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 suitesetSessionDefaults — set session context (reportSuiteId + globalCompanyId)findSegments — discover and select comparison segmentsfindMetrics — resolve metric IDsrunReport — one call per segment per metric, plus one unsegmented call for sizing context
Phase 0 — Setup
- Confirm report suite with
findReportSuites / setSessionDefaults.
findReportSuites(globalCompanyId: "<gcid>", page: 0, limit: 10)
setSessionDefaults(globalCompanyId: "<gcid>", reportSuiteId: "<rsid>")
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: "<start>",
endDate: "<end>",
limit: 1
)
# allVisitorVisits = summaryData.totals[0]
runReport(
dimensionId: "variables/page",
metricIds: "metrics/visits",
segmentIds: "<segmentId>",
startDate: "<start>",
endDate: "<end>",
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: "<metricId>", # note: "metricIds" not "metricId"
segmentIds: "<segmentId>", # note: "segmentIds" not "segmentId"
startDate: "<start>",
endDate: "<end>",
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_<YYYY-MM-DD_HHMMSS>.html.
HTML template
Read 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_<YYYY-MM-DD_HHMMSS>.html and open:
open /tmp/aa_segment_comparator_report_<YYYY-MM-DD_HHMMSS>.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."
- Confirm report suite.
- Find segments: "Mobile Devices" and "Desktop" (or offer to create them).
- Confirm metrics: visits, bounce rate, orders, conversion rate, revenue.
- Date range: last 30 days.
- Preview: "2 segments × 5 metrics + 1 sizing call = 11 reports. Proceed?"
- Run all reports; announce progress.
- Context: Mobile = 48.2k visits (38.7%), Desktop = 72.4k visits (58.2%).
- Matrix: Desktop wins 3 of 5 metrics. Conversion rate spread 61%.
- Generate HTML report and open.
- 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."