Tracking and implementation

Knowing which metrics to track is useless without the infrastructure to actually measure them. Here’s how to build a metrics stack that scales from seed stage to Series C without becoming a nightmare.

The metrics stack

At a high level, your tracking infrastructure has three layers:

Layer 1 — Data sources: Your product database (PostgreSQL, MySQL), billing platform (Stripe, Zuora), CRM (HubSpot, Salesforce), and product analytics (Mixpanel, Amplitude).

Layer 2 — Data warehouse: Centralize everything in one place. Snowflake, BigQuery, or a managed warehouse like Basedash Warehouse that handles provisioning automatically.

Layer 3 — BI and dashboards: Turn raw data into charts, dashboards, and insights. This is where your team actually interacts with metrics. Tools like Basedash use AI to generate dashboards from natural language, so you don’t need a dedicated analyst to get started.

How much infrastructure do you need?

Don’t over-engineer early. A seed-stage startup connecting Basedash directly to their Postgres database and Stripe account can track every metric in this guide. When you outgrow direct connections, Basedash Warehouse gives you a managed warehouse with 750+ Fivetran connectors — no infrastructure to set up or maintain. You get the benefits of a warehouse without hiring a data engineer.

Setting up tracking

Step 1: Define your events

Create a tracking plan before writing any code. For each event, document:

• Event name — use a consistent naming convention (user_signed_up, not SignUp or User Sign Up)
• Trigger — when exactly does this fire?
• Properties — what context does it carry?

user_signed_up
  user_id: string
  signup_source: "organic" | "paid" | "referral"
  plan_type: "free" | "trial" | "paid"
  timestamp: ISO 8601

Step 2: Start with your primary KPI

If MRR is your primary KPI, start by connecting your billing system. If WAU is your primary KPI, start by tracking meaningful user actions in your product database.

Don’t try to instrument everything at once. Get your primary metric right, then add secondary metrics one at a time.

Step 3: Connect your data sources

Building dashboards

Design principles

Lead with your primary KPI. The first thing anyone sees should be the number that matters most.

Show trends, not snapshots. A single number is meaningless without context. Always show time series and comparisons to targets or previous periods.

Keep it scannable. If your dashboard requires scrolling to find the key insight, redesign it. Top-level dashboards should have 5–8 metrics max. With AI-powered tools like Basedash, you can describe the dashboard you want in plain English and iterate on it conversationally — no SQL or chart configuration required.

Separate operational from strategic. Daily ops dashboards (support volume, system health) serve a different audience than monthly strategic dashboards (MRR, unit economics).

Dashboard structure for startups

Automated alerts

Set up alerts for the metrics that require immediate action — not for everything.

Worth alerting on:

• MRR growth drops below target for 2+ weeks
• Churn rate spikes above threshold
• Runway drops below 9 months
• System uptime falls below 99.5%

Not worth alerting on:

• Minor fluctuations in daily active users
• Individual customer behaviors
• Metrics you can’t act on in real-time

The best alert includes: what happened, how it compares to normal, and who should investigate. “MRR growth dropped to 6% MoM (target: 12%). @finance, please investigate churn spike in Enterprise segment.”

Metrics review cadence

The weekly review is the most important ritual. Keep it under 30 minutes, focused on: (1) how did the primary KPI move, (2) why, (3) what are we doing about it.

Common mistakes

Inconsistent definitions. If marketing calculates CAC differently than finance, you’ll argue about numbers instead of making decisions. Document every metric’s formula and source of truth.

No data ownership. Assign one person as the owner of data quality. If nobody owns it, data rots fast.

Over-investing in infrastructure. Your first dashboard doesn’t need Airflow, dbt, and a Snowflake warehouse. Start simple, add complexity when simple breaks.

Tracking everything, analyzing nothing. Raw event tracking without regular analysis is just storage costs. Only track what you’ll actually look at.