---
title: Data
category: product
entity_type: skill
price: $15
canonical: https://forgehouse.ai/skills/data/
lang: en
hreflang_alt: https://forgehouse.ai/tr/skiller/data/
last_updated: 2026-06-20
---

# Data

> Analytics and data query patterns for metrics, dashboards, reports, KPIs, and funnel analysis

A library of analytics and reporting query patterns for the metrics that matter: active users, growth, retention cohorts, funnels, and revenue. It pairs ready-to-adapt SQL with an event-tracking schema and dashboard components, so you can stand up reliable KPIs and visualizations instead of writing aggregation logic from scratch.

## Use cases
- Calculating active users (daily, weekly, monthly) and stickiness ratios
- Building cohort retention and week-over-week growth analyses
- Measuring a multi-step conversion funnel from visit to purchase
- Computing revenue metrics: recurring revenue, revenue per user, lifetime value
- Defining a structured analytics event schema for client-side tracking
- Optimizing slow aggregation queries with indexes and materialized views

## Benefits
- Stand up trustworthy KPI dashboards quickly with proven query patterns instead of guesswork
- Spot retention and funnel leaks early with cohort and step-conversion analyses
- Keep dashboards fast under load through indexed queries and materialized aggregations
- Track product behavior consistently with a well-structured event schema

## What’s included
- User-metric queries for active users, stickiness, signups, and growth rate
- A cohort retention SQL pattern and a multi-step conversion funnel query
- Revenue queries for recurring revenue, average revenue per user, and lifetime value
- A typed analytics event interface and a client-side tracking function
- Reusable dashboard components for metric cards and time-series charts
- Query-optimization patterns with indexes and a refreshable materialized view

## Who it’s for
Developers and analysts building metric dashboards, KPI reports, and funnel analyses on a SQL data store.

## How it runs
Every dashboard here starts from a question, not a table. The skill writes the metric SQL precisely, wires funnels and revenue views into single queries, and indexes the hot paths before anything slows down.
1. Starts the schema from the question, not the table: how will this data be queried decides the design, then defends it in layers with RLS, app-level auth and input validation.
2. Defines the core metric SQL precisely instead of vaguely: DAU, WAU, MAU and the stickiness ratio, week-over-week growth with window functions, and cohort retention that tracks each signup week across its following weeks.
3. Builds the conversion funnel as one query: distinct users counted at each step from page view to purchase, step-to-step percentages plus overall conversion, so the leak point is visible in a single table.
4. Covers the revenue side with MRR by month, ARPU per paying user and LTV with both average and median, because a few whales distort the average and the median tells the honest story.
5. Ships a typed event tracking schema: every event carries an id, name, user and anonymous id, timestamp, properties and context including UTM campaign data, sent through one track() implementation rather than scattered calls.
6. Optimizes before the dashboard slows down: composite indexes on the common query paths, expensive aggregations moved into materialized views refreshed daily, and closes with the checklist of retention policy and privacy compliance.

## FAQ
### These are SQL patterns. Do they assume a specific database, or do I adapt them to mine?
They are written as ready-to-adapt SQL, so you fit them to your own data store rather than expecting a one-click connector. Most patterns lean on standard SQL, with the table and column names swapped for yours.

### How much rework before these queries run on my actual schema?
The shape of each query, the metric logic for active users, cohorts, or funnels, is the part you keep, while the table and event names are what you change. The event-tracking schema it ships shows the structure these queries expect.

### Does it set up the tracking, or assume the data is already there?
It queries data you are already capturing and gives you a schema to model events against, but it does not collect the events for you. If the underlying tracking is not in place, those metrics have nothing to read.

## Price
$15, one-time, no subscription. VAT included.

Related guide: [AI for data analytics](https://forgehouse.ai/guides/ai-data-analytics/)