A learning goal or research question starts by asking: *What are people already doing that we want to better understand?* It’s not about what we think they need, but about deepening our curiosity around their current behavior and approach.
**First, get into the physics of a business opportunity and frame it up. You should ask people in your ideal customer segment (and sometimes yourself if the motivation is internal):**
1. When did you buy/start using Path X?
2. When did you have the first thought that you might need something like Path X?
3. What were you doing in that period before adopting Path X?
4. Why did you not just continue with what you were doing before but instead adopt Path X, (*especially if there was a time elapsed between the thought of the struggle and the adoption of Path X*)?
5. What is bad about that (what you were doing before)?
6. Before you used/bought Path X, what were you picturing would be different after you used/bought Path X?
**What we ask ourselves:**
- Does this address a high-level focus area/theme of the business? If not, why do this now?
- Do we feel comfortable, or do we feel uncomfortable, about shaping this framed opportunity?
This step is critical because it helps us falsify—it helps us to find out **what to say no to**, not by describing the *good* that will happen by solving xyz problems/opportunities but **by knowing what goes wrong when we *****don't***** do something** about xyz problems/opportunities.
**We ask the following questions when we are trying to decide which shaped solutions to select next:**
- Does this problem/opportunity still matter?
- Does the time budget feel right?
- Is the solution attractive/does it satisfy the key criteria laid out when shaping (does it address its fitness function well, i.e., when the current outcome improves, then the solution works.)?
- Are the right people available?
- Can we deliberately allocate talent to this project for the time we want to spend on it without dragging them into too much unplanned/reactive work?
**For more on this:**
2. Prioritizing Under Highly Variable Conditions