\n | Agency project<\/td>\n | Kantar, Ipsos<\/td>\n | Quote per study<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n<\/div>\n\n\n\n Budget for panel incentives separately. A monadic test of four concepts at 200 per cell needs 800 completes, so per-response pricing adds up fast.<\/p>\n\n\n\n Step-by-step guide to running a concept test<\/strong><\/h2>\n\n\n\nA concept test follows seven steps, and skipping the first two causes most failed studies. Write the decision down before you write any questions.<\/p>\n\n\n\n \n- Define the decision.<\/strong>
State what you will do with each outcome, such as “launch if purchase intent beats the benchmark.”
<\/li>\n\n\n\n- Set the target audience.<\/strong>
Screen for people who buy or influence the purchase, not just anyone.
<\/li>\n\n\n\n- Build the concept card.<\/strong>
Use one image, a headline, and a short description. Keep the format identical across concepts.
<\/li>\n\n\n\n- Choose the method.<\/strong>
Pick monadic for clean reads or sequential monadic when your audience is small.
<\/li>\n\n\n\n- Write the questionnaire.<\/strong>
Cover appeal, purchase intent, uniqueness, believability, and value. Add open-ended follow-ups. Keep each question short and scored on the same scale.
<\/li>\n\n\n\n- Field and clean.<\/strong>
Randomize order, remove speeders, and check quotas.
<\/li>\n\n\n\n- Analyze and decide.<\/strong>
Compare top-box scores, test for significance, and read the open text.<\/li>\n<\/ol>\n\n\n\nHow do you measure concept test results?<\/strong><\/h2>\n\n\n\nYou measure a concept test by comparing top-box scores against a benchmark and checking that differences are statistically significant. A top-box score is the percentage of respondents choosing the highest one or two ratings on a scale.<\/p>\n\n\n\n Track these core measures for each concept:<\/p>\n\n\n\n \n- Purchase intent:<\/strong> Top-two-box share on a five-point “how likely to buy” scale<\/li>\n\n\n\n
- Appeal:<\/strong> How much respondents like the idea overall<\/li>\n\n\n\n
- Uniqueness:<\/strong> How different it feels from current options<\/li>\n\n\n\n
- Believability:<\/strong> Whether people trust the claims<\/li>\n\n\n\n
- Value for money:<\/strong> Perceived worth at the stated price<\/li>\n\n\n\n
- Open-text reasons:<\/strong> The “why” behind each score<\/li>\n<\/ul>\n\n\n\n
Set a pass mark before fielding. Use past launches or category norms as the benchmark, and avoid declaring a winner on gaps smaller than your margin of error.<\/p>\n\n\n\n What do real concept tests look like?<\/strong><\/h2>\n\n\n\nThree illustrative scenarios show how the method works across industries. These are composite examples, not named client results.<\/p>\n\n\n\n A packaged food brand<\/h3>\n\n\n\nThe brand tests three flavor concepts monadically with 200 shoppers each. One concept wins on purchase intent but scores low on believability.<\/p>\n\n\n\n \n- Action:<\/strong> Rewrite the claim and retest only that concept<\/li>\n<\/ul>\n\n\n\n
A B2B software team<\/h3>\n\n\n\nThe team has a niche audience of 150 IT buyers. It runs a sequential monadic test on three feature bundles in randomized order. Open-text answers reveal that one bundle confuses buyers.<\/p>\n\n\n\n \n- Action:<\/strong> Drop that bundle and field the other two at scale<\/li>\n<\/ul>\n\n\n\n
A bank testing a new card<\/h3>\n\n\n\nThe bank tests two rewards structures with a pricing question series. Top-two-box intent looks equal. The price-threshold answers show one structure tolerates a higher annual fee.<\/p>\n\n\n\n \n- Action:<\/strong> Launch that structure with the higher fee<\/li>\n<\/ul>\n\n\n\n
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