Case Studies
Explore modeled studies of a foldable iPhone, protein drinks and data center proposals, with the decisions, methods and next tests behind each.
See how the offer, audience and question shape the next decision.
These research stories use modeled audiences to examine product offers, buying occasions and local proposals. Explore what was tested, what the findings suggest and the evidence a next test needs.
Who would buy the $1,999 iPhone Duo?
Product and pricing · Modeled offer study
An offer study explores why a foldable upgrade appeals, who would pass and how different price menus change modeled qualification.
The decision: Which buyers and use cases justify testing a premium foldable offer?
Read for: Audience differences, price comparisons and what a Duo sale would replace.
The article and episode use different offer scenarios and measures: modeled qualification in the article; top-two-box consideration in the episode.
When do protein and caffeine belong together?
Concept and occasion · Illustrative concept study
An unofficial Red Bull concept study moves from three drink formats to a clear citrus offer, asking how timing and existing protein habits shape modeled demand.
The decision: Which format, use occasion and starting audience deserve a physical product test?
Read for: Occasion comparisons, first-purchase and repeat-purchase definitions, and substitution.
Created independently by Sapien; Red Bull did not commission the study.
Which data center commitments deserve a local test?
Community and infrastructure · Modeled resident study
Eleven hypothetical proposals examine bill protection, physical impact, community benefits and enforceable guarantees.
The decision: Which commitments should a project team take into local resident research?
Read for: Offer groups, predicted support and the concern left unresolved after each proposal.
The modeled differences become questions for research with residents at a real site.
Check the task behind the finding.
Inspect the human reference, audience, comparison measure and discrepancies before applying a modeled finding to a new decision.