Executive Summary

E-commerce discovery was built for an era where shoppers searched with keywords and navigated through filters. That model is increasingly misaligned with how modern consumers, especially Gen Z think, search, and decide.

Today’s shoppers don’t start with keywords. They start with intent:

• an occasion

• a budget

• a mood or aesthetic

• a set of constraints

In this research note, we analyze how Gen Z expresses shopping intent, why traditional site search fails to interpret it, and how conversational shopping is emerging as a structurally better model for discovery in fashion e-commerce.

This is not a product pitch. It is a behavioral and systems-level analysis.

1. The Shift: From Keyword Search to Intent Expression

How e-commerce search was designed

Most e-commerce search systems assume that users:

• Know exactly what they want

• Can compress intent into 1–3 keywords

• Are comfortable refining results through filters

This worked when shopping was transactional and utility-driven.

How Gen Z actually shops today

Gen Z expresses shopping needs the same way they talk to friends or sales associates — in full sentences, rich with context.

Examples we repeatedly observed:

• “Something for a beach party, not too flashy, under $100”

• “Office wear but relaxed, neutral colors”

• “Oversized hoodie, Korean aesthetic, not too baggy”

This aligns with broader consumer research:

• McKinsey reports that Gen Z prefers interactive, personalized, and conversational digital experiences over static interfaces
  https://www.mckinsey.com/industries/retail/our-insights/true-gen-generation-z-and-its-implications-for-companies

The core shift:

Users are no longer searching for products.
They are describing situations.

2. What We Observed Across 100+ Real Discovery Queries

We analyzed over 100 real fashion discovery queries from Gen Z users (ages 18–27).

Every meaningful query contained multiple intent layers:

A single query often encoded 4–6 intent dimensions simultaneously.

Traditional search engines are built to extract:

• keywords

• sometimes filters

They are not built to reason across intent dimensions.

3. Why Traditional E-commerce Search Fails (Systemically)

The issue is not bad UX or poor tagging. It’s architectural.

Key failure points we observed:

1. Keyword dependency

   • If intent terms aren’t exact tags, results degrade.

2. Rigid filter mental model

   • Users don’t think in filter order.

3. Zero-result dead ends

   • No recovery or clarification loop.

4. No understanding of “soft” constraints

   • “Not too flashy” is invisible to filters.

Industry research supports this:

• Baymard Institute shows that over 30% of e-commerce site searches return irrelevant or zero results, leading to abandonment
  https://baymard.com/research/ecommerce-search

From the merchant’s perspective, this loss is silent.
Users don’t complain, they just leave.

4. Fashion Makes the Problem Worse (and More Important)

Fashion discovery is inherently:

• subjective

• emotional

• context-driven

Vogue Business highlights that fashion search breaks down because style, mood, and intent cannot be reduced to keywords
https://www.vogue.com/article/what-defined-fashion-tech-in-2025

Yet most fashion stores still rely on:

• keyword search

• static collections

• manual merchandising

This mismatch disproportionately affects:

• first-time visitors

• Gen Z shoppers

• mobile traffic

5. Conversational Shopping: What It Actually Means

Conversational shopping is often misunderstood.

It is NOT:

• a scripted chatbot

• a FAQ assistant

• a popup saying “Can I help you?”

It IS:

• an intent-understanding system

• that can interpret natural language

• ask clarifying questions

• refine results dynamically

• and maintain context within a session

In other words:

Discovery becomes a dialogue, not a transaction.

This aligns with consumer expectations:

• Capgemini found that 71% of consumers want generative AI integrated into shopping experiences, especially for personalization and guidance
  https://www.capgemini.com/news/press-releases/71-of-consumers-want-generative-ai-integrated-into-their-shopping-experiences/

6. Why This Directly Impacts Conversion & Revenue

Search users are high-intent users.

Shopify reports that:

• Visitors who use site search convert 2–3× higher than those who don’t - when search works
  https://www.shopify.com/in/blog/how-to-optimize-your-search-and-discovery-experience-on-shopify

When search fails:

• High-intent traffic exits first

• Bounce rates rise

• Merchants misattribute the loss to “just browsing.”

In reality, intent existed, the system failed to capture it.

7. What Merchants and Agencies Can Do Today

This shift does not require rebuilding the entire store.

Forward-looking teams are already:

1. Auditing long-tail, descriptive search queries

2. Tracking zero-result searches as lost intent

3. Treating discovery as a flow, not a page

4. Unifying catalog metadata and language

5. Experimenting with conversational entry points

The goal is not “adding chat”.
The goal is resolving intent.

8. The Bigger Transition: From Web Commerce to Agentic Commerce

We believe commerce is moving through three phases:

1. Web commerce – pages, categories, filters

2. Mobile commerce – apps, plugins, performance

3. Agentic commerce – AI systems that understand, assist, and act

Conversational shopping is the first visible layer of this transition.

Not because it is novel,
but because it mirrors how humans naturally buy.

Closing Note

If this perspective resonates, we’re opening up early access to a small group of merchants and agencies who want to experiment with conversational, intent-driven shopping before it becomes mainstream.

You can join the waitlist here:
👉 https://eldor.ai/waitlist

Early access members get:

• A hands-on walkthrough of our conversational shopping system

• A simple audit of their store’s discovery and “AI readiness.”

• Direct feedback loops with our founding team

No commitments, just early learning together.