loss-aversion-engine

To add loss aversion mechanics, implement configurable decay rules for streaks, gear, and ranks alongside visual progress indicators. This makes losses salient to reduce churn in habit-forming apps.

Loss aversion in gamification leverages the greater psychological impact of losses over gains to boost retention. It uses streak decay and sunk cost amplification to discourage abandonment and motivate return visits.

Yes, you can prevent rage quitting by configuring recovery mechanics like streak freezes, comeback bonuses, and Amnesty Week. These features support re-engagement after inactivity rather than punishing users permanently.

Rank decay and gear degradation improve retention by subtly reducing leaderboard positions and modeling equipment wear for inactive users. This incentivizes return visits to recover lost status and maintain progress.

The primary limitation of using loss aversion is the risk of rage quitting if penalties feel too harsh. You must balance decay rules with sufficient recovery mechanics like streak freezes to prevent users from abandoning the app entirely.