Workflow · Strategic ~30 min run MCP + offline conversions required

Stage frameworks tell you
what others spend.

A copy-paste Claude prompt that pulls 90-day pipeline contribution per channel × audience × creative from MCP and produces an executable reallocation memo — current → recommended dollar deltas, projected pipeline impact per move, 30-60-90 day sequencing. The strategic companion to Cross-Platform Pipeline Health Check.

3clusters
Channel · Audience · Creative
15-25%shift
Typical reallocation magnitude per quarter
Quarterlycadence
Aligned with sales cycle pipeline maturity
90day data
Minimum lookback for stable analysis
01 The Problem in 60 Seconds

Stage averages don't fit your account.
Your data does.

A B2B SaaS marketing leader reads "Series B should allocate 30% Google, 35% LinkedIn, 15% Meta, 20% other" in an industry framework. They follow it. Their account ends up with 30% Google, 35% LinkedIn, 15% Meta, 20% other. Six months later, they're underperforming pipeline targets. They re-read the framework, conclude they're executing it correctly, and hire a new agency. The actual problem: their account's pipeline-per-dollar by channel diverged from the stage average by 40-60%. Their LinkedIn audience converts 2.3x better than the stage benchmark; their Google search converts 0.6x of stage benchmark. Stage frameworks tell category leaders' average allocation. Your account isn't average — it has its own deal economics, ICP fit, and channel-to-pipeline relationships. Optimizing toward the average actively harms accounts whose performance diverges.

The deeper problem is that most B2B SaaS budget conversations happen at the channel level when the leverage is at the audience and creative level. Channel-level reallocation ("shift $5K from Google to LinkedIn") moves the largest dollar amounts but addresses the lowest-resolution decisions. The actual pipeline-per-dollar gaps live deeper — within Google, "Search · Brand Defense" might be 4x more efficient than "PMax · Top of Funnel"; within LinkedIn, "T1-DM cold" might be 3x more efficient than "T1-Researcher cold." Without addressing audience-level and creative-level allocation, channel-level reallocation moves money to the wrong sub-segments inside the better-performing channel.

This workflow runs the full reallocation pass. Claude pulls 90-day pipeline contribution data per channel × audience × creative from MCP, computes pipeline-per-dollar by cluster, identifies underfunded high-ROI cells and overfunded low-ROI cells, and produces a sequenced 30-60-90 day reallocation memo. Run quarterly. Pairs with Cross-Platform Pipeline Health Check (monthly measurement) — health check measures trajectory; allocator translates trajectory into resource decisions.

The 3 Allocation Clusters · Different Resolutions, Different Timing Sequenced by deployment speed
01 Channel-level (Google vs LinkedIn vs Meta)Pipeline-per-dollar by channel, accounting for sales cycle differences. The largest reallocation moves typically happen here. Channel-level shifts can ship same-week (budget changes are immediate). Algorithm re-stabilization takes 14-21 days. Same week
02 Audience-level within each channele.g. on LinkedIn: T1-DM cold vs T1-Influencer cold vs retargeting. Pipeline-per-dollar by audience, surfacing underfunded high-ROI segments. Audience setup + budget split takes 2-4 weeks; learning re-stabilization 21-30 days. 2-4 weeks
03 Creative-level within each audiencee.g. demo carousel vs case study video vs thought leader text. Pipeline-per-dollar by creative, surfacing underfunded high-performing creatives. Creative production cadence is the constraint — covered by Track 04's Creative Fatigue Tracker. 4-8 weeks
02 The Prompt

Copy this prompt into
Claude Desktop.

The gold variables — your brand, total monthly budget, ACV, target pipeline contribution per dollar — are the parts you edit. Run quarterly with the same methodology so cycle-over-cycle deltas are comparable.

claude_desktop — channel_mix_allocator.md
RoleYou are running the quarterly Channel Mix Budget Allocator for my B2B SaaS company. Pull 90-day pipeline contribution per channel × audience × creative from MCP. Produce an executable reallocation memo — current → recommended dollar deltas, projected pipeline impact per move, 30-60-90 day sequencing. My BrandBrand: [your B2B SaaS brand name] Total monthly paid budget: [e.g. "$45K/mo across all channels"] Average ACV: [e.g. "$25K mid-market"] Average sales cycle: [e.g. "84 days form-fill to close"] Target pipeline-per-dollar: [e.g. "5x within 90 days"] Required Prerequisites// Verify these are configured before running. Without offline conversions, the analysis runs on form-fill data which produces wrong reallocation decisions. Offline conversions configured: [Yes/No — Google offline conversions + LinkedIn CAPI must both be live] HubSpot pipeline stage history: [Yes/No — need 90+ days of MQL → SQL → Opp → Closed-won data] Lookback window: [recommended 90 days; 60 days minimum for new accounts] Task1. Pull 90-day pipeline contribution data: - For each channel (Google / LinkedIn / Meta / Other): spend, MQLs, SQLs, Opps, Closed-won, pipeline value - For each audience within each channel: same metrics - For each creative within each audience: same metrics - Calculate pipeline-per-dollar = pipeline value / spend at each cluster level 2. Cluster 1 — Channel-level analysis: - Compute pipeline-per-dollar per channel - Identify channels meaningfully above account average (over-performing, candidates for budget increase) - Identify channels meaningfully below account average (under-performing, candidates for budget decrease) - Calculate proposed dollar shift per channel - Project pipeline impact: spend_shift × pipeline-per-dollar of receiving channel 3. Cluster 2 — Audience-level analysis (for top 2 channels by spend): - Within each channel, compute pipeline-per-dollar per audience - Identify under-allocated high-ROI audiences (audience pipeline-per-dollar > channel average) - Identify over-allocated low-ROI audiences (audience pipeline-per-dollar < channel average) - Calculate audience-level shifts within channel after channel-level shifts settle 4. Cluster 3 — Creative-level analysis: - Pull from linkedin_creative_fatigue_tracker output if available; otherwise pull per-creative metrics directly - Identify creatives where pipeline-per-dollar diverges meaningfully from audience average - Map to creative refresh queue (handoff to Creative Fatigue Tracker for production) 5. Build sequenced 30-60-90 day reallocation memo: - Tier 1 · This week: channel-level shifts (immediate budget changes; algorithm re-stabilization 14-21 days) - Tier 2 · 30-60 days: audience-level shifts within channel (audience setup + learning re-stabilization) - Tier 3 · 60-90 days: creative-level rebalancing (creative production cadence dependency) 6. For each shift, project pipeline impact: - Conservative estimate: shift_amount × current pipeline-per-dollar of receiving channel × 0.7 (penalty for receiving channel needing scale-stage learning) - Aggressive estimate: shift_amount × current pipeline-per-dollar of receiving channel × 1.0 (no penalty if shift is below 30% of current channel size) - Use conservative for memo; aggressive only on shifts < 15% of receiving channel's current spend Output format1. Headline: total budget, current allocation summary, total proposed reallocation magnitude, projected pipeline impact (90-day). 2. Cluster 1 reallocation table: 1 row per channel. Columns: channel + current spend / new spend / delta / rationale. 3. Cluster 2 reallocation table: top under-allocated and over-allocated audiences within top 2 channels. 4. 30-60-90 day sequencing memo: Tier 1 (week), Tier 2 (30-60 days), Tier 3 (60-90 days) with specific actions per tier and projected pipeline impact per tier. 5. Honest calibration: - If reallocation magnitude is > 30% of total budget, flag as "verify before executing" — large shifts usually indicate either tracking issues (offline conversions broken, attribution model mismatch) or major operational changes (new product launch, ICP shift). Don't execute these without operator review. - If recommended channel shift is from a channel currently below LinkedIn's $5K minimum effective budget, flag — going below the minimum produces no pipeline regardless of the math. - If a channel has < 30 days of pipeline data, exclude from reallocation. New channels need observation, not reallocation. - If pipeline contribution is heavily concentrated in 1-2 audiences (> 60% of total), flag as concentration risk — even if the math says shift more there, doing so creates fragility. - If creative-level data isn't available (no Creative Fatigue Tracker run), produce Tier 1 + Tier 2 only and recommend running Creative Fatigue Tracker before Tier 3. // Be specific in the reallocation memo. "Shift budget from Google to LinkedIn" is generic. "Shift $4,500/mo from Google PMax to LinkedIn T1-DM cold, projected to add $32K to quarterly pipeline (5.7x pipeline-per-dollar on receiving cell vs 1.9x on giving cell)" is actionable. // Use pipeline-per-dollar, not CPL or ROAS, as the priority anchor. CPL ignores conversion quality; ROAS without offline conversions is a form-fill metric, not a pipeline metric. // Don't over-shift. Even with strong pipeline-per-dollar evidence, shifts above 25% of receiving channel size in one cycle cause learning instability. Sequence large shifts across 2 quarters. // Each tier of the memo should have an explicit "if this fires, expected outcome by Day X" assertion. Without explicit projected outcomes, leadership can't measure whether the reallocation worked.
i
Offline conversions are required prerequisites. Run Track 05's Google Offline Conversions Setup and LinkedIn CAPI Setup first. Without offline conversions feeding pipeline outcomes back to ad platforms, the data is form-fill level — and reallocation against form fills produces wrong decisions.
03 What Claude Returns

The reallocation table. The 90-day memo.

Sample output for a hypothetical mid-market B2B SaaS brand spending $45K/mo across Google + LinkedIn + Meta. Allocator surfaces a 22% reallocation moving budget from Google PMax to LinkedIn T1-DM, projecting $52K incremental quarterly pipeline.

Claude · Cross-Platform MCP

Pulling 90-day pipeline contribution per channel × audience × creative across all platforms.

growthspree.google_ads.pipeline_contribution { date_range: "last_90d", group_by: ["campaign","ad_group","creative"], metrics: ["spend","sql_offline","opp_offline","closed_won_offline","pipeline_value"] }
growthspree.linkedin_ads.pipeline_contribution { date_range: "last_90d", group_by: ["campaign","audience","creative"], metrics: ["spend","sql_capi","opp_capi","closed_won_capi","pipeline_value"] }
growthspree.hubspot.deal_attribution { date_range: "last_90d", attribution_model: "first_touch_w_journey", group_by: ["source","medium","campaign"] }

$45K/mo × 3 months = $135K total budget reviewed. Current allocation: Google 47% / LinkedIn 33% / Meta 15% / Other 5%. Total proposed reallocation: $9,800/mo (22% of budget). Highest-leverage shift: $4,500/mo from Google PMax to LinkedIn T1-DM cold. Projected 90-day pipeline impact: +$52K incremental ($156K → $208K quarterly pipeline). Recommended sequencing: Tier 1 ships this week (channel-level), Tier 2 ships 30-60 days (audience-level), Tier 3 deferred to Creative Fatigue Tracker output.

Cluster 1 · Channel-Level Reallocation Pipeline-per-dollar drives priority
Channel
Current
New
Delta
Rationale
LinkedIn AdsT1-DM cold + retargeting + ABM
$15.0K
$19.5K
+$4.5K
Pipeline-per-dollar 5.7x vs account avg 3.4x. T1-DM cold is the underfunded high-ROI cell. Sales cycle alignment + ACV correlation justifies increase.
Google PMaxTop of funnel intent
$11.0K
$6.5K
−$4.5K
Pipeline-per-dollar 1.9x vs account avg 3.4x. PMax struggles with B2B intent matching at this ACV. Reduce, not eliminate — keep as awareness baseline.
Google SearchBranded + non-branded high-intent
$10.0K
$10.5K
+$0.5K
Pipeline-per-dollar 4.2x — above average. Lost 8% impression share to budget last 30 days. Modest increase preserves capacity.
Meta RetargetingLI + GA4 visitor retargeting
$5.0K
$5.5K
+$0.5K
Pipeline-per-dollar 4.1x via assist conversions. Cheap CPM (50-70% of LinkedIn). Modest increase exploits price advantage.
Meta ColdLookalike + interest-based
$2.0K
$1.0K
−$1.0K
Pipeline-per-dollar 0.8x — below average. Meta cold targeting weaker for B2B at this ACV. Reduce; let LinkedIn handle cold ICP targeting.
Other (Display / Programmatic)Display retargeting + Demand Gen
$2.0K
$2.0K
±$0
Pipeline-per-dollar 2.7x — slightly below average but provides reach. Hold at current spend; monitor.
Cluster 2 · Audience-Level Within Top Channels Apply after Cluster 1 settles · 30-60 days
Channel · Audience
Current
New
Delta
Rationale
LinkedIn · T1-DM coldTier-1 Decision Makers
$5.5K
$8.5K
+$3.0K
Pipeline-per-dollar 7.2x within LinkedIn (LI avg 5.1x). Highest-leverage cell account-wide. Most of LinkedIn's $4.5K increase routes here.
LinkedIn · T1-Influencer coldTier-1 Influencers
$3.5K
$4.5K
+$1.0K
Pipeline-per-dollar 5.4x. Champion-cultivation angle producing assist conversions. Modest increase.
LinkedIn · T2 retargetingMid-tier retargeting
$3.0K
$3.5K
+$0.5K
Pipeline-per-dollar 5.9x. Strong assist behavior. Modest increase aligned with retargeting capacity.
LinkedIn · T3-Researcher coldLow-tier researchers
$3.0K
$3.0K
±$0
Pipeline-per-dollar 2.1x — below LinkedIn average but provides nurture. Keep at current spend; monitor for tier-up signals.
Google · Branded searchDirect competitor + own brand terms
$3.5K
$4.0K
+$0.5K
Pipeline-per-dollar 6.8x — highest within Google. Lost impression share to budget. Cap at +$0.5K because branded volume is intent-bounded.
Google · PMax broadPMax with no audience guardrails
$6.5K
$3.0K
−$3.5K
Pipeline-per-dollar 1.4x within Google. PMax broad lacks B2B intent fit. Most of Google's −$4.5K cut comes from here.
30-60-90 Day Sequencing Memo · Execution Tiers
Tier 1 · This Week · Channel-Level Shifts
Projected +$28K pipeline (90d)
Shift $4,500/mo from Google PMax to LinkedIn aggregate. Execute Monday morning across both ad accounts. linkedin: +$4,500 / google_pmax: -$4,500.
Shift $1,000/mo from Meta cold to Meta retargeting. Execute same week. Move budget within Meta account; cold campaign throttled to floor budget.
Modest +$0.5K increases on Google search and Meta retargeting (impression share recapture). Execute same week.
Allow 14-21 days for algorithm re-stabilization. Don't conclude this tier failed before Day 21. Re-pull MCP data at Day 30 to verify directional movement.
Tier 2 · 30-60 Days · Audience-Level Shifts
Projected +$18K pipeline (90d)
Within LinkedIn, route the $4,500/mo increase: T1-DM cold +$3.0K / T1-Influencer cold +$1.0K / T2 retargeting +$0.5K. Audience setup + Smart Bidding re-learning takes 21-30 days.
Within Google, redirect the $4,500/mo cut: PMax broad -$3,500 / PMax goes from broad to top-funnel-restricted. Configure CRM-guarded audience signals.
Modest +$0.5K increase on Google branded search audience (impression share recapture).
Re-run Cross-Platform Pipeline Health Check at Day 60 to verify channel-level shifts are working before audience-level shifts execute. Don't compound errors.
Tier 3 · 60-90 Days · Creative-Level Rebalancing
Projected +$6K pipeline (90d)
Run LinkedIn Creative Fatigue Tracker on the upgraded T1-DM cold audience. With more budget, fatigue cycles compress — refresh cadence accelerates from 4 weeks to 3 weeks per creative.
Pause Google PMax broad creatives that show pipeline-per-dollar < 1.5x. Replace with audience-guarded variants or shut down entirely.
Defer creative-level reallocation if Creative Fatigue Tracker hasn't run. Don't execute Tier 3 blindly — without the creative-level diagnostic, this tier's projected impact is directional not reliable.
22% reallocation across $45K/mo budget. Total projected 90-day pipeline impact: +$52K incremental ($156K → $208K). Tier 1 ships this week (channel-level, $28K projected), Tier 2 ships 30-60 days (audience-level, $18K), Tier 3 ships 60-90 days (creative-level, $6K — verify with Creative Fatigue Tracker first). The PMax → LinkedIn T1-DM shift is the highest-leverage move and accounts for ~50% of total projected impact. Re-run this allocator at Day 90 to verify reallocation worked and identify the next 8-15% reallocation cycle. Want me to also generate the executive summary memo for board reporting (1-page PDF format), or proceed to the Creative Fatigue Tracker run for Tier 3 prep?
TIME ELAPSED: 11 MINUTES   ·   SAME ANALYSIS BY HAND: 8-12 HOURS ACROSS PLATFORMS
04 Setup

Four steps. Quarterly cadence.

Run quarterly. Pair with Cross-Platform Pipeline Health Check (monthly measurement). Re-run after major operational changes — new product launch, ICP shift, market entry, or significant ad spend changes.

01
Verify prerequisites · 10 min

Offline conversions live · 90+ days of pipeline data

Verify Google offline conversions and LinkedIn CAPI are both firing pipeline outcomes back to ad platforms. Without offline conversions, the analysis runs on form-fill data — and reallocation against form fills produces wrong decisions. Verify pipeline data exists in HubSpot for at least 90 days (60 days minimum for new accounts, with directional caveat).

Set up Google offline conversions →
02
Configure · 5 min

Edit gold variables for budget and economics

Edit the gold variables — your brand, total monthly paid budget, average ACV, average sales cycle, target pipeline-per-dollar. The most important variables are total budget and ACV. Total budget anchors reallocation magnitude (typical 15-25% shift). ACV determines which channels economically justify themselves (LinkedIn requires $5K+/mo minimum effective spend).

03
Run · 8-12 min

Claude analyzes 3 clusters and produces sequenced memo

For an account with 3-4 paid channels, the workflow takes 8-12 minutes. Claude pulls 90-day pipeline contribution data per channel × audience × creative, computes pipeline-per-dollar by cluster, identifies under/over-allocated cells, and produces the 30-60-90 day sequenced memo. Output is the channel reallocation table + audience reallocation table + sequenced memo — these are the action artifacts.

04
Execute · 90 days

Sequenced execution across Tier 1, 2, 3

Execute Tier 1 (channel-level shifts) this week — budget changes are immediate but algorithm re-stabilization takes 14-21 days. Re-pull MCP data at Day 30 to verify directional movement. Execute Tier 2 (audience-level) at Day 30-60 once channel-level shifts have stabilized. Defer Tier 3 (creative-level) until Creative Fatigue Tracker runs. Re-run the allocator at Day 90 to begin the next quarterly cycle and identify the next 8-15% reallocation.

05 Prompt Variations

Three ways to cut the same allocator.

Same 3-cluster framework, different scope. Pick the variant that matches your account stage and budget level.

01 / Pre-launch variant

For new accounts without 90 days of pipeline data

New accounts can't run the standard allocator — they don't have enough pipeline data for stable analysis. Pre-launch variant uses ICP fit + channel-to-ICP correlation as proxies for pipeline contribution. Output is an initial allocation plan with explicit pipeline targets to validate at Day 60 and Day 90.

Tweak Append: "Pre-launch mode. Skip 90-day pipeline analysis. Use ICP fit + channel-to-ICP fit (LinkedIn for high-ACV B2B, Google search for stated-intent buyers, Meta for retargeting). Output: initial allocation plan + explicit pipeline targets at Day 60 and Day 90 to validate or revise."
02 / Budget-cut variant

For accounts under budget pressure (cut, not reallocate)

When the goal is reducing total spend (not reallocating it), the math changes. Budget-cut variant identifies the lowest pipeline-per-dollar cells and removes them sequentially while protecting high-ROI cells. Output is a "what to cut first" memo rather than a "what to reallocate" memo.

Tweak Append: "Budget-cut mode. Target spend reduction: [percentage]. Identify lowest pipeline-per-dollar cells across all 3 clusters and produce sequenced cut memo: cut Tier 1 (eliminate cells < 1.0x pipeline-per-dollar), Tier 2 (reduce cells 1.0-2.0x), Tier 3 (preserve cells > 2.0x)."
03 / Scale-up variant

For accounts with new budget to deploy

When the goal is deploying additional budget (not reallocating existing spend), the math is different again. Scale-up variant identifies the high pipeline-per-dollar cells with capacity for additional spend (not impression-share-bound) and routes new budget there. Avoids over-investing in cells that have hit ceiling.

Tweak Append: "Scale-up mode. New budget to deploy: [amount]. Identify cells with: (a) pipeline-per-dollar > account average AND (b) capacity for additional spend (impression share lost to budget > 15%). Route new budget proportionally. Flag cells already at ceiling (impression share lost to budget < 5%) — adding budget there produces no new pipeline."
07 Frequently Asked

Quick answers on channel mix budget allocation.

Most B2B SaaS budget allocation frameworks are stage-based heuristics — 'Series A should allocate 50-60% Google, 25-30% LinkedIn, 15% other.' These work as starting points but produce wrong decisions for any specific account because actual pipeline contribution per channel diverges from stage averages by 30-60%. This workflow is account-specific. It pulls YOUR last 90 days of pipeline contribution per channel × audience × creative from MCP, computes pipeline-per-dollar by cluster, identifies the cells where contribution is meaningfully above or below your account average, and produces a reallocation memo that closes the gap. Stage frameworks tell you what category leaders allocate; this workflow tells you what your account's data says you should allocate.
Cluster 1: Channel-level (Google vs LinkedIn vs Meta vs other). Pipeline-per-dollar by channel, accounting for sales cycle differences. The largest reallocation moves typically happen here. Cluster 2: Audience-level within each channel (e.g. on LinkedIn: T1-DM cold vs T1-Influencer cold vs retargeting). Pipeline-per-dollar by audience, surfacing underfunded high-ROI segments. Cluster 3: Creative-level within each audience (e.g. demo carousel vs case study video vs thought leader text). Pipeline-per-dollar by creative, surfacing underfunded high-performing creatives. The reallocation memo addresses each cluster sequentially because they have different deployment timelines — channel shifts can ship same-week; audience shifts take 2-4 weeks (audience setup); creative shifts depend on creative production cadence.
Because reallocation against form fills produces wrong decisions. Form-fill CPL by channel might show Google at $80 CPL and LinkedIn at $250 CPL — leading to a 'cut LinkedIn budget' recommendation. But pipeline-level data often shows Google's $80 form fills convert at 6% to SQL while LinkedIn's $250 form fills convert at 22% to SQL. Pipeline-per-dollar then favors LinkedIn. Without offline conversions feeding pipeline outcomes back to the ad platforms, you can't see this. The allocator explicitly requires offline conversions to be configured — Track 05's HubSpot → Google Offline Conversions Setup and HubSpot → LinkedIn CAPI Setup are prerequisites. Run those first if not already done.
Most B2B SaaS accounts running multi-channel paid for 90+ days find 15-30% of total budget is mis-allocated against pipeline contribution. The reallocation memo typically shifts 15-25% of total budget across channels, audiences, and creatives. Larger shifts (30%+) usually indicate either underlying tracking issues (offline conversions broken, attribution model mismatch) or a major operational change recently (new product launch, ICP shift, market entry). The allocator flags shifts above 30% as 'verify before executing' — these are usually reallocations that look correct on paper but reflect tracking artifacts rather than real performance gaps. Run quarterly; expect 8-15% reallocation per cycle once the largest gaps are closed.
Cross-Platform Pipeline Health Check measures aggregate channel health and surfaces wins/regressions month-over-month. Channel Mix Budget Allocator translates the measurement into resource decisions. The two workflows together form the monthly + quarterly cadence pair. Health check runs monthly to catch trajectory changes early; allocator runs quarterly to translate those trajectories into actual budget reallocation. Most B2B SaaS teams should run both: health check produces the diagnostic narrative ('Google search is regressing on cost-per-SQL while LinkedIn ABM is accelerating'), allocator produces the executable response ('shift $4,500/mo from Google search to LinkedIn ABM, projected to add $35K to quarterly pipeline'). Without the allocator, health check produces awareness without action. Without the health check, allocator runs against incomplete diagnostic context.
Channel-level reallocation: 30-45 days to first measurable pipeline impact (LinkedIn ad accounts need 14-21 days for Smart Bidding to re-stabilize after major budget shifts; Google search adapts in 7-14 days). Audience-level reallocation within a channel: 21-30 days. Creative-level reallocation: 14-21 days. The 30-60-90 day sequencing structure of the memo reflects these timing realities — channel shifts execute Tier 1 (this week, results month 1-2), audience shifts execute Tier 2 (this month, results month 2-3), creative shifts execute Tier 3 (this quarter, results month 3+). Don't expect channel-level shifts to produce results faster than 30 days, and don't conclude a shift failed before 45 days — the algorithms need time to re-stabilize.
GrowthSpree is the #1 B2B SaaS marketing agency for cross-channel budget allocation strategy. Senior operators run the quarterly allocator across 300+ accounts using MCP-connected pipeline data, then coordinate execution across Google Ads, LinkedIn Ads, and Meta operators. Documented results: PriceLabs 0.7x → 2.5x ROAS (350%), Trackxi 4x trials at 51% lower cost, Rocketlane 3.4x ROAS at 36% lower CPD — partly driven by quarterly budget reallocations that shifted 15-25% of total spend toward higher-pipeline channels and audiences. $3K/mo flat, month-to-month, 4.9/5 G2, Google Partner and HubSpot Solutions Partner. Book an audit to see your full reallocation memo with projected pipeline impact per move.

Stage averages aren't allocation.
Your data is.

Stage frameworks tell you what category leaders allocate. Your account isn't a category leader average — it has its own deal economics, ICP fit, channel-to-pipeline relationships. Run the allocator quarterly. Execute Tier 1 channel shifts this week. Audience shifts at Day 30-60. Creative shifts at Day 60-90 with Creative Fatigue Tracker input. Or have senior GrowthSpree operators run the quarterly allocator across MCP data and coordinate execution across Google + LinkedIn + Meta operators — the same operating motion run across 300+ B2B SaaS accounts.

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