Secondhand Shopping is On the Rise. But Poor Product Search Functionality is Hurting Online Marketplaces
Imagine this scenario: After scouring a secondhand shop for what seems like an eternity, you finally find the perfect pair of brand-name jeans with tags still on them for only $20. For some shoppers, the thrill of discovering a hidden gem in a thrift store is more rewarding than a fast-fashion find — and not just because of the price tag.
Secondhand shopping is a rising trend thanks to used clothing beginning to be seen as equal to or in some cases cooler than new clothing, coupled with consumer concerns about sustainability and ethically-sourced clothing. This trend has also spurred the growth of secondhand online marketplaces for customers who want sustainable fashion without the hassle of sorting through clothing racks. However, these online marketplaces come with their own search and discovery challenges, with clothing often mislabeled, or not labeled at all, and thus fail to yield search results in older keyword-matching search engines that match consumer queries. Because of this, secondhand e-commerce retailers risk leaving consumers empty-handed and disappointed.
Secondhand online marketplaces that are ready to take advantage of the shift toward more sustainable fashion need improved search functionality. By investing in an AI-powered solution, retailers can reduce site abandonment and boost conversions — and Curtsy, a sustainable women’s fashion online marketplace, proves this notion.
The Growing Popularity of Secondhand Shopping
The global secondhand apparel market is expected to reach $77 billion by 2025 — up from $36 billion in 2021. The booming trend toward secondhand shopping is largely driven by Gen Z and their desire for more sustainable fashion choices. And with current supply chain delays and inflation, the immediacy and affordability of thrift stores are even more appealing to consumers. With growing interest in secondhand shopping we’re seeing a rise in e-commerce secondhand marketplaces.
Secondhand marketplaces help strike the right balance for consumers who want interesting, difficult-to-find used clothing, are concerned about how their clothes are sourced, but want the convenience of online shopping. But much like thrift stores, some secondhand marketplaces can be difficult to navigate, and rarely send customers to the items they’re looking for when product search and browse functionality is weak. For example, a consumer types “all stars” into the search bar but they’re led to a no results page or a page with all clothes and jewelry featuring stars — not the Converse All Star shoes they’re trying to find. This scenario could result in disappointed shoppers abandoning the site. Given that 32 percent of customers would stop doing business with a brand they loved after a single bad experience, ensuring that customers find something appealing to them is critical to driving sales.
How Secondhand Online Marketplaces Can Drive Sales by Improving Search Functionality
Curtsy is dedicated to simplifying the thrifting process and creating a best-in-class user experience, but found that their app was regularly not surfacing the products it had that its customers wanted. There’s nothing more frustrating than turning away a shopper looking for something you have but they simply can’t find. However, by harnessing the power of an e-commerce-focused search solution, Curtsy increased conversions and gained access to:
- Optimized search functionality: Thanks to a solution with machine learning capabilities informed by clickstream data, Curtsy can now serve more relevant search results to shoppers. Improved search functionality helps boost business KPIs and reduces customer frustration by learning from clickstream data and user behavior to help users find what they’re looking for.
- Optimized browse functionality: A lot of people assume that the learnings from clickstream and user behavior are meant for search only, but having them built into browse functionality is often even more important. When someone goes into the “tops” category, for example, there can be hundreds of thousands of available products in the category. The important thing is it matters which ones you show on page one and which you show on page 1,000. This is fundamentally the same kind of AI challenge where learning user preferences from their behavior and clickstream–just like Netflix learns movie preferences and Spotify learns what kind of music you like–become especially critical.
- Autosuggest: With autosuggest also based on clickstream and AI, Curtsy can more quickly direct consumers to products and searches that are likely to appeal to them. Autosuggest capabilities can understand words and their significance, meaning the tool can correct misspellings and ascertain search intent faster than the user can type their query.
- Personalized recommendations: Curtsy implemented KPI-optimized recommendations tailored to each customer. By understanding shoppers and their online behaviors, Curtsy can create a better customer experience, drive more sales and increase basket size. When consumers thrift-shop online, they don’t want to just see “relevant” products — they want to see products that appeal to them, and they want the system to help them find the next great thing for their wardrobe. This expectation is the reason why secondhand marketplaces should do like Curtsy and invest in their product search and customer experience strategies.
With improved search functionality, browse functionality, autosuggest, and tailored recommendations, secondhand online marketplaces can meet customer expectations, reduce site abandonment, drive sales, help customers find clothing they love, and play alongside e-commerce leaders.
Eli Finkelshteyn is the founder and CEO of Constructor, AI-first site search that increases conversions and revenue.