Ymax 1.0

Context

Ymax is Yieldmo's AI-powered ad tech platform — advertisers use it to set up campaigns and deals, plan supply, build contextual targeting, manage line items, and monitor performance. I joined Yieldmo in May 2025 to take over Ymax day-to-day design from the CXO, who had been the sole designer up to that point. My role is owning feature work and ticket throughput across the platform; this case study highlights three areas where the work has been deepest.

Early on at Yieldmo, I led the PageMax interactive prototype for Cannes Lions 2025 in collaboration with the CXO. PageMax has since folded into Ymax, and the work below is what's lived in production since.

The problem

For each gap, I designed the surfaces in close partnership with product and engineering. DV360's targeting model in particular informed the line item redesign.

Contextual targeting — supply package creation

Users build supply packages by describing what they want in plain language — text, image, video, or URL — instead of building inventory lists by hand. The system scans the input, returns matching segments, and bundles them into a saved package.

The AI-Generated Attributes component shows the LLM's interpretation in plain language and populates the description field before the user commits to running the search.

Before this addition, users who clicked the create button would end up staring at an empty results table while the search ran, causing a pretty poor experience. This led to product defining five states, which I then designed the sequence for.

The Context Card on the results page uses the same component as the input modal, so users can edit and re-run their search inline.

Extended Targeting — line item redesign

Product worked with our internal users to identify what was missing in our targeting capabilities, which led to me researching how DV360 handles multi-dimensional targeting and using those patterns as reference.

Before

Internal users had been asking for frequency capping, dayparting, web-vs-app split, language scoping, multi-audience logic, and geo more specific than country level.

After

Geo targeting

Geo targeting has been updated to include four match types — City, Postal Code, DMA Region (a metro-area grouping used in ad targeting), and State — returned in a single query and mixable inside one selection.

Our internal users had also requested the ability to bulk add zip codes. The parser flags invalid input with a clear constraint message — instead of silently dropping bad rows.

Reporting and insight surfaces

This work is delivered as design. Implementation is queued for Ymax 2.0.

The dashboard is a campaign-level view — KPI tiles, spend-vs-performance over time, total spend by domain, and performance comparisons across line items, segments, and URLs.

Yieldmate is the platform's AI assistant — it surfaces real-time insights when the system recognizes either an opportunity or a concern in a user's deal or campaign. The loop is straightforward: user sees an insight, navigates to where it applies, makes a change, and the deal performs better. Working with my team lead, we explored what these recommendations should look like — surfaced where the user is already working, paired with actions they can take.

Two things shape the pattern:

1. Each insight pairs a finding with a recommended action that deep-links to where it applies. Add Supply takes the user to a pre-filtered supply search. Go to Line Items lands them on the flagged row, with the recommendation inline. The user moves from observation to action in one click.

2. Coherent across surfaces. Yieldmate looks and reads the same way wherever it appears — dashboard, line items table, supply search — so users learn the pattern once instead of re-learning it everywhere.

What shipped

What carries forward

That's the bulk of Ymax 1.0. The next case study covers Ymax 2.0, the platform rebuild currently in progress.