Naming Survey Template
Use this naming survey template to test 3-10 candidate names with the right audience and a fair setup (randomized exposure, neutral context, and a "None of these" option). You will end with a simple scorecard, clear thresholds, and a winner/shortlist rule you can paste into a stakeholder update.
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How to Run a Fair Naming Survey (Fast Setup Checklist)
- Lock the shortlist to 3-10 names: Default internal starter target: test 3-10 finalists; adjust after your baseline. If you have 20 or more candidates (internal starter target; adjust after your baseline), do an internal screen first and only take the best 3-10 to market (internal starter target; adjust after your baseline).
- Pick the exposure plan before you write questions: Default: monadic (each person rates fewer names). If you only have 3-5 names (internal starter target; adjust after your baseline) and need speed, use sequential monadic with strict randomization.
- Turn on name-order randomization: Order effects = early options get extra credit. Randomize name order and keep the same display rules for every name (same font, casing, and punctuation).
- Keep context neutral (do not teach the meaning): Default: show names with a one-line, category-only description (for example, "A project management tool for small teams"). If you must add more context, add the exact same context for every name so you do not create response bias and how to avoid it.
- Add a "None of these" option: Default: allow opt-out on forced-choice questions so you do not inflate preference for the least-bad name. If stakeholders want a winner no matter what, keep "None" but treat it as a red flag if it places top-2 (internal starter target; adjust after your baseline).
- Use consistent scales and anchors: Default internal starter target: use a 5-point Likert for ratings (Strongly disagree to Strongly agree); adjust after your baseline and if you have historical tracking. If you change scales mid-stream, you will get noisy comparisons.
- Gate with comprehension before you score "liking": Default: ask "What do you think this is?" (open text) or a simple clarity item before preference. If people cannot explain it, high "like" scores often come from guessing.
- Limit fatigue: Default internal starter target: cap at 3-5 names rated per respondent; adjust after your baseline and drop the cap if data quality stays high. If you need to cover 10 names (internal starter target; adjust after your baseline), split them across randomized groups so no one rates all 10.
- Run basic survey hygiene checks: Do a short pilot (for example, a 2-minute walkthrough as an internal starter target; adjust after your baseline), confirm mobile rendering, and remove any leading adjectives. AAPOR's Best Practices for Survey Research maps cleanly to simple actions here: keep questions neutral, keep stimuli consistent, and document who you sampled.
- Turn on the core bias controls: Default: randomize, add "None," keep context neutral, and keep the survey short. If you need more rigor, add a holdout group (each respondent sees fewer names) and follow your team's survey design best practices for question order and screening.
- Plan the post-survey checks now: Default: after you pick a winner/shortlist, run a message test, a logo/visual test, and domain/handle checks. If you are renaming, test confusion risk against your current brand.
- Write the legal disclaimer into your workflow: Survey results do not provide trademark or legal clearance. Default: treat the survey as evidence for desirability and clarity, then send the finalists to trademark counsel for clearance before launch.
Who to Survey for Name Testing (And Who Not To)
Use this section to recruit the right people for name testing so you can trust the winner. Default: sample people who match the target customer for the thing you are naming (company, product, feature, or campaign). If you serve multiple markets, set quotas and report results by segment instead of averaging them together.
Start with a simple screener + quotas plan, then build your invite list or panel criteria from it. Put the screener at the top of the template and keep it short so you do not lose good respondents.
- Audience match (required): Role/title, industry, company size, and region that match your ICP.
- Usage level (required): Default: current users + active evaluators. If you are launching into a new category, shift weight to prospects who buy similar solutions.
- Language (required): Test in the language(s) you will market in. If you plan a localized name, run separate surveys per language.
- Quotas (recommended): Set quotas for priority segments (for example, SMB vs mid-market, US vs EU, technical vs business buyers) so one group cannot dominate the average.
- Recruitment channel: Use your own lists, customer community, or a reputable panel. If you need help sourcing respondents, follow how to recruit the right respondents and document exactly where completes came from.
Employee-only voting: Default: use employees for early screening and spelling/pronunciation issues. If you need market validation, do not let internal familiarity decide the winner.
One average across different buyers: Default: segment results. If Segment A loves Name 1 and Segment B hates it, the overall average hides a launch risk.
Over-sampling existing fans: Default: balance customers and prospects. If your goal is market expansion, heavy customer samples can over-rate on-brand familiarity.
Panel quality: if you use an access panel, ask how participants are recruited, deduplicated, and managed, and how quality checks are enforced. ISO guidance on access panels (see ISO 26362 access panel requirements) translates into one practical rule: do not treat "panel" as a black box; document the supplier, screening, and QC in your results summary.
Do this next: Write your top 3-6 screener questions (internal starter target; adjust after your baseline) and set 2-4 quotas you will actually report (internal starter target; adjust after your baseline), not 12 you will ignore (internal starter target; adjust after your baseline).
Sample Size Rules of Thumb for Naming Surveys (Overall + By Segment)
Start with the decision you must make
Use sample size guidelines to set targets you can defend. Default: decide whether you need (a) a clear winner, or (b) a shortlist of 2-3 names for legal/creative follow-up (internal starter target; adjust after your baseline). If you only need a shortlist, you can run leaner and focus on the gates (clarity + negatives).
Overall completes: pick a practical default
Default internal starter target: 200-300 completes total for a standard B2B/B2C naming test with 3-10 names; adjust after your baseline. If you expect close results (small top-two gaps), push toward 400 or more completes as an internal starter target; adjust after your baseline so your ranked list is more stable.
By-segment completes: set a floor, then stop slicing
Default internal starter target: 100 or more completes in each priority segment you will use for a segment veto (for example, US buyers, enterprise buyers, or a core industry); adjust after your baseline. If a segment has fewer than 50 completes, treat it as directional as an internal starter target; adjust after your baseline and do not let it overrule the overall winner.
Quotas: match how you will interpret the scorecard
Default internal starter target: set quotas for 2-4 segments, then report those cuts in every slide and table; adjust after your baseline. If you cannot staff a segmented readout, simplify to one primary audience and one secondary audience.
Track how the sample was obtained
Default: record invites, starts, completes, and removals (speeding, straightlining, failed screeners). Use AAPOR's Standard Definitions for outcome rates so stakeholders understand the disposition of cases and do not confuse "completes" with "people who saw the invite." If you are documenting methods formally, align your write-up to the transparency expectations in the OMB Standards and Guidelines for Statistical Surveys.
Do this next: Write your sample target as a sentence you can reuse: "N=300 total, with N=120 in Segment A and N=120 in Segment B (internal starter targets; adjust after your baseline); segments below N=50 are directional only (internal starter target; adjust after your baseline)."
Name Test Design Options: Monadic vs Sequential vs Paired
| Design | When to use | Pros | Cons + bias risks | Max names per respondent | Default setup notes |
|---|---|---|---|---|---|
| Monadic Each respondent rates 1-3 names (randomly assigned) | Default for most teams, especially with 6-10 names (internal starter target; adjust after your baseline) or when you need clean comparisons by segment. | Lower fatigue; fewer contrast effects; ratings behave more like real-world first impressions. | Needs more total completes to cover all names well; requires careful quota balancing across name cells. | 1-3 (internal starter target; adjust after your baseline) | Randomly assign respondents to name cells; keep identical context; include a "None of these" in any forced-choice wrap-up. |
| Sequential monadic Each respondent rates multiple names in a randomized order | Use when you have 3-5 names (internal starter target; adjust after your baseline) and a tight timeline, and you can keep the survey short. | Efficient: every respondent covers every name; easier analysis because each person provides within-person comparisons. | Order effects if you do not randomize; fatigue if you show too many names; contrast effects (a strong name makes the next feel weaker). | 3-5 (internal starter target; adjust after your baseline) | Turn on full randomization/rotation; keep per-name question blocks identical; add a progress indicator so respondents pace themselves (a Tailored Design style best practice for online surveys). |
| Paired comparison / forced choice Respondents pick between two names at a time | Use when the top 2-3 are very close (internal starter target; adjust after your baseline) and stakeholders will only accept a head-to-head decision. | Simple output (wins/losses); forces tradeoffs; can break ties when ratings cluster. | Feels artificial if respondents want "none"; grows fast in length as names increase; can amplify framing effects if pairs are not balanced. | 2-4 names in a bracket (internal starter target; adjust after your baseline); avoid full round-robin beyond 5 (internal starter target; adjust after your baseline) | Include a "Neither" option when possible; balance pair exposure; keep context neutral and identical across pairs. |
| Default recommendation: Start with monadic (or light sequential at 3-5 names as an internal starter target; adjust after your baseline) with strict randomization, neutral context, and a "None of these" option. Switch to paired comparison only as a tie-breaker phase. | |||||
How to Score Results and Pick a Winner (Simple Naming Scorecard)
- Step 1: Set gates first (clarity + negatives)
Use this section to filter out risky names so you do not "average" your way into a bad launch. Default: apply two hard gates before you look at overall scores. If a name fails a gate, it is out (or it needs revision and a re-test).
- Clarity gate: Default internal starter target: at least 60% "clear what it is" (top-2 box on a 5-point clarity item) OR at least 60% correct/usable interpretations in the open-text "What do you think this is?" prompt; adjust after your baseline and category familiarity.
- Negatives gate: Default internal starter target: 10% or less reporting a strong negative association; adjust after your baseline and your brand risk tolerance. If you see 15% or more in any priority segment, treat it as a segment veto as an internal starter target; adjust after your baseline.
- Step 2: Normalize every dimension to a 0-100 score
Convert each rating to a 0-100 scale so you can combine items cleanly. Default internal starter mapping: map 1-5 Likert to 0, 25, 50, 75, 100; adjust after your baseline (and adjust to match your actual scale endpoints). If you used different scales, rescale them to 0-100 before combining.
- Step 3: Score the core dimensions (then weight them)
Build a scorecard you can explain in one slide. Default: weight for launch risk so clarity and negatives matter more than pure "like." If your category is unfamiliar, increase clarity weight.
- Appeal (20%): "I like this name." (internal starter target; adjust after your baseline and launch context)
- Fit (15%): "This name fits a [category] brand." (internal starter target; adjust after your baseline and launch context)
- Differentiation (15%): "This name feels distinct from competitors." (internal starter target; adjust after your baseline and launch context)
- Memorability (15%): "I would remember this name later." (internal starter target; adjust after your baseline and launch context)
- Pronounce/spell confidence (15%): "I would know how to pronounce/spell this." (internal starter target; adjust after your baseline and launch context)
- Clarity (20%): your clarity item (still keep the gate). (internal starter target; adjust after your baseline and launch context)
Structured brand-name evaluation dimensions like these are commonly used in naming assessment research (see Creating brand identity: evaluation of new brand names), and they translate well into a stakeholder-ready rubric.
- Step 4: Apply segment veto rules (do not let the average hide a failure)
Run the same gates and weights within each priority segment. Default: a name cannot win overall if it fails the clarity gate or negatives gate in any must-win segment. If you have a "primary" and "secondary" segment, allow a softer rule for the secondary segment (for example, "must not be worst" instead of "must pass").
- Step 5: Use tie-breakers you can defend
Break close scores with pre-set tie-breakers so the decision does not turn into opinion. Default: use (1) segment veto, (2) clarity, (3) negatives, then (4) pronounceability/spellability, then (5) open-ended themes. If you need one more, add a delayed recall check (follow-up survey about 24-72 hours later as an internal starter target; adjust after your baseline and your buying cycle) for the top 2.
Fluency matters because easy-to-process names tend to be recognized and remembered more readily; treat pronounceability as a real advantage, not a cosmetic preference (see Company name fluency, investor recognition, and firm value).
- Step 6: Write the decision rule into the readout
Turn your output into one sentence stakeholders can repeat. Default internal starter target: "Name X wins if it passes both gates, leads the weighted score by 5 or more points overall, and does not trigger a segment veto"; adjust after your baseline and measurement noise. If the top-two gap is smaller than 5 points (internal starter target; adjust after your baseline), call it a shortlist and move to legal + creative follow-ups.
Do this next: Build a one-page scorecard table with gates, weighted total, and segment cuts for the top 3 names (internal starter target; adjust after your baseline) before you show any wordsmithing opinions.
Frequently Asked Questions
How many names should I test in one survey?
Default internal starter target: test 3-10 finalists; adjust after your baseline. Longer lists create fatigue and noisy ratings, especially in sequential designs. If you have a long list, do a quick internal screen first, then run the external survey on the best 3-10.
Should I randomize the order of the name options?
Yes. Order effects mean early options often get extra picks just because they are seen first. Turn on randomization (or rotation) and keep the same presentation rules for every name so the test stays fair.
What is better for name testing: monadic, sequential, or paired comparison?
Default: monadic for the cleanest read, especially with 6-10 names (internal starter target; adjust after your baseline). Use sequential monadic if you only have 3-5 names (internal starter target; adjust after your baseline) and need speed, but cap the number per respondent to reduce fatigue. Use paired comparison as a tie-breaker when the top 2-3 are very close.
Can I survey employees to choose the best name?
Use employees for early screening, pronunciation/spelling issues, and obvious negative associations. For market validation, prioritize prospects and customers who match your target profile and report results by segment. Employee familiarity can inflate "fit" and "liking" scores.
How do I choose a winner if two names score similarly?
Decide tie-breakers before you field the survey. Default: apply segment veto rules first, then clarity and negatives gates, then pronounce/spell confidence, then open-ended themes and typing/spelling error rates. If the tie holds, run a short delayed-recall follow-up for the top 2.
Does this naming survey confirm trademark availability?
No. Survey feedback helps you pick names people understand and do not dislike, but it is not legal clearance. Follow up with trademark counsel, domain/handle checks, and any required regulatory review before you commit.