For years, consumers had to learn how to speak the language of search engines. Now, search engines will need to learn how to speak human.

The search bar was built for a different kind of shopping

Traditional e-commerce works particularly well when the shopper knows what the product is called and the catalogue is neatly structured. That’s why a query like “Black Gucci leather jacket size M” will deliver great results.

But real shopping intent isn’t always that clear, and can often look like this: “I want something like this, but cooler toned“, “a jacket that works for this bridesmaid dress“, or “this Pinterest outfit but for under 100€ and second-hand if possible”.  

And if you think about it, these are much closer to how people naturally think about products. For a long time, e-commerce systems were simply not very good at understanding this kind of intent. And if you spend a few minutes on some of the biggest e-commerce sites, you can already observe images, disproportionately big compared to the rest of the page, and keywords slowly moving to the background

Product discovery is becoming visual, conversational and recommendation-led

Images are a stronger starting point, because they can communicate things that are difficult to describe precisely in words: silhouette, aesthetic, styling, context or vibes. A recent study by Retail Dive found that 85% of respondents placed more weight on visual than textual information when shopping online for clothing or furniture. Google has also highlighted that a quarter of monthly Google Lens searches are shopping-related, with younger users particularly active in visual search.

At the same time, conversational AI lets shoppers describe intent in a natural way, allowing them to add constraints and refine results without translating their desires into filters and search terms. Recommendation systems add another layer of surfacing products customers may have never searched for to begin with.

The result is that search is becoming less about matching words and more about understanding intent.

Resale makes this shift more obvious

Second-hand commerce is a prime example for this.

Traditional retail starts with a clean catalogue: one product, known SKU, standardised images, structured attributes and several units in stock. Resale data is much messier. The same product can appear listed by multiple marketplaces or individual sellers. Everyone using different images, different titles, missing attributes and completely different descriptions. And each listing is unique and can disappear any moment.

In this environment, keyword search quickly reaches its limits. If two sellers describe the same product differently, the system still needs to understand that they may be comparable. If important information is missing from the description, it may need to be inferred from the image. And if a shopper does not know the exact product name, there still needs to be a way to connect their intent with the right inventory.

That is why visual and conversational discovery are particularly valuable in resale: they help bridge the gap between how people naturally express what they want and how fragmented product inventory actually exists in our database.

How we approach this at faircado

At faircado, we aggregate and normalise millions of second-hand listings across dozens of resale marketplaces. Fragmented product data, inconsistent imagery and constantly changing availability are therefore not edge cases for us – they are the environment we work in every day.

This is why we prioritised image early on, making us a Gen Z favourite. We use image matching, personalised curation, similarity search, and searching your Pinterest boards inside the app to make fragmented resale inventory easier to understand and navigate.

Gen Z is accelerating the shift

Younger consumers grew up in a visual-forward world. That’s why they are especially comfortable and pushing for these new ways of discovering products. ThredUp’s Resale Report 2026 shows that 51% of Gen Z already use AI for things such as visual search, price comparison and fit.

That matters because Gen Z is also one of the generations pushing resale further into the mainstream. The consumers most comfortable moving between social platforms, AI assistants, visual search and marketplaces are also increasingly treating second-hand as a normal part of the shopping journey.

What this means for retail

The obvious response is to add image search, an AI assistant or another new interface. But the real challenge sits underneath.

If retailers want products to be discoverable through visual, conversational and AI-led search, their product data needs to be structured well enough for those systems to understand it. Images need to become an actual source of information. And retailers need to think about discoverability beyond their own website.

As AI increasingly becomes an intermediary between consumers and websites, this change isn’t one to ignore. At the same time, a Deloitte study shows that 75% of companies cite data as the main barrier to agentic commerce, while only 44% say their data is adequate for AI.

The search bar is not disappearing. But it is becoming one interface among many. Paired with consumers who are used to ultra-convenient shopping, just relying on a search bar is a practice of yesterday.

About the author
Evoléna de Wilde d’Estmael is Co-founder & CEO of Faircado, a resale discovery platform that aggregates and normalises millions of second-hand listings across European marketplaces. She writes about resale, Gen Z consumer behaviour and the future of ecommerce. This article expands on a post Evi originally shared on her LinkedIn.

The post Beyond Keywords: How Product Discovery is Changing appeared first on faircado.

Second-hand is becoming a default option

Gen Z is becoming too economically important for retailers to treat its shopping habits as a niche trend. Global Gen Z spending is expected to reach around $12 trillion by 2030, accounting for ~19% of global spending. By spending more per capita than any prior generation of that age, they have increasingly become a powerful force in consumer markets that have to be accounted for.

At the same time, second-hand has become remarkably normal for them. According to the ThredUp 2025 Resale Report, 62% of Gen Z shopped second-hand in 2025, while 68% of Gen Z and Millennials buy second-hand. With second-hand shopping as a staple in their shopping routine, these two generations are expected to account for 71% of all resale market growth by 2030.

That changes the competitive landscape for retailers. If someone wants a pair of trainers, a handbag or a jacket, the choice is no longer simply Brand A versus Brand B. It might be new versus second-hand. Or your new product versus your own product from six months ago. For brands, that is a very different kind of competition.

Your biggest competitor might be your own product

Traditionally, retailers think about competition horizontally. Nike competes with Adidas. Zara competes with H&M. Samsung with Apple. But once second-hand becomes a normal part of the consideration, competition starts happening vertically too.

A customer considering a new €300 jacket might also find the same jacket from last season for €160, or a barely worn version of the current collection for even less. And for the customer, this won’t be considered a downgrade, it’s simply different ways of solving the same need. A well-made item can re-enter the market again and again, creating value for future owners but also competing with the brand’s newest assortment. For retailers, this can easily mean that resale of your own brand will cannibalise your sales. And this is where the economics of resale start to affect the original purchase itself.

They aren’t price-sensitive. They’re value-literate.

One of the most interesting shifts is happening before the purchase even takes place. 67% of Gen Z consider an item’s resale value before buying it. That sounds like a small behavioural detail, but economically it is a pretty fundamental change. If you expect to resell something later, the sticker price is no longer necessarily the number that determines whether something feels expensive. A consumer might start thinking more like:

purchase price – future resale value = effective cost of ownership

A €400 product that can later be resold for €250 can, in that sense, look very different from a €150 product with almost no resale value. This is why describing younger shoppers as simply “price-sensitive” doesn’t represent their reality. The same shopper might be buying a 400 € bag, while buying their festival outfit from Temu. They’re both extremely willing to spend, while remaining conscious of value.

In our DMEXCO presentation in Cologne 2026, we described this as buying with an exit strategy: resale is no longer only what happens after someone gets tired of a product. It can influence whether that person buys the product in the first place.

Resale fits Gen Z’s new way of discovery

Gen Z is not only more open to buying second-hand; it is also less attached to the traditional idea of starting a shopping journey with a specific retailer. Discovery can begin on TikTok, Instagram, Pinterest, with a creator, on a marketplace or increasingly through an AI assistant. By the time someone arrives on a retailer’s website, the product idea – and sometimes even the shortlist – may already have been formed somewhere else.

That behaviour makes resale particularly interesting. Once a shopper is comfortable moving between platforms and comparing products regardless of where they are sold, the distinction between new from this retailer and second-hand somewhere else becomes much less important. The consideration set suddenly becomes much larger: not just the products currently stocked by a handful of retailers, but potentially millions of individual items spread across dozens of resale platforms.

The problem is that this much larger universe is also far harder to navigate. Second-hand inventory is fragmented, listings are inconsistent and the same or similar product can be described in completely different ways. A shopper might know the look they want without knowing the brand, model name or exact keywords that would surface it. This is where visual and conversational search help bridge the gap between how people naturally express what they want and how resale inventory is actually structured.

You can already see that behaviour emerging. According to ThredUp data, 51% of Gen Z use AI in resale for things such as visual search, price comparison and fit, while 63% are comfortable with agentic or automated buying experiences.

At faircado, this is a problem we encounter every day. We aggregate and normalise millions of second-hand listings across dozens of resale marketplaces, where product information, imagery and availability can vary enormously from one listing to the next. By focusing on image search and integrating Pinterest boards, we made that inventory searchable, understanding products beyond the keywords. That is also why resale gives us an early view of where retail generally is heading. Gen Z customers are pushing for visual and conversational discovery, while at the same time being the ones to push resale into the mainstream.

So what does this mean for brands?

Brands by now know that they have to take resale seriously. From retailers building their own resale platforms to luxury groups like Kering exploring how its brands can participate in the secondary market. The response is clear: if customers are buying second-hand, companies want to remain part of that relationship.

But launching a resale programme is arguably the easier part. Resale also exposes much bigger questions about how a company operates in the first place. If products are expected to have a second or third life, durability matters differently. So do repairability, materials, product information and the ability to identify and authenticate an item years after its original sale. If resale value starts influencing what consumers buy new, even pricing and brand equity begin to look slightly different.

This is where resale becomes more than an additional sales channel or sustainability initiative. Taken seriously, circularity asks companies to think beyond maximising the value of the first transaction and towards maximising the value of a product across its lifetime. That can sit uncomfortably alongside a retail model still largely built around selling more units, shortening replacement cycles and encouraging the next purchase.

The real question, then, is not whether brands will participate in resale – many already are. It is how far they are willing to let the logic of resale influence the rest of the business. Will durability, residual value and multiple ownership become meaningful considerations when products are designed and commercial decisions are made? Or will resale remain a circular layer added onto an otherwise linear model?

About the author
Evoléna de Wilde d’Estmael is Co-founder & CEO of Faircado, a resale discovery platform that aggregates and normalises millions of second-hand listings across European marketplaces. She writes about resale, Gen Z consumer behaviour and the future of ecommerce. This article expands on a post Evi originally shared on her LinkedIn.

The post How Gen Z Is Changing the Economics of Resale appeared first on faircado.

Gen Z is pushing resale into the mainstream at the same time as it is changing how products are discovered, compared, valued and bought across e-commerce more broadly. The consumers who are most comfortable moving between TikTok, Pinterest, marketplaces and AI assistants are also increasingly treating second-hand as an equivalent alternative to buying new.

That puts resale at the intersection of several changes happening across commerce at once. And it makes it a particularly useful place to understand what retailers will have to adapt to next.

E-commerce is losing control of the discovery journey

For a long time, retailers could think of the shopping journey as something that largely happened within their own environment. A customer arrived, searched, compared products and eventually checked out.

But customer journeys have become more diversified over the years.

Gen Z is discovering products everywhere: TikTok, Instagram, Pinterest, YouTube, AI tools and in their group chat. 77% of Gen Z already use TikTok to find new products and 92% use AI to compare prices before buying. So, by the time they reach a retailer’s website, they aren’t arriving to make a decision, but to confirm the one they’ve already made.

That changes what it means to compete for discovery. Being good at converting traffic once it arrives is no longer enough. Products increasingly need to be visible and understandable across the environments in which consumers are actually making up their minds.

Which leads us to a second problem: even when Gen Z does come looking, most retail sites aren’t built for how they search.

Search is moving beyond the search bar

The search bar retailers have spent the last 20 years optimising has become useless to Gen Z.

Keyword search asks the customer to translate their intent into the language of a catalogue: brand, category, colour, size. But search now has turned visual, conversational and recommendation-led.

Resale makes that transition particularly visible because exact keywords have always been less reliable there. Products are photographed differently and described differently by individual sellers, so understanding the underlying item and the shopper’s intent matters much more than matching two strings of text.

The lesson for e-commerce more broadly is not that keywords disappear entirely. It is that the search bar becomes one interface among many.

AI agents make product data part of the storefront

As AI becomes more involved in product research and comparison, retailers are no longer only optimising for people browsing their own website. Products increasingly need to be understood by systems that may decide what even makes it into a shopper’s consideration set.

That shifts the role of product data. Structured attributes, consistent descriptions and rich imagery are no longer just useful for on-site search; they become part of how products are surfaced outside the retailer’s own environment. And many companies are not ready for that yet: 75% cite data as a main barrier to agentic commerce, while only 44% say their data is adequate for AI.

The product feed is becoming a new part of the storefront.

Resale has already been operating under these conditions

None of these changes belong exclusively to second-hand.

Fragmented discovery is spreading across retail. Visual and conversational interfaces are becoming more important. AI is increasingly involved in comparison.

What makes resale interesting is that these forces are already concentrated there.

Resale has had to deal with shoppers moving between platforms, fluid consideration sets, products competing across ownership cycles and discovery that cannot depend entirely on clean keywords or retailer-controlled journeys.

The category has been forced to operate with messier data, less predictable inventory and a more fragmented customer journey much earlier. As those same conditions start appearing across e-commerce more broadly, the solutions developed for resale become increasingly relevant outside of it too.

What retailers can learn from resale

The takeaway is not simply to launch a resale programme. It is to look at the capabilities resale has already been forced to build.

Retailers need to make products understandable beyond their own website, treat images as product information rather than decoration, structure data for both humans and machines, and design discovery around intent rather than relying solely on predefined categories and keywords.

At faircado, we see these shifts particularly clearly because we are already building in that environment. We work with more than 150 million second-hand items across 60+ partners, while using image matching, personalised discovery and agentic search infrastructure to make fragmented inventory easier to navigate.

About the author
Evoléna de Wilde d’Estmael is Co-founder & CEO of Faircado, a resale discovery platform that aggregates and normalises millions of second-hand listings across European marketplaces. She writes about resale, Gen Z consumer behaviour and the future of ecommerce. This piece expands on an idea Evi recently shared on LinkedIn.

The post Why Resale Is the Stress Test for the Future of E-Commerce appeared first on faircado.

The same product can look completely different to a computer

Take three people selling the same pair of trainers.

One seller uses the exact model name. Another lists them as “white Nike trainers”. A third uploads a few photos, barely writes a description and just calls them “Sneakers”.

A person can often look at those listings and immediately understand that they refer to similar, perhaps even identical, products. A conventional e-commerce system cannot necessarily make that connection.

That is a fundamental difference between resale and traditional retail. In a normal catalogue, the retailer already knows what the product is before it appears online. In resale, the system may first have to work out what the item actually is.

Brand, model, colour, material, category or condition might be buried in free text, visible only in the image or missing entirely. The same product may be described in completely different ways across marketplaces or individual sellers.

So the discovery problem begins before search even happens. Before a system can return the right product, it first needs a shared understanding of products that were never structured consistently in the first place.

Aggregating listings is not enough to succeed in resale

The obvious solution to fragmented resale supply is aggregation: bring products from multiple marketplaces together so shoppers no longer have to search each one individually.

That solves one part of the problem, but it can also create a much bigger pile of messy data.

If ten marketplaces describe products differently, combining them does not automatically create one coherent catalogue. You first have to reconcile what those listings mean. The real work happens after aggregation:

collect → normalise → enrich → match → rank

Descriptions need to be standardised. Missing attributes need to be inferred where possible. Images need to contribute product information. Similar products need to be recognised even when their titles have almost nothing in common. And all of that has to happen while the underlying inventory continues to change.

This is why we think of resale discovery less as a search problem and more as a product intelligence problem.

What this looks like at faircado

At faircado, we aggregate more than 150 million second-hand items across 60+ partners, combining fragmented inventory from across the resale market.

As a second-hand aggregator, we understand the value of big inventory. But we also understand that more inventory only helps if people can navigate it, and we manage to surface the right product at the right time.

Image matching, personalised curation, and similarity search for sold-out items are only some of the ways we are tackling this issue. The interface can evolve, but it only works if the underlying products have first been made understandable.

Availability makes the problem harder

Then there is inventory.

A traditional retailer might have hundreds of units sitting behind one product page. A second-hand listing usually represents exactly one object. Once it sells, that listing is gone.

This means resale discovery is happening on top of inventory that is constantly changing. Search therefore cannot simply index a fixed catalogue and rank it well. The system continuously needs to understand new supply, remove unavailable products and find alternatives when the best match disappears.

That combination – unique products, imperfect data and volatile availability – is what makes resale substantially harder than simply adding another product category to an existing e-commerce stack.

Resale is not just another category

This is the part we think retailers entering resale can easily underestimate.

Adding second-hand inventory is not quite the same as adding beauty, electronics or another product vertical to an existing shop. Those categories usually enter a system after somebody has already created the catalogue.

Resale asks the system to create that understanding itself. And as more retailers build resale programmes, distinguishing the two is increasingly important. The challenge is not merely getting used products into the existing infrastructure. It is building infrastructure capable of dealing with products that were never designed to fit neatly into it.

That is ultimately why resale discovery remains difficult – and why solving it is so interesting.

About the author
Evoléna de Wilde d’Estmael is Co-founder & CEO of Faircado, a resale discovery platform that aggregates and normalises millions of second-hand listings across European marketplaces. She writes about resale, Gen Z consumer behaviour and the future of ecommerce. This piece expands on ideas Evi frequently shares on LinkedIn: making second-hand the first choice requires more than aggregating supply – it requires infrastructure that can normalise, enrich and understand fragmented resale inventory.

The post Why Resale Has a Discovery Problem appeared first on faircado.

Agentic commerce – where AI agents discover, compare, and increasingly transact on a shopper’s behalf — is already here. Most players are putting their head in the sand and pretending it’s not for them. But that’s like being in the year 2000 and not believing in the internet.

Here’s the reality.

1. Young people shop by image, not text

Young people today shop by image, not lengthy product descriptions and keywords. This changes everything in a platform’s architecture: semantic vs. visual search. A resale catalogue built for keyword queries isn’t built for the way the next generation actually browses.

2. AI surfaces are becoming the new storefront

More and more shoppers use new AI surfaces: Gemini, ChatGPT, Perplexity, and others. Which means product catalogues need to be normalised, enriched, and conversation-ready to be visible. Most aren’t. If an agent can’t read and understand your listings, your inventory effectively doesn’t exist in that channel.

3. Payment providers are becoming shopping platforms

Payment providers “own” the customers and know them best – so they’re becoming the shopping platforms. From the shopper’s agent to the brand’s agent, they’re best placed to facilitate and authenticate transactions. Whoever sits closest to the payment sits closest to the sale.

Why I started building faircado

I started to build faircado the day I realised that, on this beautiful planet:

• We throw a rubbish truck full of clothes into landfill every second
• We already have enough clothes to dress the next 6 generations
• Yet we still produce 100 billion new garments every year

This is insanity.

We HAD to find a way to do better than this.

So we built the first system able to aggregate, normalise, and enrich millions of second-hand product listings from all across the internet into one place — so it would become super easy for anyone to find what they were looking for.

Image matching, Pinterest integration, virtual try-on… it quickly became one of Gen-Z’s favourite apps. That’s given us incredible insights into the evolution of shopping behaviours, and I’m honestly afraid of how things will end for “traditional” players who refuse to see the urgency.

Because making second-hand the first choice of every consumer will only work with a strong, future-proof resale infrastructure. Onwards and upwards!

What I usually get asked

What is agentic commerce?

Agentic commerce is a way of shopping online, where AI agents discover products, compare options, and increasingly also complete purchases on a shopper’s behalf. This is replacing the “old-school” way of shopping through browsing, text search, and buying manually.

Why does agentic shopping matter for resale?

Second-hand listings are very different from regular e-commerce data. Second-hand catalogues are messy and non-standardised by nature. If listings aren’t normalised, enriched, and readable by AI agents, they won’t surface in agent-driven discovery – leaving resale invisible in the channel where a growing share of shoppers, especially Gen-Z, now start.

How can resale platforms prepare for agentic shopping?

By investing in visual (not just text) search, normalising and enriching product data so it’s conversation-ready for AI surfaces, and understanding the growing role of payment providers in authenticating and facilitating agent-led transactions.

PS 1: Read McKinsey & Company’s new State of the Consumer 2026 to learn more about “the new tech-driven path to purchase: how people discover and decide is changing rapidly.”

PS 2: I’ll be on stage across Europe over the next few months to talk about the future of shopping – and the role of resale, agentic commerce, and Gen-Z in it. Hit me up if you’re planning to be at TechBBQ in August, DMEXCO in September, or Recommerce Buzz in October. I’d love to chat!

Evolena de Wilde is the Founder & CEO of faircado; a version of this piece first appeared on Evolena’s LinkedIn.

The post What does agentic commerce mean for resale? appeared first on faircado.