The best examples of 3 ANOVA examples in market research

Why marketers care about ANOVA (with real examples front and center)

Most marketing teams don’t ask, “Which statistical test should we run?” They ask:

• Did the new ad lift purchase intent more than the old one?
• Which price point performs best across segments?
• Is our redesign actually improving satisfaction, or are we fooling ourselves?

ANOVA (Analysis of Variance) is the workhorse behind many of those answers. The best examples of 3 ANOVA examples in market research usually fall into three buckets:

• Comparing several groups at once (one-way ANOVA)
• Comparing groups plus another factor like age or channel (two-way ANOVA)
• Comparing the same respondents over time or conditions (repeated-measures ANOVA)

Below, we’ll walk through three flagship designs and expand them into 6–8 real examples that mirror what brands, agencies, and UX teams are doing right now.

One-way ANOVA: Comparing multiple marketing options at once

When you’re comparing more than two groups—three ad concepts, four price points, five landing pages—a one-way ANOVA is often the first stop. These are the most intuitive examples of 3 ANOVA examples in market research, because they map directly to everyday A/B/C/D tests.

Example 1: Testing three ad creatives for a CPG brand

Imagine a consumer packaged goods (CPG) brand launching a new snack line. The team has three TV/video creatives:

• Concept A: Family-focused, emotional storytelling
• Concept B: Humor-driven, short-form spots
• Concept C: Product-first, feature-heavy

They recruit 600 respondents from a national online panel and randomly assign them to one of the three concepts. Each person watches a single ad and then rates purchase intent on a 1–7 scale.

A one-way ANOVA compares the mean purchase intent across the three groups. Instead of running multiple t‑tests (A vs. B, A vs. C, B vs. C) and inflating the Type I error rate, ANOVA evaluates whether at least one ad concept differs from the others.

If the ANOVA p-value is below 0.05, the team follows up with post-hoc tests (like Tukey’s HSD) to see which pairs are different—maybe Concept B significantly outperforms A and C. This is one of the cleanest real examples of 3 ANOVA examples in market research: one dependent variable (purchase intent), one factor (ad concept), three levels.

Example 2: Optimizing subscription pricing tiers

A streaming service wants to understand which monthly price for a new ad-supported tier delivers the best balance of stated likelihood to subscribe.

They test four price points: \(5.99, \)7.99, \(9.99, and \)11.99. Each respondent sees one price and rates their likelihood to subscribe (0–10 scale).

Here, a one-way ANOVA evaluates whether the average likelihood score differs across the four price points. The marketing team can then:

• Identify the price with the highest mean likelihood
• Use confidence intervals to understand uncertainty
• Combine with elasticity modeling to estimate revenue trade-offs

This example of one-way ANOVA is common in pricing research, conjoint pre-tests, and willingness-to-pay studies.

Example 3: Comparing satisfaction across three store formats

A retail chain pilots three store formats:

• Urban micro-stores
• Standard suburban stores
• Large-format destination stores

Customer satisfaction is measured via post-visit surveys. A one-way ANOVA compares mean satisfaction scores across the three formats. If the ANOVA shows differences, the retailer can decide whether to expand, tweak, or retire a format.

These three cases—ad testing, pricing, and format evaluation—are classic examples of 3 ANOVA examples in market research that analysts run every quarter.

Two-way ANOVA: When segment and context both matter

Market research rarely cares about just one factor. You might want to know not only which ad works best, but also for whom and in which channel. That’s where two-way ANOVA comes in.

Two-way ANOVA analyzes the effect of two categorical factors on a continuous outcome, plus their interaction. Many of the best examples of 3 ANOVA examples in market research are really two-way ANOVA problems dressed up as segmentation questions.

Example 4: Ad performance by age group and platform

A fintech app is testing three video ads across two platforms: YouTube and TikTok. They care deeply about how Gen Z vs. Millennials respond.

Design:

• Factor 1: Ad concept (A, B, C)
• Factor 2: Age group (Gen Z, Millennial)
• Outcome: Brand favorability (1–7 scale)

A two-way ANOVA answers three questions at once:

• Is there a main effect of ad concept? (Some ads better overall?)
• Is there a main effect of age group? (One age group more favorable overall?)
• Is there an interaction? (Does the best ad depend on age?)

If the interaction is significant, the team may find that Ad C is strongest for Gen Z while Ad A resonates more with Millennials. That’s not just a statistical nuance; it’s a media planning decision.

This is a textbook example of two-way ANOVA in market research, and it’s exactly the kind of question digital-first brands are asking in 2024 and 2025 as they optimize creative by audience.

Example 5: Price sensitivity by income and region

A national telecom provider is testing three introductory price offers for home internet. They suspect that income level and region (urban vs. rural) both influence price sensitivity.

Design:

• Factor 1: Price offer (Offer 1, Offer 2, Offer 3)
• Factor 2: Income segment (Low, Mid, High)
• Outcome: Likelihood to switch providers (0–10)

A two-way ANOVA can show, for example:

• A strong main effect of price (lower prices increase likelihood overall)
• A main effect of income (lower-income households more likely to switch overall)
• A price × income interaction (high-income households are less sensitive to price differences)

For marketers, that interaction is gold. It supports differentiated offers by segment while staying within regulatory and brand guidelines.

If you’re looking for examples of 3 ANOVA examples in market research that actually shift strategy, this kind of price-by-segment analysis is high on the list.

Example 6: UX satisfaction across device and task type

A SaaS company wants to understand how device type (desktop vs. mobile) and task type (billing, reporting, admin) affect task satisfaction after a redesign.

Design:

• Factor 1: Device (Desktop, Mobile)
• Factor 2: Task type (Billing, Reporting, Admin)
• Outcome: Satisfaction (1–10)

Two-way ANOVA reveals whether:

• Mobile is weaker overall
• Reporting tasks are more painful overall
• The pain point is specifically mobile reporting (an interaction)

This is a very modern, 2024-era example of two-way ANOVA in UX research, where cross-device and cross-task experiences matter for churn and NPS.

For readers who want to go deeper into two-way designs and interactions, many university stats resources break this down clearly; for instance, see introductory materials on ANOVA from UCLA’s Institute for Digital Research and Education.

Repeated-measures ANOVA: Tracking the same people over time

The third pillar in our examples of 3 ANOVA examples in market research is repeated-measures ANOVA. Instead of comparing different groups, you track the same respondents across multiple time points or conditions.

This is common in brand tracking, UX iteration, and longitudinal product tests.

Example 7: Brand attitude before and after a national campaign

A health insurance company launches a national campaign aimed at improving perceptions of trustworthiness and clarity of coverage.

They survey the same panel of consumers at three time points:

• T1: One month before the campaign
• T2: Immediately after the campaign ends
• T3: Three months post-campaign

Each wave includes a 1–7 scale for brand trust. A repeated-measures ANOVA tests whether mean trust scores change across T1, T2, and T3 within the same individuals.

This design controls for individual differences (some people are just more trusting or more skeptical) and focuses on within-person change. It’s a staple in brand tracking and campaign effectiveness studies.

For examples of longitudinal analysis in health and social research—often using similar techniques—resources from the National Institutes of Health provide solid methodological background, even if the context is clinical rather than marketing.

Example 8: Iterative UX testing of a checkout flow

An e-commerce brand is reworking its checkout flow. They recruit a panel of frequent shoppers and expose each participant to three versions of the checkout process over several weeks:

• Version 1: Current checkout
• Version 2: Streamlined with fewer fields
• Version 3: One-page checkout with autofill

After each version, participants rate ease of use and likelihood to complete purchase.

Because the same people experience all three versions, this is a classic repeated-measures ANOVA setup. The analysis looks at whether mean ease-of-use scores differ across the three versions within respondents.

If Version 3 significantly increases ease of use and completion likelihood, the team has strong evidence to justify development investment and rollout.

Example 9: Tracking satisfaction across subscription lifecycle stages

Subscription businesses live and die on retention. A streaming platform follows a cohort of new subscribers and surveys them at:

• Onboarding (week 1)
• Month 1
• Month 3
• Month 6

They measure overall satisfaction and perceived value. A repeated-measures ANOVA checks whether satisfaction drops over time, and if so, at which stage the decline is statistically meaningful.

This kind of analysis connects directly to churn interventions—for example, triggering personalized offers around the point where satisfaction starts to fall.

For those interested in the underlying statistics behind repeated-measures and longitudinal designs, the National Center for Education Statistics and many .edu sites offer accessible introductions to within-subjects ANOVA and mixed models.

How these ANOVA examples fit into 2024–2025 market research trends

The best examples of 3 ANOVA examples in market research today don’t live in isolation. They’re wrapped inside broader analytics stacks:

• Ad testing platforms: Many self-serve tools use one-way or two-way ANOVA under the hood when comparing creative scores across concepts and segments.
• Experimentation in product and UX: Teams running continuous A/B/n tests often rely on ANOVA-style models when there are multiple variants or repeated measures.
• Brand and customer experience tracking: Longitudinal panels and trackers regularly apply repeated-measures ANOVA or its more flexible cousins (like mixed-effects models) to understand changes over time.

A few practical notes based on how teams are working in 2024–2025:

• Sample sizes are getting bigger, thanks to online panels and in-product experiments. That means ANOVA assumptions (like normality via the central limit theorem) are more often reasonable.
• Non-normal data (e.g., skewed satisfaction scores) is still common. Analysts often supplement ANOVA with nonparametric tests or generalized models.
• Multiple comparisons are everywhere—dozens of segments, many creatives. Post-hoc corrections (Tukey, Bonferroni, FDR) matter more than ever.

If you’re building your own analyses in R, Python, or even Excel, it’s worth reviewing assumption checks and effect size measures (like eta-squared) from reputable academic sources such as Harvard’s statistics teaching materials.

Pulling it together: When to use which ANOVA design

To recap the landscape of examples of 3 ANOVA examples in market research, think in terms of the question you’re asking and the data you have:

• Use one-way ANOVA when you’re comparing 3+ independent groups on a single outcome.
  • Ad concepts, price points, store formats.
• Use two-way ANOVA when you have two categorical factors and care about interactions.
  • Creative × age, price × income, device × task type.
• Use repeated-measures ANOVA when the same respondents are measured multiple times or under multiple conditions.
  • Pre/post campaign, multiple UX versions, lifecycle tracking.

Most real examples are messy: you may have more than two factors, or a mix of within- and between-subjects elements. In those cases, analysts move toward mixed ANOVA or linear mixed models—but the logic is the same as in these core designs.

If you keep these three patterns in mind, you’ll recognize ANOVA opportunities everywhere in your research roadmap—and you’ll know how to talk to your data science team about them.

FAQ: ANOVA examples in market research

Q1. What are some common examples of ANOVA in market research?
Common examples include comparing multiple ad creatives on purchase intent, testing several price points on likelihood to buy, evaluating satisfaction across store formats, examining ad performance by age and platform, and tracking brand attitudes before and after a major campaign.

Q2. Can you give an example of ANOVA with more than one factor?
Yes. A classic example of two-way ANOVA is testing three ad concepts across two age groups (Gen Z and Millennials) and measuring brand favorability. The analysis shows whether some ads work better overall and whether the best ad depends on age, capturing both main effects and interactions.

Q3. How do repeated-measures ANOVA examples include real marketing data?
Repeated-measures ANOVA is used when the same respondents are surveyed multiple times, such as measuring brand trust before, immediately after, and months after a national campaign, or asking the same panel to rate different UX versions of a checkout flow.

Q4. When should I avoid ANOVA in my research?
You should be cautious with ANOVA when group sizes are extremely unbalanced, when assumptions like approximate normality and similar variances are badly violated, or when your outcome is categorical (e.g., yes/no). In those cases, alternatives like logistic regression or nonparametric tests may be more appropriate.

Q5. Are the best examples of 3 ANOVA examples in market research only for large brands?
Not at all. Smaller brands and startups can use the same logic with smaller samples: compare multiple ad variants, price points, or onboarding flows. The math scales down; the main difference is how much confidence you can place in small-sample estimates.