Argos Out of Stock Trolley Recommendations

Imagine the frustration of shopping online, adding items to your trolley, and then finding out at checkout that some of them are out of stock. This disappointment has been a key driver of abandonment at Argos. In response, we sought to mitigate this frustration by introducing tailored recommendations for alternative products through our existing recommendation API. The goal was to help customers find substitutes for unavailable items, enhancing their shopping experience and reducing trolley abandonment.

Date: Q4 2020 - Q2 2021

Lead Product Designer: Alex Dawson

Problem

Argos faced significant challenges during the pandemic, including stock shortages, delays caused by the Suez Canal blockage, and other supply chain disruptions. With 25% of trolley visits showing out-of-stock (OOS) products, the customer experience was suffering, leading to missed sales opportunities.

Our task was to reduce the disappointment felt by customers when encountering OOS products and offer them relevant alternatives in real time to keep them engaged and prevent them from leaving the site.

Users & Audience

We identified several customer personas through research:

1. Undecided Shoppers: These customers have a general idea of what they want but are open to alternatives if they encounter something better.

2. Selective Shoppers: These customers approach buying with a mixed strategy—sometimes impulsive, sometimes deliberate, depending on the product.

3. Decisive Shoppers: These users know exactly what they want and stick to their plan, typically ignoring distractions or recommendations.

4. Browsers: A small group that enjoys exploring product options and relies heavily on reviews and visuals for inspiration.

Further segmentation revealed how different customers respond to recommendations:

• Trusting: These customers have bought recommended products before and are open to them again.

• Open but Skeptical: These customers haven't purchased recommendations yet but are willing to do so if the recommendation meets their criteria.

• Self-Reliant: These customers prefer to make their own decisions and rarely trust product recommendations.

• Burned by Bad Recommendations: This smallest group had a bad experience with a recommendation and is unlikely to trust them again.

Roles & Responsibilities

I was the Lead Product Designer on the project, collaborating with two Product Managers, the Engineering team, and stakeholders from the availability department. My responsibilities spanned the entire project lifecycle, from research to design and testing, ensuring we delivered a user-centric solution within tight deadlines.

Scope & Constraints

We had a limited timeframe of a few months to complete the project and couldn’t modify the existing recommendation API. This required creative problem-solving to deliver value without overhauling the backend infrastructure.

Discover

The discovery phase started with stakeholder interviews to understand the pain points and limitations of our current recommendation system. I collaborated with Product Managers, engineers, and recommendation engine experts to map out how recommendations were currently being displayed and what we could improve.

We conducted extensive research, including:

• Usability testing of the existing OOS experience.

• Competitor analysis to benchmark against leading e-commerce players.

• Customer feedback from Forsee surveys and user interviews.

• Heuristic analysis of the trolley page to identify usability issues.

With limited existing assets for the trolley page, I recreated the entire trolley flow for both desktop and mobile to enable future prototyping and testing.

Define

Through data synthesis, we identified common themes around customer frustration with out-of-stock items. Customers were not only annoyed by the lack of availability but also felt abandoned, with little support to help them find alternatives.

Key insights included:

• Desire for Collection Options: Many customers wanted the same product available at a different store for collection, even if it meant travelling further.

• Pre-order Capability: Customers expressed a preference for pre-ordering items that were OOS, giving them the choice to wait or shop elsewhere.

• Open to Recommendations: While customers were open to recommendations, they needed to feel like genuine replacements—anything less felt like an attempt to upsell.

From these insights, we formed hypotheses around factors that influence the success of a recommendation:

1. Location Proximity: Recommendations for nearby stock could reduce trolley abandonment.

2. Price Sensitivity: Customers were less likely to accept higher-priced recommendations.

3. Product Familiarity: Customers were more likely to accept recommendations for everyday items like batteries, compared to higher-investment items like electronics.

Develop

Armed with customer insights, we began ideating solutions. We produced multiple design variations for the trolley page, focusing on how to present recommendations alongside OOS products. Key design elements included:

• In-line recommendations: Displayed directly next to OOS products.

• Quick view side drawer: Allowed customers to easily compare alternative products with details like reviews and pricing.

• One-click replacement: Enabled customers to replace OOS items in their trolley without navigating away from the page.

We rapidly iterated these designs through 13 rounds of usability testing, which included 84 talk-out-loud studies and surveys with 350+ participants.

Deliver

Our final solution is displayed in stock recommendations directly next to OOS products. Customers could explore alternatives through a side drawer, view detailed product information, and replace the OOS item with a single click. The design was fully responsive across desktop and mobile, utilizing existing design patterns and components from our design system.

The engineering team implemented the solution through a series of JIRA tickets, followed by extensive QA and A/B testing. Our MVP was designed to be scalable, with additional features such as image carousels and category-specific recommendations saved for future iterations.

Outcomes & Lessons

The A/B test was conducted with 50% of customers over 25 days, resulting in:

• 0.5% increase in conversion for customers shown in stock recommendations.

• 0.2% increase in revenue per visit.

• 0.2% increase in units per order.

• 0.9% decrease in exit rate.

After the successful test, the solution was rolled out to 100% of Argos customers and is currently being tested on Habitat. We plan to further improve the experience through copy adjustments, additional recommendation types, and category links.

Key Lessons:

• A small, well-timed recommendation can significantly reduce trolley abandonment.

• Customers value transparency—showing them why a recommendation is relevant builds trust.

• Managing customer disappointment in out-of-stock situations is key to maintaining brand loyalty.

Through this project, we made the shopping experience more seamless and supported customers in recovering their purchases even when products were unavailable.