SaaS
Your product analytics shows what users do. It rarely shows why they churn before payback — or which activation steps actually predict long-term retention.
- Tie product usage to churn — before it happens
- Build MRR/NDR dashboards your board trusts
- Unify fragmented BI into one source of truth
- Identify which activation steps predict expansion
Why this matters
Product Analytics That Predicts Retention
Most SaaS teams track feature usage but cannot connect it to retention. We build cohort models that tie activation milestones to long-term revenue — so you know which onboarding steps actually matter.
Revenue Metrics Your Board Actually Trusts
Your MRR, NDR, and LTV numbers should not come from three different spreadsheets. We build unified revenue analytics — from trial conversion to expansion — so your CFO and investors see one source of truth.
CAC Payback Visibility Across Channels
You are spending on paid, content, and PLG — but do you know which channel produces users that actually expand? We build attribution models that track from first touch to payback period, so you invest where it compounds.
Metric Trees That Connect Teams
When Product, Marketing, and Finance track different numbers, decisions slow down. We build metric tree frameworks that cascade from company-level KPIs to team-level actions — everyone sees how their work moves the needle.
How We Work With SaaS Teams
Every SaaS company has different data maturity. We start by understanding where you are — then build only what moves the needle fastest.
Diagnose Your Data Gaps
We audit your existing analytics stack, identify blind spots in your metric coverage, and map which decisions are currently being made without data — and which data exists but nobody trusts.
Build the Analytics Layer
We design and deploy the data models, pipelines, and dashboards that connect product usage to revenue outcomes — starting with the metrics that have the highest decision value.
Validate and Iterate
Analytics is not a one-time project. We validate models against real outcomes, tune dashboards based on how your team actually uses them, and identify the next highest-value analysis.
Transfer Ownership
Your team should be able to run this without us. We document everything, train your analysts, and hand over a system your team actually adopts — not a shelfware report.
Key KPIs
User Engagement & Retention
Revenue & Growth
Customer Acquisition & Marketing Efficiency
Operational & Sales Performance
Results we've delivered
A $25M ARR B2B SaaS with 200 employees suffered from data silos, no single source of truth, and rising churn.…
A $12M ARR SaaS platform had zero product analytics and no A/B testing capability. We built their experimentation infrastructure from…
Delivered a complete data strategy and 12-month roadmap for a $6M pet-tech marketplace in 4 weeks. Unified 10+ fragmented data…
Automated LinkedIn client reporting for a marketing agency — reduced monthly report generation from 2-3 days to fully automated. Enabled…
Advanced dashboards and real-time data tools, enabling actionable insights into user behavior, revenue trends, and performance metrics for smarter decision-making
Advanced analytics boosted AI Sales with revenue tracking, AI vs. human performance insights, real-time alerts, and improved bot strategies for…
Implementation Roadmap
Define
Identify KPIs and data solutions to track business performance.
Develop
Create a tailored data analytics system.
Deploy
Provide knowledge to use the new data system for business growth.
Frequently asked questions
What metrics matter most for SaaS analytics?
The core SaaS metrics are MRR/ARR, churn rate, LTV, CAC, and LTV:CAC ratio. Beyond these, track expansion revenue, net revenue retention (NRR), time-to-value, and activation rate. The right mix depends on your stage — early-stage focuses on activation and retention, growth-stage on unit economics.
How do you handle SaaS data from multiple sources?
We build a unified data warehouse (Snowflake or BigQuery) that ingests from your product database, Stripe/billing system, CRM, and marketing tools. dbt models create a single customer entity that ties subscriptions, usage, and support interactions together.
What does a typical SaaS analytics engagement look like?
Phase 1 (weeks 1-2): audit current metrics and data sources. Phase 2 (weeks 3-6): build the data warehouse, define metric logic, create executive dashboards. Phase 3 (ongoing): iterate on self-serve analytics, cohort analysis, and predictive models for churn.
Can you help reduce churn with data?
Yes. We build churn prediction models using product usage signals, support ticket patterns, and billing behavior. Typical outcome: identifying at-risk accounts 30-60 days before cancellation, enabling proactive intervention that reduces churn by 15-25%.
Your data team is drowning in dashboards but starving for answers
We build the analytics infrastructure that connects product usage to revenue outcomes — so you stop guessing and start compounding.
Book a Discovery Call →