Forecasting Tool
Publica by IAS
Forecasting is critical for publishers planning upcoming quarters, but in Publica, the experience was fragmented, unclear, and difficult to trust. Key data points were scattered across the UI, required inputs were missing, and outputs lacked transparency.
This project reimagined forecasting as a centralized, reliable tool that brings all critical inputs and outputs into one clear, intuitive experience.
Business Challenge
Publica by IAS is a CTV ad server platform that helps publishers optimize ad delivery, unify auctions, and maximize streaming ad revenue through transparent data insights.
Forecasting, however, presented several challenges:
Inputs were spread across multiple pages
Key targeting parameters were missing
Output values were difficult to interpret and trust
User feedback consistently highlighted confusion around what was included in forecasts particularly audience segments — making it hard to confidently plan campaigns and quarterly revenue.
“They don’t love all of these red flags of stuff that don’t get included in the forecast, particularly the audience segment.”
Objective
Design a centralized forecasting experience that:
Integrates all critical targeting inputs
Clearly communicates forecast outputs and confidence
Improves trust, adoption, and long-term product stickiness
Role
Lead Product Designer, Publica by IAS
Owned end-to-end design from research through handoff
Led user interviews, synthesis, and ideation
Partnered closely with PM and engineering
Strategic Approach
Research
To understand the root causes behind low trust and usability issues, I conducted:
Competitive analysis
Internal and external user interviews
I led the research process from planning and facilitation to synthesis and collaborative ideation with the PM. The insights uncovered directly shaped how inputs, outputs, and data transparency were approached in the new design.
Key User Insights
Publishers need to understand audience reach before committing to campaigns or deals
Forecasted values lacked clarity around meaning and likelihood of achievement
Users wanted forecasting to reflect all available targeting parameters, including content, audience segments, and frequency capping
Solution Framework
UI/UX Overhaul: Centralize forecasting into a single experience with clear inputs and outputs
Forecasting Logic Updates: Introduce additional input options to improve accuracy
Backend Data Enhancements: Support more targeting parameters for precise, trustworthy projections
Design Process
Visual Evolution
With a strong foundation from research, the focus shifted to blending existing components with new visuals to support clarity and ease of use.
UX Goals
Simplify input selection by clearly surfacing available targeting parameters
Make outputs easy to digest through visualizations and structured tables
Wireframes
I began with wireframes to establish information hierarchy and define how inputs, outputs, and supporting context would live on a single page.
Component Exploration
I audited existing components and explored new UI patterns to support the updated experience, including:
Forecast Inputs: Flexible, buildable fields for dates, delivery settings, and targeting
Forecast Outputs: A clear, scannable table for quick insights
Data Visualization: Graphs to break down forecasts by channel or tier
Lo-Fi Mocks & Iteration
With the layout defined, lo-fi mocks allowed rapid iteration on structure and flow. I reviewed designs with my design manager and PM, gathering feedback and clarifying open questions.
Several rounds of iteration followed, refining the experience to better address user pain points while aligning with product and technical constraints.
Decision Points
I presented the evolving designs to Engineering to validate feasibility and define a clear V1 versus future V2 scope. Based on these discussions, I adjusted the designs to ensure a smooth handoff and realistic implementation plan.
Final Design
The final experience delivers a clear, transparent forecasting workflow:
Loading State: Sets expectations while forecasts are processed
Improved Inputs: Added missing targeting parameters to increase forecast accuracy
Actionable Outputs: Visualized data with flexible “View By” options, downloadable tables, and clear breakdowns
Together, these changes improve trust in the data and enable faster, more confident decision-making.
Loading State
Populated
Shipped & Reflections
I supported QA and design iteration throughout development, addressing blockers and refining the experience as needed. The forecasting tool is on track for beta in Q3 and GA in Q4 2025 (update: successfully released in production!).
Leading this project end to end deepened my understanding of forecasting workflows and reinforced the importance of close collaboration across design, product, and engineering. The result is a more reliable, intuitive experience that better supports publishers’ planning and growth.