# cja-funnel-health-check > Analyzes a multi-step conversion funnel in Adobe Customer Journey Analytics to find where users drop off and which steps have the worst leakage. - Skill: `adobe/cja-funnel-health-check` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds add adobe/cja-funnel-health-check` - Raw SKILL.md: https://api.skillmd.com/api/skills/adobe/cja-funnel-health-check/raw - Safety review: CAUTION (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics, Coding & Dev Tools, Marketing & Growth, Data Analysis - Tags: Adobe Cja, Conversion Rate, Customer Journey Analytics, Drop Off, Fallout, Funnel Analysis, Html Report, Segment - License: Apache-2.0 - Author: Adobe (https://skillmd.com/u/adobe), verified publisher - Updated: 2026-07-06 - Page: https://skillmd.com/skills/adobe/cja-funnel-health-check --- # Funnel Health Check (Customer Journey Analytics) Turn a plain-English funnel description into a quantified step-by-step conversion analysis. The output identifies the biggest leakage point in the funnel and provides dimension-based breakdowns to show which audience or channel has the worst drop-off. Funnel health checks are most valuable when a team suspects a specific step is broken but hasn't quantified it. This skill does the quantification in a single conversation turn. --- ## CJA MCP Tools Used - `findDimensions` — resolve page name, event, or other dimensions for step filtering - `findMetrics` — get the base metric to measure (sessions, visitors, events) - `searchDimensionItems` — find exact dimension values for step names - `runReport` (with `adhocSegments`) — measure visitor counts at each funnel step - `findSegments` — use existing segments as funnel step filters if applicable - `describeSegment` — understand segment logic before applying as a funnel step --- ## Phase 0 — Setup 1. Call `findDataViews` to list available data views. 2. If the user hasn't specified a data view, present the list and ask which to use. 3. Call `setDefaultSessionDataViewId` with the chosen ID. 4. Ask the user to define the funnel stages if not already specified (e.g., "What are the steps in the funnel you want to analyze?"). ## Phase 1 — Define Funnel Steps ### 1.1 Parse the user's funnel description Extract the step sequence from the user's plain-English description: - "Homepage → Product Page → Cart → Purchase" - "Registration → Onboarding Step 1 → Onboarding Step 2 → Activated" - "Landing Page → Lead Form → Thank You Page" Each step must resolve to a measurable condition in CJA. A step can be: - **Page view**: user viewed a specific page (resolved via page name dimension) - **Event/metric**: user triggered a specific action (e.g., "Added to Cart") - **Segment membership**: user meets a pre-built segment condition ### 1.2 Clarify ambiguous steps If any step is vague (e.g., "checkout" without a page name), ask one question: > "For the 'Checkout' step — should I look at people who visited a page > containing 'checkout' in the URL, or those who triggered a specific event > like 'Cart Add'?" Do not ask more than one clarifying question at a time. ### 1.3 Resolve page names to dimension values For page-based steps, call `searchDimensionItems` to find the exact dimension values that match the step name. First call `findDimensions` with a semantic search like `"page name url"` to confirm the correct dimension ID — in most CJA data views this is `variables/web.webPageDetails.name`, not `variables/page`: ``` searchDimensionItems( dimensionId: "variables/web.webPageDetails.name", searchAnd: "", startDate: "", endDate: "", page: 0, limit: 10 ) ``` Present matches to the user if there are multiple candidates: > "I found these pages matching 'checkout': /checkout/start, /checkout/payment, > /checkout/review. Should I use '/checkout/start' as the entry to the checkout > step?" --- ## Phase 2 — Measure Each Step For each funnel step, construct an ad hoc segment that filters to visitors/sessions that reached that step. Then run a report measuring the base metric (usually Unique Visitors or Sessions) with that segment applied. ### 2.1 Construct ad hoc segments for each step A "reached step N" ad hoc segment is: - Container: Visit or Person (use Visit for session-level funnels, Person for cross-visit journeys) - Condition: Page Name equals "" OR Event occurred ### 2.2 Run reports for each step Run one `runReport` per step with the ad hoc segment applied. The `adhocSegments` parameter takes fully-formed CJA segment definition objects — NOT a simplified shorthand. The correct structure is: ``` runReport( dimensionIds: "variables/web.webPageDetails.name", metricIds: "metrics/visitors", startDate: "", endDate: "", page: 0, limit: 1, adhocSegments: [{ "func": "segment", "version": [1, 0, 0], "container": { "func": "container", "context": "visitors", "pred": { "func": "streq", "val": { "func": "attr", "name": "variables/web.webPageDetails.name" }, "str": "" } } }] ) ``` Use `context: "visitors"` (person-level) for cross-visit funnels. Use `context: "visits"` (session-level) for within-session funnels. The metric value comes from `summaryData.filteredTotals[0]` in the response. The dimension and value used in the predicate should match what was found via `searchDimensionItems` in Phase 1 — use the exact `value` string returned. **Important**: Each step uses a cumulative filter — measure "visitors who EVER reached this step in the period," not "visitors who ONLY visited this page." This produces the classic funnel waterfall. Capture `stepCount[i]` for each step i from 1 to N. --- ## Phase 3 — Compute Funnel Metrics For each step-to-step transition: - `stepConversionRate[i→i+1]` = stepCount[i+1] / stepCount[i] × 100 - `dropOff[i→i+1]` = stepCount[i] − stepCount[i+1] - `dropOffRate[i→i+1]` = 100 − stepConversionRate[i→i+1] Overall funnel: - `overallConversionRate` = stepCount[N] / stepCount[1] × 100 - `biggestDropOffStep` = argmax(dropOff[i→i+1]) — the step with the most visitors lost --- ## Phase 4 — Optional: Segment the Funnel If the user wants to compare funnel performance across audiences or channels, run the same step reports filtered by a dimension or segment: ``` runReport( dimensionIds: "variables/device_type", metricIds: "metrics/visitors", startDate: "", endDate: "", page: 0, limit: 10, adhocSegments: [{ /* step N filter — same structure as Phase 2 */ }] ) ``` Note: use `findDimensions` with `searchQuery: "device type mobile desktop"` to confirm the device dimension ID for the data view (often `variables/device_type`). This shows step-N visitor counts broken down by device type (or channel, or country). If one segment has a dramatically lower conversion through the worst step, that's the target for optimization. Common comparisons to suggest: - Device type (mobile vs desktop conversion often differs significantly) - Marketing channel (paid vs organic users may convert differently) - New vs returning visitors --- ## Phase 5 — Generate HTML Funnel Report Generate the funnel report inline and write to `/tmp/cja_funnel_health_check_report_.html`. ### HTML Template ```html Funnel Health Check — {ORG_NAME} — {FUNNEL_NAME}

Funnel Health Report

{ORG_NAME} Funnel Health

Step-by-step conversion for the {FUNNEL_NAME} journey across {DATE_RANGE}, with the biggest leakage point surfaced.

📅 {DATE_RANGE} 📊 {DATA_VIEW} 🕔 Prepared {GENERATED_DATE}

Funnel Summary

Entered Funnel

{STEP_1_COUNT}

Step 1 visitors

Completed Funnel

{STEP_N_COUNT}

Reached final step

Overall Conversion

{OVERALL_CVR}%

End-to-end rate

Biggest Drop-Off Step

{WORST_STEP_NAME}

▼ Worst leakage

Worst Step Drop-Off

{WORST_STEP_DROPOFF}%

Of visitors lost at this step

Funnel Visualization

Step-by-Step Analysis

▾

Step	Visitors	% of Step 1	Step Conversion	Drop-Off	Drop-Off Rate	Visual

Segment Breakdown at Worst Step

▾

Segment / Dimension	Entered Worst Step	Passed Worst Step	Conversion	vs. Average

Optimization Recommendations

▾

``` --- ## Workflow Summary 1. Parse funnel steps from user description. 2. Clarify any ambiguous steps (one question at a time). 3. Resolve page names with `searchDimensionItems`. 4. Run one `runReport` per step with ad hoc segment filter; capture visitor counts. 5. Compute step conversion rates, drop-off counts, and rates. 6. Identify the biggest drop-off step. 7. Optionally run dimension breakdown at the worst step. 8. Generate HTML report with Chart.js funnel visualization. 9. Write to `/tmp/cja_funnel_health_check_report_.html`. 10. Open with `open /tmp/cja_funnel_health_check_report_.html`. 11. Summarize inline: "Overall conversion: X%. Biggest drop-off at [Step N]: Y% of users abandon. Mobile users drop off at a 2× higher rate than desktop." --- ## Important Guardrails - **Read-only analysis.** Never modify segments, calculated metrics, or project definitions automatically. - **Confirm funnel stages before running.** Ambiguous stage definitions produce misleading results — clarify with the user first. - **Note attribution model.** Funnel conversion rates depend on the attribution model in the data view; mention it in the report. - **Flag incomplete data.** If any stage returns zero or suspiciously low counts, note possible tracking gaps before drawing conclusions. - **Cap date range.** Funnel analysis over very long date ranges (>90 days) can be slow; suggest 30-day windows as default. - **Never assume stage order.** Confirm with the user that the stages are sequential and mutually exclusive before calculating drop-off rates. ## Example Interaction > "Check the health of our checkout funnel — I want to see where people are dropping off." 1. **Setup:** Call `findDataViews`, user selects their e-commerce data view. Call `setDefaultSessionDataViewId`. 2. **Define funnel:** Ask "What are the checkout stages?" User replies: "Product View → Add to Cart → Checkout Start → Purchase." 3. **Analysis:** Run `runReport` for each stage transition over the last 30 days. Calculate drop-off rates: Product View→Cart 22%, Cart→Checkout 58%, Checkout→Purchase 71%. 4. **Findings:** Identify the Cart→Checkout step as the highest drop-off (78% fall off). Segment by device type to find mobile conversion is 40% lower than desktop. 5. **Report:** Present a funnel visualization with drop-off rates per stage, top exit segments, and 3 prioritized recommendations. ## Recommendations Logic - **Worst step drop-off > 60%**: "Critical leakage — this step is broken or the user expectation is misaligned. Prioritize UX investigation." - **Mobile drop-off > 2× desktop**: "Mobile UX at this step needs attention — consider a dedicated mobile flow or simplified form." - **Specific channel drop-off > 40% worse than average**: "Users from this channel may have mismatched intent — review landing page alignment." - **Step 1 count < 1,000**: "Funnel entry volume is too low for statistical confidence. Check the step definition or expand the date range."