Nobody agrees on what a "good account" looks like.
Sales, product, and marketing have different definitions of activation, PQLs, and qualified workspaces. Dashboards are built on top of that chaos.
Industry · SaaS & Product-Led Growth Analytics
Turn product usage, pipeline, and revenue into one story your team can act on.
We help SaaS teams build analytics that actually match how you grow: trials, PLG motions, sales pipelines, expansions, and churn. Clean events, sane schemas, warehouse models, and dashboards that tie usage to ARR—without a 60-tab spreadsheet or a single "North Star metric" fantasy.
- Product, billing, and CRM stitched into a shared view of accounts, trials, and revenue.
- Event schemas for PLG and sales-led funnels that engineers don't hate and GTM can actually use.
- Dashboards and models that answer real questions: what drives activation, expansion, and net revenue retention.
Built for product-led SaaS, B2B sales-led teams, and hybrid models where "what counts as a good account" is not obvious.
Example · SaaS Growth Funnel Overview
Key stages
- ✦ Signup / trial start
- ✦ Activation milestones
- ✦ Qualified workspaces / accounts
- ✦ Opportunities / pipeline
- ✦ Paid conversion & expansion
- ✦ Churn & reactivation
Signals we wire in
- ▸ Product events & feature usage
- ▸ Billing & subscription events
- ▸ CRM stages & account owners
- ▸ Marketing source & campaign
- ▸ Health scores & fit signals
Result: one growth view across product, RevOps, and marketing— where "good" accounts, levers, and risks are obvious.
SaaS Reality
You don't need more charts. You need your product, pipeline, and revenue to finally speak the same language.
Most SaaS analytics fails because it's built tool-first instead of model-first. We start with your growth motions, pricing model, and sales process—then design events, schemas, and pipelines that serve that reality.
PLG data is trapped in product tools.
Amplitude, PostHog, Mixpanel, or custom tracking exists—but it’s not joined cleanly to CRM, billing, or GA4. So decisions rely on anecdotes instead of patterns.
Attribution ignores usage and expansion.
Boards and execs get channel attribution reports that stop at signup or first deal. Nobody sees which motions drive expansion, retention, or high-LTV cohorts.
DIY event schemas are collapsing under their own weight.
Years of one-off events, random naming, and legacy flows make it risky to change anything—so experiments slow down, not speed up.
Foundations First
We start with your growth model, then design the tracking—not the other way around.
PLG, sales-assisted, pure enterprise, usage-based pricing—each needs a different measurement spine. We co-design that spine with product, RevOps, and engineering before touching GA4, GTM, or dashboards.
Event schema & entity model
- Design a sane event dictionary covering users, accounts/workspaces, and environments.
- Align activation, PQL, SQL, and opportunity definitions before coding anything.
- Create versioned specs engineers and analysts can both live with.
Identity & stitching strategy
- Decide how you track users vs accounts vs workspaces across tools.
- Handle invites, multi-seat accounts, and multiple products in one model.
- Minimize fragile client-side joins by leveraging warehouse and server-side events.
Source-of-truth decisions
- Pick where "truth" for revenue, seats, and churn lives (billing vs CRM vs product).
- Make GA4 and BI consumers of that truth—not fragile mini-CRMs.
- Document how conflicting numbers are resolved so teams don’t re-litigate every QBR.
Governance & change management
- Put guardrails around who can create new events and funnels.
- Add review processes for schema changes that impact downstream models.
- Make it easy to deprecate and migrate events instead of letting cruft pile up.
SaaS Data Architecture
A SaaS analytics stack designed around accounts, revenue, and product behavior.
The goal: any key question—from "which channels drive high-LTV accounts?" to "which features correlate with net retention?"—can be answered without five different ad-hoc queries and a fight over whose numbers are right.
Product & App Layer
- Web and app events for core features, collaboration, and value moments.
- Tracking workspaces/accounts, roles, and plans—not just anonymous users.
- Feature flags and experiments emitting structured events.
Typical tech
Custom event tracking, Segment / RudderStack / Snowplow, GA4, product analytics (Amplitude, PostHog, Mixpanel), feature flag tools.
Billing & Monetization Layer
- Subscriptions, invoices, and usage-based charges from Stripe, Chargebee, Recurly, or custom billing.
- Plan changes, downgrades, pauses, and add-ons.
- Trial-to-paid conversion and discount logic.
Typical tech
Billing webhooks into warehouse, server-side events into GA4/Ads, revenue tables keyed by account/workspace.
CRM & GTM Systems
- Leads, contacts, accounts, and opportunities in HubSpot/Salesforce/Pipedrive.
- Sales stages, owners, lost reasons, and forecast categories.
- Outbound, inbound, and PLG-assisted motion signals.
Typical tech
Reverse ETL into CRM, CRM → warehouse sync, lifecycle events back into analytics as clean events.
Warehouse, Models & BI
- Unified account and user models across product, billing, and CRM.
- Cohort and lifecycle analyses (activation, expansion, churn).
- Self-serve dashboards for growth, product, RevOps, and leadership.
Typical tech
BigQuery/Snowflake/Redshift, dbt or SQL models, Looker Studio / Power BI / Mode, semantic layers for metrics.
Experimentation & Optimization
- Feature and pricing experiments with clean exposure and outcome definitions.
- Channel and motion experiments (PLG vs sales-led, self-serve vs assisted).
- Playbooks for what to test next based on bottlenecks.
Typical tech
Experimentation platforms, GA4 experiments, in-house frameworks wired to warehouse metrics.
Selected SaaS Engagements
From noisy product data to clear revenue stories.
The specifics change—vertical, ACV, motion—but the patterns repeat: messy tracking, misaligned definitions, and big bets made on partial views. Our job is to replace that with a stack your whole GTM org can trust.
PLG SaaS with freemium & trials
Problem: Millions of events, weak identity strategy, and no agreement on activation. Growth, product, and RevOps saw different funnels and "PQL" definitions.
What we did: Redesigned the event schema around accounts/workspaces, implemented warehouse-first identity, and wired activation + expansion models into BI and CRM.
Impact: Teams aligned on what "good" looks like, experiments targeted clear bottlenecks, and leadership had one set of growth metrics.
B2B sales-led SaaS with long cycles
Problem: Marketing attribution stopped at MQL, and sales stages lived only in CRM. No clean view from first touch → opportunity → closed-won → expansion.
What we did: Mirrored CRM lifecycle events into analytics, joined them with marketing + product data in the warehouse, and built account-level pipeline analytics.
Impact: Go-to-market leadership could see which channels and motions created durable pipeline and revenue—not just leads.
Hybrid PLG + sales-assisted
Problem: Self-serve, assisted, and enterprise deals all followed different paths with inconsistent tracking. Pricing experiments were flying blind.
What we did: Standardized journeys into a unified account model, wired pricing and packaging experiments into warehouse metrics, and exposed clear reports for each motion.
Impact: The team could confidently shift spend and effort between PLG and sales-led motions, backed by NRR and payback numbers—not gut feel.
For product & growth
Clear activation, habit, and expansion metrics for your product-led motions—with dashboards that show what actually drives upgrades and retention, not just DAUs.
For RevOps & sales leadership
Account and pipeline analytics grounded in reality—deal velocity, conversion, expansion, and churn by segment, motion, and playbook.
For founders & finance
Clean, auditable metrics for board decks and planning: NRR, GRR, CAC payback, LTV by cohort, and channel/motion ROI—backed by a stack you actually understand.