GTM analytics and measurement framework that builds metrics architecture, dashboard specs, and alert systems. Use when: GTM metrics, launch metrics, measurement framework, KPIs, dashboard design, what should we measure, analytics framework.

Launch Pulse (VITAL Metrics Architecture)

A comprehensive measurement framework engine that designs the full analytics stack for tracking launch success -- from metric definitions through dashboard specifications to automated alert systems. VITAL ensures you measure what matters, detect problems early, and attribute results accurately across channels and touchpoints.

When to Use

Designing the measurement framework for an upcoming launch
Defining KPIs and success criteria for GTM initiatives
Building dashboard specifications for different stakeholder audiences
Setting up alert systems to detect launch issues early
Choosing an attribution model for multi-channel campaigns
Establishing baselines and targets before a launch
Creating a metrics review cadence and reporting rhythm

What You'll Need

Critical inputs (ask if not provided):

Product name and launch type (GA, beta, feature, expansion)
Target audience and customer segments
Business objectives and success criteria (revenue, adoption, pipeline targets)
Marketing channels being activated (from demand-engine)
Sales motion (PLG, sales-led, hybrid)
Analytics tools and data infrastructure in use

Nice-to-have:

Historical baselines for similar launches or products
Current analytics setup and gaps
Dashboard tools and BI platforms available
Data team capacity and timeline
Attribution tools currently deployed
Customer journey maps (from journey-architect)

Process

Step 1: Build the VITAL Metrics Pyramid

The VITAL pyramid organizes metrics into five layers, each building on the one below. Start from the base (Volume) and work upward to Loyalty.

Layer	Focus	Time Horizon	Audience	Signal Type
V - Volume	Reach and awareness	Daily	Marketing	Leading
I - Intent	Engagement and interest	Daily/Weekly	Marketing + Sales	Leading
T - Traction	Pipeline and conversion	Weekly	Sales + Revenue	Leading/Lagging
A - Adoption	Product usage and value	Weekly/Monthly	Product + CS	Lagging
L - Loyalty	Retention and advocacy	Monthly/Quarterly	CS + Executive	Lagging

Step 2: Define Metrics Per Layer

For each VITAL layer, define 4-8 specific metrics with full specifications.

V -- Volume Metrics (Top of Funnel)

#	Metric	Definition	Source	Frequency	Owner	Leading/Lagging
V1	Website Traffic	Unique visitors to launch/product pages	GA4	Daily	Marketing	Leading
V2	Impressions	Total ad impressions across paid channels	Ad platforms	Daily	Demand Gen	Leading
V3	Social Reach	Unique accounts reached on social platforms	Social tools	Daily	Social	Leading
V4	PR Mentions	Press coverage and article mentions	Media monitoring	Daily	Comms	Leading
V5	Content Views	Blog, video, and resource page views	CMS/GA4	Daily	Content	Leading
V6	Event Registrations	Webinar/event signups	Event platform	Weekly	Events	Leading

I -- Intent Metrics (Mid-Funnel)

#	Metric	Definition	Source	Frequency	Owner	Leading/Lagging
I1	MQLs	Marketing qualified leads by scoring criteria	MAP	Daily	Demand Gen	Leading
I2	Demo Requests	Inbound requests for product demonstration	CRM	Daily	Sales	Leading
I3	Trial Signups	Free trial or freemium account creations	Product	Daily	Growth	Leading
I4	Content Engagement	Downloads, time-on-page, return visits	GA4/MAP	Weekly	Content	Leading
I5	Email Engagement	Open rate, click rate, reply rate	MAP	Weekly	Email	Leading
I6	Pricing Page Views	Visits to pricing/packaging pages	GA4	Daily	Marketing	Leading

T -- Traction Metrics (Pipeline)

#	Metric	Definition	Source	Frequency	Owner	Leading/Lagging
T1	SQLs	Sales qualified leads accepted by sales	CRM	Weekly	Sales	Leading
T2	Pipeline Created	Dollar value of new pipeline from launch	CRM	Weekly	Revenue	Leading
T3	Win Rate	Deals won / deals in pipeline	CRM	Monthly	Sales	Lagging
T4	Sales Cycle Length	Average days from SQL to closed-won	CRM	Monthly	Sales	Lagging
T5	Deal Size	Average contract value of launch deals	CRM	Monthly	Revenue	Lagging
T6	MQL-to-SQL Rate	Conversion rate from MQL to SQL	CRM/MAP	Weekly	RevOps	Leading

A -- Adoption Metrics (Product)

#	Metric	Definition	Source	Frequency	Owner	Leading/Lagging
A1	Activation Rate	% of signups completing key onboarding action	Product analytics	Weekly	Product	Lagging
A2	Time to Value	Median time from signup to first value moment	Product analytics	Weekly	Product	Lagging
A3	DAU/WAU Ratio	Daily active / weekly active users (stickiness)	Product analytics	Weekly	Product	Lagging
A4	Feature Adoption	% of users using launch feature within 30 days	Product analytics	Weekly	Product	Lagging
A5	Usage Depth	Actions per session or per user per week	Product analytics	Weekly	Product	Lagging

L -- Loyalty Metrics (Retention)

#	Metric	Definition	Source	Frequency	Owner	Leading/Lagging
L1	NPS	Net Promoter Score from post-launch survey	Survey tool	Monthly	CS	Lagging
L2	Retention Rate	% of users/accounts active after 30/60/90 days	Product analytics	Monthly	CS	Lagging
L3	Expansion Revenue	Upsell/cross-sell revenue from launch cohort	CRM	Monthly	Revenue	Lagging
L4	Referral Rate	% of customers generating referrals	CRM/Product	Monthly	Growth	Lagging
L5	Support Satisfaction	CSAT score on support interactions	Support tool	Weekly	Support	Lagging

Step 3: Set Targets and Thresholds

For each metric, establish baselines and progressive targets with RAG thresholds.

Target-Setting Template:

Metric	Baseline	30-Day Target	90-Day Target	365-Day Target	Red (<)	Yellow	Green (>)
V1: Website Traffic
I1: MQLs
T2: Pipeline
A1: Activation Rate
L2: Retention Rate

RAG Threshold Guidelines:

Level	Definition	Action Required
RED	Below 70% of target for 3+ consecutive periods	Immediate investigation, escalation to leadership, corrective action plan
YELLOW	70-90% of target for 2+ consecutive periods	Root-cause analysis, optimization plan within 1 week
GREEN	90%+ of target	Continue execution, look for scale opportunities
BLUE (bonus)	120%+ of target	Investigate why, document learnings, consider increasing investment

Step 4: Design Dashboard Architecture

Build four dashboard tiers for different audiences and cadences.

Dashboard Tier Map:

Dashboard	Audience	Metrics Count	Refresh	Format
Executive	C-suite, VPs	5-7 top-level	Weekly	One-page scorecard
Operations	Marketing, Sales leads	15-20 operational	Daily	Multi-tab dashboard
Campaign	Channel managers	Per-channel deep dive	Real-time	Channel-specific views
Product	Product, Engineering	Adoption and usage	Daily	Product analytics tool

Executive Dashboard Specification (5 metrics):

Position	Metric	Visualization	Comparison	Alert
Hero	Pipeline Created (T2)	Number + trend line	vs. target, vs. last launch	< 70% of target
Top-left	MQLs (I1)	Number + bar chart	vs. target by week	< 70% of target
Top-right	Activation Rate (A1)	Percentage gauge	vs. baseline	< 50%
Bottom-left	Win Rate (T3)	Percentage + trend	vs. company average	< historical -10pp
Bottom-right	NPS (L1)	Score + distribution	vs. baseline, vs. industry	< 20

Step 5: Configure Alert System

Define threshold-based alerts with escalation paths and response protocols.

Alert Configuration Matrix:

Alert Name	Metric	Trigger Condition	Severity	Channel	Recipient	Response Protocol
Pipeline Drop	T2	<70% weekly target, 2 weeks	Critical	Slack + Email	VP Sales, VP Marketing	Emergency pipeline review within 24h
Activation Cliff	A1	<50% activation rate	Critical	Slack + Email	VP Product, PM	UX investigation, onboarding audit
MQL Drought	I1	<60% daily target, 5 days	High	Slack	Demand Gen lead	Channel audit, budget reallocation
CAC Spike	T1/Budget	CAC >150% of target	High	Email	Marketing, Finance	Channel pause, spend review
Churn Signal	L2	Retention <80% at 30 days	High	Slack + Email	CS lead, Product	Churn cohort analysis, intervention
Traffic Surge	V1	>200% of daily average	Info	Slack	Marketing	Investigate source, capitalize if organic

Escalation Ladder:

Severity	First Response	Escalation	Timeline
Critical	Metric owner + VP	C-suite if unresolved	24h to action plan, 48h to resolution
High	Metric owner + manager	VP if unresolved	48h to action plan, 1 week to resolution
Medium	Metric owner	Manager if unresolved	1 week to action plan
Info	Metric owner (log only)	No escalation	Document and review in weekly sync

Step 6: Attribution Model Design

Define how credit is assigned across channels and touchpoints.

Attribution Model Comparison:

Model	How It Works	Best For	Limitation
First-Touch	100% credit to first interaction	Understanding awareness drivers	Ignores nurture and conversion
Last-Touch	100% credit to final interaction	Understanding conversion drivers	Ignores awareness and nurture
Linear	Equal credit across all touches	Fair distribution when unsure	Overweights low-impact touches
Time-Decay	More credit to recent touches	Shorter sales cycles	Undervalues awareness
Position-Based	40% first, 40% last, 20% middle	Balanced, most recommended	Arbitrary weighting
Self-Reported	Ask buyers "how did you hear about us?"	Dark social, word-of-mouth	Recall bias, limited scale

Recommended Approach:

Primary: Position-based (40/20/40) for automated attribution
Secondary: Self-reported "How did you hear about us?" on signup and demo forms
Validation: Compare models quarterly -- if they diverge significantly, investigate

Attribution Data Requirements:

Requirement	Tool/Source	Status	Gap
UTM tracking on all links	URL builder + GA4
CRM-MAP integration	CRM + MAP
Multi-touch tracking	Attribution tool
Self-reported field	Form builder
Offline event tracking	CRM manual + import

Output

Save to outputs/launch-pulse/

Deliverables:

VITAL Metrics Framework -- Complete pyramid with 25-30 defined metrics, each with definition, source, frequency, owner, baseline, targets (30/90/365), RAG thresholds, and leading/lagging classification
Dashboard Specifications -- Four-tier dashboard architecture (Executive, Operations, Campaign, Product) with metric placement, visualization types, comparison logic, and refresh cadences
Alert Rules -- Threshold-based alert system with trigger conditions, severity levels, notification channels, recipients, response protocols, and escalation ladders
Attribution Model -- Recommended multi-model attribution approach with data requirements, implementation checklist, and quarterly validation protocol

Chain Connections

Receives from: launch-command (launch plan and workstreams), demand-engine (channel strategy and targets), budget-allocator (spend allocation for ROI tracking)
Feeds into: growth-loop (adoption and retention metrics), signal-radar (market performance signals), launch-debrief (actuals vs targets)
Enhanced by: journey-architect (touchpoint mapping for attribution), financial-analyst (unit economics for ROI thresholds)