Analyzing Funnels
Quick Start
To analyze a funnel:
- Clarify the user journey the user wants to measure
- Use the Altertable MCP server to create the funnel with the right steps, window, and ordering
- Query the funnel data and calculate drop-off rates per step
- Identify the biggest bottleneck and present actionable findings
When to Use This Skill
- User asks about conversion rates, drop-offs, or completion rates
- Analyzing a multi-step process (signup, checkout, onboarding, feature adoption)
- Comparing funnel performance across segments or time periods
- Identifying where users abandon a flow
Core Workflow
Step 1: Define the Funnel
Ask the user (or infer from context) what journey to measure. You need:
- Steps: Ordered list of events from entry to conversion goal
- Conversion window: Maximum time allowed to complete the funnel
- Use short windows (30 min) for session flows like checkout
- Use medium windows (24 hr) for day-bounded flows
- Use long windows (7+ days) for consideration flows like onboarding
- Ordering: Strict (exact sequence required) or Any (steps in any order)
Default to strict ordering unless the user specifies otherwise.
Step 2: Preview and Create the Funnel
Use the Altertable MCP server to:
- Preview the funnel first to validate step definitions and check the data looks correct
- Once validated, create the funnel insight (or funnel insight discovery to save and share it)
- Query the funnel to retrieve per-step user counts
Step 3: Calculate Metrics
For each step transition, compute:
| Metric | Formula |
|---|
| Step conversion rate | Users at step N+1 / Users at step N |
| Step drop-off rate | 1 - Step conversion rate |
| Overall conversion rate | Users at final step / Users at step 1 |
Step 4: Identify Bottlenecks
Find the step transition with:
- The largest absolute user drop-off
- The largest percentage drop-off
- Any unexpected pattern (e.g., later steps dropping more than early steps)
Step 5: Present Results
Present results as a step-by-step breakdown:
- Show each step with user count, conversion rate, and drop-off rate
- Highlight the primary bottleneck
- Provide a concise recommendation tied to the bottleneck (what to investigate or improve)
Format example:
Step 1: Page View - 10,000 users
Step 2: Add to Cart - 1,200 users (12.0% conversion, 88.0% drop-off) <-- biggest drop
Step 3: Checkout Started - 800 users (66.7% conversion, 33.3% drop-off)
Step 4: Purchase Complete - 720 users (90.0% conversion, 10.0% drop-off)
Overall conversion: 7.2% (720 / 10,000)
Bottleneck: Step 1 to Step 2 -- 88% of users drop off before adding to cart.
Segmented Analysis
When comparing funnels across segments (device, traffic source, user type):
- Always compare identical step definitions and time periods
- Call out which segment has the worst conversion and at which step
- Account for sample size -- small segments can produce misleading rates
Common Pitfalls
- Wrong conversion window: Too short cuts off legitimate conversions; too long inflates rates with unrelated sessions. Match the window to the expected user behavior.
- Too many steps: Including minor intermediate events dilutes the analysis. Keep funnels to 3-7 meaningful steps.
- Too few steps: Jumping from entry to conversion hides where users actually drop off.
- Ignoring ordering: Using "any" ordering when the flow is inherently sequential produces misleading results.
- Comparing mismatched periods: Ensure segments or time comparisons use the same date ranges and funnel definitions.
- Not previewing before creating an insight: Always preview funnel results to verify step definitions are correct before saving.
Reference Files