60% Drop-off at Checkout. The Fix Wasn’t a New Feature.
“Prioritized conversion and monetization instead of adding new features, by choosing not to build.”
A 4-month product contract at a 50K+ MAU career platform. Users were engaged, just not converting, and the fix wasn’t the 20-item feature backlog everyone expected.
High traffic, flat conversion, and a product/engineering relationship losing time to rework.
Where interested users went
Checkout was the leak
1. Reached checkout100%
2. Completed purchase40%
60% of users who reached checkout abandoned there: the single largest, most concentrated point of loss in the funnel.
Conversion stagnation
40K MAU, but flat paid subscriber growth; CAC wasn’t recovering.
NPS declining
User satisfaction trending down quarter over quarter, raising churn and referral risk.
Engineering-product misalignment
Incomplete specs meant 40% of engineering time went to clarification instead of building.
02
The problem to solve first
Conversion-first, freeze features. Two other directions got ruled out.
Ruled out: keep shipping the 20-item backlog
Users were engaged, not unconvinced. More features wouldn’t fix a checkout problem.
Ruled out: NPS-driven feature triage
NPS is a lagging signal. It would point at symptoms, not the checkout drop-off itself.
03
What I considered instead
Deprioritizing features was the decision with the most disagreement.
Weighted decision matrix
Deprioritize 15 features, or split engineering time?
Criterion
Weight
Conversion-first (chosen)
Split engineering
Speed to measurable impact
×3
3 (9)
1 (3)
Short-term stakeholder trust
×1
1 (1)
3 (3)
Revenue / conversion impact
×3
3 (9)
1 (3)
Engineering focus and quality
×2
3 (6)
1 (2)
Weighted total
25
11
Scores run 1–3 (3 = strongest), multiplied by each criterion's weight.
Divided attention meant mediocre results on both. Stakeholders were disappointed short-term, but the 22% subscriber lift proved the focus was right.
Tradeoff 01 · Testing
Invest 2 weeks building proper A/B testing infrastructure
Alternative considered
Ship changes untested and measure aggregate metrics
Why this one
Without it, we’d never know what actually worked. It delayed the first optimization by 2 weeks, but every iteration after was 3x faster with a clear winner.
Tradeoff 02 · Handoffs
Write detailed technical specs upfront, even for “obvious” features
Alternative considered
Quick verbal handoffs, iterate on engineering questions
Why this one
Verbal handoffs were causing 30% rework. Specs take more PM time but dropped rework to 5% and let engineering build with confidence.
04
What I’d do differently
Three real mistakes from a 4-month engagement.
Mistake 01
Took 2 weeks to realize the backlog needed pruning, not executing
Impact
Spent the first 2 weeks planning features that shouldn’t have been built at all.
What I’d do instead
Start with funnel analysis before touching the backlog.
Mistake 02
Didn’t push harder for a user research budget
Impact
Some decisions leaned on quantitative data alone, missing qualitative context on why users dropped off.
What I’d do instead
Advocate for 5–10 user interviews a month, even informal ones.
Mistake 03
Over-documented some specs, under-documented others
Impact
An inconsistent engineering experience: some features shipped smoothly, others bumpy.
What I’d do instead
Build spec templates and checklists for consistency across every feature.
05
How this connects to outcomes
A 4-month contract, measured on subscribers and satisfaction.
+1,100
Paid subscribers
22% lift in 4 months
+60%
NPS improvement
Detractor count cut in half
2
Features delivered
Deliberately few; conversion was the bet
50K+
Platform reach
Monthly active users served
Get in touch
Let’s talk about what you’re building.
Interested in how I can help your team cut through backlog bloat and fix the funnel first? I’d like to hear what you’re working on.