1. Study snapshot
Study name: momentum1
Core question: Would you buy this energy drink?
Primary method: A/B Monadic Test
Secondary read: Key Driver Analysis

2. Study parameters
Question card features
| Category | Features |
| --- | --- |
| image | Various energy drink visual patterns |
| title | Various energy drink brand names |
| price | Various price points |
| description | Standard energy drink description with varying feature emphasis |

User features
| Category | Features |
| --- | --- |
| Age Years (width 10, start 18) | 18-27; 28-37; 38-47 |
| Gender | Male; Female |
| Energy Drink Consumption Frequency | Daily; Weekly; Monthly; Rarely |
| Primary Purchase Channel | Supermarket; Convenience store; Online; Gym |

3. How this study is used
Goal: The researcher uses this study to explore how different question card features (image, title, price, description) and user demographic features affect Yes/No purchase intent for an energy drink. The output supports early product feature exploration by identifying which signals drive higher acceptance.
Typical launcher: Product team exploring energy drink concepts.
Industry or company context: Beverages / product development
Research stage: Early explanatory study
Why run this study now: The researcher uses this study to explore how different question card features (image, title, price, description) and user demographic features affect Yes/No purchase intent for an energy drink. The output supports early product feature exploration by identifying which signals drive higher acceptance.
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 consumption frequency or brand familiarity measures if the next study needs to distinguish trial vs habitual buyers.

4. Results summary
- Participants: 333
- Yes rate: 56.46% (188 yes / 145 no)
- Model used: Random Forest (69.4% accuracy)
- Strongest positive signals: image = voltshift_clean_performance_can.png
- Strongest negative signals: image = voltshift_neon_can_optimized_1400_88.jpg

5. Value for the launcher
In this study, Yes responses to "Would you buy this energy drink?" were more common among respondents with image = voltshift_clean_performance_can.png, while No responses were more common among respondents with image = voltshift_neon_can_optimized_1400_88.jpg. 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 consumption frequency or brand familiarity measures if the next study needs to distinguish trial vs habitual buyers 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 image = voltshift_clean_performance_can.png, while validating those signals in broader tests.
- Next test: Add consumption frequency or brand familiarity measures if the next study needs to distinguish trial vs habitual buyers.
- 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.
