Smarter search results on G2.com

Timeline

Summer 2025

Mentored by

Allison Horrell, Aryn Silverberg

Overview

I owned G2's core search experience, delivering a full search page redesign and shipping 3 MVP changes during my internship that drove an 8.5% lift in user conversion on search pages. My work defined the new product card design pattern and AI interaction patterns now used across the product.

Context

G2 is a software marketplace platform that connects sellers with buyer insights

Buyers discover and compare products, while sellers pay for market insights and buyer-intent data. However, the user engagement that drives buyer-intent data was declining: 48% of buyers searching on G2 left without clicking a single result.

The problem

G2 has the answers, but search doesn’t surface them, so buyers leave.

I started out by evaluating the existing experience, looking at competitors, and, most importantly, hearing from real software buyers through a preliminary user test I conducted to truly understand the buyer perspective.

Annotated legacy G2 search page showing hidden filters, category jargon, and an unexplained AI control — The search page was still optimized for SEO, not buyer needs, and tacking on AI wouldn’t fix it
The buyer side of G2 had long been neglected: the legacy search page had hidden and limited filters, massive product cards filled with jargon, and an AI sparkle that provided no reason to click.

Buyer quote paired with analytics showing low engagement with G2 search filters — Software buyers search for specific needs, but filters on G2 don’t meet them there.
In my usability testing with six buyer-persona participants, everyone described specific requirements and prioritized shortlisting results immediately, but user analytics show that the current filters fall short.

How-might-we workshop with engineers, a data analyst, and a product design mentor — I led an HMW brainstorm with my team to explore the possibilities and limitations of G2’s search data with the people who know it best.

Low-fidelity explorations for tailored filtering, personalized results, and contextual AI prompts — I explored design interventions for the opportunities we identified:

Initial Design

How far can we personalize a search results page to each buyer’s needs?

After exploring different intervention points from the search bar to the category pages, I decided to focus on the search results page. I designed a smarter, personalized search experience built on G2’s existing data infrastructure, pointing users to G2 AI only when helpful.

Search Assistant translating a buyer’s written needs into active and suggested filters — 1. Buyers describe their needs; G2 surfaces the right filters

Personalized G2 product cards showing how each result matches the buyer’s needs — 2. Showing the right information, in the right amount, on product result cards

Contextual G2 AI entry points shown within product cards and empty search results — 3. G2 AI entry points appear when buyers would actually want them—not all the time

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The search page was still optimized for SEO, not buyer needs, and tacking on AI wouldn’t fix it

Software buyers search for specific needs, but filters on G2 don’t meet them there.

I led an HMW brainstorm with my team to explore the possibilities and limitations of G2’s search data with the people who know it best.

I explored design interventions for the opportunities we identified:

1. Buyers describe their needs; G2 surfaces the right filters

2. Showing the right information, in the right amount, on product result cards

3. G2 AI entry points appear when buyers would actually want them—not all the time