Attribute App

Role

Lead Product Designer

Product Model

B2B SaaS · Data visualization

Domain

Ad Attribution & Performance Analytics

Core Challenge

Interpreting data over time

This case study is a reconstructed representation of real work. Product names and identifying details may have been altered for confidentiality.

Multiple devices running Attribute App, showing responsiveness
Attribute App on a screen

Summary

Attribute is a B2B SaaS platform that helps radio stations and advertisers evaluate ad performance. I partnered closely with product and engineering to design systems that made real-world, asynchronous data understandable—supporting comparison across time and placements and enabling more confident ad spend decisions.

Key Decisions

Total Visits by Hour, updated 24 min ago

Handling data integrity

Attribution numbers changed after ads aired. I made it clear when data wasn’t final so users didn’t jump to conclusions—reducing misreads and increasing trust.

AI insight showing over time, shoter creatives performed better during commuter hours

Supporting user judgment

The product provided context without auto-optimizing too early—keeping users in control while still guiding decisions.

Ad sessions by time of day and placement

Designing for comparison

I made it easy to compare performance across time and placements instead of spotlighting single metrics—helping teams focus on patterns, not spikes.

Time and date adjustment ranges for specific ad campaigns

Making time flexible

Users could adjust time/date ranges and aggregation without losing context or data integrity.

Total Visits by Hour, updated 24 min ago

Handling data integrity

Attribution numbers changed after ads aired. I made it clear when data wasn’t final so users didn’t jump to conclusions—reducing misreads and increasing trust.

Ad sessions by time of day and placement

Designing for comparison

I made it easy to compare performance across time and placements instead of spotlighting single metrics—helping teams focus on patterns, not spikes.

AI insight showing over time, shoter creatives performed better during commuter hours

Supporting user judgment

The product provided context without auto-optimizing too early—keeping users in control while still guiding decisions.

Time and date adjustment ranges for specific ad campaigns

Making time flexible

Users could adjust time/date ranges and aggregation without losing context or data integrity.

Annotations

1

Top-level metrics separate traffic, visits, and new users, helping users distinguish between awareness and conversion signals rather than collapsing everything into a single score.

1

Top-level metrics separate traffic, visits, and new users, helping users distinguish between awareness and conversion signals rather than collapsing everything into a single score.

1

Top-level metrics separate traffic, visits, and new users, helping users distinguish between awareness and conversion signals rather than collapsing everything into a single score.

2

Shaded regions indicate attribution windows that are still in progress. This signals uncertainty and prevents users from treating early data as final.

2

Shaded regions indicate attribution windows that are still in progress. This signals uncertainty and prevents users from treating early data as final.

2

Shaded regions indicate attribution windows that are still in progress. This signals uncertainty and prevents users from treating early data as final.

3

Upcoming spots connect historical performance to what’s scheduled next.

3

Upcoming spots connect historical performance to what’s scheduled next.

3

Upcoming spots connect historical performance to what’s scheduled next.

4

Complete vs ongoing status helps contextualize performance across the week.

4

Complete vs ongoing status helps contextualize performance across the week.

4

Complete vs ongoing status helps contextualize performance across the week.

Annotated image of Attribute App dashboard