Attribution & Channels
True channel performance using Sealmetrics last-click data — no GA4 modeling, no sampling.
MCPs required: Sealmetrics MCP
SEAL-021 — Channel revenue ranking, last 90 days
Using Sealmetrics MCP for site {site_id}, return the channel ranking by real revenue for the last 90 days.
Group by utm_source / utm_medium pair (or by Sealmetrics channel rule if more meaningful). For each row: channel, entrances, conversions, conversion rate, revenue, revenue per session, % of total revenue, 90-day vs prior-90-day delta.
Sort by revenue desc. Highlight the top 3 growing channels and the top 3 shrinking channels.
End with one-line recommendations for budget reallocation.
SEAL-022 — Best and worst utm_source by CR
For site {site_id}, query Sealmetrics MCP for the last 60 days.
For every utm_source with at least 500 entrances:
- Entrances, conversions, CR, revenue, AOV, bounce rate, % bot.
Rank from highest CR to lowest. Split the list into 3 tiers: top, middle, bottom.
For the top tier, recommend which to scale (more budget, more landing variants).
For the bottom tier, recommend which to pause and which to fix (label each: pause / fix).
SEAL-023 — Best and worst utm_term keywords
For site {site_id}, query Sealmetrics MCP for the last 90 days.
Top 10 utm_term values by conversions (best performers): term, entrances, conversions, CR, revenue.
Bottom 10 utm_term values among those with at least 200 entrances and 0 or near-zero conversions (worst performers): same columns.
For each bottom-10 term suggest: pause, change match type, or rewrite the landing page that receives most of its traffic.
SEAL-024 — Zero-conversion campaigns to pause
Using Sealmetrics MCP for site {site_id}, list every utm_campaign with more than 1000 entrances in the last 30 days and zero macro conversions.
For each: campaign name, source, medium, entrances, micro-conversions, top landing pages, average bounce rate.
Output a "candidates to pause" table. For each candidate, indicate whether to "pause immediately" (no micros either) or "investigate landing fit" (micros present but no macros).
SEAL-025 — Campaign cannibalization detector
For site {site_id}, query Sealmetrics MCP for the last 60 days.
Find every pair of utm_campaigns that share more than 60% of the same landing pages in their traffic distribution.
For each pair return: campaign A, campaign B, % of landings shared, entrances each, conversions each, plausible cannibalization risk (high/medium/low based on overlap and source-medium).
End with: which campaign in each pair should be kept, which should be paused or repositioned.
SEAL-026 — Direct traffic spike investigation
Using Sealmetrics MCP for site {site_id}, check whether direct traffic (utm_source = direct or null) increased by more than 25% week over week.
If yes:
1. List the top 20 landing pages receiving the extra direct traffic.
2. For each, pull the top referrers and the top browser and device families.
3. Detect dark social patterns (Slack, Discord, internal tools) or possible UTM stripping.
Output: "Yes/No spike" verdict + investigation table + 3 hypotheses for what is generating it.
SEAL-027 — Channel decay month over month
For site {site_id}, query Sealmetrics MCP and compare entrances and conversions by channel for current month vs previous month.
List channels where entrances dropped more than 20% MoM. For each, pull the top 5 utm_campaigns responsible for the decline.
Output: 1 main "decay" table + a nested list of campaigns to investigate per channel. End with a 3-bullet executive summary.
See also
Was this page helpful?