AI-powered rental fashion: Smarter recommendations for a sustainable future

Fashion is one of the most polluting industries in modern times. It produces about 10 per cent of the UK's annual carbon footprint – more than all international flights and maritime shipping combined. Research from Climatetrade, 2023, shows it also consumes vast amounts of water and generates millions of tons of waste. According to Fashion Transparency Index 2023, the constant demand for cheap clothing has made the industry a "key driver of human rights abuses". One of the largest study into clothing habits ever undertaken by climate action NGO WRAP shows the UK's wardrobes hold 1.6 billion items of unworn clothes.

To address some of these challenges, a new business model of subscription rental fashion has sprung up in recent years. Established brands like LK Bennett and Ann Taylor, along with startups such as Hurr, My Wardrobe HQ, nuuly, and Armoire, now offer rental services that allow multiple customers to “share” a much larger wardrobe, reducing the need for constant new purchases. These companies operate on a monthly subscription model, enabling members to rent and swap garments within each period. Returned garments are inspected, cleaned, and recirculated, ensuring efficient inventory use. The rental fashion business model has potential for reducing waste, lowering emissions, and extending the lifecycle of clothing, all the while keeping customers stylish and satisfied.

The challenge of keeping customers engaged

As operations researchers, my colleagues and I, together with a PhD student, Jiannan Xu, were drawn to rental fashion because of the operational complexity it presents. We partnered with a rental fashion startup and analysed its customer data. One issue stood out from the analysis: customers were not staying on the platform long enough. Many left after the initial discounted trial period, citing dissatisfaction with the style or fit of their rented items.

To improve retention and enhance the customer experience, we developed a novel approach to optimising personalised recommendations for rental fashion businesses. Our work focused on two key challenges:

• Encouraging customers to subscribe – many companies offer discounted trials, yet potential subscribers hesitate because they’re unsure whether they’ll find styles they like.

• Retaining customers beyond the trial period – a large percentage of cancellations stem from product fit uncertainty. Customers may select items based on images and descriptions, only to find that they don’t meet expectations in person. This mismatch leads to frustration, increased returns, and lower renewal rates.

Smarter assortment optimisation with AI

To tackle these issues, we developed an AI-powered algorithm that improves how rental companies curate product recommendations. Instead of relying on generic recommendation engines or brute-force optimisation, our model develops and optimises a stochastic decision model to account for the unique structure of rental fashion businesses. Specifically, it considers:

• Initial selection uncertainty – predicting which items are most likely to encourage customers to subscribe.

• Renewal uncertainty – identifying which items will maximise customer retention after the first rental cycle.

By carefully balancing these factors and using smart mathematical approximations, we developed a fast, scalable, and personalized ranking algorithm—one of the simplest and quickest methods for optimising the assortment curation process. Essentially, our algorithm gives a personalised score to each garment, whereby the score weighs in the expected consumer surplus (“signal”) and the uncertainties regarding the customer’s initial and post-wear valuations (“noise”). It thus has parallels with finance and engineering applications where good decisions are made by balancing signal-to-noise ratios.

Alternative methods to our algorithm would be either too slow for real-time use, or ineffective as they do not optimise the business objective. In extensive numerical simulations, we show our algorithm ranks and refines product assortments in milliseconds, compared to minutes using brute-force optimisation. We further show that, even under stress-testing scenarios, our algorithm can achieve results within 6.5 per cent of the best possible profit outcomes, making it both effective and scalable.

Practical benefits for rental fashion companies

Beyond technical advancements, our research has real-world implications for the rental fashion industry. By ensuring that customers receive better-matched recommendations, businesses can reduce unnecessary returns and improve overall satisfaction. This leads to:

• Fewer shipments and exchanges, reducing logistics costs.

• Better use of inventory, maximising item circulation.

• Higher customer retention rates, strengthening long-term profitability.

The future of AI in subscription-based rentals

Beyond rental fashion, the principles behind our AI-powered optimisation model extend to subscription-based rentals of experience goods in general. This includes electronics rentals, furniture subscriptions, and toy libraries, where selecting the right assortment is key to customer satisfaction and retention. By refining product recommendations, businesses can optimise customer engagement, reduce churn, and improve profitability.

Our research is among the first to explore assortment optimisation in this space, setting the stage for further studies in inventory and subscription management. While rental businesses may not solve all the environmental challenges, smarter, AI-powered recommendations can significantly enhance business viability and the overall customer experience—paving the way for a smarter, more efficient future in rental commerce.