Logging a custom event and building a simple funnel chart is genuinely achievable — comprehensive behavioral cohort analysis at real product scale is the deeper, ongoing part this stays clear of.
Build it
Effort: a weekendRunning cost: $0/mo to start
Why CanICodeThis says Build it
Logging a custom event (name, properties, timestamp) tied to a user is straightforward CRUD
A basic funnel chart (how many users did step A, then B, then C) is bounded aggregation
For a single product's core metrics, this is genuinely a weekend build
The parts that'll cause problems
Retention cohort analysis (grouping users by signup week and tracking behavior over time) needs real, carefully-designed aggregation queries
Real-time dashboards at high event volume need genuine data-pipeline engineering to stay fast
Statistical significance on any embedded experiment results shares this register's own A/B-testing caution
How I'd build this
frontend
Astro
database
Turso or a Postgres host
hosting
Cloudflare Pages
MVP scope
Log a custom event with properties and a timestamp
A funnel chart for a fixed sequence of steps
Basic counts and time-series charts
Postpone to v2
Retention cohort analysis
Real-time dashboards at scale
Experiment significance testing
What it actually costs to build
AI agent — with a subscription (Claude Pro/Max, Cursor, etc.)
$0 marginal
AI agent — pay-per-use API, no subscription
$43–$86 one-time
Hosting, once it's running
$0/mo (free tier)
Domain name, if you want your own
~$12/yr
Existing tools solving a similar problem typically run $28–$28/mo — see how they compare below.
The build prompt
Build me a basic product analytics tool, genuinely achievable in a weekend:
1. Log custom events with properties tied to a user — straightforward CRUD, a single write per tracked action.
2. A funnel chart for a fixed sequence of steps (how many users did A, then B, then C) — bounded aggregation.
3. Basic counts and time-series charts using an existing charting library.
That's real, useful tracking for a single product's key metrics. Out of scope: retention cohort analysis (grouping users by signup week and tracking behavior over time), which needs real, carefully-designed aggregation queries, and real-time dashboards at high event volume, which need genuine data-pipeline engineering to stay fast.
