Free pilot for fashion stores: improve onsite recommendations (conversion/AOV)

Topic summary

Offer of a no-cost pilot for fashion retailers to enhance on-site product recommendation systems with the goal of increasing conversion rates and average order value (AOV). Conversion refers to turning site visitors into purchasers; AOV is the average revenue per transaction.

  • Details, scope, and implementation specifics are not provided in the available content. No posts, attachments, or media are visible.
  • No feedback, questions, or opposing views are recorded, so stakeholder interest and requirements are unclear.
  • No decisions, commitments, or participant sign-ups are documented.

Key unknowns:

  • Eligibility (store size, platform compatibility) and integration needs (data access, tech stack).
  • Pilot structure (duration, success metrics, support level) and privacy/compliance considerations.
  • Expected outcomes beyond improved conversion/AOV and any costs after the pilot.

Status: Unresolved/ongoing due to missing discussion content. Action needed: share pilot specifics and next steps (how to apply, timelines, measurement plan) to progress.

Summarized with AI on January 27. AI used: gpt-5.

Hi! We’re looking for a small number of Shopify fashion/apparel stores to run a free pilot focused on one thing: improving onsite product recommendations and measuring lift in conversion and/or AOV.

The problem: many stores end up showing bestsellers/new arrivals to everyone, especially for first-time visitors, which can limit relevance and revenue.

What we’re piloting: a “Bring your taste” experience where shoppers can opt in to bring a lightweight preference profile (high-level signals like what they tend to like/buy and “kept vs returned” tendencies). That profile is used to power more relevant onsite recommendations.

Pilot scope: we’ll test recommendations in one or two placements (e.g., PDP and/or cart drawer) and help you measure impact (A/B or a clean before/after baseline depending on traffic).

Recommendations are one of the few levers that can impact both conversion and AOV, but they’re often limited by sparse per-customer history. We think “Bring your taste” can reduce that gap by making preferences available earlier (opt-in), and we want to measure whether the lift is real.