Data-Driven Product Decisions

Product companies generate enormous amounts of data — user behavior, feature usage, conversion funnels, retention patterns, and support interactions. The difference between good and great product companies is how effectively they use this data to make decisions. At Nexis Limited, analytics dashboards power product decisions across all four SaaS products.

Metrics That Matter

Product Metrics

  • Daily Active Users (DAU) / Monthly Active Users (MAU): Measures engagement breadth. The DAU/MAU ratio indicates stickiness — values above 20% suggest good daily engagement.
  • Feature Adoption Rate: Percentage of users who adopt a new feature within 30 days of release. Low adoption suggests poor discoverability, unclear value, or implementation problems.
  • Retention Rate: Percentage of users who return after their first day, week, and month. Retention is the single most important product metric.
  • Time to Value: How long it takes a new user to experience the product's core value. Shorter is better.

Business Metrics

  • Monthly Recurring Revenue (MRR): Total recurring revenue from subscriptions.
  • Customer Acquisition Cost (CAC): All costs to acquire one new customer.
  • Lifetime Value (LTV): Predicted total revenue from a customer over their relationship with your product.
  • Churn Rate: Percentage of customers who cancel their subscription in a given period.
  • LTV/CAC Ratio: Should be above 3x for a healthy SaaS business.

Building a Data Pipeline

A data pipeline moves data from source systems (databases, APIs, event streams) through transformation into analytics-ready storage. Common architecture:

  • Extract: Pull data from application databases, third-party APIs, and event tracking systems.
  • Transform: Clean, validate, deduplicate, and structure data for analysis.
  • Load: Store transformed data in a data warehouse (BigQuery, Redshift, or PostgreSQL for smaller scale).
  • Orchestrate: Schedule and monitor pipeline runs with tools like Airflow or Dagster.

Dashboard Design

Effective dashboards communicate insights quickly and clearly:

  • Each dashboard should answer one question or serve one audience.
  • Put the most important metrics at the top.
  • Show trends over time, not just current values.
  • Include context — is this number good or bad? Show targets and historical comparisons.
  • Keep dashboards focused — 5-8 charts per dashboard maximum.

Event Tracking

Client-side and server-side event tracking captures user interactions — page views, button clicks, feature usage, and error occurrences. Design your event tracking schema carefully:

  • Use a consistent naming convention (noun_verb: "button_clicked", "page_viewed").
  • Include contextual properties (page, component, user segment).
  • Track both actions (what users did) and outcomes (what resulted).
  • Document every event and property in a tracking plan.

Conclusion

Data analytics is not about collecting data — it is about making better decisions. Start with the metrics that directly relate to your product and business goals, build pipelines to make data accessible, and create dashboards that drive action. Let data inform decisions, but do not let it replace product intuition and customer empathy.

Need help building your analytics practice? Our team designs data pipelines and analytics dashboards.