Voice Branding Quantitative Analysis

Running Marimo Notebooks

Running on Ct-105 for shared access:

uv run marimo run 02_quant_analysis.py --headless --port 8080

Batch Report Generation

The quant report can be run with different filter combinations via CLI or automated batch processing.

Single Filter Run (CLI)

Run the report script directly with JSON-encoded filter arguments:

# Single consumer segment
uv run python 03_quant_report.script.py --consumer '["Starter"]'

# Single age group
uv run python 03_quant_report.script.py --age '["18 to 21 years"]'

# Multiple filters combined
uv run python 03_quant_report.script.py --age '["18 to 21 years", "22 to 24 years"]' --gender '["Male"]'

# All respondents (no filters = defaults to all options selected)
uv run python 03_quant_report.script.py

Available filter arguments:

--age — JSON list of age groups
--gender — JSON list of genders
--ethnicity — JSON list of ethnicities
--income — JSON list of income groups
--consumer — JSON list of consumer segments

Batch Runner (All Combinations)

Run all single-filter combinations automatically with progress tracking:

# Preview all combinations without running
uv run python run_filter_combinations.py --dry-run

# Run all combinations (shows progress bar)
uv run python run_filter_combinations.py

# Or use the registered CLI entry point
uv run quant-report-batch
uv run quant-report-batch --dry-run

This generates reports for:

All Respondents (no filters)
Each age group individually
Each gender individually
Each ethnicity individually
Each income group individually
Each consumer segment individually

Output figures are saved to figures/<export_date>/<filter_slug>/.

Jupyter Notebook Debugging

The script auto-detects Jupyter/IPython environments. When running in VS Code's Jupyter extension, CLI args default to None (all options selected), so you can debug cell-by-cell normally.

Adding Custom Filter Combinations

To add new filter combinations to the batch runner, edit run_filter_combinations.py:

Checklist

Open run_filter_combinations.py
Find the get_filter_combinations() function
Add your combination to the list before the return statement:

# Example: Add a specific age + consumer cross-filter
combinations.append({
    'name': 'Age-18to24_Consumer-Starter',  # Used for output folder naming
    'filters': {
        'age': ['18 to 21 years', '22 to 24 years'],
        'consumer': ['Starter']
    }
})

Filter keys must match CLI argument names:
- age — values from survey.options_age
- gender — values from survey.options_gender
- ethnicity — values from survey.options_ethnicity
- income — values from survey.options_income
- consumer — values from survey.options_consumer
Check available values by running:

from utils import QualtricsSurvey
S = QualtricsSurvey('data/exports/2-2-26/...Labels.csv', 'data/exports/.../....qsf')
S.load_data()
print(S.options_age)
print(S.options_consumer)
# etc.

Test with dry-run first:

uv run python run_filter_combinations.py --dry-run

Example: Adding Multiple Cross-Filters

# In get_filter_combinations(), before return:

# Young professionals
combinations.append({
    'name': 'Young_Professionals',
    'filters': {
        'age': ['22 to 24 years', '25 to 34 years'],
        'consumer': ['Early Professional']
    }
})

# High income males
combinations.append({
    'name': 'High_Income_Male',
    'filters': {
        'income': ['$150,000 - $199,999', '$200,000 or more'],
        'gender': ['Male']
    }
})

Notes

Empty filters dict = all respondents (no filtering)
Omitted filter keys = all options for that dimension selected
Output folder names are auto-generated from active filters by QualtricsSurvey.filter_data()

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