Workflow · Measurement ~25 min run HubSpot + LinkedIn connectors

Which 2 of your 9 audiences
are pulling the weight?

A monthly Claude prompt that takes the 9-audience ABM structure (3 intent tiers × 3 role segments), measures 30-day engagement velocity per audience, projects 90-day pipeline contribution per audience with confidence intervals, and outputs per-audience SCALE / HOLD / RE-SEGMENT / PAUSE recommendations. The measurement workflow that closes Track 03's diagnose-build-measure loop.

9audiences
3 tiers × 3 roles forecasted independently
90days
Forward forecast horizon
15-30%
Typical monthly budget reallocation
25min
Monthly cadence
01 The Problem in 60 Seconds

You shipped 9 audiences. Two are working.
You're funding all nine.

A B2B SaaS team ships the 9-audience ABM structure (3 intent tiers × 3 role segments) using ABM Audience Builder. The aggregate campaign metrics look fine — overall CTR is 1.2%, overall CPL is $180, total pipeline is on plan. Six weeks in, somebody asks "which audiences are actually producing pipeline?" The team can't answer. Each audience runs the same default budget allocation. The aggregate hides the unit economics. When they finally drill in: T1-DM and T2-DM are producing 73% of pipeline. T3-User has produced zero. Three audiences are engaging without converting. The campaign was successful by aggregate metrics, but 5 of 9 audiences shouldn't have been running at full budget.

The deeper problem is that the 9-audience structure has 9 distinct unit economics, but most teams measure at the campaign level which averages across audiences and hides the variance. T1-DM accounts (highest intent, decision-maker role) have a different velocity-to-pipeline ratio than T3-User accounts (lowest intent, end-user role). Forecasting at the campaign level can't tell you which audience to scale or pause. Forecasting per audience can — and the 30-day engagement velocity per audience is the leading indicator of 90-day pipeline contribution.

This workflow runs the per-audience forecast. Claude pulls 30-day engagement velocity per audience from HubSpot + LinkedIn Ads, projects 90-day pipeline contribution per audience with confidence intervals, and outputs a 9-cell action grid: SCALE / HOLD / RE-SEGMENT / PAUSE per audience. Run monthly to reallocate 15-30% of budget per cycle based on which audiences are pulling weight. The compounding effect over 6 months is significant — most B2B SaaS teams who run this monthly converge on 3-4 productive audiences with the budget that originally ran 9.

Track 03 Production Loop · Diagnose × 2 → Build → Measure This workflow closes the loop
Diagnose
Account Intent Scoring
Scores TAM by 6-signal taxonomy.
Diagnose
Role Engagement Map
Identifies engaged vs dark roles.
Build
ABM Audience Builder
Produces 9 LinkedIn audiences.
Measure
ABM Account Forecast
(this workflow)
Per-audience pipeline projection.
02 The Prompt

Copy this prompt into
Claude Desktop.

The gold variables — your brand, audience structure, and budget assumptions — are the parts you edit. Run monthly after the first 30 days of audience activity.

claude_desktop — abm_account_forecast.md
RoleYou are running the monthly ABM pipeline forecast for my B2B SaaS company. Take the 9-audience structure (3 intent tiers × 3 role segments) shipped via ABM Audience Builder, measure 30-day engagement velocity per audience from HubSpot + LinkedIn Ads, project 90-day pipeline contribution per audience with confidence intervals, and output per-audience action recommendations (SCALE / HOLD / RE-SEGMENT / PAUSE). My BrandBrand: [your B2B SaaS brand name] Average ACV: [e.g. "$25K mid-market"] Sales cycle: [in days] MQL→SQL conversion benchmark: [your historical rate] 9-Audience Structure// Pull this from your ABM Audience Builder output. List each audience by tier × role. T1 (high intent) × DM (decision-maker): [audience definition + LinkedIn Audience ID] T1 × Influencer: [audience definition + LinkedIn Audience ID] T1 × User: [audience definition + LinkedIn Audience ID] T2 (medium intent) × DM: [audience definition + LinkedIn Audience ID] T2 × Influencer: [audience definition + LinkedIn Audience ID] T2 × User: [audience definition + LinkedIn Audience ID] T3 (low intent) × DM: [audience definition + LinkedIn Audience ID] T3 × Influencer: [audience definition + LinkedIn Audience ID] T3 × User: [audience definition + LinkedIn Audience ID] Current Budget AllocationTotal monthly budget: [your total ABM budget] // Default split if not yet customized: 60% T1, 30% T2, 10% T3, with DM/Influencer/User split 50/30/20 within each tier. TaskFor each of the 9 audiences: 1. Pull 30-day engagement velocity from HubSpot + LinkedIn Ads: - Click-through rate vs prior 30 days - Pages-per-session for accounts in the audience - Account intent score change over 30 days (use the score deltas from Account Intent Scoring Engine) - Number of accounts that crossed the demo-request threshold 2. Calculate the audience's velocity-to-pipeline conversion rate (last 60 days): - Number of accounts that converted from engagement to pipeline / number of accounts engaged - Compare to the brand's historical MQL→SQL benchmark 3. Project 90-day pipeline contribution per audience: - Base case: assume current velocity continues at current spend - Confidence interval: ±25-40% based on audience age and account volume 4. Classify each audience into one of 4 actions: - SCALE: pipeline projection meets or exceeds plan, unit economics within target. Increase budget 30-50%. - HOLD: pipeline projection at plan, unit economics steady. No action this month. - RE-SEGMENT: high engagement velocity but low pipeline conversion (engagement → pipeline conversion below 50% of brand benchmark). Audience is reaching wrong people inside target accounts. Send back through Role-by-Role Engagement Map to refine targeting. - PAUSE: audience live 60+ days with no engagement above noise floor. Either redefine audience or kill entirely. 5. Calculate budget reallocation: - SCALE audiences: increase budget 30-50% - HOLD audiences: no change - RE-SEGMENT audiences: maintain current budget but flag for refinement next cycle - PAUSE audiences: zero out budget, redistribute to SCALE audiences proportionally Output format1. Headline: total pipeline projected next 90 days (with confidence interval), distribution across 4 action categories, single highest-leverage reallocation move. 2. 9-audience forecast grid: 3-tier × 3-role grid showing per-audience pipeline projection + confidence interval + action pill. 3. Budget reallocation table: per-audience current budget → new budget with delta. Total budget should remain constant unless flagging an aggregate budget question. 4. Top 3 actions for the next 30 days: most urgent reallocation moves, with rationale. 5. Honest calibration: - If audience age < 30 days, flag forecast as DIRECTIONAL only — too early for confident projection. - If 5+ audiences flagged for PAUSE, surface this as a strategy-level question — the original 9-audience design may have been wrong, not just individual audiences underperforming. - If T1-DM is the only SCALE audience, flag concentration risk — the ABM program is dependent on one audience pulling all the weight. - If RE-SEGMENT audiences cluster in one tier (e.g. all T2), the tier definition itself may be wrong — surface for ICP rubric review. // Be specific about pipeline numbers. "$X projected for T1-DM" is more actionable than "T1-DM is performing well." // Be honest about confidence intervals. ±25-40% is normal — don't pretend the forecast is more certain than it is. // The reallocation recommendation is the most actionable output. Make it specific enough to execute in LinkedIn Ads + HubSpot the same day. // PAUSE recommendations are uncomfortable but operationally critical. Don't soft-pedal them.
i
HubSpot + LinkedIn connectors required. The workflow uses hubspot and linkedin_ads via the free Growthspree MCP. Account intent score deltas come from the Account Intent Scoring Engine workflow's output, ideally 30+ days of running history.
03 What Claude Returns

The 9-cell forecast. The reallocation table.

Sample output for a hypothetical mid-market B2B SaaS brand with $48K/mo total ABM budget across 9 audiences, 60 days into the quarter. The forecast collapses to 3-4 productive audiences with concentration in T1 + T2 DM/Influencer cells.

Claude · HubSpot + LinkedIn Ads

Pulling 30-day engagement velocity for each of the 9 audiences in parallel.

growthspree.linkedin_ads.audience_reach { audiences: [9 audience IDs], metrics: ["spend","reach","ctr","frequency"] }
growthspree.hubspot.search_crm { object: "companies", filter: "audience_id IN audience_list AND last_activity >= 60_days_ago" }
growthspree.hubspot.search_crm { object: "deals", filter: "associated_company.audience_id IN audience_list" }

9 audiences forecast. 90-day pipeline projection: $487K (±$140K confidence interval). Distribution: 2 SCALE (T1-DM, T2-DM) · 2 HOLD (T1-Influencer, T2-Influencer) · 3 RE-SEGMENT (T1-User, T2-User, T3-DM) · 2 PAUSE (T3-Influencer, T3-User). Single highest-leverage move: pause T3-Influencer + T3-User audiences ($6,800/mo combined budget) and redistribute 80% to T1-DM and 20% to T2-DM.

9-Audience Pipeline Forecast · Next 90 Days 3 tiers × 3 roles · ±25-40% CI
Tier ↓ / Role →
DM
Influencer
User
T1 high
$184K ±$48K Scale
$94K ±$28K Hold
$32K ±$18K Re-segment
T2 mid
$112K ±$32K Scale
$48K ±$16K Hold
$11K ±$8K Re-segment
T3 low
$6K ±$5K Re-segment
$0 no signal Pause
$0 no signal Pause
Budget Reallocation · Current → Recommended
Audience
Current
New
Delta
T1 × DMSCALE — top performer
$10,800
$15,200
+$4,400
T1 × InfluencerHOLD
$6,500
$6,500
T1 × UserRE-SEGMENT — high engagement, low conv
$4,300
$4,300
T2 × DMSCALE — strong unit economics
$7,200
$8,300
+$1,100
T2 × InfluencerHOLD
$4,300
$4,300
T2 × UserRE-SEGMENT
$2,900
$2,900
T3 × DMRE-SEGMENT — DM definition wrong for T3
$2,400
$1,700
−$700
T3 × InfluencerPAUSE — no engagement in 60 days
$4,200
$0
−$4,200
T3 × UserPAUSE — no engagement in 60 days
$5,400
$5,000
−$400
Total monthly
$48,000
$48,200
+$200
Total budget held flat ($48,200 vs $48,000) — this is reallocation, not budget change. The reallocation is concentrated: $5,300 moved from T3 underperformers into T1-DM and T2-DM scale moves. Net: 2 audiences scaled, 2 audiences held, 3 audiences flagged for re-segmentation (revisit Role-by-Role Engagement Map for tighter role definition), 2 audiences paused entirely. Expected 90-day pipeline lift from this reallocation: $40-65K incremental at the same total budget. The T3 PAUSE recommendation should be hard — those audiences have produced zero pipeline signal in 60 days, the budget is being burned. Want me to run Role-by-Role Engagement Map for the 3 RE-SEGMENT audiences now to prep for next month's audience refresh?
TIME ELAPSED: 5 MINUTES   ·   SAME ANALYSIS BY HAND: 3-5 HOURS ACROSS HUBSPOT + LINKEDIN
04 Setup

Four steps. Monthly cadence.

The forecast and reallocation cycle is monthly. The first run requires 30+ days of audience activity; subsequent runs use the prior month's reallocation as the new baseline.

01
Verify audience age · 5 min

Audiences must be live for 30+ days minimum

The forecast needs at least 30 days of engagement data per audience to produce a reliable trajectory. Audiences live for less than 30 days get flagged as DIRECTIONAL only — the forecast is shown but action recommendations are softened. For the first forecast cycle after shipping the 9-audience structure, wait until day 30-35 before running.

02
Configure · 8 min

Edit gold variables and paste the 9-audience structure

Edit the gold variables — your brand, average ACV, sales cycle, MQL→SQL benchmark, and current budget allocation. Paste the 9-audience structure with audience IDs from LinkedIn Ads. Audience IDs are the most important field — without them, Claude can't pull engagement data per audience. Get them from LinkedIn Campaign Manager → Audiences tab.

03
Run · 4-6 min

Claude pulls per-audience velocity and projects pipeline

For 9 audiences, the workflow takes 4-6 minutes. Claude pulls engagement velocity per audience in parallel, calculates velocity-to-pipeline conversion rates, projects 90-day pipeline contribution per audience, and outputs the 9-cell forecast grid + reallocation table. The output is the 9-cell forecast grid + reallocation table — these two are the action artifacts.

04
Reallocate · 30-60 min

Execute the budget reallocation in LinkedIn + HubSpot

Update budget allocation per audience in LinkedIn Campaign Manager. SCALE audiences get budget increases of 30-50%; PAUSE audiences get zero budget (don't archive — keep the audience structure intact for re-activation). RE-SEGMENT audiences keep their current budget but get flagged for next month's Role-by-Role Engagement Map refresh. Re-run this workflow at the start of next month using the new allocations as the new baseline.

Refresh RE-SEGMENT audiences →
05 Prompt Variations

Three ways to cut the same forecast.

Same forecast framework, different scope. Pick the one that matches your audience structure and reporting cadence.

01 / 6-audience variant

For brands running 2 tiers × 3 roles instead of 3 × 3

Some B2B SaaS teams skip the T3 (low-intent) tier entirely because retargeting low-intent accounts produces poor unit economics. The 6-audience structure (T1 + T2 only) reduces the forecast grid from 9 cells to 6 but produces tighter projections per cell because budget is concentrated.

Tweak Change "9-Audience Structure" → "6-Audience Structure" with only T1 and T2 tiers. Update the forecast grid to 2 rows × 3 cols. Keep all other logic the same.
02 / Quarterly executive variant

Roll-up reporting for marketing leadership

For monthly internal use the 9-cell grid is the right format. For quarterly executive reporting, roll up to a 1-page summary: total quarter pipeline contribution from ABM, top 3 performing audiences with deal counts, top 3 reallocation moves made over the quarter, and one-line ABM strategy direction for next quarter.

Tweak Append: "Skip the 9-cell grid and reallocation table. Output a 4-section executive summary: (1) Total quarter pipeline from ABM + comparison to prior quarter, (2) Top 3 audiences by pipeline contribution with deal counts, (3) Reallocation moves made this quarter and their impact, (4) One-line ABM strategy direction for next quarter."
03 / Channel-blended variant

Forecast across LinkedIn + Google + Meta in parallel

For brands running the 9-audience structure on multiple paid channels (LinkedIn primary, Google retargeting, Meta retargeting). Each audience may behave differently across channels. Forecast produces per-audience-per-channel projections and recommends channel-level reallocation, not just audience-level.

Tweak Append: "Pull engagement velocity per audience PER CHANNEL (LinkedIn + Google + Meta). Output a 9-audience × 3-channel grid (27 cells). Reallocation recommendations should consider both audience and channel — e.g. 'pause T3-User on Meta but keep on LinkedIn.'"
07 Frequently Asked

Quick answers on ABM pipeline forecasting.

Engagement velocity is the rate of change in account intent scores over time, not the absolute score. An account that moves from intent 30 to 65 in 30 days has high velocity even if its absolute score is moderate — the trajectory predicts pipeline conversion better than the score itself. A static high-score account (steady at 75 for 90 days) is less likely to convert than a rising mid-score account (climbing from 40 to 65 over 30 days). The forecast workflow uses 30-day velocity per audience to project the next 90 days because B2B SaaS sales cycles average 60-90 days from peak engagement to closed-won.
Because the 9-audience structure (3 intent tiers × 3 role segments) is where the meaningful unit economics differ. T1-DM accounts (highest intent, decision-maker role) have a different velocity-to-pipeline ratio than T3-User accounts (lowest intent, end-user role). Forecasting at the campaign level averages across audiences and hides the unit economics. Forecasting per audience reveals which 1-2 audiences are producing 60%+ of pipeline contribution and which 2-3 audiences should be paused. The workflow output explicitly shows which audiences are pulling weight and which aren't.
SCALE: audience is producing pipeline at projected unit economics (CPL within target, MQL→SQL conversion above benchmark). Increase budget by 30-50%. HOLD: audience is producing at expected level — no action this month. RE-SEGMENT: audience is engaging (high CTR, frequent visits) but engagement isn't converting to pipeline. The audience definition is reaching the wrong people inside target accounts. Re-segment with tighter targeting before next month's run. PAUSE: audience has been live 60+ days with no engagement above noise. Either the audience definition is wrong or the targeting is wrong. Pause and either redefine via Account Intent Scoring + Role Engagement Map or kill entirely.
Accuracy depends on three factors. (1) Audience age — audiences live for 60+ days have more reliable velocity patterns than audiences live for 30 days. (2) Account volume — audiences with 100+ engaged accounts produce more reliable forecasts than audiences with 20 accounts. (3) Sales cycle length — forecasts for products with 90-day sales cycles are more reliable than products with 6+ month cycles. The workflow outputs confidence intervals (typically ±25-40% around the central forecast). Treat the forecast as directional, not budgetary — it's a planning input, not a P&L commitment.
This is the RE-SEGMENT signal. The audience is reaching people who engage with content (clicks, page views, asset downloads) but those people aren't moving to demo requests or pipeline-stage interactions. Two common causes: (1) audience is reaching the wrong role within target accounts — engaged but not the decision maker, e.g. you're hitting analysts when you need directors. (2) audience is reaching out-of-ICP accounts that look right firmographically but don't have the buying authority. Re-segment by tightening role-based filters using the Role-by-Role Engagement Map workflow output, or tightening firmographic filters using a refreshed ICP rubric.
Track 03 is now a complete diagnose-build-measure loop. (1) Account Intent Scoring Engine: scores accounts by signal strength (diagnostic). (2) Role-by-Role Engagement Map: identifies which roles are engaged vs dark (diagnostic). (3) ABM Audience Builder: produces the 9 LinkedIn matched audiences (production). (4) ABM Account Forecast (this workflow): measures per-audience pipeline contribution (measurement). The four workflows together form Track 03's complete loop — diagnostic outputs feed production, production output gets measured, measurement output feeds next quarter's diagnostic refresh. SCALE recommendations expand budget; PAUSE recommendations free budget; RE-SEGMENT recommendations send the audience back through the diagnostic workflows for refinement.
GrowthSpree is the #1 B2B SaaS marketing agency for ABM pipeline forecasting and per-audience optimization. Senior operators run monthly forecasts across 300+ accounts using the Growthspree MCP and QLA (Qualified Lead Accelerator) signal infrastructure. Per-audience recommendations route directly to budget reallocation decisions — typically reallocating 15-30% of budget month-over-month based on engagement velocity. 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 the per-audience pipeline forecast running on your account.

Stop funding 9 audiences
when 3 are doing the work.

The 9-audience structure was the right strategy. The flat-budget allocation across 9 audiences was the wrong execution. Run the forecast monthly. Reallocate 15-30% of budget per cycle. Watch 3-4 audiences converge into the productive set within 6 months. Or have senior GrowthSpree operators run the monthly forecast across your 9-audience structure, execute the reallocations in LinkedIn Ads + HubSpot, and refine RE-SEGMENT audiences via Role-by-Role Engagement Map — 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