The ROI of AI in Luxury Retail: Reducing Returns
The luxury fashion industry is facing a growing problem: returns. According to a recent report by McKinsey & Company, the average return rate for luxury goods is 30%. This means that for every three items that a luxury retailer sells, one of them will be returned.
With my background in the luxury retail industry, specifically in Africa, I have gained a comprehensive understanding of the issues that omnichannel retailers face, with returns being a significant challenge.
There are a number of factors that contribute to high return rates in the luxury fashion industry. One is the high price of luxury goods. When customers spend a lot of money on an item, they are more likely to be hesitant to keep it if it doesn’t fit or look the way they expected.
Another factor is the lack of opportunity or apprehension to try on luxury goods before buying them.
Most luxury retailers do not have the best reputation for great service- especially for the disenfranchised. According to this 2022 Vox exclusive report, Department stores have a long, complicated history of racist exclusion. In 2021, BOF reported that the purchasing habits of Black Americans, who have traditionally shown a strong interest in luxury products, are now undergoing a significant transformation, carrying significant consequences for brands.
The high return rate has become a major problem for luxury retailers since the start of the pandemic. Dealing with returns incurs costs and can have detrimental effects on the brand’s standing, compounded by potential stock shortages.
There are a number of ways that luxury retailers can use AI to reduce returns. One way is to use Artificial Intelligence (AI) powered virtual try-on tools. These tools allow customers to see how an item will look on them before they buy it. This can help to reduce the number of returns caused by fit issues.
Another way that AI can be used to reduce returns is to personalize the shopping experience. AI can be used to track customer preferences and to recommend products that are likely to be a good fit. This can help to reduce the number of returns caused by customers buying items that they don’t actually want.
AI can also be used to improve the customer service experience. AI-powered chatbots can answer customer questions and resolve issues quickly and efficiently. This can help to reduce the number of returns caused by customer dissatisfaction.
Here are some specific examples of how luxury retailers are using AI to reduce returns:
- Farfetch is using AI to power its Wanna virtual try-on tool. Wanna allows customers to see how a product will look on them in real time, using their own smartphone camera. This has helped to reduce Farfetch’s return rate by 20%.
- Mytheresa is using AI to personalize the shopping experience. Mytheresa’s AI-powered algorithm tracks customer preferences and recommends products that are likely to be a good fit. This has helped to reduce Mytheresa’s return rate by 15%.
Recognizing the need for a data-driven approach to personalize their offerings, Mytheresa aimed to provide the most sought-after luxury fashion designers to customers across 133 countries and in eight different languages.
By partnering with Kibo, they efficiently streamlined their testing and personalization strategy across various markets, emphasizing swift implementation, robust testing capabilities, and the generation of valuable customer insights.
Moreover, Kibo facilitated the integration of different workflows seamlessly, ensuring a flawless and enjoyable customer experience, which remains a fundamental principle for Mytheresa.
- The RealReal is using AI-powered chatbots to improve the customer service experience. The RealReal’s chatbots can answer customer questions and resolve issues quickly and efficiently. This has helped to reduce The RealReal’s return rate by 10%.
AI presents a robust solution for luxury retailers seeking to minimize returns. Implementing AI-powered tools like virtual try-on features, personalized shopping experiences, and AI-driven chatbots can significantly enhance the customer journey, leading to a decrease in return rates.
In addition to the aforementioned examples, luxury retailers can explore the following approaches:
1. Leverage AI to predict customers with a higher likelihood of returning items. This valuable insight enables targeted interventions through personalized recommendations and additional support to mitigate potential returns.
2. Analyze return data using AI algorithms to uncover underlying patterns and root causes of returns. This analysis aids in identifying specific areas for improvement and implementing corrective measures.
3. Utilize AI to devise innovative return policies, such as extending return windows or offering free return shipping. Such flexible policies can enhance customer satisfaction and reduce hesitation in making purchases.
By integrating AI strategies like these, luxury retailers can effectively reduce return rates while elevating the overall customer experience.
Comparative Analysis for Clients with a Personalized Approach
The advancement of deep learning technology empowers applications to access and learn from vast amounts of data independently. This progress enables machines to develop an enhanced understanding of consumers, including their preferences, dislikes, and individual habits.
For instance, specific deep learning algorithms allow “price-sensitive” consumers to leverage artificial intelligence, connecting them with luxury suppliers worldwide, and enabling them to find the most affordable prices for luxury goods based on historical data. A great example is Lyst, the $2BN fashion technology company, and premium shopping app founded in 2010.
By merging the capabilities of AI and human insights, a comparative analysis for clients becomes possible. Deep learning algorithms, driven by AI, help identify and connect consumers with the best available prices for luxury products.
This technology facilitates seamless interactions between luxury suppliers and consumers globally, fostering an environment where consumers can make informed purchasing decisions based on their unique preferences and budgetary considerations.
Google AR & Fashion Retailers
Google is expanding its presence in the AR try-on market by introducing its own virtual try-on tool. This new tool seeks to provide online shoppers with a similar level of confidence they experience when trying on clothes in physical stores, ensuring that they make well-fitting purchases.
In an announcement on June 14, Google expressed its commitment to enhancing the online shopping experience by introducing two features. The first feature, virtual try-on for apparel, utilizes generative AI to showcase clothing on a diverse range of real models. The second feature offers new filters to help shoppers find exactly what they are looking for.
By tapping on products marked with a “Try On” badge during their searches, customers in the United States can virtually try on women’s tops from various brands including Anthropologie, Everlane, H&M, and LOFT.
Google clarified that although it seeks to gain a larger share of the AR market, it does not intend to monetize this effort (lol)
Instead, the company emphasizes the importance of collecting data from the try-on tool to improve the platform. This data, combined with the growing adoption of the feature by brands and retailers, will drive faster enhancements and refinements to benefit shoppers.
