1. Study snapshot
Study name: clarity3
Core question: Would you buy this water bottle?
Primary method: A/B Monadic Test
Secondary read: Key Driver Analysis

2. Study parameters
Question card features
| Category | Features |
| --- | --- |
| image | Wave_pattern.png; Triangular_tessellation.png; Checker_grid.png; Chevron__herringbone_.png; Concentric_circles.png; Fine_hexagonal_honeycomb.png; Topographic_contour_lines.png; Vertical_stripes.png; Minimal_dotted_matrix.png |
| title | AquaSync; PureCycle; Weter; SummitFlow; H2Woah; NeoFlow Steel; EverSteel |
| price | 9.99; 19.99; 29.99; 39.99; 49.99; 59.99; 69.99; 79.99; 89.99; 99.99 |
| description | 750 mL reusable stainless steel water bottle. |

User features
| Category | Features |
| --- | --- |
| Age Years (width 10, start 18) | 18-27; 28-37; 38-47 |
| Reusable Water Bottle Uses per Week (width 5) | 0-4; 5-9; 10-14 |
| Workplace Primary Type | Office; Hybrid; Field-based |

3. How this study is used
Goal: The researcher uses this study to test water bottle concepts against a more targeted audience segment. The output supports audience-specific optimization by identifying which signals are strongest within the defined demographic slice.
Typical launcher: Product team validating water bottle concepts with a target audience.
Industry or company context: Consumer packaged goods / product development
Research stage: Early explanatory study
Why run this study now: The researcher uses this study to test water bottle concepts against a more targeted audience segment. The output supports audience-specific optimization by identifying which signals are strongest within the defined demographic slice.
What the team gets: A clearer read on the tested decision and the audience patterns behind it.
Why this matters: It reduces uncertainty around the exact decision being tested before the team spends more on execution or a larger follow-up study.
Larger plan: The strongest next move is to add usage frequency or brand familiarity measures if the next study needs to distinguish first-time vs repeat buyers in this segment.

4. Results summary
- Participants: 400
- Yes rate: 54.50% (218 yes / 182 no)
- Model used: Ridge Regression (70.3% accuracy)
- Strongest positive signals: office open plan configuration = Yes, commute length minutes = 90-<120, in person meetings per week = 15-19
- Strongest negative signals: office open plan configuration = No, commute length minutes = 0-<30, in person meetings per week = 5-9

5. Value for the launcher
In this study, Yes responses to "Would you buy this water bottle?" were more common among respondents with office open plan configuration = Yes, commute length minutes = 90-<120, and in person meetings per week = 15-19, while No responses were more common among respondents with office open plan configuration = No, commute length minutes = 0-<30, and in person meetings per week = 5-9. This gives the team a grounded starting point for follow-up tests with the segments and card features that showed stronger or weaker Yes rates here. The next study could test that by adding usage frequency or brand familiarity measures if the next study needs to distinguish first-time vs repeat buyers in this segment and checking whether similar Yes-rate patterns persist when another factor varies.

6. What to do next
- Use now: Use the current highest-Yes pattern as the working route in this context, especially office open plan configuration = Yes, commute length minutes = 90-<120, and in person meetings per week = 15-19, while validating those signals in broader tests.
- Next test: Add usage frequency or brand familiarity measures if the next study needs to distinguish first-time vs repeat buyers in this segment.
- Do not over-read: These insights apply to this study's design, sample, and question framing, not proof that the same Yes/No pattern will hold unchanged in live settings.
