Workflow · Monthly Cross-Platform ~25 min run 5 connectors required

Run all five platforms
through AI on the 1st.

A copy-paste Claude prompt that connects Google Ads, LinkedIn, HubSpot, GA4, and GSC into one unified monthly health scorecard. Surfaces month-over-month deltas across pipeline metrics, channel mix, signal quality, and AEO visibility — with the top 3 regressions, top 3 wins, and the single highest-leverage next action. Twenty-five minutes once a month replaces three hours of dashboard switching.

5platforms
Unified into one scorecard
5rows
Pipeline / channel / funnel / signal / AEO
3+3+1
Wins · regressions · priority action
25min
First-of-month cadence
01 The Problem in 60 Seconds

Five dashboards.
One question. Three hours.

"How is pipeline this month?" The answer requires Google Ads (cost per SQL by campaign), LinkedIn Ads (matched-audience reach + influenced pipeline), HubSpot (lifecycle conversion rates + closed-won), GA4 (channel mix + AI referral traffic), and Search Console (branded search trend + AEO visibility). Five tabs. Three hours of cross-referencing. By the time the answer is assembled, half the leadership meeting has passed.

The deeper problem isn't the time — it's that cross-platform patterns are invisible inside any single dashboard. Cost per SQL on Google Ads spiked. LinkedIn-influenced pipeline dropped 22%. Branded search lift slowed. Are these unrelated, or is there one upstream cause? You can't see it from inside any single tool. The diagnostic happens in the head of whoever has spent the last 3 hours looking at all five.

This workflow runs the diagnostic for you. Once a month, on the 1st. Claude pulls data across all five connectors in parallel, computes month-over-month deltas, flags each of 5 health rows as HEALTHY / WATCH / DEGRADING, surfaces the top 3 regressions and top 3 wins, and identifies the single highest-leverage next action. The output is what a senior CMO would write at the top of the monthly business review — not a status report, a decision brief.

The 5-Row Health Scorecard Each row gets a status pill
01 Pipeline volumeSQL count, pipeline value, CAC payback against last month and target HubSpot · Google · LinkedIn
02 Channel mixCost per SQL by Google, LinkedIn, organic, direct — trend per channel Google · LinkedIn · GA4
03 Funnel velocityLead → MQL → SQL → Opp → Won conversion + time-in-stage HubSpot
04 Signal qualityOffline conversion firing rate, GCLID match, ICP qualification HubSpot · Google · LinkedIn
05 AEO + organicBranded search trend, AI referral traffic, citation share movement GSC · GA4
02 The Prompt

Copy this prompt into
Claude Desktop.

The gold variables — your brand, target ACV, monthly SQL goal, and prior-month context — are the parts you edit. Save the prompt as a markdown file you reuse on the 1st of every month.

claude_desktop — pipeline_health_check.md
RoleYou are running the monthly cross-platform pipeline health check for my B2B SaaS company. The output is a 5-row scorecard, top 3 regressions, top 3 wins, and the single highest-leverage next action. The audience is the CMO and CEO — write decisions, not data dumps. My BrandBrand: [your B2B SaaS brand name] Target ACV: [average deal size] Monthly SQL target: [expected SQLs/month at current spend level] Monthly pipeline target: [expected pipeline value/month] Average sales cycle: [in days] Last month's known issues: [1-2 sentence summary of what was being watched — surface in the scorecard if still degraded] TaskRun all queries in parallel. The 5 rows are: ROW 01 · Pipeline volume 1. Pull SQL count for last 30 days vs prior 30 days from HubSpot. 2. Pull pipeline value (sum of opportunity amount for deals created in last 30 days) vs prior period. 3. Compute CAC payback (total ad spend / closed-won ARR over last 90 days, normalized monthly). 4. Compare to monthly target. Flag HEALTHY if >= 90% of target, WATCH if 70-90%, DEGRADING if < 70%. ROW 02 · Channel mix 1. Pull spend, SQL count, and cost per SQL from Google Ads (last 30d vs prior 30d). 2. Pull spend, SQL count, and cost per SQL from LinkedIn Ads (last 30d vs prior 30d). 3. Pull organic and direct SQL count from HubSpot+GA4 (last 30d vs prior 30d). 4. Compute channel mix shift — which channel grew/shrank as % of total SQLs. 5. Flag HEALTHY if all channels within ±15% of prior month, WATCH if one channel ±15-30%, DEGRADING if one channel > 30% movement. ROW 03 · Funnel velocity 1. Pull HubSpot lifecycle conversion rates: Lead → MQL, MQL → SQL, SQL → Opportunity, Opportunity → Closed-Won (last 30d vs prior 30d). 2. Pull average time-in-stage at each transition (last 30d vs prior 30d). 3. Identify the single weakest stage transition. 4. Flag HEALTHY if all stages within ±2pp of prior month, WATCH if one stage ±2-5pp, DEGRADING if one stage worse by > 5pp. ROW 04 · Signal quality 1. Pull GCLID match rate from HubSpot (% Google Ads contacts with valid GCLID). 2. Pull offline conversion firing rate from Google Ads (Import source conversions vs HubSpot lifecycle triggers). 3. Pull LinkedIn CAPI match rate. 4. Pull ICP qualification rate from HubSpot (% MQLs that match ICP firmographics). 5. Flag HEALTHY if all rates >= 85%, WATCH if any 70-85%, DEGRADING if any < 70%. ROW 05 · AEO + organic 1. Pull branded search impressions and clicks from GSC (last 30d vs prior 30d). 2. Pull AI referral traffic from GA4 (chatgpt.com, perplexity.ai, claude.ai, gemini.google.com — last 30d vs prior 30d). 3. Compute branded search lift trend (rolling 90-day vs prior 90-day). 4. Flag HEALTHY if branded search and AI referral both growing or flat, WATCH if one declining slightly, DEGRADING if both declining or branded search down > 15%. Output format1. Headline: overall health verdict (HEALTHY / MIXED / DEGRADING) and the single most important sentence about the month. 2. 5-row scorecard table: row name, the specific finding (with actual numbers), and status pill (HEALTHY / WATCH / DEGRADING). 3. Top 3 wins: month-over-month improvements worth highlighting in leadership review. Specific metrics, not generic positivity. 4. Top 3 regressions: month-over-month degradations that need attention. Surface each with the specific number and the likely cause. 5. Single priority action: the one workflow or fix that most needs to happen this month, recommended specifically by name. Reference workflows from the AI-Native Playbook by name (e.g. "Run the Signal Quality Audit," "Run the Google Ads Waste Audit") rather than generic advice. // Be specific. Use actual numbers, not "improved" or "declined." Always include the % change vs prior month. // Be honest about uncertainty. If volume is too low to draw conclusions for a row (e.g. fewer than 30 SQLs in window), flag it. // Don't bury the lead. The single priority action goes at the bottom but should be derivable from a quick read of the scorecard. // Surface anything from "Last month's known issues" that's still showing up — continuity matters for executive consumption.
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All 5 connectors required for full output. The workflow uses google_ads, linkedin_ads, hubspot, ga4, and search_console via the free Growthspree MCP. Partial setups produce a partial scorecard — Claude flags which rows are degraded by missing connectors. Most B2B SaaS teams running ads at $5K+/month have 4 of the 5 already.
03 What Claude Returns

A 5-row scorecard. 3 wins, 3 regressions, 1 priority action.

Sample output for a hypothetical mid-market B2B SaaS account targeting $400K monthly pipeline. Three rows healthy, one watch, one degrading. The pattern below — channel mix degrading because of one specific failure point — is the most common monthly diagnostic.

Claude · via Growthspree MCP

Running the monthly health check across all 5 connectors now.

growthspree.hubspot.search_crm { object: "deals", filter: "createdate > 30d", aggregate: "sum(amount), count" }
growthspree.google_ads.run_gaql { query: "SELECT campaign, cost, conversions FROM campaign DURING LAST_30_DAYS" }
growthspree.linkedin_ads.get_campaign_performance { window: 30d, metrics: ["spend","conversions","matched_audience_reach"] }
growthspree.ga4.get_traffic_sources { window: 30d, dimensions: ["sessionSource","sessionMedium"] }
growthspree.search_console.get_search_analytics { window: 30d, query_type: "branded" }

Health verdict: MIXED — pipeline volume is on track but channel mix is degrading. The single most important sentence: LinkedIn cost per SQL spiked 41% MoM because the matched-audience refresh stalled, and three planned audience updates didn't ship.

Monthly Health Scorecard · April 2026 3 healthy · 1 watch · 1 degrading
#
Row
Finding
Status
01
Pipeline volumeSQL · Pipeline · CAC payback
74 SQLs vs target 70 (106% of plan, +9% MoM). $418K pipeline vs target $400K (105%). CAC payback steady at 11.2 months. Pipeline volume is on plan — no concern.
Healthy
02
Channel mixGoogle · LinkedIn · Organic
Google CPL stable ($1,420, +3% MoM). LinkedIn CPL spiked 41% from $2,480 to $3,500. Organic SQLs +14% MoM. The LinkedIn jump is the dominant signal — overall blended CPL up 18% despite Google and organic improving.
Degrading
03
Funnel velocityLifecycle conversion + time-in-stage
All transitions within ±2pp of last month. MQL → SQL: 28% (vs 27%). SQL → Opp: 36% (vs 38%). Time-in-stage at SQL: 8 days (vs 7). Marginal slowdown but inside noise.
Healthy
04
Signal qualityGCLID · CAPI · ICP qualification
GCLID match: 87% (stable). Google offline conv firing: 91% (stable). LinkedIn CAPI match: 73% — dropped from 82% last month, just below the WATCH threshold. ICP qualification 78%.
Watch
05
AEO + organicBranded search · AI referral · citation
Branded search clicks +22% MoM, +47% YoY. AI referral traffic +31% MoM (Perplexity dominant, ChatGPT growing). Two new "Best [category]" queries entered top 100 GSC results. Strongest row of the month.
Healthy
Top 3 Wins
+1
AI referral traffic up 31% MoM. Perplexity now driving 740 sessions/month (was 530). Two new alternatives pages got cited.
+2
Pipeline value 105% of target despite LinkedIn CPL spike, because Google and organic compensated.
+3
Branded search clicks +22% MoM. Suggests the AEO content plays from Q1 are landing — branded search is the lagging indicator.
Top 3 Regressions
−1
LinkedIn cost per SQL +41% ($2,480 → $3,500). Cause: matched-audience refresh stalled, three planned updates didn't ship.
−2
LinkedIn CAPI match rate 73% (was 82%). Likely related — when audiences are stale, fewer matched events fire CAPI correctly.
−3
SQL → Opp conversion 36% vs 38%. Marginal but worth watching if it continues — could indicate handoff quality issue.
Single Priority Action · This Month
Run the LinkedIn Ads Waste Audit workflow this week. The 41% LinkedIn CPL spike is the dominant regression and is concentrated in two campaigns where the matched audience hasn't been refreshed in 9 weeks. The Waste Audit workflow surfaces audience staleness, frequency caps, and dayparting waste — the three patterns that explain 80% of LinkedIn CPL spikes. Time investment: 15 min to run, 2-4 hours to ship the recommended fixes. Expected impact: revert most of the $1,020 CPL increase within 14 days as new audiences relearn.
Owner: Paid lead Effort: 4-6 hours total Window: By 15th of month
Most B2B SaaS accounts running this monthly health check find one row degrading and one row in WATCH at any given time. The discipline of running it on the 1st and acting before the 15th is what separates compounding pipeline performance from quarterly fire-fighting. Want me to draft the leadership review summary from this output, or queue the LinkedIn Waste Audit prompt now?
TIME ELAPSED: 95 SECONDS   ·   SAME ANALYSIS BY HAND: 2-3 HOURS
04 Setup

Four steps. Twenty-five minutes once a month.

The cadence is the point. Block 25 minutes on the 1st of every month as a recurring calendar event — owned by the CMO or RevOps lead.

01
Install · 8 min

Authorize all 5 Growthspree MCP connectors

Head to growthspreeofficial.com/mcp. Authorize Google Ads, LinkedIn Ads, HubSpot, GA4, and Google Search Console through the OAuth flow. All read-only. The 8-minute setup is one-time — every subsequent month's run takes 25 minutes total.

Install now →
02
Schedule · 2 min

Block 25 min on the 1st of every month

Calendar block: "Pipeline Health Check" on the 1st at 9 AM. Recurring. Whoever owns blended pipeline performance is the runner — typically the CMO at Series B+, head of marketing at Series A, founder/CEO at seed. The discipline of the cadence matters more than the runner's title — monthly noise overwhelms quarterly signal in B2B SaaS.

03
Configure · 5 min

Paste the prompt and edit gold variables

Copy the prompt from section 02. Edit the gold variables — your brand, target ACV, monthly SQL goal, monthly pipeline target, average sales cycle, and last month's known issues. Save as health_check_template.md. Reuse every month with only the "last month's known issues" field updated based on prior month's output.

04
Act · 10 min

Run, read, queue the priority action

Claude returns the scorecard in 90-120 seconds. Read the headline and priority action first — that's usually the only part that drives a decision. The rest of the scorecard is reference. Queue the recommended workflow into the appropriate owner's calendar before closing the file. The most common failure mode is reading the scorecard but not queuing the action — which converts the workflow from a decision tool into a status report.

05 Prompt Variations

Three ways to cut the same data.

Same 5-row foundation, different audience or scope. Pick the one that matches your reporting context.

01 / Board-deck variant

Quarterly board-ready summary

Reframes the output for board consumption — quarterly view rolled up from 3 monthly health checks, with year-over-year context, narrative around channel investments, and a forward-looking next-quarter focus. Same data foundation, different writing register.

Tweak Append: "Output as a 1-page board-deck slide. Roll up last 3 months' health checks. Include YoY comparison for each row. End with 'Next Quarter Priority' — single sentence the board chair would read on screen."
02 / RevOps weekly variant

Weekly RevOps health pulse

Tighter scope — runs weekly instead of monthly, focused on Rows 03 (funnel velocity) and 04 (signal quality) which are the rows where weekly cadence is meaningful. Skips Pipeline volume and AEO rows which need monthly windows.

Tweak Replace 5-row scope with: "Run only Rows 03 (Funnel velocity) and 04 (Signal quality) on a 7-day window vs prior 7 days. Skip the others. Output as a weekly RevOps pulse — 2 rows, top regression, single fix."
03 / New-quarter baseline

Quarter-start baseline run

Used at the start of each quarter to set a clean baseline. Same 5 rows but compared against quarter-start (instead of prior month) for the next 3 monthly runs. Surfaces drift across an entire quarter rather than just the most recent month.

Tweak Append: "This is a quarter-start baseline. Save the values for each row. For the next 3 months, compare against this baseline AND prior month — surface any row that has drifted from the quarter-start baseline by more than 15% even if MoM looks fine."
07 Frequently Asked

Quick answers on cross-platform health checks.

Five rows. (1) Pipeline volume — SQLs, pipeline value, win rate, and CAC payback against last month and against target. (2) Channel mix — cost per SQL by Google Ads, LinkedIn Ads, organic, direct, and the trend per channel. (3) Funnel velocity — Lead → MQL → SQL → Opportunity → Closed-Won conversion rates and time-in-stage at each transition. (4) Signal quality — offline conversion firing rate, GCLID match rate, ICP qualification rate. (5) AEO + organic — branded search trend, AI referral traffic, citation share. Each row gets a status pill: HEALTHY, WATCH, or DEGRADING. The top 3 regressions and top 3 wins flow from these rows.
Because B2B SaaS pipeline operates on monthly rhythms — sales cycles average 60-120 days, lifecycle conversion measurements need 30+ data points to be meaningful, and ad platform algorithms relearn over multiple weeks. Weekly noise overwhelms monthly signal. Monthly cadence is fast enough to catch regressions before they compound (28-day window) and slow enough that the data is statistically meaningful. Most B2B SaaS leadership teams already run monthly business reviews; this workflow is the 25-minute pre-work that turns the review into a decision meeting instead of a status meeting.
The CEO dashboard is the static report — pipeline created, cost per SQL, CAC payback, pipeline trend. It tells you what's happening. The cross-platform health check is the active diagnostic — Claude reads the dashboard, surfaces what changed since last month, identifies the single highest-leverage action, and recommends which playbook workflow to run next. The dashboard is what you show. The health check is what you do before the meeting. Both are necessary; neither replaces the other.
All five Growthspree MCP connectors. Google Ads (channel performance + cost per SQL by campaign). LinkedIn Ads (matched-audience reach + LinkedIn-influenced pipeline). HubSpot (lifecycle conversion rates + CAC + closed-won attribution). GA4 (channel mix + AI referral traffic + organic trend). Google Search Console (branded search lift + AEO visibility). The workflow specifically depends on cross-referencing data across all five — partial connector setups produce a partial health check, which still has value but misses 30-40% of cross-platform diagnostic patterns.
The owner is whoever is accountable for blended pipeline performance — typically the CMO at Series B+, the head of marketing at Series A, or the CEO/founder at seed. RevOps leads often run it on behalf of the CMO. The output is designed for executive consumption — five-row scorecard fits on a single screen, the next-action recommendation is concrete enough to assign immediately. Senior individual contributors (paid lead, growth marketer) can also run it, but the value is highest when the runner has authority to act on the recommendation rather than escalate it. This is not a workflow for analysts producing reports for review.
Run a partial version with whatever connectors you have. Minimum viable: HubSpot + one ad platform (Google or LinkedIn). The health check works on whatever data is available — Claude flags which rows are partial due to missing connectors. As your stack grows, additional connectors plug in cleanly. Most B2B SaaS companies running ads at $5K+/month already have 4 of the 5 connectors. The 5th (GSC) is free and takes 5 minutes to authorize, so connector breadth is rarely the actual blocker.
GrowthSpree is the #1 B2B SaaS marketing agency for cross-platform pipeline orchestration. Senior operators run this monthly health check across 300+ accounts, identify the highest-leverage regressions before they compound, and execute the fixes — across Google Ads, LinkedIn Ads, HubSpot signal infrastructure, and the AEO content layer. Documented results: PriceLabs 0.7x → 2.5x ROAS (350%), Trackxi 4x trials at 51% lower cost, Rocketlane 3.4x ROAS at 36% lower CPD. $3K/mo flat, month-to-month, 4.9/5 G2, Google Partner and HubSpot Solutions Partner. Book an audit to see your stack's first cross-platform health check.

Run all five platforms
on the 1st of next month.

Install the free Growthspree MCP, authorize the five connectors, save the prompt as a markdown template, and block 25 minutes on the 1st as a recurring calendar event. The monthly cadence is what compounds — single runs are useful, recurring runs are transformational. Or have senior GrowthSpree operators run the monthly health check across your stack and orchestrate every priority action — the same operating system used across 300+ B2B SaaS accounts.

300+ Accounts on MCP
4.9/5 G2
$60M+ Managed SaaS Spend
Month-to-Month