Why use A/B/C testing instead of A/B?

• Explore a wider range of creative or strategic ideas
• Identify a clear winner from more than two options
• Understand how each option ranks in the minds of consumers

A common example of A/B/C concept testing:

How it fits into your research journey

Step 1: Define your goal and concepts

Step 2: Choose the right test method

• Monadic Testing – each respondent sees only one concept. This reduces bias and simulates a real-world reaction.
• Sequential Monadic Testing – each respondent sees all concepts, but in randomized order. Helps compare side-by-side but risks fatigue.

Step 3: Build diagnostic and comparative questions

Step 4: Balance your survey design

Step 5: Analyze with care

Expert Insights Tip:

When running A/B/C concept testing in SurveyMonkey, ensuring fairness in how each concept is presented is essential. If one concept is always shown first, it may unintentionally influence respondents’ perceptions or fatigue responses – skewing your results. Balancing exposure and randomizing order are key best practices in any concept comparison.

Why randomization matters

Respondents often react more positively to the first concept they see, simply because it's presented first (this is known as a “primacy effect”). To reduce this bias, randomizing the order of concepts is a must. In SurveyMonkey, this can be done using the built-in “Randomization” feature under the question logic or page settings.

Tips for setting up balanced concept rotation in SurveyMonkey

• Use question blocks: If each concept includes images, descriptions, or different sets of follow-up diagnostics, create a block (or page) for each. Then apply randomization at the block level.
• Rotate concept order within a question: For simpler surveys—like comparing taglines or ads—you can use a multiple choice question with randomized answer order (one per concept).
• Limit concept fatigue: Avoid showing all concepts to one person if you have more than three or four. Instead, assign concepts randomly so each respondent only sees a subset.
• Use skip logic when needed: In more advanced setups, use skip and display logic to control how concepts and questions are matched to specific segments, if applicable.

Example: Balanced exposure in a fictional test

Let’s say you want to compare three new snack packaging designs (Concept A, B, and C). Each includes an image and a short product description. You would place each concept on a separate page and then randomize the page order, so each respondent sees the designs in a different sequence. This ensures that no one concept consistently benefits from the “first impression” effect.

Balancing concept exposure not only builds fairness into your survey design, but also improves the validity of your consumer insights and the overall quality of your concept testing results.

Once your A/B/C concept test is complete in SurveyMonkey, the next step is turning raw data into actionable insights. Knowing how to interpret the results gives you confidence in making smarter, evidence-backed business decisions – whether you're choosing a product idea, creative execution, or messaging strategy.

Start with your key metrics

Identify which diagnostic measures matter most for your business goals. Are you optimizing for purchase intent, uniqueness, brand fit, or emotional connection? SurveyMonkey’s advanced features allow you to view question-level summaries and filter responses by concept, helping you isolate performance on each attribute.

Look beyond averages. Averages are helpful, but deeper analysis often reveals more. Compare distributions, look for outliers, and evaluate differences between segments – such as age groups or current customers vs. non-users – when appropriate.

Step-by-step approach to analysis

• Segment responses by concept: Use built-in filters to isolate data from respondents who evaluated Concept A, B, or C separately.
• Compare diagnostics side by side: Review scores for each concept across your testing criteria (e.g., appeal, relevance, uniqueness, purchase intent).
• Run statistical comparisons: If you want to know whether differences are meaningful, use SurveyMonkey’s analyze tools or export to Excel/SPSS and run t-tests or ANOVA for significance.
• Look for consistent wins: A concept that comes out on top across multiple metrics – not just one – is more likely to succeed in the market.

Reading between the numbers

Sometimes, results are close or mixed. For example, Concept A might score highest in purchase intent, while Concept C leads in uniqueness. In this case, consider your strategic priorities. If short-term sales are the goal, you may lean toward Concept A. If standing out in a crowded market is more important, Concept C might be better aligned.

Example: Fictional comparison of snack bar concepts

Let’s say your survey showed the following:

• Concept A had the highest appeal overall (83%)
• Concept B scored best for health perception (74%)
• Concept C drove the most purchase intent (68%) among younger consumers

You’d want to evaluate performance across all your business priorities. If your strategy is expanding with health-conscious messaging, Concept B may be favored—even if it wasn’t the overall “winner.”

In short, effective analysis isn’t just about who “won.” It’s about understanding the nuances and trade-offs to make research-informed decisions backed by real consumer insights.

Survey tools like SurveyMonkey make concept testing more accessible than ever. But access alone doesn’t guarantee success. Even a well-designed A/B/C test can fall short if diagnostics are off, logic is misapplied, or insights are interpreted in isolation. Pairing DIY tools with expert guidance helps avoid these pitfalls and maximize the strategic value of your research.

When DIY survey tools benefit from expert support

It’s easy to build and launch a survey – it’s much harder to ensure the survey is answering the right questions in the right way. That’s where partnering with consumer insights professionals adds value. On Demand Talent, like the experts at SIVO Insights, bring real-world experience across industries and use their strategic mindset to help companies:

• Clarify research objectives tied to business outcomes
• Design more robust survey structures and logic
• Ensure balanced sampling and concept exposure
• Interpret findings with context and strategic translation
• Avoid common missteps that compromise data quality

DIY doesn’t mean “do it alone”

As more companies lean on DIY research tools to act fast and stretch budgets, they also recognize the need for flexible, scalable expertise. Insights professionals from SIVO’s On Demand Talent network can be brought in project-by-project, role-by-role – helping teams move faster, stay aligned with business goals, and extract deeper value from their concept tests.

Unlike freelancers or generic consultants, On Demand Talent are seasoned insights experts who know how to guide survey design, diagnose concept performance, and deliver insights that stick. Plus, they can train internal teams to stretch their tools further, building long-term research capabilities along the way.

Example: A startup using SurveyMonkey with expert support

A fictional food startup launched their first A/B/C concept test for a new product line using SurveyMonkey. Their team had marketing chops, but limited survey experience. By working with an On Demand Talent expert, they improved their survey flow, added missing diagnostics, and ensured their sampling aligned with their target audience. The results? Faster decisions and greater leadership confidence in the path forward.

In an era where speed, flexibility, and tools like SurveyMonkey lead the way, real insights still require real expertise. With the right partner in place, DIY survey tools become not just faster – but smarter.