1. Study snapshot
Study name: momentum2
Core question: Would you buy this energy drink?
Primary method: Segmentation model
Secondary read: Key Driver Analysis

2. Study parameters
Question card features
| Category | Features |
| --- | --- |
| image | Fixed reference image |
| title | Fixed reference title |
| price | Fixed reference price |
| description | Fixed reference description |

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 user demographic features affect Yes/No purchase intent for an energy drink while holding question card features constant. The output supports audience segmentation by identifying which demographic groups show higher or lower acceptance.
Typical launcher: Product team exploring energy drink audience segments.
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 user demographic features affect Yes/No purchase intent for an energy drink while holding question card features constant. The output supports audience segmentation by identifying which demographic groups show higher or lower 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 within segments.

4. Results summary
- Participants: 333
- Yes rate: 64.86% (216 yes / 117 no)
- Model used: Ridge Regression (74.4% accuracy)
- Strongest positive signals: caffeinated drinks per day (width 2) = 4-5, gym workout visits per week (width 3) = 6-8, age years (width 10, start 18) = 18-27
- Strongest negative signals: caffeinated drinks per day (width 2) = 0-1, gym workout visits per week (width 3) = 0-2, caffeinated drinks per day (width 2) = 2-3

5. Value for the launcher
In this study, Yes responses to "Would you buy this energy drink?" were more common among respondents with caffeinated drinks per day (width 2) = 4-5, gym workout visits per week (width 3) = 6-8, and age years (width 10, start 18) = 18-27, while No responses were more common among respondents with caffeinated drinks per day (width 2) = 0-1, gym workout visits per week (width 3) = 0-2, and caffeinated drinks per day (width 2) = 2-3. 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 within segments 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 caffeinated drinks per day (width 2) = 4-5, gym workout visits per week (width 3) = 6-8, and age years (width 10, start 18) = 18-27, 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 within segments.
- 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.
