Laravel Metrics Laravel Package

Product Decisions This Supports

• Data-Driven Dashboards: Enables rapid development of real-time analytics dashboards (e.g., SaaS KPIs like MRR, churn, or e-commerce metrics like sales trends, inventory turnover). Reduces frontend/backend coupling by providing structured, query-optimized data for charts (e.g., Chart.js, Highcharts).
• Roadmap Efficiency:
  • Time-Series Aggregations: Eliminates manual SQL for periodic trends (daily/weekly/monthly) or custom ranges, accelerating feature delivery (e.g., "Add customer retention trends" → 1 day vs. 1 week).
  • Multi-Dimensional Analysis: Supports segmented metrics (e.g., "Revenue by product category over time") via labelColumn and groupData, reducing backend complexity for complex reports.
  • Prototyping: Validates metrics hypotheses quickly (e.g., A/B test performance) without building full data pipelines.
• Build vs. Buy:
  • Avoid Custom Solutions: Replaces ad-hoc SQL or raw Eloquent queries for time-series data, reducing technical debt.
  • Extensibility: Allows customization (e.g., PostgreSQL-specific optimizations) while leveraging pre-built functionality for 80% of use cases.
  • Cost-Effective: MIT-licensed alternative to commercial tools (e.g., Mixpanel, Amplitude) for self-hosted analytics.
• Use Cases:
  • SaaS Metrics: MRR/ARR growth, churn rates, feature adoption (e.g., User::metrics()->countByMonth()->trends()).
  • E-Commerce: Sales velocity, inventory turnover, customer lifetime value (e.g., Order::metrics()->sum('amount')->byWeek()->trends()).
  • Internal Tools: Operational dashboards (e.g., support ticket resolution time, API call volumes).
  • Marketing: Campaign performance (e.g., Lead::metrics()->countByDay()->groupData(['source' => ['organic', 'paid']])->trends()).
  • Finance: Expense tracking, revenue recognition (e.g., Invoice::metrics()->sum('amount')->byMonth()->metricsWithVariations()).

When to Consider This Package

• Adopt When:
  • Your Laravel app requires time-series aggregations (e.g., trends, comparisons) for dashboards or reports.
  • You need to reduce backend complexity for periodic data (e.g., daily/weekly/monthly) without sacrificing performance.
  • Your team lacks dedicated data engineers but needs scalable analytics (supports MySQL, PostgreSQL, SQLite).
  • You’re building multi-tenant SaaS and need tenant-specific metrics (e.g., Tenant::all()->each(fn ($tenant) => $tenant->metrics()->...)).
  • You want to avoid raw SQL for time-based queries while maintaining flexibility (e.g., custom date ranges, labels).
  • Your roadmap includes A/B testing, cohort analysis, or segmented metrics (e.g., by user role, region).
• Look Elsewhere If:
  • You need real-time streaming analytics (use Kafka + custom processing).
  • Your dataset exceeds millions of rows without indexing (consider dedicated tools like ClickHouse or Druid).
  • You require advanced statistical functions (e.g., moving averages, forecasting) beyond basic aggregates (sum, avg, count).
  • Your stack is non-Laravel (e.g., Django, Node.js) or uses non-SQL databases (e.g., MongoDB).
  • You need sub-second latency for high-frequency queries (this package abstracts but doesn’t optimize for ultra-low latency).
  • Your use case involves geospatial data or graph-based metrics (e.g., network analysis).

How to Pitch It (Stakeholders)

For Executives: "This package lets us build analytics dashboards 10x faster by automating the heavy lifting of time-series queries—think of it as ‘Turbo Mode’ for metrics. Instead of spending weeks writing SQL for trends like ‘monthly revenue by product category,’ we’ll do it in hours. For example, tracking SaaS churn or e-commerce sales velocity becomes as simple as a single method call. It’s like hiring a junior data engineer overnight, but without the salary. We’ll save dev time, ship features faster, and get better insights sooner—all while keeping control of the data."

For Engineers: *"This is a batteries-included solution for time-series metrics in Laravel. It handles:

• Complex aggregations (daily/weekly/monthly trends, custom ranges) with a fluent API.
• Multi-dimensional grouping (e.g., groupData(['status' => ['pending', 'shipped']]) for segmented charts).
• Database agnosticism (MySQL, PostgreSQL, SQLite) with zero config.
• Performance (optimized queries under the hood, no N+1 issues).

Use it for:

• Dashboards (e.g., User::metrics()->countByMonth()->trends()).
• Reports (e.g., Order::metrics()->sum('amount')->byYear()->metricsWithVariations()).
• Prototyping (e.g., ‘Let’s test this hypothesis in 10 minutes’).

No more:

• Writing repetitive SQL for GROUP BY + DATE_TRUNC.
• Debugging off-by-one errors in date ranges.
• Maintaining custom query builders for every new metric type.

Example:

// Before: 30+ lines of SQL + manual date handling
// After: 3 lines of readable code
LaravelMetrics::query(Order::query())
    ->sum('amount')
    ->byMonth(12)
    ->labelColumn('payment_method')
    ->trends();

Works with Eloquent, Query Builder, or raw SQL. Supports Laravel 11/PHP 8.2+."*

For Designers/Product Managers: "This tool gives us flexibility to iterate on dashboards without blocking on backend work. Need to show ‘weekly active users by region’? Done. Want to compare ‘this month vs. last month’ for a KPI? One line. It’s like having a drag-and-drop analytics builder, but built into our codebase. We’ll ship insights faster and avoid misaligned expectations with engineering."