1. Study snapshot
Study name: clarity2
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 refine water bottle concepts by testing a narrower set of visual and pricing variations against the same purchase-intent question. The output supports concept refinement by confirming which signals remain strong after the first round.
Typical launcher: Product team refining reusable water bottle concepts.
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 refine water bottle concepts by testing a narrower set of visual and pricing variations against the same purchase-intent question. The output supports concept refinement by confirming which signals remain strong after the first round.
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.

4. Results summary
- Participants: 810
- Yes rate: 49.14% (398 yes / 412 no)
- Model used: Ridge Regression (71.2% accuracy)
- optimal price: $67.87 (this value represents the tested price that maximized revenue per product view in this study, not necessarily long-run market revenue)
- Strongest positive signals: workplace primary type = Office, age years (width 10, start 18) = 38-47, reusable water bottle uses per week (width 5) = 10-14
- Strongest negative signals: workplace primary type = Field-based, reusable water bottle uses per week (width 5) = 0-4, price = 79.99

5. Value for the launcher
In this study, Yes responses to "Would you buy this water bottle?" were more common among respondents with workplace primary type = Office, age years (width 10, start 18) = 38-47, and reusable water bottle uses per week (width 5) = 10-14, while No responses were more common among respondents with workplace primary type = Field-based, reusable water bottle uses per week (width 5) = 0-4, and price = 79.99. The optimal price is about $67.87, representing the tested price that maximized revenue per product view in this study, not necessarily long-run market revenue. 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 and checking whether similar Yes-rate patterns persist when another factor varies.

6. What to do next
- Use now: Use $67.87 as the current price anchor suggested by this study, while validating its effect on Yes rates in broader tests.
- Next test: Add usage frequency or brand familiarity measures if the next study needs to distinguish first-time vs repeat buyers.
- Do not over-read: These insights apply to this study's design, sample, and question framing. The optimal price of $67.87 reflects the tested setup here, not guaranteed long-run market performance.
