Executive Summary
A Shopify review app serving merchants globally had entered a 12-month revenue decline. Monthly recurring revenue was slipping, early-funnel churn had grown unmanageable, and the team kept shipping features that were not moving the business.
I joined as Design Manager for the Vertical, owning product design for the app, leading a team of 6 designers (1 Lead, 3 Senior, 1 Mid, 1 Junior), and partnering directly with the Head of Division on product strategy and prioritization.
Over the course of the role, I redirected the team from a feature-output mindset to an outcome-driven one, using user data and opportunity cost analysis to reframe what was worth shipping. The result was a full reversal of the revenue trend and a durable shift in how the team operated.
Outcomes at a glance:
MRR recovered from $79K to $96K (+21.5%) in 6 months
Top-funnel churn cut from 65% to 23% (first 3 hours post-install)
Widget activation success raised from ~72% to 92%
Pricing anchor test delivered 3 to 4x conversion lift on the $49.95 plan
Average widget types used per merchant grew from 1.3 to 3.9
Team competency gaps closed across 3 of 6 designers through structured coaching
This case study is anonymized. Product names, proprietary assets, and specific merchant identifiers are withheld to respect confidentiality. Metrics and outcomes are real.
Context and Diagnosis: Why the Numbers Were Slipping
When I took over design for the Vertical, the product was not broken in any obvious way. It had a mature feature set, a long merchant list, and a functional paywall. But MRR had declined for 12 consecutive months, and no one on the team could point to a single root cause.
I treated the situation as a diagnostic problem before a design problem. In the early weeks of the role, I triangulated three data sources to isolate where merchants were actually leaving:
Amplitude funnels and retention cohorts to map behavior across the install-to-activation journey
Full-session replays for installed-but-inactive merchants
Customer Success transcripts and chat logs to surface recurring language around friction and cancellation
A clear pattern emerged: most churn happened within the first 1 to 3 hours after install, well before merchants ever touched a paid feature. Three root causes were doing most of the damage.
Outdated widget visuals. The review widget looked dated against modern Shopify themes. Some merchants were leaving 1-star ratings specifically about CSS clashes before even evaluating the feature.
Overloaded onboarding. The flow asked merchants to configure too many things up front, forcing cognitive load before any value was demonstrated.
Poorly timed paywall. The paywall surfaced late in the journey and failed to communicate plan value at the moment of decision.
The implication was uncomfortable. The roadmap at the time was built around net-new features, but the product was losing merchants before any new feature would ever reach them. Diagnosing this clearly was the precondition for the harder decision that followed.
Strategic Reframe: Shifting from Feature Output to User Outcome
Presenting the diagnosis was the easy part. Convincing the organization to change course was not.
The prevailing assumption across Product and Engineering was that growth would come from new capabilities. My counter-argument, grounded in data, was that core experience fixes would outperform net-new features at this stage of the product's life. I used three tools to make the case:
Opportunity cost framing. For every multi-month feature investment, I mapped what the equivalent team effort on core experience repairs could return, based on current churn and activation economics. The ratio was not close.
Competitive teardown. I benchmarked the app's onboarding, widget visuals, and paywall against direct competitors in the Shopify review category. We were behind on surface-level polish in ways that merchants were visibly punishing us for.
North Star Metric refinement. The existing NSM tracked output activity, which rewarded shipping over impact. I worked with the Head of Division to refine it around a retention-weighted activation signal, so that the team's definition of success aligned with what actually drove MRR.
Two decisions followed directly from this reframe. Neither was popular.
Deprioritized a 3-month automation feature that had already been scoped and partially committed. Scope risk and ROI modeling indicated the payoff window would not materialize in time to affect the revenue curve.
Killed an AI translation experiment early after a rapid validation round showed more than 70% of merchants disabling it shortly after trying it. Low trust made the feature a net-negative experience.
Both calls freed meaningful team capacity and redirected it to the core experience work that actually moved the numbers. The team's cultural reset started here: saying no became a legitimate design contribution.
Execution: Rebuilding the Core Experience
With the team realigned around outcome, we ran 4 coordinated interventions over 6 months. Each was scoped tightly, measured independently, and prioritized in the order that would compound the fastest.
1. Onboarding redesign, reducing cognitive load
The previous flow asked merchants to configure multiple settings before the widget was ever visible on their store. I redesigned it into a 3-step flow:
Set Branding
Customize Widget
Enable App Embed
Each step was designed to deliver a quick visual win that built early trust. The highest-friction step, widget activation, previously required manual theme editing and broke roughly 1 in 4 merchants. I simplified it into a one-click action using Shopify's App Embed API.
Widget activation success: ~72% → 92%
Top-funnel churn, first 3 hours: 65% → 23%
2. Proactive CS triggers, recovering stalled merchants
Passive drop-off was being treated as unrecoverable. I worked with CS and Engineering to build behavior-based triggers that auto-flagged stalled merchants and routed them to targeted chat and email recovery flows. This turned drop-off into an active recovery channel without adding CS headcount, and closed the loop between design decisions and downstream merchant outcomes.
3. Paywall redesign and pricing anchor test
The paywall was redesigned with a welcome sub-step, features regrouped by USP rather than by plan tier, and clearer visual contrast between plans. We then ran a pricing anchor A/B test segmented by merchant spending behavior.
For high-willingness-to-pay segments, we reversed the default plan order so the highest plan appeared first. The single change delivered a 3 to 4x conversion lift on the $49.95 plan: 119 conversions in the test variant vs 31 in control.
Median paywall decision time: 1m 55s → 45s (-61%)
4. Widget visual redesign and ecosystem consolidation
The review widget was visually redesigned to fit modern Shopify theme conventions, resolving long-standing CSS compatibility complaints that had been driving 1-star reviews.
Alongside this, I led an audit of the widget catalog. We retired 2 legacy widget types with near-zero adoption and consolidated the offering from 9 to 7, reducing maintenance load for Engineering and simplifying the merchant's mental model.
Average widget types used per merchant: 1.3 → 3.9 (3x)
Together, these 4 interventions produced the MRR recovery from $79K to $96K (+21.5%) over 6 months, on a trendline that had been declining for the previous 12.
Leadership and DesignOps: Building the System That Produced the Outcome
The design outcomes above were downstream of how the team was set up to operate. Three leadership investments carried as much weight as any individual redesign.
Team competency mapping and coaching
Early in the role, I assessed each of the 6 designers against a competency framework covering craft, systems thinking, cross-functional influence, and validation design. 3 of the 6 had clear gaps against the role expectations for their level.
I built structured 1:1 coaching plans for each of the 3, with specific development outcomes tied to defined review checkpoints rather than vague "get better at X" goals. Progress was visible across the following review cycles in all 3 cases.
AI integration into the design workflow
I integrated AI tooling into multiple stages of the design process:
User research synthesis and JTBD mapping
First-draft PRD and spec writing
Prototype generation for faster validation
This raised team velocity without adding headcount. More importantly, it freed senior designers from execution-heavy tasks so they could focus on architecture, critique, and strategic partnership with Product.
Design rituals and evidence-based decision culture
I established a regular design critique cadence where every decision of significance had to be defended with evidence: Amplitude data, session replay, CS signal, or a structured experiment. This shifted the team's default posture from "here is my solution" to "here is the problem, the evidence, and why this is the right bet."
The cultural shift was the hardest part of the work and the most durable. By the end of the engagement, the team was proposing deprioritizations on its own and pushing back on PO requests with data rather than opinion. That capability is what persists after any single designer leaves.
Outcomes and Reflections
Consolidated outcomes:
Metric | Before | After | Change |
|---|---|---|---|
MRR | $79K | $96K | +21.5% |
Early churn (first 3 hours) | 65% | 23% | -42pp |
Widget activation success | ~72% | 92% | +20pp |
Paywall decision time | 1m 55s | 45s | -61% |
Widget types used per merchant | 1.3 | 3.9 | ~3x |
Paywall conversion, high-WTP segment | Baseline | 3 to 4x | Segmented lift |
What I would do differently:
Diagnose faster. The diagnosis phase was defensible given the complexity, but I could have compressed it by parallelizing the competitive teardown with the data triangulation.
Surface the deprioritization case earlier. The call to kill the 3-month automation feature was the right one, but the internal politics would have been lighter if I had framed the opportunity cost argument in Week 1 rather than several weeks in.
Invest in the design system in parallel. We shipped the widget redesign successfully, but without a stronger component system underneath, each downstream improvement took longer to ship than it should have. A design-system investment earlier would have compounded across every intervention.
Principles I carried into this work:
Diagnose before prescribing. In a feature-factory environment, the strongest strategic move is often refusing to design until the problem is actually understood.
Say no as a design contribution. Deprioritizing the automation feature and killing the AI translation experiment were among the highest-leverage calls of the year. Neither produced a single shipped pixel.
The team is the product. Core experience improvements compound, but a team that can diagnose, prioritize, and defend decisions independently compounds faster.
