Behavioral Strategy Case Studies
Jason Hreha·
Updated July 10, 2026
The goal is not storytelling. It’s evidence-backed pattern recognition: which behavior was selected, whether it fit the population, and what happened next.
Publication standard #
This library lists only cases that include:
- at least one Evidence Ledger ID, and
- at least one concrete source behind that evidence (primary source, peer-reviewed paper, filing, or clearly labeled company-reported material).
Draft case notes are kept out of the public index until they meet that standard.
How to read a case #
Each case opens with the headline result, then tells the story:
- Background: the company or system, the context, and (where relevant) the popular story the case corrects
- What actually drove the outcome: the gating behavior, who performed it, and the system choices that enabled it
- Case facts box: population, target behavior, measurement window/denominator, key metric, fit ratings, and Evidence Ledger IDs at a glance
- Behavior Fit Assessment: Dispositional Fit, Capability Fit, Context Fit (scores are labeled as examples unless backed by data)
- Evidence hygiene: quantitative claims are either sourced with a primary source + date, referenced via the Evidence Ledger, or explicitly marked approximate; results carry source-quality labels (peer-reviewed, SEC filing, company-reported, press-reported)
For reuse and implementation, see the machine-readable case and measurement templates in the Toolkit (Specs & Templates).
Case synthesis (the recurring pattern) #
Across domains, the pattern is consistent: initiatives win when they match solutions to existing behavioral patterns rather than trying to force new ones. See Synthesis for a broader cross-case analysis.
Published cases #
Featured #
- Spain’s ONT: System Enablement, Not Defaults
- 401(k) Auto-Enrollment: Defaults Work After Fit
- Slack vs Glitch: Same Team, Different Behavior Fit
- Instagram’s Pivot to Photo Sharing
- Airbnb Trust System: Trust Enables High-Stakes Sharing
- Spotify Discover Weekly: Choice Overload → Automation
- Zoom Remote Work Surge: Friction Removal Under Constraints
- Robinhood Zero-Commission Trading: Fee Friction Removal
- Duolingo: Micro-Lessons Beat Traditional Study
- M-PESA: Infrastructure Enables Behavior at Scale
All published cases (by domain) #
Finance & FinTech #
- 401(k) Auto-Enrollment: Defaults Work After Fit
- Acorns: Round-Ups Piggyback on Spending
- YNAB vs Mint: Budgeting Behavior Requires Dispositional Fit
- Robinhood Zero-Commission Trading: Fee Friction Removal
- M-PESA: Infrastructure Enables Behavior at Scale
Health & Fitness #
- ClassPass: Reduce Friction to Try a Class
- Gym Membership Churn: Misfit Behaviors Create Predictable Drop-Off
- Peloton: Home Context Changes Feasibility
- Strava: Social Feedback Reinforces Repeat Action
- Couch to 5K: Scaffolding Lowers the Barrier to Start
- Meditation Apps: High Drop-Off When the Behavior Is Misfit
- Digital Health Onboarding: Friction Kills Early Adherence
- HIV Adherence: Context and Constraints Dominate Motivation
- Meal Kit Churn: Convenience Has a Context Half-Life
Technology & Media #
- Slack vs Glitch: Same Team, Different Behavior Fit
- Discord: Community Behaviors Compound When the Constraints Are Right
- Figma: Multiplayer Collaboration Removed a Coordination Barrier
- Instagram’s Pivot to Photo Sharing
- Spotify Discover Weekly: Choice Overload → Automation
- TikTok vs Vine: Creation Friction and Distribution Constraints
- YouTube Pivot: Behavior Fit Beat Product Vision
- Netflix vs Blockbuster: Convenience and Behavior Chains Win
- Waze: Crowdsourcing Works When Contribution Friction Is Low
- Zoom Remote Work Surge: Friction Removal Under Constraints
Platforms & Trust #
Workplace & Services #
- Open Offices: A “Culture” Intervention That Backfired
- Server Training: Process Enablement Changes Service Behaviors
- Proposify: Time-to-First-Benefit Predicts Adoption