Success Stories
Simple Logging vs afterchange.io
Simple Logging
- Only records "what happened"
- Shows numbers but no insights
- Requires manual analysis
- No before/after comparison
- Doesn't answer "Did this decision work?"
afterchange.io
- Answers "What, Why, What Impact"
- Automatically compares before/after data. AI summarizes if you want.
- Test hypothesis, see results
- Automatic before/after comparison
- Data-driven decision making
Time Savings
31 work days saved annually
Real-World Scenarios
See how afterchange.io adds value to your daily work
Did New Feature Increase Purchases?
'payment_completed' event logged 150 times
Is this good or bad?
Before: 100/week
After: 150/week
+50% increase! Time to invest in marketing
Did Bug Fix Actually Reduce Errors?
'checkout_error' event decreased
By how much?
Decision: "Checkout bug fix"
Before: 50 error/day
After: 5 error/day
-90%! Check timeouts for remaining 5 errors
Did UI Change Increase or Decrease Clicks?
'button_click' event exists
Did it improve or worsen?
Hypothesis: "Green button +30% clicks"
Before: 1000 (blue)
After: 950 (green)
Hypothesis wrong! Revert back
Real-World Use Cases
Signup Button Redesign
Problem
User signup conversion was below industry benchmarks. The signup button was small and positioned in a low-visibility area.
Decision Made
Redesigned the signup button with a larger size, brighter color (blue to green), and moved it to the header sticky position. Conducted A/B test with 50% traffic split.
Hypothesis
"Users will complete signup more frequently, leading to increased registration rate"
Tracked Metric
Result
Signup completion rate increased by 34% (from 2.1% to 2.8%). Hypothesis confirmed. Change rolled out to 100% of users.
CPU Temperature Optimization
Problem
IoT device CPU was overheating during peak load, reaching 85°C and causing thermal throttling. This reduced performance and shortened device lifespan.
Decision Made
Implemented new cooling algorithm that dynamically adjusts fan speed based on workload prediction. Also optimized CPU scheduling to distribute load more evenly.
Hypothesis
"CPU temperature will decrease under load, reducing thermal throttling and improving device reliability"
Tracked Metric
Result
Average CPU temperature dropped by 18% (from 82°C to 67°C). Thermal throttling incidents reduced by 92%. Device stability improved significantly.
Database Query Optimization
Problem
API response times were degrading as user base grew. Database queries for user dashboard were taking 2-4 seconds, causing poor user experience.
Decision Made
Added composite indexes on frequently queried columns, implemented query result caching with Redis, and optimized N+1 query patterns in ORM.
Hypothesis
"API response time will decrease, improving user experience and reducing server load"
Tracked Metric
Result
Average API response time improved by 73% (from 2.8s to 750ms). Server CPU usage decreased by 40%. User satisfaction scores increased.
Developer Laptop Investment ROI
Problem
Engineering team was working on 4-year-old laptops. Build times averaged 12 minutes, hot reload took 8-10 seconds. Developer satisfaction surveys showed frustration with tooling performance.
Decision Made
Invested $75,000 in new MacBook Pro M3 Max laptops for 15-person engineering team. Set up webhook integration with GitHub to track commit frequency and build completion as productivity proxies.
Hypothesis
"Better hardware will reduce friction in development workflow, leading to increased code commits and faster iteration cycles"
Tracked Metric
Result
Daily commits increased by 42% (from 38 to 54 commits/day). Build completion events up 35%. Developer satisfaction NPS improved from 6.2 to 8.7. ROI achieved in 4 months through faster delivery.
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