Funnel Helper

Diagnose and optimize full conversion funnels for paid traffic from Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and Shopify Ads campaigns.

Purpose

Core mission:

• Analyze conversion funnel drop-off by stage.
• Identify bottlenecks from ad click to checkout or lead submit.
• Recommend stage-specific optimization actions.
• Define funnel experiment roadmap and expected impact.

When To Trigger

Use this skill when the user asks for:

• conversion funnel diagnosis
• CVR optimization planning
• landing page and checkout improvement sequence
• funnel experiment design tied to ROAS/CPA goals

High-signal keywords:

• conversion, funnel, checkout, cvr
• cpa, roas, traffic, landing page
• campaign, optimize, retarget

Input Contract

Required:

• funnel_stage_metrics
• traffic_source_breakdown
• conversion_goal
• observation_window

Optional:

• session_replay_notes
• form_or_checkout_logs
• segment_breakdowns
• experiment_history

Output Contract

1. Funnel Stage Health Scorecard
2. Bottleneck Priority Ranking
3. Optimization Actions by Stage
4. Experiment Roadmap with KPI impact
5. Monitoring and Iteration Rules

Workflow

1. Normalize funnel definitions and stage metrics.
2. Rank drop-off severity and opportunity size.
3. Map root causes (message mismatch, UX friction, trust gap, etc.).
4. Recommend stage-specific actions and experiments.
5. Define monitoring thresholds and iteration cadence.

Decision Rules

• If top-funnel CTR is strong but CVR is weak, prioritize LP and checkout fixes.
• If add-to-cart is strong but purchase is weak, prioritize trust/payment friction fixes.
• If retargeting conversion is low, review audience freshness and offer relevance.
• If funnel data is sparse, run diagnostic experiments before major redesign.

Platform Notes

Primary scope:

• Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads

Platform behavior guidance:

• Keep funnel interpretation tied to traffic intent by channel.
• Distinguish ad-side and on-site bottlenecks before action.

Constraints And Guardrails

• Do not infer funnel causes without stage-level evidence.
• Keep test queue prioritized by expected impact and effort.
• Avoid simultaneous high-impact changes that break attribution clarity.

Failure Handling And Escalation

• If stage definitions are inconsistent, output a canonical funnel mapping first.
• If missing checkout data blocks diagnosis, request minimum event payload.
• If conversion drops sharply during active changes, trigger rollback review.

Code Examples

Funnel Health Schema

text

stages:
  - impression_to_click
  - click_to_viewcontent
  - viewcontent_to_addtocart
  - addtocart_to_checkout
  - checkout_to_purchase
primary_metric: stage_cvr

Bottleneck Prioritization Rule

text

impact_score = dropoff_pct * traffic_volume * margin_weight
sort_by: impact_score_desc

Examples

Example 1: CVR collapse

Input:

• Click volume stable, purchases down

Output focus:

• stage bottleneck map
• immediate fixes
• monitor plan

Example 2: Checkout friction

Input:

• Add-to-cart high, checkout completion low

Output focus:

• checkout friction hypotheses
• test sequence
• expected lift range

Example 3: Funnel rebuild plan

Input:

• Multi-channel traffic with inconsistent landing paths

Output focus:

• canonical funnel design
• stage KPI definitions
• experiment roadmap

Quality Checklist

• Required sections are complete and non-empty
• Trigger keywords include at least 3 registry terms
• Input and output contracts are operationally testable
• Workflow and decision rules are capability-specific
• Platform references are explicit and concrete
• At least 3 practical examples are included