---
title: "Naming a New Feature: How to Pre-Test 20 Candidates with AI Panels | Minds"
canonical_url: "https://getminds.ai/blog/feature-naming-ai-panels-before-launch"
last_updated: "2026-05-21T11:28:14.723Z"
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  description: "Stop shipping feature names that don't survive contact with customers. Test 20 candidate names with a synthetic panel in 30 minutes and pick the one that lands."
  "og:description": "Stop shipping feature names that don't survive contact with customers. Test 20 candidate names with a synthetic panel in 30 minutes and pick the one that lands."
  "og:title": "Naming a New Feature: How to Pre-Test 20 Candidates with AI Panels | Minds"
  "twitter:description": "Stop shipping feature names that don't survive contact with customers. Test 20 candidate names with a synthetic panel in 30 minutes and pick the one that lands."
  "twitter:title": "Naming a New Feature: How to Pre-Test 20 Candidates with AI Panels | Minds"
---

May 21, 2026·Product·Minds Team

# **Naming a New Feature: How to Pre-Test 20 Candidates with AI Panels**

Stop shipping feature names that don't survive contact with customers. Test 20 candidate names with a synthetic panel in 30 minutes and pick the one that lands.

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# Naming a New Feature: How to Pre-Test 20 Candidates with AI Panels

Feature naming is the most under-invested decision in product. A name lives in your UI, your changelog, your sales decks, your help center, and your customers' Slack conversations forever. Yet most teams pick the name in a 30-minute Zoom, ship it, and only discover it does not work when CS starts correcting the same misunderstanding 50 times in week one.

The reason is structural. Real customer research on naming is expensive and slow. Recruiting 20 customers for a 1-hour comprehension interview takes 2 weeks, costs $4k to $8k, and delivers feedback after the engineering team has already committed the name to the codebase. So teams skip it.

In 2026, synthetic panels close that gap. You can pre-test 20 candidate feature names in 30 minutes against a panel that mirrors your user base. This is the playbook.

## What "good naming research" actually has to answer

Stop optimizing for the name your team likes. Naming is a 4-axis decision:

1. **Comprehension.** When a new user sees this name in your nav bar with no tooltip, do they correctly guess what the feature does?
2. **Category-fit.** Does this name signal the right mental category? A feature named "Insights" sets a different expectation than one named "Reports" or "Pulse."
3. **Differentiation.** Does this name distinguish the feature from the 5 other things in your product that sound similar?
4. **Memorability.** A week from now, can the customer recall the name when talking to a colleague?

Most internal naming debates collapse these 4 axes into "which name sounds best." Synthetic panels let you measure each axis separately and pick the name that wins on the dimensions that matter for your distribution model.

## The 30-minute naming workflow

This is the loop. It works for new product surfaces, repackaged features, plan tier names, and even internal tool naming where mis-naming costs onboarding time.

### Step 1: Generate 20 to 30 candidate names (10 minutes)

Use a separate LLM session, not your panel, to brainstorm. Feed it the feature spec, the user problem, your product's existing naming patterns, and 3 to 5 examples of competitor naming for reference. Ask for 30 names across 4 patterns:

1. Descriptive (what it does, plainly): "Bulk Edit", "Audience Builder"
2. Metaphorical (what it feels like): "Spotlight", "Compass", "Pulse"
3. Proper-noun branded (a name you own): "Atlas", "Helix", "Forge"
4. Action-led (verb-first): "Match Audience", "Compare Versions"

Drop the weakest 10. You should have 20 candidates entering the panel test.

### Step 2: Build the comprehension panel (5 minutes)

Spin up 30 to 50 personas that match your typical new-user profile. Be explicit about their prior tool experience. "Marketing manager who has used HubSpot for 2 years and just signed up for our trial" is a sharper panel than "marketer."

For B2B SaaS naming research, weight the panel 70 percent toward new prospects and 30 percent toward 30-day-old customers. For consumer naming, match the panel to your registered-user demographics.

### Step 3: Run the comprehension test (10 minutes)

Show each candidate name one at a time with no description, no icon, no tooltip. Ask three diagnostic questions per name:

1. Looking at this name in our product's navigation, what do you expect this feature to do? Be specific.
2. Which existing tool or feature does this remind you of, if any?
3. On a scale of 1 to 5, how confident are you in your guess?

The panel returns expected-functionality answers per name. You are looking for two patterns:

- **Tight clustering.** All 30 personas describe the feature the same way. That is a clear name.
- **High average confidence.** Personas rated themselves 4 or 5 on the confidence scale. That is a name that does not require a tooltip to work.

Names with high comprehension variance ("could be reporting? or maybe an alert system?") are eliminated even if the average preference is high. You do not want a name that requires onboarding to explain itself.

### Step 4: Run the differentiation pass (5 minutes)

Take your top 5 from comprehension. Show them in the context of your full product nav. Ask the panel: "If you saw these 5 features in one product, which feature does each one do? Are any of them confusing or overlapping?"

This catches the trap where each name tests fine in isolation but creates overlap in the context of your full product surface. It is the #1 mistake in product naming and almost never caught by traditional research either.

## Real example: how this changes a launch

Take a feature for cross-team feedback collection. Internal favorite: "Echo." Sounds clever, has a nice metaphor, the team likes it.

Run the comprehension panel and you discover:

- 40 percent of new users guess "Echo" is an integration with audio or voice tools.
- 30 percent guess it is an analytics feature that "echoes" user behavior back at you.
- Only 20 percent guess anything close to feedback collection.
- Average confidence: 2.4 out of 5. Very low.

Same panel rates "Feedback Hub" or "Pulse" with 85 percent accurate comprehension and confidence averaging 4.1. "Echo" loses on the only axis that matters for a feature name: does the new user know what it does?

Without the panel, "Echo" ships, the team feels clever, and CS spends 3 months explaining what the feature actually is. The panel costs 30 minutes and saves 3 months of confused customers.

## Global naming research: the multiplier

Naming gets exponentially harder when the feature ships globally. The classic risk is a name that is fine in English and embarrassing in another language. Less obvious but more common is the name that translates cleanly but loses category-fit in a different market.

Synthetic panels handle this in minutes per locale. Run the same comprehension test against a German panel, a Spanish panel, a Turkish panel. You will find:

- 1 to 2 candidate names that work cross-market (these are usually proper-noun branded).
- Descriptive names that need to be re-translated rather than transliterated.
- Names that work in 5 markets but flop in the 6th, often for reasons your team would never have predicted.

This is the workflow that previously took 6 to 10 weeks of global research agency work and $40k to $80k. With panels it takes a half-day per locale and the cost of API calls.

## Where synthetic panels do not replace human research

Naming has a final stage that panels cannot do well. Once you have your top 2 candidates, run them past 5 to 10 actual customers in a 15-minute call. You are not retesting comprehension at that point. You are checking emotional resonance, brand alignment, and the gut-level pattern matching that humans do better than any panel.

The right ratio is:

- Synthetic panel does the breadth work (20 candidates to 2 finalists).
- Human research does the depth work (2 finalists to 1 winner).

Treat them as complementary, not competing. The panel removes 90 percent of the work so the human research can focus where it matters.

## What to ship into your team's process this week

Three changes worth making to your naming workflow today:

1. **Add a "comprehension gate" before any feature name ships.** A name that fails comprehension testing does not go into docs, UI, or announcement. No exceptions.
2. **Reserve "internal favorite" status only for names that also win the panel test.** This prevents the most common naming failure, which is a smart-sounding name that nobody understands.
3. **Build a panel library tied to your ICP segments.** Once you have 3 or 4 saved panels, every future naming decision compresses from 30 minutes to 10. The marginal cost of naming research drops to almost zero.

The teams that build this workflow ship product surfaces with names that land on day one. The teams that skip it ship names that need 6 months of onboarding material to explain themselves. The difference shows up in activation, retention, and the volume of "what is this feature actually for" tickets your CS team receives.