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Data Breakdowns

Segmenting and comparing data across different dimensions

Updated today

Data breakdowns let you segment your results by different dimensions—personas, themes, or specific question answers. This reveals patterns and differences that aren't visible in aggregate data.

What Are Breakdowns?

A breakdown splits your data by a dimension to compare subgroups. For example:

Without breakdown:

65% of respondents are satisfied

With breakdown by age:

  • Age 18-34: 72% satisfied

  • Age 35-54: 61% satisfied

  • Age 55+: 58% satisfied

Now you can see that younger respondents are more satisfied.

Breakdown Types

By Persona

Compare results across AI-generated consumer segments:

Persona

Satisfaction

Quality Seekers

78%

Budget Conscious

52%

Convenience Driven

71%

Use this to understand how different customer types respond.

By Theme

Compare results based on themes extracted from qualitative responses:

Theme

Agreement

Mentioned "value"

82%

Mentioned "quality"

75%

Mentioned "service"

68%

Use this to connect quantitative answers with qualitative feedback.

By Question Answer

Compare results based on how people answered another question:

Frequency of Use

Likelihood to Recommend

Daily users

8.5/10

Weekly users

7.2/10

Monthly users

5.8/10

Use this for cross-tabulation analysis.

How to Apply Breakdowns

In Chart Views

  1. View any question chart

  2. Click Compare By or the breakdown selector

  3. Choose your dimension:

    • Persona

    • Theme

    • Another question

  4. The chart updates to show segmented data

In Tables

  1. Navigate to data tables view

  2. Select breakdown dimension

  3. View statistics for each segment

  4. Sort by segment values

Interpreting Breakdown Data

Statistical Significance

When comparing segments, consider:

  • Sample size per segment (n=)

  • Margin of error for each group

  • Practical significance vs. statistical significance

💡 Tip: Small segments (under 30 responses) may show patterns that aren't reliable. Look for consistent patterns across multiple questions.

Reading Breakdown Charts

Stacked bar charts show:

  • Total responses per segment

  • Proportion of each answer

  • Visual comparison across groups

Side-by-side charts show:

  • Separate chart per segment

  • Same scale for comparison

  • Easy visual pattern recognition

Tables show:

  • Exact numbers and percentages

  • Statistical measures

  • Sortable columns

Common Analysis Patterns

Finding Differences

Look for segments where:

  • Results differ significantly from average

  • Patterns are opposite to other segments

  • Specific options are over/under-represented

Identifying Drivers

Use breakdowns to understand:

  • What factors correlate with satisfaction

  • Which segments are most valuable

  • What drives specific behaviors

Validating Findings

Cross-check patterns:

  • Do the same patterns appear in multiple questions?

  • Are results consistent with qualitative feedback?

  • Do breakdowns make logical sense?

Breakdown Examples

Satisfaction by User Type

Question: How satisfied are you?

Segment

Very Satisfied

Satisfied

Neutral

Dissatisfied

Power users

45%

35%

15%

5%

Casual users

22%

38%

28%

12%

New users

18%

30%

35%

17%

Insight: Power users are significantly more satisfied. Focus on converting casual users to power users.

Feature Interest by Persona

Question: Which features would you use?

Feature

Quality Seekers

Budget Conscious

Convenience Driven

Premium materials

78%

23%

41%

Faster delivery

52%

68%

89%

Price alerts

31%

91%

45%

Insight: Each persona has distinct feature preferences. Tailor messaging accordingly.

NPS by Theme

Question: How likely to recommend?

Theme Mentioned

Promoters

Passives

Detractors

"Easy to use"

68%

24%

8%

"Good value"

55%

30%

15%

"Needs improvement"

12%

28%

60%

Insight: "Easy to use" strongly correlates with promotion. Address improvement concerns.

Best Practices

Choosing Breakdowns

  • Start broad - Persona breakdowns give high-level view

  • Then narrow - Question breakdowns for specific analysis

  • Use themes - Connect quantitative and qualitative

Sample Size Considerations

Segment Size

Reliability

n > 100

High confidence

50 < n < 100

Moderate confidence

30 < n < 50

Use with caution

n < 30

Directional only

Reporting Breakdowns

When presenting breakdown analysis:

  1. Show the key insight first

  2. Provide supporting data

  3. Note sample sizes

  4. Acknowledge limitations

  5. Recommend actions

Combining with Filters

Breakdown + Filter

You can combine breakdowns with filters:

  1. Apply a filter (e.g., "Only completed responses")

  2. Then apply a breakdown (e.g., "By persona")

  3. View segmented data within your filtered set

Multiple Dimensions

For complex analysis:

  • Filter to specific population

  • Apply primary breakdown

  • Note patterns

  • Try alternative breakdowns for validation

Export and Sharing

Exporting Breakdown Data

Include breakdowns in exports:

  • PDF reports can include segmented charts

  • CSV exports include segment columns

  • Slides can show breakdown comparisons

Presenting Breakdowns

For stakeholder presentations:

  • Lead with the insight, not the data

  • Use clear visualizations

  • Highlight actionable differences

  • Provide context for interpretation

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