS significantly reduces the time and effort required for product discovery.
Higher conversion rates: the mobile-first design and AR capabilities aimed to improve customer confidence and satisfaction, leading to increased sales.

Conclusion

This case study illustrates the potential of integrating AI and social networking in the retail sector. By focusing on user needs and harnessing innovative technologies, Retailor creates a unique shopping experience that exceeds modern consumer expectations. The vision showcases a holistic approach to retail, where community, technology, and fashion converge to inspire and engage customers.