Turning a Dead-End into an Opportunity.

Dead ends in e-commerce, like out-of-stock items, can become opportunities for discovery

What’s the user problem? The user problem that’s been identified

At Farfetch, 15% of landing product detail pages are for out-of-stock items, leading many users to abandon their session without exploring alternatives. This results in lost conversions and disengaged users, highlighting a gap in the offered experience and missed conversion opportunities. Furthermore, Out of Stock pages look very similar to In-Stock Pages.

What’s the goal? The results which show that the change was successful

Turning these Out-of-Stock dead-end moments into opportunities for engagement, discovery, and inspiration. A measurable impact would include reduced abandonment rate and churn rates after an Out-of-Stock page is visited, alongside increased time-on-site, as users are encouraged to explore and stay connected with the broader catalog.

Spending time in the Problem Space

Mapping the problem and current experience

After aligning on metrics and mapping the problem with the PM (image above), I asked the team to discuss the page from a first-time user’s perspective, capturing feedback on post-its during the review. We also consulted Customer Support for their insights and role-played as luxury associates informing users about out-of-stock items. These activities helped uncover key jobs-to-be-done for the next steps.

Confirm product identity - Help users quickly verify they've found their desired item, not to be confused with in-stock pages.
Communicate availability status clearly - Provide immediate, unambiguous out-of-stock messaging
Smart alternative suggestions - Present relevant options that match original specifications (color, style, size)

Jobs-to-be-done

The first step is clear: ensuring users recognize the product they were searching for. Once that’s achieved, we can focus on opportunities. Think of it as a shopping assistant saying, "We don’t have this… but let me help!"

To explore this, we selected a few algorithms and A/B tested the least confident option:

Same product, different model or color: Not tested, as we were confident in its relevance when available.
Same brand + same category (e.g., More Mules from Burberry).
Similar pieces (e.g., Other Mules).

The goal is to deliver an experience that flows from relevance to discoverability—starting with what’s most likely to match the user’s intent and transitioning to broader options. For instance, it’s easier to infer someone wants a similar shoe than to guess they’re looking for a pink one without explicit data.

Design and Hypothesis Testing

Proven Impact

The rollout of smart recommendations for out-of-stock product detail pages (OOS PDPs) significantly improved user engagement and conversion metrics:

Single Page Visits: Users encountering out-of-stock items were more likely to explore suggested alternatives, reducing drop-offs and increasing browsing behavior.
Add-to-Bag Actions & Revenue: A clear boost in Add-to-Bag actions contributed to a notable increase in GMV, backed by high-confidence testing results.

Performance of Recommendation Variants
Among the tested variants, Variant C proved most effective. By prioritizing recommendations for similar products over broader designer or category suggestions, it drove higher engagement rates and conversions:

Revenue Impact: The test drove an increase in GMV ranging from +$2.35M to +$6.76M, depending on the scenario, with a high statistical significance of 99.9%.

To further enhance the experience, my team and I iterated on related features: