Copartner, Fintech Signals App
Fintech Signals App Case Study
I clarified the journey from research signal to action so users could move with more confidence and less confusion.
About Copartner
Copartner is a mobile finance app that delivers research ideas and buy/sell signals from vetted analysts on a subscription. This case study covers the path from receiving a signal to acting on it, the point at which users were losing confidence and calling support.
The problem
Copartner delivered good signals and then abandoned users at the hardest moment: knowing what to actually do with one, by when, and whether it was still valid.
User pain
- Users saw signals but were unsure what action to take next.
- Subscription value was not clear at decision moments.
- Signal credibility context was scattered.
Business pain
- Signal consumption did not convert to action reliably.
- Support load increased around execution ambiguity.
- Subscription intent dropped due to unclear payoff.
Product gap
- No cohesive bridge between signal insight and action.
- Trust indicators lacked clear prioritization.
- CTAs were detached from risk context.
What I owned
Owned
- Product strategy, user flows, and information architecture
- Wireframes, interaction model, and high-fidelity UI
- Validation synthesis and iteration decisions
- Developer handoff with behavior states and edge cases
Collaborated
- PM on scope, sequencing, and KPI alignment
- Engineering on technical feasibility and state visibility
- Stakeholders on domain constraints and rollout priorities
Out of scope
- Backend implementation and infrastructure changes
- Pricing, growth campaigns, and sales operations
- Post-launch paid acquisition strategy
The results
+24%
signal-to-action conversion
-32%
support questions about next steps
4.3/5
clarity score in validation
Users moved from signal insight to action with clearer confidence cues. Core action funnel became more deterministic and predictable. Reduced support load and improved paid intent quality.
The approach
I took the signal itself as the unit of the journey, notification through to a placed action, and traced where confidence leaked out of it.
Discover
Mapped user and business friction through interviews, workflow audits, and benchmark analysis.
Prioritize
Ranked opportunities by impact on activation, task success, and decision confidence.
Design
Built IA, interaction patterns, and high-fidelity solutions with clear state behavior.
Validate
Tested critical flows, measured drop-offs, and iterated before development handoff.
The decisions
The calls that shaped the product, with what each one cost. Anything crossed out here was a real option at the time.
Decision 1: Signal card model
Signal cards lacked clear decision structure.
Considered
- Data-heavy card
- Action-first card
- Modal detail model
Chose
Action-first card with confidence and risk context
Gave up
Reduced visible secondary indicators
On what evidence
Users asked, "What should I do now?" repeatedly
What happened
Signal-to-action conversion improved by 24%
Decision 2: Subscription framing
Users could not evaluate premium value quickly.
Considered
- Price-first upsell
- Outcome-first framing
- Delayed paywall
Chose
Outcome-first framing with real workflow examples
Gave up
More content required in paywall context
On what evidence
Interviews showed value uncertainty before price concern
What happened
Higher intent quality in paid funnel
Decision 3: Execution handoff
Users dropped while moving from signal to broker action.
Considered
- External link handoff
- Guided handoff
- Broker-agnostic modal
Chose
Guided handoff with checklist and status cues
Gave up
Added one transition layer
On what evidence
Support logs showed confusion at handoff stage
What happened
Support questions reduced by 32%
The work
Every signal now carries its own next step and validity window on the card itself, so 'where do I act' stopped being something users had to ask a human.
Signal feed
Make action priority and confidence visible immediately.
Decision details
Provide enough context for confident buy/sell choice.
Premium path
Tie subscription value to real decision outcomes.
Targets, set up front
Metric
Baseline
Target
Result
Measured in
Signal-to-action
Baseline 38%
Target 48%+
Result 47%
pilot cohort
Support ticket share
Baseline 22%
Target <17%
Result 15%
30-day window
Flow clarity score
Baseline 3.4/5
Target 4.0+
Result 4.3/5
usability survey
Outcomes in detail
More actionable behavior
Lower ambiguity burden
Higher trust and confidence
Reflection
What worked
Action-first signal cards reduced decision ambiguity immediately.
What to improve
Introduce personalized signal explanations by risk profile.
Next experiment
Test outcome previews before broker handoff for high-volatility markets.
Vinit Chaudhary
I'll personally review your brief and get back to you within two hours.
Product Designer
Contact
I usually respond to all digital enquiries within 2 business hours.
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