A growing healthcare SaaS company came to us with a paradox that is increasingly common in the industry: they had more data than ever before and less ability to use it. Despite investing heavily in HubSpot, their analytics infrastructure, and multiple point solutions, the leadership team still could not answer fundamental questions about their business with confidence.
The result of our engagement: over 20 purpose-built KPI dashboards that transformed how every department — from the executive team to individual contributors — made decisions.
The Challenge
The company provided a SaaS platform used by healthcare organizations to manage patient engagement, communications, and compliance workflows. They had grown rapidly, achieving eight-figure ARR with a customer base of hospitals, health systems, and medical groups across the United States.
Data existed everywhere but informed nothing. The company tracked data across HubSpot (marketing and sales), their own product analytics platform, a billing system, a customer support tool, and various spreadsheets maintained by different teams. Each system had its own metrics, its own definitions, and its own version of reality.
The executive team flew blind. Board meetings required weeks of preparation as the operations team manually compiled data from multiple sources, reconciled conflicting numbers, and built presentations that were often outdated by the time they were delivered. The CEO could not get a real-time answer to basic questions: What is our current ARR? How is pipeline tracking against target? What is our net revenue retention?
Departmental silos created conflicting narratives. Marketing reported lead volume was up. Sales reported pipeline quality was down. Customer success reported retention was stable. Finance reported revenue growth was decelerating. Each team's data was technically correct within its own context, but the lack of integrated reporting meant nobody could see the full picture or identify where the disconnects originated.
No leading indicators. All reporting was backward-looking — what happened last month, last quarter, last year. There were no dashboards tracking the leading indicators that would predict future performance: pipeline coverage ratios, customer health scores, expansion signals, or churn risk indicators.
Our Approach
Phase 1: Data Audit and Architecture (Weeks 1-4)
We started by mapping every data source, documenting what each system tracked, and identifying the overlaps, gaps, and conflicts. This audit revealed several critical findings:
- Marketing and sales defined "lead" differently, creating a 40% discrepancy in reported lead volume
- Customer lifetime value was calculated three different ways by three different teams
- Revenue data in HubSpot did not match the billing system due to sync errors that had gone undetected for months
- Product usage data — a critical signal for retention and expansion — was not connected to the CRM at all
We designed a data architecture that designated HubSpot as the system of record for all customer-facing data, established standardized definitions for every key metric, and defined the integration points needed to unify the data.
Phase 2: Data Integration and Cleanup (Weeks 5-8)
With the architecture defined, we executed the technical work:
CRM data cleanup. Deduplicated 15,000 contact records and standardized key fields (company names, industries, lifecycle stages). Resolved the revenue data discrepancy by rebuilding the billing system sync with proper field mapping and validation.
Product data integration. Connected the product analytics platform to HubSpot, creating a daily sync that updated customer records with usage metrics: active users, feature adoption, login frequency, and engagement scores. This data became the foundation for customer health scoring.
Definition alignment. Facilitated workshops with marketing, sales, customer success, and finance to align on standardized definitions for 30 key metrics. Documented each in a metrics dictionary that became the reference for all future reporting.
Phase 3: Dashboard Design and Build (Weeks 9-16)
We built 22 dashboards across four tiers, each designed for a specific audience and decision context.
Executive tier (4 dashboards):
- Company health overview: ARR, growth rate, net revenue retention, cash metrics
- Pipeline and revenue: Pipeline coverage, forecast vs. actual, bookings trend
- Customer health: NRR, churn rate, expansion rate, customer health score distribution
- Operational efficiency: CAC, LTV:CAC ratio, payback period, burn rate
Department tier (8 dashboards):
- Marketing performance: Lead generation, MQL conversion, channel ROI, campaign attribution
- Sales pipeline: Pipeline by stage, velocity, win rate, rep performance
- Customer success: Health scores, renewal pipeline, expansion pipeline, risk accounts
- Product engagement: Feature adoption, usage trends, activation rates
- Finance: Revenue recognition, billing accuracy, cash flow
Operational tier (6 dashboards):
- Daily lead flow and response times
- Weekly pipeline changes (adds, advances, losses)
- Campaign performance tracking
- Support ticket volume and resolution
- Product uptime and performance
- Data quality scorecard
Individual tier (4 dashboards):
- Sales rep performance (personalized for each rep)
- CSM account portfolio view
- Marketing campaign manager view
- Executive board prep summary
Phase 4: Training and Adoption (Weeks 17-20)
Each dashboard was accompanied by documentation explaining what it measured, how to interpret the metrics, and what actions to take when metrics deviated from targets. We ran team-specific training sessions that walked through real scenarios: "If this number drops, here is what you investigate and here is what you do."
The Results
Decision speed increased dramatically. The CEO reported that questions that previously took days to answer — requiring manual data pulls, reconciliation, and analysis — could now be answered in real time from the executive dashboards. Board meeting preparation time dropped from three weeks to two days.
Marketing-sales alignment improved measurably. With standardized definitions and shared dashboards, the 40% lead volume discrepancy disappeared. Both teams now referenced the same numbers, which shifted meetings from "whose data is right?" to "what should we do about these numbers?"
Customer churn prediction became possible. By integrating product usage data with CRM records, the customer success team could now identify at-risk accounts 60-90 days before renewal based on declining engagement scores. This early warning system enabled proactive intervention that reduced churn in the first quarter of use.
Revenue forecasting accuracy improved to within 5%. The pipeline dashboards with stage-specific conversion rates and weighted forecasting gave finance a reliable revenue prediction for the first time in the company's history.
Data quality became a managed process. The data quality scorecard — tracking field completion, duplicate rates, and sync health — gave the operations team a standing mechanism for maintaining the data infrastructure. Quality issues were caught weekly rather than discovered during quarterly board prep.
Key Takeaways
Start with definitions, not dashboards. The most valuable output of the entire engagement was the metrics dictionary — 30 standardized definitions that every team agreed on. Without aligned definitions, dashboards just automate disagreements.
Connect product data to the CRM. For any SaaS company, product usage data is the most predictive signal for retention, expansion, and churn. If it is not in your CRM, your customer success team is operating without their most important tool.
Build dashboards for decisions, not display. Every dashboard we built was designed around a specific decision it should enable. The executive dashboard enabled resource allocation decisions. The sales pipeline dashboard enabled forecasting decisions. The customer health dashboard enabled retention intervention decisions. Dashboards that do not connect to decisions do not get used.
Layer the insights. The four-tier dashboard architecture ensured that each audience saw the information relevant to their role and decision scope. The CEO did not need to see daily lead flow. Individual reps did not need to see company-level cash metrics. Layering prevents information overload while maintaining comprehensive coverage.
The company went from spending weeks compiling unreliable reports to having real-time, trustworthy analytics across every function. That shift — from data chaos to analytical clarity — became a genuine competitive advantage in how quickly and confidently they could act on the information their business generated every day.