Tiantian Zhang
1stCollab
Adding dashboard feature to a SaaS influencer marketing platform and improving brand automation and progress visibility
Project Overview
1stCollab is an AI-powered influencer marketing platform that helps brands launch and manage influencer campaigns at scale. It streamlines the process of sourcing creators, negotiating rates, reviewing content, handling payments, and tracking performance.
In short, it enables brands to book and manage large numbers of influencers efficiently.
I was contracted to design the creator progress dashboard. The goal was to give brands clear visibility into campaign performance, both at the overall funnel level and at the individual creator level, reducing the need for manual updates and internal follow ups.
Role
User Flow
Wireframing
UI Design
Time
April 2024 - May 2024
Go to Site ->
Team
Product Designer
Product Manager
Engineer
The problem
Once a campaign was launched, brands had limited visibility into what was happening in real time. This created uncertainty, frequent back and forth with internal teams, and reduced trust in the platform.
Two core gaps emerged:
Limited end to end campaign visibility
Brands could not clearly track where campaigns stood in the pipeline or how they were performing across the funnel. Without a consolidated view of booking progress, content status, and performance metrics, it was difficult to identify drop offs or improve outcomes proactively.
No visibility at the individual creator level
When focusing on specific creators, brands had no direct way to monitor progress or status. This led to manual follow ups and reliance on internal teams for updates.
Wireframing Key Decisions
Goal: Clarify information hierarchy for campaign tracking while incorporating effective filtering.
1
Organized filters top-down to give users progressive control over campaign data.
2
Prioritized showing overall funnel metrics first, then granular creator progress. Highlighting the two filter placement, one is overall, and one at the activities line. All could influence the showing of the creator's details.
3
Designed a stepper list view so users could quickly assess the status of each creator at a glance.
2
3
1
Funnel Hierarchy Design Decision 01
I explored multiple dashboard structures to determine how brands should consume campaign performance data.
I explored multiple dashboard structures to determine how brands should consume campaign performance data. An early version visually emphasized weekly activity, which fragmented the hierarchy and made it harder to identify overall campaign health.
Through iteration with the team, I prioritized cumulative funnel metrics as the primary narrative, followed by recent activity as a secondary layer. This clarified the relationship between long term performance and short term actions, reducing cognitive load and improving scan efficiency.
Individual Creator Tracking Design Decision 02
A core design challenge was making creator progress transparent and easy to scan.
The campaign pipeline consists of six stages:
Activated | Contacted | Responded | Submitted Bid | Signed Contract | Completed
Each stage can exist in five possible states:
Completed | Waiting to Start | In Progress | Waiting for Brand Action | Terminated
To reduce ambiguity and improve scannability, I designed a consistent visual system for each stage and its corresponding status. This allowed brands to quickly identify bottlenecks, stalled creators, or required actions without reading detailed logs.
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If I had more time
Given more time, I would prioritize user testing to observe how brands interpret funnel data in context. Direct feedback would help validate assumptions and uncover edge cases that static mockups cannot reveal.
I would also experiment with alternative visualization approaches through lightweight A/B testing to better understand how different presentations influence decision speed.
What I Learned
This experience strengthened my appreciation for structured thinking. Designing dashboards requires clarity above all else, and I learned how deliberate hierarchy choices shape how users interpret data.
Collaborating with PMs and engineers also reinforced the importance of aligning design decisions with business goals and technical realities.