The Problem
Everything was manual. Nothing scaled.
Aspire’s core value proposition was helping brands scale influencer marketing. But scaling campaigns while every creator action required manual intervention was a direct ceiling on that promise — and on revenue. Every new campaign meant more headcount, not more efficiency. Churn risk was real: brands were hitting walls that Aspire’s competitors were beginning to automate around. The automation builder wasn’t a feature request — it was a strategic bet on whether Aspire could become the operational layer for influencer marketing, not just a discovery tool.
Before this system existed, brand managers running influencer campaigns had no automation support whatsoever. Approving creators, sending briefs, triggering offers, gifting products, reviewing content, categorising creators, sending payments - every action required a human to do it manually, for every creator, in every campaign. At scale, that meant hiring more people instead of building better tools.
Competitors offered basic if/then rules. We needed something that could handle the full complexity of influencer workflows - non-linear, multi-step, conditional, and usable by non-technical brand managers.
“Your campaign launch is days away, but you’re still chasing unsigned briefs, product requests, and deliverables. Scaling creator campaigns shouldn’t feel like a last-minute scramble.”
— The problem we set out to solve
My Role
End to end - from design to production
Role
Senior Product Designer
Scope
Discovery · Research · PRD Collaboration · UX Design · Prototyping · Validation · Eng Handoff
Research
15+ independent customer calls · 10+ calls with PM · 25+ customers total
Methods
Competitor Analysis · Customer Interviews · Prototype Validation · Figma · React Flow
I owned design end to end - from shaping the problem with the PM to shipping with engineering. I also made the case to leadership for the AI builder - the feature they initially pushed back on - and saw it through to launch.
Discovery
25+ customers. 4 core problems.
Before touching Figma, I ran 15+ discovery calls independently and attended 10+ more alongside the PM - covering brand managers, marketing directors, account managers, and agency teams managing multiple client accounts. I paired this with thorough competitor analysis across tools like Later, Traacker, and Zapier to understand where the market ceiling was. Four problems came up repeatedly across every conversation:
Automate the full campaign lifecycle
Brands wanted to automate offers, product sends, member adds, brief sends and follow-ups - the entire workflow, not just emails.
Build automations without being technical
Users aren't engineers. They needed to describe what they wanted in plain English and have the system figure out the rest.
Understand what actually ran
Once automations were live, brands had no visibility into which ran, which failed, and which creators were affected.
Get started in minutes, not hours
Setting up automations from scratch was too slow. They needed ready-made templates for the most common workflows.
Design Decisions
Five decisions that defined the system
Each of these decisions came from direct customer evidence - not product intuition or internal debate.
Making the builder usable by non-technical users
Early prototypes revealed that finding and adding blocks wasn't discoverable - users struggled to search for actions and couldn't intuitively add paths. The fix was layered: contextual on-canvas guides per block, a pre-built test automation with dummy data, and an onboarding flow for first-time users. When we tested the MVP, brand users understood the flow immediately. Adoption spiked - big brands sent unsolicited feedback saying the system felt intuitive.
“Let them feel the system before they build in it.”
Parallel paths - the feature no competitor had
Every competitor offered yes/no conditional splits. But a single creator event often needed to trigger multiple independent actions simultaneously - notify the creator, alert the team, AND send to content review, all at once. The hardest design problem was visual differentiation: users kept reading parallel paths as conditions. The solution was color-coded branches with named paths, making it clear these were independent execution streams.
“None of our competitors - Later, Traacker, or any influencer marketing tool - had shipped parallel execution.”
AI builder - the fight to build it, and why it was worth it
Leadership's objection was specific: timeline pressure and system complexity. Aspire's data model is significantly more intricate than Zapier's — reliably mapping plain-English intent to the right triggers, conditions, and actions across 85+ triggers and 70+ actions wasn't a solved problem. The risk of shipping something that hallucinated wrong automations was real. I made the case in three parts. First, customer quotes showing that non-technical users were abandoning the manual builder mid-flow — this was a retention problem, not just a UX preference. Second, a working prototype demonstrating the parsing accuracy on Aspire's actual data model. Third, a competitive framing: no influencer marketing tool had shipped AI-assisted workflow building. Being first here was a defensible moat. Leadership approved it. Once shipped, users could describe entire automations in plain English — the system would parse intent, confirm its understanding, then build the flow. The feature reduced time-to-first-automation for new users by removing the cold-start problem entirely.
“Describe your workflow in plain English. The system builds it.”
Run history - making failure legible
Once automations ran across hundreds of creators, brands had no visibility into what happened. If an offer send failed after an approval succeeded, they found out weeks later when creators followed up. The design tracked each member's journey independently, highlighted success and failure at the node level, and allowed one-click retry of just the failed paths.
“Every member. Every step. Every failure - with one-click retry.”
Scheduling - build now, run later
Teams building Black Friday or holiday automations weeks in advance needed to hold them until the right moment. Recurring ambassador workflows needed to run on a cadence without manual re-triggering. Rather than adding complexity to the canvas, scheduling was surfaced at the publish step - activate now, schedule for later, or save as draft. This kept the building experience clean.
“Activate now. Schedule for later. Save as draft.”
The System
A complete automation ecosystem
The final system wasn't just a campaign feature - it became a platform-wide automation infrastructure covering the complete Aspire product lifecycle.
Visual Canvas Builder
Drag, connect, and configure nodes with auto-layout
85+ Triggers & 70+ Actions
Covering the entire Aspire platform, not just campaigns
AI-Assisted Building
Describe your workflow in plain English
Parallel Execution Paths
Multiple simultaneous branches from a single trigger
43 Pre-built Templates
Common workflows ready to activate in two clicks
Run History & Retry
Per-member execution trace with failed path recovery
Scheduling
Activate now, schedule for later, or save as draft
Test Mode
Run automations with dummy data before going live
How it works
01
Choose a trigger
Application approved, brief signed, product delivered, reward unlocked
02
Add conditions
Specify what must be true for the automation to run
03
Define the action
Send email, trigger reminder, generate promo codes, add to groups
