You are leading a redesign of the notification system for a productivity app that currently sends frequent task reminders. Users report notification fatigue, while stakeholders want to increase engagement with more personalized alerts. How would you decide which notifications to keep, modify, or remove, and what criteria would guide your decisions?

Apply the RICE framework to score each notification type (Reach, Impact, Confidence, Effort). 1) Map all current notifications and collect usage metrics (open rates, opt‑outs). 2) Segment users by behavior and preference to identify high‑value groups. 3) Score each notification with RICE, prioritize those with highest score, and plan phased removal or redesign. 4) Prototype changes, run A/B tests, and iterate based on engagement and satisfaction metrics.

I would start by inventorying all notifications and gathering quantitative data—open rates, dismissal rates, and engagement spikes. Next, I’d segment users by usage patterns and preferences to surface high‑value groups. Using the RICE framework, I’d score each notification type: Reach (how many users see it), Impact (potential to improve task completion), Confidence (data certainty), and Effort (development cost). Notifications scoring low on RICE would be candidates for removal or consolidation. For high‑scoring alerts, I’d prototype a more contextual, user‑controlled experience—e.g., a preference center that lets users choose frequency and type. Finally, I’d run A/B tests to validate that the redesigned system reduces fatigue while sustaining or boosting engagement, using metrics like opt‑out rate, task completion, and NPS.