Product Analytics Dashboard

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SaaS & Technology

Usage Analytics Dashboard for SaaS Platform

Real-time product usage, cohorts, retention, and feature adoption metrics for data-driven product decisions.

SaaS automation case study featuring reporting and integration.

100%

Data-Driven Decisions

+9%

Retention Improvement

Full

Feature Visibility

Overview

A SaaS company lacked visibility into how customers interacted with their product. The product team was guessing priorities without data, and there was no cohort or retention tracking in place.

Business Context

The product team was making roadmap decisions based on customer requests and stakeholder opinions rather than actual usage data. Features that required significant engineering investment sometimes saw minimal adoption, while high-value capabilities went underdeveloped because their impact wasn't visible. Customer success couldn't identify at-risk accounts until they churned, and the executive team couldn't answer basic questions about product engagement during board meetings. Engineering resources were being allocated based on who made the loudest case rather than what would drive the most value. Investors were asking for product metrics during due diligence that the company simply couldn't provide, potentially affecting fundraising outcomes. The company recognized that becoming truly data-driven required not just collecting data, but making it accessible and actionable for every team. ZapWizards was engaged to build a comprehensive product analytics infrastructure that would provide the visibility needed to operate at the next level of sophistication and satisfy investor expectations.

How We Built It

We designed a complete analytics data pipeline starting with event capture from the application layer. Mixpanel SDK integration captures granular user interactions — feature usage, navigation patterns, session duration, and custom business events specific to the platform's domain. ETL pipelines built with Make.com transform raw event data and load it into BigQuery for analysis, with automated data quality checks that flag anomalies in event volume or schema. The custom Retool dashboard surfaces real-time metrics including daily/weekly/monthly active users, feature adoption rates, and session depth analytics. Cohort analysis capabilities allow the product team to compare behavior across signup periods, pricing tiers, and customer segments. Retention curves visualize how engagement changes over time, identifying critical periods where intervention can improve outcomes. Usage heatmaps reveal which features drive the most engagement and which are being ignored despite development investment. A churn risk scoring system analyzes usage patterns to identify accounts showing early warning signs, automatically alerting customer success teams. HubSpot integration ensures that usage insights are accessible directly within the CRM, enabling sales and success teams to have context-rich conversations. Automated weekly reports summarize key metrics and trends for the executive team.

Challenges

No consolidated timeline of events

No cohort or retention tracking

No visibility into feature adoption

Product team guessing priorities

What We Delivered

Data ingestion engine syncing app events → DB

Real-time analytics dashboard in Retool

Cohort analysis (DAU, WAU, MAU)

Activation metrics and usage heatmaps

Alerts for churn-risk customers

Tech Stack

Retool, BigQuery, Mixpanel API, Make.com, HubSpot, SQL ETL pipelines

Results

100%

Data-Driven Decisions

+9%

Retention Improvement

Full

Feature Visibility

Strategic Impact

The shift to 100% data-driven decision making transformed product development from an opinion-based process to an evidence-based discipline. The 9% retention improvement came from identifying and addressing specific friction points that were causing users to disengage — patterns that had been invisible without cohort analysis. Feature development prioritization improved dramatically as the team could now see actual usage data for existing features and make informed predictions about new capabilities. Customer success became proactive rather than reactive, reaching out to at-risk accounts before they churned based on early warning signals. The executive team gained confidence in board discussions, answering questions about product health with specific metrics rather than anecdotes. Engineering resources are now allocated based on impact data, eliminating waste on features that customers don't value. Sales conversations improved as reps can now speak to specific product value based on usage patterns similar customers demonstrate. The analytics infrastructure has become foundational to the company's operations, with every team from sales to support relying on the insights it provides. Marketing uses the data to build targeted campaigns for users based on their engagement level and feature adoption. Investor reporting is now straightforward, with key metrics readily available.