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Core Builds6 weeksSaaS

Stripe Says One Number, Dashboard Says Another

A growing SaaS and marketing company was drowning in disconnected systems - Salesforce for sales, QuickBooks for financials, Google Analytics for web traffic, and product-specific databases all living in silos. Leadership was making decisions based on week-old spreadsheets manually compiled by the team, with no way to see the full customer journey or business health in real-time.

Data IntegrationAnalyticsSaaS Metrics

The Challenge

The company was facing several critical challenges:

Key Issues:

  • Salesforce for sales pipeline and customer data
  • QuickBooks for financial transactions and invoicing
  • Google Analytics for website traffic and user behavior
  • Product databases for usage metrics and feature adoption
  • Spreadsheets for everything else cobbled together manually
  • Leadership making decisions based on week-old data from manually compiled spreadsheets
  • No single source of truth - conflicting numbers depending on which system you looked at
  • Team spending hours each week copying data between systems for basic reporting
  • Impossible to see the full customer journey from website visit → sales conversation → product usage → renewal
  • No foundation for advanced analytics like lead scoring or churn prediction

Business Impact:

The founder knew they needed better data infrastructure but didn't know where to start or how to prioritize what to build.

The Solution

Built a unified data platform that brought all disparate systems together, automated manual workflows, and established a strategic roadmap for analytics maturity.

Our Approach:

  • Phase 1 - Unified Data Foundation (Weeks 1-3): Created single source of truth in BigQuery by connecting all critical systems, designed unified data model, implemented data quality checks
  • Phase 2 - Self-Service Dashboards (Weeks 4-5): Built Looker dashboards for sales, finance, product, and customer health - all updated in real-time
  • Phase 3 - Strategic Roadmap (Week 6): Worked with founder to shift thinking from "reporting what happened" to "predicting what's next", mapped current state to future capabilities
  • Analytics Maturity Roadmap: Now (real-time dashboards) → 6 months (lead scoring, funnel analysis) → 12 months (churn prediction, ML-powered forecasting)

The Results

Real-time dashboards replaced manual weekly reports. Leadership shifted from "what happened last week?" to "what should we do next?" Strategic roadmap positioned the company to scale analytics capabilities as they grow.

Manual Reporting
15hrs → automated
Eliminated weekly data compilation
Data Sources
8 tools unified
Single source of truth established
Decision Velocity
Real-time
Leadership makes decisions with current data
Customer Journey
Full visibility
Track from first visit through renewal
Dashboard Access
Entire company
Self-service for all teams
Timeline
6 weeks
From scattered data to unified platform

Additional Results:

  • Team productivity: Freed up hours per week previously spent on data wrangling
  • Strategic clarity: Founder now thinks in terms of metrics, not gut feel
  • Scalable foundation: Platform can grow with the company - ready for advanced analytics
  • Cultural shift: Team meetings shifted from debating what the numbers are to discussing what to do about them

Technical Details

Architecture:

Modern data stack built on Google Cloud Platform

Technology Stack:

  • Data Warehouse: Google BigQuery (serverless, pay-per-query)
  • BI Tool: Looker for self-service dashboards
  • ETL: BigQuery scheduled queries, custom Python scripts for API connections
  • Data Sources: Salesforce API, QuickBooks API, Google Analytics API, PostgreSQL (product DB)
  • Transformation: SQL-based data modeling with automated refresh schedules
  • Automation: Daily data refresh, automated quality alerts, scheduled dashboard distribution

Technical Highlights:

  • Unified customer dimension: Links Salesforce accounts, QuickBooks customers, GA users, and product user IDs
  • Activity fact tables: Sales activities, financial transactions, web events, product usage events
  • Incremental loads: Only pull new/changed data to minimize API usage and cost
  • Dashboards: Executive (revenue, growth, CAC, LTV), Sales (pipeline, conversion rates), Customer Health (usage, engagement, renewal risk)
  • Analytics frameworks: North Star Metric, Pirate Metrics (AARRR), Analytics Maturity Model, Data-Driven Decision Making
  • Future capabilities: Data model includes features for lead scoring, churn prediction, forecasting, customer segmentation
  • Cost-effective: BigQuery serverless model fit startup budget vs traditional warehouse

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