Signalboard - Case Study
My Approach
I approached this as a systems problem, not just a design one. The goal: make insights actionable and workflows adaptable, without creating more noise.
I focused on three core principles:
Signals – Quickly surface what’s performing across TikTok, Instagram, and other channels. Not just views or likes, but deeper patterns, engagement spikes, audience sentiment, and visual trends that actually drive results.
Suggestions – Make it easier to act on insights. Whether that’s reposting a video with a stronger hook, adjusting the format, or shifting the tone—recommendations should feel like support, not automation.
Structure – Design content systems to be modular and reusable. Signalboard helps track which components—CTAs, visuals, captions—perform best, so teams can remix and build smarter without starting from scratch.
What I Explored
A tiered interface that visually ranks content based on performance indicators like completion rate, watch-through time, and platform-specific benchmarks. Less dashboard, more quick-read heatmap.
In-context creative prompts that appear where decisions happen. Instead of waiting for a weekly report, teams would get lightweight guidance right inside their campaign tools—like reusing a high-performing thumbnail or shifting the timing of a post.
A modular content system that breaks down assets into remixable parts. If a particular caption style or visual format is resonating, it can be reused across campaigns—creating a more efficient way to scale what works.
My Approach
I approached this as a systems problem, not just a design one. The goal: make insights actionable and workflows adaptable, without creating more noise.
I focused on three core principles:
Signals – Quickly surface what’s performing across TikTok, Instagram, and other channels. Not just views or likes, but deeper patterns, engagement spikes, audience sentiment, and visual trends that actually drive results.
Suggestions – Make it easier to act on insights. Whether that’s reposting a video with a stronger hook, adjusting the format, or shifting the tone—recommendations should feel like support, not automation.
Structure – Design content systems to be modular and reusable. Signalboard helps track which components—CTAs, visuals, captions—perform best, so teams can remix and build smarter without starting from scratch.
What I Explored
A tiered interface that visually ranks content based on performance
Contextual AI nudges that suggest actions like reposting, format changes, or tone shifts
A modular content structure designed for flexibility and reuse
What It Shows
The biggest takeaway was that most teams don’t need more data, they need clearer signals. This project helped me focus on decision support instead of just reporting. I also explored how to keep AI useful without letting it overwhelm the workflow. Everything in the prototype is meant to reduce time spent guessing or switching between tools.
Summary
Signalboard is a concept to help content teams quickly see what’s working and decide what to do next. Instead of sorting through analytics dashboards, teams get a focused, visual cue system to drive their next move. The focus wasn’t just on making things look better, but on reducing friction, supporting reuse, and keeping teams aligned without adding extra complexity.
What It Shows
The biggest takeaway was that most teams don’t need more data, they need clearer signals. This project helped me focus on decision support instead of just reporting. I also explored how to keep AI useful without letting it overwhelm the workflow. Everything in this study is meant to reduce time spent guessing or switching between tools.
Summary
Signalboard is a concept to help content teams quickly see what’s working and decide what to do next. It brings together performance signals, AI suggestions, and modular design to make content decisions easier and more consistent. The focus wasn’t just on making things look better, but on reducing friction, supporting reuse, and keeping teams aligned without adding extra complexity.
A systems-first prototype for faster, and smarter content decisions
By: Sarah Volynsky
Overview
Signalboard is a system I envisioned to help content teams make faster, smarter decisions about what to post next. It uses AI to rank creative assets based on real signals—engagement, sentiment, visual traits—and recommends clear next steps like “reuse,” “revise,” or “reformat.”
The Challenge
Content teams operate across fragmented systems, planning in one tool, tracking performance in another, and executing across multiple channels. This slows down feedback loops, hides valuable insights, and often leads to reactive (rather than strategic) decisions. I wanted to explore how a unified, intelligent layer could close those gaps.
A systems-first prototype for faster, and smarter content decisions
By: Sarah Volynsky
Overview
Signalboard is a lightweight concept exploring how content performance data and AI-powered suggestions can reduce friction across the creative workflow. Instead of adding another dashboard, it acts as a decision layer by helping teams quickly identify what’s working, what needs to change, and how to respond in real time.
The Challenge
Content teams operate across fragmented systems, planning in one tool, tracking performance in another, and executing across multiple channels. This slows down feedback loops, hides valuable insights, and often leads to reactive (rather than strategic) decisions. I wanted to explore how a unified, intelligent layer could close those gaps.
