Summary
A story about how to quantify and use survey data to create audience clusters that are statistically representative of the people that use your online product or service. Edgar joins the dots between multiple research/pattern finding/storytelling techniques, such as proto-personas, affinity mapping, survey creation, quantification of qualitative data, organic clustering of audiences, user interviews, journey maps, and business modeling canvas. It makes research tangible and actionable: instead of being an end-point that provides stakeholders with insights, it takes stakeholders through an inclusive and structured process they can be part of.
Key Insights
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Seeing research as a product with stakeholders as users increases engagement and adoption.
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Involving stakeholders early through proto persona workshops helps frame relevant research questions and reduces bias.
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Combining quantitative clustering via correlation matrices with qualitative interviews produces richer, actionable personas.
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Statistical validity criteria (95% confidence, 5% margin) can guide survey sample sizes even in large user populations.
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Behavioral and demographic data clusters need emotional and narrative overlays to become useful user personas.
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Mapping pain points from qualitative research into hypotheses with measurable outcomes aids prioritization.
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Prioritizing opportunities by business impact, ease of implementation, and user volume clarifies focus areas.
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Using the business model canvas in divergent and convergent sessions helps find innovative ways to deliver value propositions.
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Small iterative deliverables and open conversations reduce bias and increase the agility of research processes.
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Life-stage variables (e.g., marital status) can be more relevant than traditional demographics like age for some user behaviors.
Notable Quotes
"Research should be done the same way products are built, with stakeholders as your users."
"We aimed for research that is actionable, not just insightful or pretty to look at."
"If you don’t bring stakeholders into the research journey, they won’t believe or use the data."
"Proto personas created by cross-department participants helped us build unbiased, relevant survey questions."
"We used a Python algorithm with a correlation matrix to identify meaningful clusters from survey responses."
"Clusters describing demographics and behaviors alone were not enough; we needed emotional personas to find value propositions."
"Creating hypotheses from pain points with measurable success criteria helped prioritize which to pursue."
"Delivering research in small, lean increments allowed us to iterate fast and reduce bias."
"Marital status mattered because car buying decisions often involve family members, not just the individual."
"Diverging and converging around the business model canvas helped us test and prototype delivery methods for the value propositions."
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