SaaS metrics dashboard
MRR, ARR, churn, net revenue retention, expansion and cohort views, calculated from Stripe and your database.
Data Analytics · SaaS
SaaS Analytics, Churn Prediction and AI Usage Insights
One view of revenue, product usage and AI cost per customer, with churn prediction that tells customer success which accounts need attention this week.
Overview
SaaS analytics that explains growth and churn
Most SaaS teams have plenty of data and few answers. MRR lives in Stripe, product events in Segment, Mixpanel or PostHog, support history in Intercom and account details in your own database. The board deck takes a week to assemble, and nobody can say with confidence why last month's churn spiked. SaaS analytics brings those sources together so revenue, usage and customer health finally line up.
We build the pipeline and the models behind it. Data flows from billing, product events, CRM and your application database into a warehouse such as BigQuery, Snowflake or PostgreSQL. We model it so MRR, net revenue retention, activation and expansion are calculated one way for everyone. Dashboards show founders, product and customer success what they need. On top, churn prediction scores every account on usage trends, support signals and billing events, so your CS team knows who to call before renewal, not after cancellation.
AI products add a metric most dashboards miss: what each customer costs you to serve. AI usage analytics tracks tokens, requests and model spend per tenant and per feature, next to the revenue that tenant brings in, so you can see which plans make money and which features need cheaper models. The approach builds on dashboards we've delivered before, including the admin analytics panel for AA JoyLand's WhatsApp assistant and the predictive analytics in IWMCRM, our own AI CRM.
Use cases
AI Data Analytics use cases in SaaS
Product analytics for SaaS
Activation funnels, feature adoption and retention by cohort, plan and segment.
Churn prediction
Account-level risk scores based on usage, support and billing signals, refreshed daily for customer success.
AI usage analytics
Token, request and model cost by tenant, feature and plan, set against revenue to show true margin.
Expansion signals
Spot accounts hitting plan limits or adding users quickly, and route them to sales at the right moment.
Feedback and ticket themes
AI groups support tickets, reviews and survey answers by theme so product sees what customers keep asking for.
How it works
How it works
- 01Connect your sourcesPipelines pull from Stripe, product analytics, CRM, helpdesk and your application database.
- 02Model the metricsData is cleaned and joined by account, and core SaaS metrics are defined once so every report matches.
- 03Dashboards by roleFounders, product, finance and customer success each get the views they act on.
- 04Predictions run on topChurn, expansion and usage models score accounts on fresh data every day.
- 05Alerts reach the right peopleRisk changes and anomalies are pushed to Slack, email or your CRM as tasks.
Capabilities
Key features
Unified SaaS data model
Billing, usage, support and CRM data joined by account in one warehouse.
Explainable churn scores
Each risk score shows the signals behind it, so CS knows what to talk about on the call.
Per-tenant AI cost tracking
Model spend broken down by customer, feature and model, next to revenue.
Natural-language questions
Ask "which accounts on the Pro plan dropped usage this month?" and get an answer with the query behind it.
Board-ready reporting
Investor and board metrics generated on schedule from the same definitions as your daily dashboards.
Privacy and access controls
Row-level security, field masking and audit logs, with GDPR-friendly handling of personal data.
Integrations
Works with your SaaS stack
- Stripe
- Segment
- Mixpanel
- PostHog
- Intercom
- HubSpot
- PostgreSQL
- BigQuery
- Snowflake
- Metabase
- Looker Studio
Using something else? If it has an API, a database or a webhook, we can connect to it.
Outcomes
Benefits for SaaS teams
Churn you can see coming
Customer success acts on risk scores weeks ahead, instead of reading cancellation emails.
Margins on AI features
Knowing cost per tenant lets you price plans properly and target expensive features for optimization.
Faster board prep
Metrics are already calculated and consistent, so reporting takes hours instead of a week.
Product decisions on evidence
Adoption and retention data shows which features earn their place on the roadmap.
Our work
Related Work
Products built by The Botss
Product
IWMCRM
I Will Manage (IWM) CRM is an AI-powered SaaS platform purpose-built for pharma distribution companies, designed to replace manual operations with intelligent automation and real-time insights. Developed by The Botss, the platform centralizes lead, vendor, product, and team management while leveraging AI-driven communication, predictive analytics, and seamless integrations with tools such as Microsoft 365, Google Workspace, Zoom, and Twilio.
Explore IWMCRM ProductCommify.ai
Commify.ai is an AI-driven platform designed to help small and medium-sized e-commerce businesses automate. By integrating directly with Shopify or WooCommerce stores, Commify.ai enables companies to deploy AI chatbots for 24/7 support, generate and edit product descriptions and images, and reduce manual workload, allowing merchants to focus on growth and sales.
Explore Commify.aiFAQ
AI Data Analytics for SaaS: FAQs
What is SaaS analytics?
It's the combination of revenue, product usage and customer data used to understand how a subscription business grows. Typical outputs are MRR and retention dashboards, activation funnels, cohort analysis and churn forecasts.
How does churn prediction work for SaaS?
A model learns from accounts that cancelled in the past, looking at signals like falling logins, fewer active users, support complaints and failed payments. It then scores current accounts so customer success can act early.
Do we have enough data for churn prediction?
It depends on how many customers and cancellations you have. With a small base we start with rule-based health scores and move to a trained model as history builds up. We check your data during discovery and say plainly what's possible.
What is AI usage analytics?
It's tracking how customers use your AI features and what each use costs: tokens, requests, model choice and spend per tenant and feature. Set against revenue, it shows whether your AI features make or lose money.
Can you build a SaaS metrics dashboard from Stripe data?
Yes. Stripe is usually the source for MRR, churn and expansion. We combine it with your database and product events so revenue metrics can be broken down by plan, segment and feature usage.
Do we need a data team to run this?
No. We build and maintain the pipelines and models, and dashboards are designed for founders and managers. If you have data people, we work with them and hand over cleanly.
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Know which customers are leaving before they do.
Tell us the three questions your dashboards can't answer today. We'll show you the data model and churn signals that answer them.