"A Day in the Life" is a qualitative research method where teams shadow or observe customers in their natural environments. By documenting customer activities, jobs, pains, and gains, it provides deep insights into real-world behavior, helping to close the "say-do" gap between what customers claim and what they actually do.
Cost
Relatively low, though you may need to compensate participants if your observation disrupts their routine.
Evidence
Observing real-world behaviors provides more accurate insights than interviews, though it remains weaker compared to quantitative experiments.
Metrics
- Number of identified customer jobs, pains, and gains
- Frequency of observed behaviors tied to hypotheses
- Insights from customer quotes.
Success Criteria
- Clear identification of actionable insights to update the Value Proposition Canvas.
- Documentation of patterns and key customer behaviors.
Setup Time
Short. Define observation goals, select participants, and obtain consent.
Run Time
Long. Requires observing several hours per participant over one or multiple days.
Risk Categories
Ideal for testing the...
Desirability: Discover if your product addresses real customer needs.
Capabilities
Basic research skills are required to collect, organize, and analyze observational data. Working in pairs helps reduce biases and capture diverse perspectives.
Setup
- Prepare:
Define the observation plan. Create a schedule and decide on data collection tools (e.g., notebooks or apps). - Permission:
Secure consent from participants. Explain the purpose and how the data will be used. - Logistics:
Coordinate with relevant stakeholders, such as retail managers or participants’ employers.
Run
- Follow participants through their day. Use a “fly on the wall” approach to minimize interference.
- Document activities, noting jobs, pains, gains, and any surprising behaviors.
- Avoid direct interaction; focus on observation.
Analyze
- Compile all notes and observations.
- Use affinity mapping to identify common themes or patterns.
- Update your Value Proposition Canvas based on findings.
- Plan follow-up experiments to validate insights.
Additional Information
This experiment pairs well with data-driven methods like Customer Support Analysis or Search Trend Analysis to cross-validate observed behaviors with quantitative evidence.