Previous Experience
Before initiating my first diary studies, I developed an app specifically for researchers who utilize this method. While the project evolved, even modern diary tools remain far from the original prototype I conceptualized. This experience highlighted some inherent challenges, even in basic steps like participant recruitment. For example, platforms such as Respondent.io and Lyssna (which uses the User Crowd panel) lack essential features, like calendar sharing between projects at the same platform, to prevent overlapping projects in the moderator’s schedule. However, leveraging three primary time zones with a 12-hour difference allowed us to distribute interviews effectively across diverse audiences.
Screening & Pre-Interviews
To ensure a motivated sample that would remain engaged throughout the study, we conducted a rigorous selection process. Out of 442 applicants: • only 19 were invited to 30-minute pre-interviews. • 10 were ultimately selected using a screener and client input. The result was a 100% retention rate from first diary day to final interviews.
Diary Execution
Although a structured diary scenario with daily questions and specific topics was prepared, we opted for manual question delivery via messaging apps. This approach allowed us to adapt to participants’ circumstances, such as illness or fridge leaking, and avoid overwhelming them with repetitive queries. Personalized communication proved more effective than automated messages in maintaining engagement and collecting meaningful responses.
Operations & Insights
I believe incorporating AI to analyze diary entries—extracting themes, pain points, and solution patterns—could be highly beneficial. However, manually reviewing entries can provide unique insights shaped by the researcher’s or client’s background and perspective. Balancing these methods ensures a deeper understanding of the data.
Artifacts
In qualitative studies, raw data often gains clarity and impact depending on how it is represented. For this project, I focused on creating artifacts to support client decision-making.
Attributes and Related Pains
I developed a list of business attributes linked to specific pain points. For example, seasonality affects both gyms and general practitioners in similar ways (lower workload in summer, higher in winter). The goal was to create a modular system of attributes that could identify potential pain points and gains not just in the reviewed industries but in any sector. However, determining the prevalence of these attributes across business types would require further quantitative research.
Solution Mapping
I mapped current and potential solutions along two axes: social vs. technical and manual vs. fully automated. This visualization provided a trajectory for transitioning from current practices to more advanced or suggested solutions. Pain points generally followed a path from the manual-social quadrant (e.g., reading competitors’ Google Reviews) to the technical-automated quadrant (e.g., receiving real-time updates on competitors’ potential bottlenecks). However, I identified opportunities in the social-automated quadrant, which combines minimal technical resources with automated processes. This area offers a promising middle ground for scalable yet human-centric solutions.