Using Research to Determine Unique Value Proposition
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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Research often fails when stakeholders are not involved or don't trust the data sources and methodologies.
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Treating research as a product, with stakeholders as users, drastically increases engagement and usability.
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Using proto-persona workshops with cross-department stakeholders helps generate unbiased, relevant survey questions.
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Clustering survey data via Python and correlation matrices can reveal meaningful user segments with statistical significance.
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Statistical validity (e.g., 95% confidence at 5% margin) is critical for stakeholder buy-in in large user bases.
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Behavioral and demographic clusters need to be augmented with emotional attributes to create actionable personas.
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Affinity mapping large qualitative datasets reduces noise and distills coherent narratives and pain points.
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Mapping pain points to measurable hypotheses enables prioritization based on user impact and implementation effort.
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Divergent and convergent workshops using tools like the business model canvas help explore and refine value proposition delivery.
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Iterative, lean research with frequent stakeholder feedback helps identify and minimize bias effectively.
Notable Quotes
"Research should be done the same way products are built, with stakeholders as your users."
"If you frame the research in terms your stakeholders understand, they will become research-hungry."
"We don’t make big deliverables because they create friction; instead, we create shared understanding through conversations."
"Proto-persona sessions helped us avoid departmental bias and uncover the questions stakeholders really cared about."
"We used a 0.8 correlation threshold to tightly cluster similar survey responses, balancing cluster count and inclusion."
"Clusters alone aren’t personas; we turned clusters into interview screeners and then emotional personas."
"We wrote hypotheses as we believe this pain point matters, here’s how we test it, and how success looks."
"Different stakeholders tackle the delivery problem differently, which gave us necessary divergent thinking."
"Biases surface as you iterate, so lean approaches help you find and fix them quicker."
"Marital status mattered because some people buy cars for their spouses or kids, not just themselves."
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