Slack Rebuilds Notification System, Reports 5X Increase in Settings Engagement

Slack Rebuilds Notification System, Reports 5X Increase in Settings Engagement - InfoQ

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Slack Rebuilds Notification System, Reports 5X Increase in Settings Engagement

Apr 21, 2026 2 min read

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• Leela Kumili

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Slack has rebuilt its notification system, introducing a unified architecture to improve consistency and user control across platforms. The redesign addresses long-standing issues with fragmented notification preferences and unpredictable behavior, which had become a significant source of user frustration and support overhead as the platform scaled.

According to Slack’s engineering team, notification-related issues were among the most common drivers of customer support tickets. The root cause was an accumulation of multiple preference models with differing semantics, resulting in inconsistent behavior between desktop and mobile clients. Users often found it difficult to understand why they received certain notifications or missed others.

Frances Coronel, Senior Software Engineer at Slack, highlighted the challenge in a LinkedIn post, stating,

Notifications at Slack were one of our most complex, legacy-laden systems.

The new system replaces four legacy preference paradigms with a simplified model centered on three options: all messages, mentions, or mute. A key architectural change separates notification intent from delivery. This decoupling distinguishes what activity generates a notification from how it is delivered, such as push alerts or in-app updates. The approach enables scenarios where users can follow all activity within the application while limiting push notifications to high-priority events.

!Image 12/filters:no_upscale()/news/2026/04/slack-new-notification-system/en/resources/1slacknotificationexample-1776312789032.jpeg)

_Before and After Simplifying Notifications (Source: Slack Blog Post)_

Commenting on the redesign approach, Paweł Spychalski, Senior Full-Stack Developer, wrote in a LinkedIn post,

Simplification requires courage. The Slack team didn't add features; they removed complexity. They went from four preference systems to one.

To preserve existing user configurations, Slack avoided large-scale data migrations and instead introduced a read-time transformation layer. This layer maps legacy preferences into the new model while maintaining user intent. For example, prior configurations that disabled notifications are interpreted under the new system as limited visibility with push delivery disabled. This approach allowed Slack to deploy the new architecture incrementally without disrupting user experience.

The redesign also introduces a hierarchical preference model that standardizes behavior across platforms. Desktop and mobile clients now share consistent logic and state definitions, reducing discrepancies that previously occurred when settings were configured on different devices. Achieving this required refactoring legacy mobile implementations and aligning frontend and backend representations of notification state.

Slack reports that the new system resulted in a fivefold increase in user engagement with notification settings and a measurable reduction in notification-related support tickets. The company also observed that a majority of users adopted the default configuration focused on mentions and direct messages, suggesting improved alignment between system design and user expectations. The notification redesign reflects this principle by focusing on simplifying abstractions and improving consistency rather than incrementally extending legacy behavior.

Advanced visibility options, such as badge indicators for unread messages, also recorded notable engagement. The company observed that the majority of users adopted the default configuration focused on mentions and direct messages, while more granular per-channel overrides were used less frequently, indicating improved alignment between default settings and user expectations.

About the Author


#### **Leela Kumili**

Leela is a Lead Software Engineer at Starbucks with deep expertise in building scalable, cloud-native systems and distributed platforms. She drives architecture, delivery, and operational excellence across the Rewards Platform, leading efforts to modernize systems, improve scalability, and enhance reliability. In addition to her technical leadership, Leela serves as an AI Champion for the organization, identifying opportunities to improve developer productivity and workflows using LLM-based tools and establishing best practices for AI adoption. She is passionate about building production-ready systems, enhancing developer experience, and mentoring engineers to grow in both technical and strategic impact. Her interests include platform engineering, distributed systems, developer productivity, and bridging technical solutions with business and product goals.

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