Funnel design
Skill Mattakushi432/Claude-Code-Skills-Custom-DevTools-Pack/plugins/devtools-pack/skills/funnel-design
When to activate: funnel design, TOFU MOFU BOFU, conversion funnel, marketing funnel, funnel optimization, drop-off analysis, multi-touch attribution, pipelineFrom its SKILL.md
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Funnel Design
Funnel Stages
TOFU / MOFU / BOFU Model
TOFU (Top of Funnel) — Awareness
Goal: Capture attention of people who have the problem
Content: Blog posts, social, podcasts, videos, SEO
Metric: Unique visitors, impressions, brand searches
MOFU (Middle of Funnel) — Consideration
Goal: Educate and build trust with interested prospects
Content: Guides, webinars, case studies, comparison pages
Metric: Lead captures, email subscribers, content downloads
BOFU (Bottom of Funnel) — Decision
Goal: Convert ready-to-buy prospects
Content: Demos, trials, pricing, testimonials, proposals
Metric: Trials, demos, qualified pipeline, closed-won
Extended Funnel (with Retention)
Awareness → Interest → Consideration → Intent → Purchase → Onboarding → Retention → Advocacy
Funnel Mapping Process
Step 1 — Document Current State
For each stage, answer:
- What is the user trying to accomplish?
- What content/touchpoints exist here?
- What is the conversion rate TO the next stage?
- What is the biggest drop-off reason?
Funnel Mapping Template
| Stage | User Goal | Touchpoints | Entry Volume | Exit Volume | Conv Rate | Drop Reason |
|---|---|---|---|---|---|---|
| Awareness | Understand problem exists | Blog, SEO, ads | 100,000 | 5,000 | 5% | Irrelevant traffic |
| Interest | Learn more | Newsletter, retargeting | 5,000 | 1,500 | 30% | Low email open rate |
| Consideration | Evaluate options | Webinar, case studies | 1,500 | 450 | 30% | Trust gap |
| Intent | Make decision | Trial, demo | 450 | 90 | 20% | Friction in signup |
| Purchase | Buy | Pricing, checkout | 90 | 45 | 50% | Price objection |
Step 2 — Identify Biggest Drop-off
Leakage = (Stage_In - Stage_Out) × Stage_Value
Fix the stage with the highest leakage first — it compounds improvements downstream.
Content by Funnel Stage
TOFU Content Types
- SEO blog posts — target informational keywords
- Problem-aware social content — LinkedIn/Twitter threads
- Podcasts / YouTube — long-form educational
- PR / media features — credibility and reach
- Paid awareness — broad targeting, non-conversion objective
MOFU Content Types
- Lead magnets: checklists, templates, calculators, benchmarks
- Email nurture sequences: 5–10 email series educating on problem → solution
- Webinars: live demonstration of expertise
- Case studies: proof that your solution works
- Comparison content: "[Your product] vs [Competitor]" pages
BOFU Content Types
- Free trial / freemium: self-serve evaluation
- Live demo / sales call: human-guided evaluation
- ROI calculator: quantify value before purchase
- Security/compliance docs: reduce enterprise risk perception
- Customer references / calls: peer validation
- Proposal templates: accelerate procurement
Conversion Points Design
Friction Reduction Checklist (per conversion point)
- Is the ask proportional to where user is in the journey?
- Is there a lower-friction alternative for users not ready?
- Is the value exchange clear before the ask?
- Is the form the minimum viable length?
- Is there social proof at the point of conversion?
- Is there a risk reducer (guarantee, no CC, cancel anytime)?
Progressive Profiling
Don't ask for everything upfront:
Conversion 1 (TOFU): Email only
Conversion 2 (MOFU): Email + company name
Conversion 3 (BOFU): Full qualification (company size, use case, timeline)
Drop-off Analysis
Diagnosis Framework
For each high-drop stage, test hypotheses:
| Hypothesis Category | Diagnostic Question | How to Test |
|---|---|---|
| Messaging mismatch | Does the content promise match what they find? | User interviews, exit surveys |
| Trust gap | Do visitors believe the claims? | Social proof A/B, review count |
| Friction | Is the ask too hard at this stage? | Form field reduction test |
| Wrong audience | Are the right people arriving? | Segment analysis |
| Timing | Are we asking too soon / too late? | Behavioral triggers |
Exit Survey Questions
- "What stopped you from [completing action] today?"
- "What would have made you more likely to [action]?"
- "What were you hoping to find that you didn't?"
Funnel Optimization Process
ICE Prioritization for Funnel Tests
| Idea | Impact (1–10) | Confidence (1–10) | Ease (1–10) | ICE Score |
|---|---|---|---|---|
| Add testimonials to pricing page | 7 | 8 | 9 | 8.0 |
| Simplify trial signup form | 8 | 7 | 7 | 7.3 |
| Add exit-intent popup on blog | 5 | 6 | 8 | 6.3 |
Weekly Funnel Review Cadence
- Pull conversion rates at each stage (vs prior week, vs target)
- Flag any stage with >10% week-over-week decline
- Review top traffic sources by quality (conversion rate, not volume)
- Review top-performing content for each stage
- Assign one experiment per high-drop stage
Multi-Touch Attribution
Attribution Models
| Model | Logic | Best For |
|---|---|---|
| First touch | 100% credit to first touchpoint | Measuring awareness channels |
| Last touch | 100% credit to converting touchpoint | Measuring conversion channels |
| Linear | Equal credit to all touchpoints | Balanced view |
| Time decay | More credit to recent touchpoints | Short sales cycles |
| U-shaped | 40% first, 40% last, 20% middle | Account-based, high-intent |
| Data-driven | ML-assigned credit | Mature data teams |
Attribution Tooling
- Simple: UTM tracking + GA4 + CRM source field
- Mid-market: HubSpot attribution reports, Segment
- Enterprise: Rockerbox, Northbeam, Triple Whale, Ruler Analytics
UTM Standard
utm_source = traffic source (google, linkedin, newsletter)
utm_medium = channel type (cpc, email, social, organic)
utm_campaign = campaign name (q1-launch, brand-awareness)
utm_content = specific ad/link variant (hero-cta, sidebar-link)
utm_term = paid keyword (for search ads)
Full Funnel KPI Dashboard
| Metric | Formula | Frequency |
|---|---|---|
| Traffic by channel | Sessions per source | Weekly |
| Lead conversion rate | Leads / Visitors | Weekly |
| MQL conversion rate | MQLs / Leads | Weekly |
| SQL conversion rate | SQLs / MQLs | Weekly |
| Pipeline value | Open SQLs × avg deal size | Weekly |
| Win rate | Closed-won / Total SQLs | Monthly |
| CAC by channel | Channel spend / New customers from channel | Monthly |
| Time to convert | Avg days first touch → closed | Monthly |
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