# Aa Segment Performance Comparator > Compares the performance of two or more audience segments across key metrics side by side, using Adobe Analytics data to determine winners, losers, and spreads for each metric. - Skill: `adobe/aa-segment-performance-comparator` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add adobe/aa-segment-performance-comparator` - Raw SKILL.md: https://api.skillmd.com/api/skills/adobe/aa-segment-performance-comparator/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics, Marketing & Growth, Data Analysis - Tags: Adobe Analytics, Audience Analysis, Html Report, Reporting, Segment Comparison - License: Apache-2.0 - Author: Adobe (https://skillmd.com/u/adobe), verified publisher - Updated: 2026-07-06 - Page: https://skillmd.com/skills/adobe/aa-segment-performance-comparator --- # Segment Performance Comparator (Adobe Analytics) Compare the performance of two or more audience segments across key metrics side by side to understand how different visitor groups behave. Uses direct segment-vs-segment comparison to determine a winner, loser, and spread for each metric, with a separate context panel showing segment sizing. > **AA Call Budget:** AA's `runReport` accepts a single `segmentId` per call. > For N segments × M metrics the comparison requires N×M calls, plus 1 > baseline call for the segment-size context panel. For 3 segments × 5 > metrics = 16 calls. Limit to 4 segments and 6 metrics for practical > performance. Always confirm the segment/metric list with the user before > starting. --- ## AA MCP Tools Used - `findReportSuites` — select report suite - `setSessionDefaults` — set session context (reportSuiteId + globalCompanyId) - `findSegments` — discover and select comparison segments - `findMetrics` — resolve metric IDs - `runReport` — one call per segment per metric, plus one unsegmented call for sizing context --- ## Phase 0 — Setup 1. Confirm report suite with `findReportSuites` / `setSessionDefaults`. ``` findReportSuites(globalCompanyId: "", page: 0, limit: 10) setSessionDefaults(globalCompanyId: "", reportSuiteId: "") ``` --- ## Phase 1 — Select Segments Ask the user which segments to compare. If not specified, prompt: > "Which visitor audiences would you like to compare? For example: > Mobile vs. Desktop, New vs. Returning, Paid Search vs. Organic, or > specific named segments from your library." Search for and confirm each segment: ``` findSegments(page: 0, limit: 50) # Filter locally by name. Built-in IDs: "Paid_Search", "Purchasers", "Return_Visits" ``` > **Note:** `findSegments` does not accept a `searchTerm` parameter. Retrieve all segments > and filter by name locally. Built-in template segments have short IDs like "Paid_Search" > that can be passed directly as `segmentIds` in `runReport`. If the user requests a segment that doesn't exist by name, offer to build it first using the aa-segment-builder skill, or suggest the closest existing segment from search results. Limit: 4 segments maximum per comparison. Advise this limit upfront. --- ## Phase 2 — Select Metrics Ask the user which metrics to compare. Suggest a balanced mix: - **Volume:** `metrics/visits` - **Engagement:** `metrics/pageviews`, `metrics/bouncerate`, `metrics/pagespervisit` - **Conversion:** `metrics/orders`, conversion rate calculated metric - **Revenue:** `metrics/revenue` Call `findMetrics` to resolve each metric ID: ``` findMetrics(expansions: "componentType,categories", page: 0, limit: 200) # Filter locally by name. Key IDs: metrics/visits, metrics/revenue, metrics/orders, metrics/bouncerate ``` Limit: 6 metrics maximum. Confirm the final list with the user: > "I'll compare these 3 segments across 5 metrics. This requires 16 report > calls (3 segments × 5 metrics + 1 sizing call). OK to proceed?" --- ## Phase 3 — Select Date Range Ask for or confirm the analysis period: - Last 7 days (good for quick comparison) - Last 30 days (recommended default) - Last 90 days (for seasonal smoothing) - Custom range --- ## Phase 4 — Run Comparison Reports ### 4.1 Segment sizing (context only) Run a single unsegmented call for `metrics/visits` to get the total population size, then one call per segment for `metrics/visits` to compute each segment's share of total. These sizing values populate the context panel — they are **not** used in the comparison matrix. ``` runReport( dimensionId: "variables/page", metricIds: "metrics/visits", startDate: "", endDate: "", limit: 1 ) # allVisitorVisits = summaryData.totals[0] ``` ``` runReport( dimensionId: "variables/page", metricIds: "metrics/visits", segmentIds: "", startDate: "", endDate: "", limit: 1 ) # segmentVisits = summaryData.totals[0]; shareOfTotal = segmentVisits / allVisitorVisits × 100 ``` > Reuse these results if `metrics/visits` is already a comparison metric. ### 4.2 Per segment per metric For each segment × metric combination: ``` runReport( dimensionId: "variables/page", metricIds: "", # note: "metricIds" not "metricId" segmentIds: "", # note: "segmentIds" not "segmentId" startDate: "", endDate: "", limit: 1 ) # Total = summaryData.totals[0] ``` > Read totals from `summaryData.totals[0]` (not `rows[]`). `dimensionId` is required — use any dimension with `limit: 1` for aggregate totals. Segment IDs are the raw `id` field from `findSegments`. Track progress: "Fetching Segment 2 of 3, metric 3 of 5..." --- ## Phase 5 — Build the Comparison Matrix The matrix compares segments directly to each other — no baseline column. For each metric row, compute: | Computed Value | Formula | |---|---| | Segment value | Raw from `runReport` | | Winner | Segment with the best value for this metric | | Loser | Segment with the worst value for this metric | | Spread | (max − min) / max × 100 | | Significant? | `true` if spread > 10% | For metrics where lower is better (bounce rate, cost per acquisition), invert the winner/loser logic — the segment with the **lowest** value wins. Mark these metrics clearly in the report. ### 5.1 Segment profile summary For each segment, compute an overall performance profile: - **Wins:** count of metrics where this segment ranks #1 - **Losses:** count of metrics where this segment ranks last - **Biggest edge:** metric where this segment outperforms others by the widest spread - **Visits share:** percentage of total visits from the context panel --- ## Phase 6 — Generate HTML Comparison Report Build the comparison report inline and write to `/tmp/aa_segment_comparator_report_.html`. ### HTML template Read [`template.html`](template.html) and use it verbatim. Do not improvise the HTML structure or CSS — only fill in the `{PLACEHOLDER}` tokens (`{ORG_NAME}`, `{DATE_RANGE}`, `{REPORT_SUITE}`, `{GENERATED_DATE}`, `{SEGMENT_NAMES_SUMMARY}`, `{SEGMENT_NAME}`, `{COLOR}`, `{VISITOR_COUNT}`, `{NUM_SEGMENTS}`, `{NUM_METRICS}`, `{NUM_SIGNIFICANT}`, `{OVERALL_WINNER}`, `{METRIC_NAME}`, `{VALUE}`, `{WINNER_SEGMENT}`, `{SPREAD}`, `{INSIGHT_TEXT}`) and repeat segment chips, matrix rows, and insight boxes once per data item. Use the `cell-winner` / `cell-loser` classes per Phase 5 winner/loser rules. **Section titles — no phase prefix**: Section headings in the HTML report must **not** include the phase number. Use the plain section name only (e.g., "Segment Comparison" not "Phase 2 — Segment Comparison", "Metric Details" not "Phase 3 — Metric Details"). Write to `/tmp/aa_segment_comparator_report_.html` and open: ```bash open /tmp/aa_segment_comparator_report_.html ``` --- ## Inline Summary (Always Deliver) Always follow the HTML report with a text summary: ``` Segment Comparison — [Date Range] | Report Suite: [Name] Segment Context: Mobile 48,200 visits (38.7%) Desktop 72,400 (58.2%) Mobile Desktop Winner Spread ──────────────── ─────── ──────── ───────── ────── Visits 48,200 72,400 Desktop 33% Bounce Rate 61.4% 40.1% ✓ Desktop 35% ✦ Conversion Rate 1.2% 3.1% ✓ Desktop 61% ✦ Revenue $9,400 $31,200 Desktop 70% ✦ ✦ = spread > 10% ✓ = winner Key findings: - Desktop converts 2.6× better (3.1% vs 1.2%). Prioritize mobile checkout. - Paid Search (not shown) has highest CVR at 4.8% — most efficient channel. ``` --- ## Guardrails - Confirm segments and metrics with the user before starting — the call count is N×M and can grow quickly. - For bounce rate and other "lower is better" metrics, invert the winner logic — the segment with the **lowest** value wins. Label these metrics clearly in the report (e.g., "↓ lower is better"). - If a segment returns very few visits (< 1,000), note that results may not be statistically reliable. - Do **not** show baseline delta percentages (segment vs. All Visitors) in the comparison matrix. Segments are subsets of the total population, so count-metric deltas are always negative and misleading. Use the context panel for segment sizing instead. --- ## Example Interaction > "Compare our mobile and desktop visitors on conversion metrics." 1. Confirm report suite. 2. Find segments: "Mobile Devices" and "Desktop" (or offer to create them). 3. Confirm metrics: visits, bounce rate, orders, conversion rate, revenue. 4. Date range: last 30 days. 5. Preview: "2 segments × 5 metrics + 1 sizing call = 11 reports. Proceed?" 6. Run all reports; announce progress. 7. Context: Mobile = 48.2k visits (38.7%), Desktop = 72.4k visits (58.2%). 8. Matrix: Desktop wins 3 of 5 metrics. Conversion rate spread 61%. 9. Generate HTML report and open. 10. Insight: "Desktop is your primary conversion engine. Mobile drives volume (39% of visits) but converts at 1.2% vs. Desktop's 3.1% — a 61% spread. Prioritize mobile checkout optimization for the biggest conversion lift opportunity."