DTC Conversion Rate Optimization Analyzer
Diagnose DTC conversion leaks and prioritize A/B tests by revenue impact
What You Can Do
You can input your DTC conversion funnel data—including traffic, conversion rates by stage, user feedback, and session recordings—and Claude will identify where conversions statistically leak, synthesize root causes from multiple data sources, and generate a prioritized roadmap of A/B tests with confidence intervals and resource estimates. The skill produces investment-grade optimization recommendations that justify budget allocation to your team and stakeholders.
Features
Maps conversion stages, calculates drop-off percentages, and flags statistically significant bottlenecks vs. normal variance
Combines GA4/Shopify analytics with support tickets, session recordings, and survey data to uncover root causes
Produces multiple improvement hypotheses per leakage point, each with evidence citations and mechanistic explanations
Estimates expected lift per hypothesis using conversion benchmarks, AOV, and traffic volume; ranks by projected revenue impact
Defines test scope, sample size requirements, confidence intervals, and implementation complexity for each recommendation
Sequences tests by impact-to-effort ratio, accounting for dev/design capacity and interdependencies
Formats findings as stakeholder decks with before/after scenarios and financial projections
Example Output
Input: Q3 analytics showing 2.1% site conversion (down from 2.4%), 18% cart abandonment (vs. 14% industry avg), 42% mobile checkout drop-off, plus 47 support tickets mentioning "shipping costs surprised me."
Output:
Leakage Diagnosis:
- Mobile checkout: -4.2% vs. desktop (statistically significant, p<0.01). Root cause: Shipping calculator appears after users enter payment info; discovered via session replays showing 63% cart abandonment immediately after shipping cost reveals.
- Cart abandonment: +4% vs. benchmark. Root cause: No exit-intent offer; competitors test 10% discounts on abandonment flows.
Top 3 Hypotheses (by expected revenue impact):
- Early shipping cost transparency — Move shipping calculator to cart page (pre-checkout). Expected lift: +0.3% conversion = $48K quarterly revenue. Implementation: 2-3 dev days. Confidence: 87% (based on industry data + session patterns).
- Exit-intent recovery offer — Trigger 10% discount on cart abandonment after 20s. Expected lift: +0.15% conversion = $24K quarterly. Implementation: 1 dev day. Confidence: 72%.
- Trust badges on mobile — Add payment/shipping guarantees above fold on mobile checkout. Expected lift: +0.08% conversion = $13K quarterly. Implementation: Design only. Confidence: 64%.
What's Included
- SKILL.md instruction file with funnel mapping framework and hypothesis generation prompts:
- Conversion funnel analysis template (pre-built spreadsheet for entering stage data and calculating leakage):
- Root cause questioning framework (questions to ask of each bottleneck using data synthesis):
- A/B test prioritization matrix (impact vs. effort scoring tool):
- Revenue impact calculator (template for estimating lift and ROI per hypothesis):
- Investor pitch template (slide deck structure for presenting optimization roadmap):
Who It's For
- DTC e-commerce managers optimizing mature brands with 1,000+ monthly transactions
- Head of growth or VP of performance marketing planning quarterly A/B test roadmaps
- Analytics leads diagnosing conversion stalls and generating hypotheses for product teams
- E-commerce operators seeking data-driven framework to prioritize between competing optimizations
- Conversion rate optimization specialists building investment cases for funnel improvements
Best For
- Diagnosing plateaued or declining conversion rates quarter-over-quarter
- Identifying statistical funnel leakage points and root causes
- Prioritizing A/B tests by expected revenue impact vs. resource cost
- Synthesizing analytics data with qualitative feedback to generate actionable hypotheses
- Creating investor-ready optimization roadmaps with financial projections
- Structuring multi-quarter conversion optimization programs with resource constraints
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