analyzing-funnels

Analyzing Funnels

Quick Start

To analyze a funnel:

1. Clarify the user journey the user wants to measure
2. Use list_events and render_insight to validate the funnel steps, window, and ordering
3. Query the funnel data and calculate drop-off rates per step
4. 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:

1. Call list_events to confirm exact event names and recent volume
2. Call render_insight with kind: funnel to validate step definitions and check the data looks correct
3. Once validated, call create_insight with kind: funnel if the user wants a saved chart
4. Call create_discovery if the bottleneck or finding should be reviewed or notified
5. Use the rendered or saved insight results 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

• Funnel parameters
• Conversion metrics