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Drafted Shorthand
The draft Shorthand story starts from getting a resemblance from the audience, where mentioning whether they have ever thought about why auto insurance premiums increase or decrease when relocating, even when there isn’t much change in coverage. I briefly talks about how insurance work, basically managing risk. This project is about whether auto insurance premiums have an impact on the risk in the area.
Then walks the reader through four acts:
The primary audience is the general public, specifically people considering relocating to a new state who want to understand whether the insurance costs they’ll face are proportionate to the actual road risk in that state.
A secondary audience is policymakers and insurance regulators who may use this analysis to evaluate whether state-level premium structures reflect actuarially fair risk assessment.
| Goal | Questions to Ask |
|---|---|
| Assess clarity of the narrative | Does the opening attract the reader to continue exploring the story. |
| whether the audience is well-defined | Who do you think about the intended audience |
| Make this data as reliable as possible | was the data good enough to exlain as this invove many uncontrollable variables? |
| Questions | Interview 1 (PPM) | Interview 2 (PPM) | Interview 3 |
|---|---|---|---|
| Does the opening feel relevant to readers? | Yes, nice flow | agreed | gGood intro that brings the reader’s attentioin graduately |
| Who do you think this story is for? | general public but might be a bit too broad | general public, suggested to maybe in a smaller scale | policy maker but no sure how they can use this info |
| Research synthesis | Anticipated changes for Part III |
|---|---|
| Findings or observations from interviews | Describe what, Zif any changes you anticipate making to address the observation. |
| Audience definition is too broad | Narrow the primary audience to people relocating across state line, reframe the call to action around a specific decision |
| High-level scope needs to consider many variables | try to reduce affectable variable (dominant factors) |
| continue with developing visualizations to refine the data | Use tableau with actual data instead of draft |
Due to the scale of the project, the data accuracy of the data is not guaranteed.
All referenced datasets are mentioned at Project I
Claude was used to help structure and draft the user research protocol, interview script, and Part II writeup template based on notes from interviews and project context provided by the author. All analytical judgments, interview content, and project direction reflect the author’s own work.