Blog
How Biotechs Can Squeeze Value from Lazy Data
Why biotech data sits underutilized, why off-the-shelf solutions fall short, and how an external data team delivers quick wins under $150K.
By Ross KatzRead
Your forecast is an argument, not a number.
Revenue operations breaks when each team trusts a different system. We build the data infrastructure that connects pipeline, billing, and customer data into a single revenue model your whole leadership team can trust.
RevOps Analytics
Sales tracks deals in the CRM. Finance tracks invoices in the billing system. Marketing tracks attribution in its own platforms. Customer success tracks renewals in a spreadsheet. Each system tells a different story about the same revenue.
7
Systems unified in a typical revenue data model
28
Reverse ETL pipelines built at a single client
2 hrs
CRM sync frequency across billing, pipeline, and application data
Use Cases
Engagements where this is the right work.
Pipeline-to-cash reconciliation
Your CRM tracks opportunities. Your billing system tracks invoices. Finance tracks recognized revenue. These three numbers should tell the same story. We build the data model that reconciles them into a single revenue view.
Revenue forecasting and actuals
We build models that combine pipeline snapshots, historical win rates by segment and deal size, seasonal patterns, and renewal timing to produce forecasts you can defend to your board, then track actuals against projections so your model improves over time.
Sales operations and performance analytics
Close rate by segment, rep, lead source, deal size, product line, and quarter. We build the dimensional models your sales leadership needs to diagnose where deals stall, which reps need coaching, and which segments are worth pursuing.
Financial forecasting and lead prioritization
Connect pipeline data to financial planning models so your CFO can forecast cash flow from sales projections. Score and prioritize leads based on historical conversion patterns so your team focuses on the deals most likely to close.
Customer health and expansion tracking
Product usage, support tickets, NPS scores, renewal dates, and expansion revenue connected into a single customer health view that flags churn risk before it shows up as a lost deal.
Our Process
A structured approach that delivers results at every stage.
01
Map Your Revenue Data Sources
Your CRM works fine for managing deals. Your billing system works fine for generating invoices. The problem is that nobody built the layer that connects them. Without a unified revenue data model, every cross-system question requires someone to export CSVs, match records by hand, and produce a one-off analysis that is stale by the time it reaches the slide deck.
Output: System inventory across CRM, billing, marketing, and product
02
Build a Unified Revenue Model
We connect your CRM, billing, marketing, support, and financial systems into a single revenue data model in your data warehouse. That includes pipeline and forecasting models that combine CRM snapshots with historical win rates and seasonal patterns, conversion and sales cycle analytics broken down by segment, rep, lead source, and deal size, customer lifecycle models that connect acquisition cost to usage, support, and expansion revenue, and financial forecasting that ties pipeline projections to cash flow planning.
From our podcasts: Maximizing Sales Efficiency with AI and Data Tools.
Output: Funnel-to-renewal model with pipeline, win-rate, and retention metrics
03
Prove the Value
We built a reverse ETL pipeline for a financial services platform that synchronized data between their CRM, accounting system, and application database, giving their team a unified view from first touch through ongoing revenue. The engagement started as a pipeline integration project and expanded into a long-term partnership because the unified data kept surfacing new questions worth answering.
For a national education organization, we built revenue forecasting models including quarterly forecast-vs-actual tracking, sales KPI dashboards, and enterprise sales revenue reporting that combined CRM data, billing data, and enrollment data into models that finance and sales leadership both trusted.
We have unified ticketing, events, and transaction data for sports organizations and built customer segmentation that fed directly into CRM for targeted campaigns. We have built quantitative customer personas from demographic, behavioral, and purchase data across automotive and consumer divisions, replacing gut-feel segmentation with models built on transaction history.
Output: Forecast accuracy report and rep-level performance scorecard
Insights
Perspectives from our team on revops analytics.
Blog
6 Ways Biotech Manufacturers Can Unlock Data Value
A practical guide for biotech CMOs to unlock value from manufacturing data through process optimization, compliance, digital twins, and predictive maintenance.
By Ross KatzRead
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How to Start Event-Based Marketing: A Bottom Up Approach
A bottom-up approach to event-based marketing using data integration, customer behavior analysis, and automation to optimize marketing spend.
By Ross KatzRead
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117 Key Questions for E-Commerce Leaders
117 questions e-commerce leaders should answer across finance, marketing, sales, operations, support, technology, and BI — prioritized by business impact.
By Ross KatzRead
Frequently Asked
Questions
We already have Salesforce reports. Why do we need a data warehouse for RevOps?
Salesforce reports tell you what is in Salesforce. They cannot join that data to your billing system, marketing platform, support tool, or general ledger. A data warehouse lets you build models that connect pipeline activity to invoiced revenue, calculate true customer acquisition cost across channels, and produce forecasts that combine CRM data with historical patterns.
How do you handle messy CRM data?
Every CRM we have worked with has data quality problems. We build pipelines that extract CRM data into the warehouse, normalize and deduplicate it, and surface quality metrics so your ops team can see where the problems are and fix them at the source. We build a clean analytical layer on top of the CRM, not a replacement for it.
Which CRMs and accounting systems do you work with?
We have built revenue data models across multiple CRMs including HubSpot, Salesforce, and vertical-specific CRMs, integrated with accounting systems like Xero, QuickBooks, and others. We build reverse ETL pipelines to push enriched data back into your CRM and model activity data alongside financial data in the warehouse. The analytical layer is CRM-agnostic.
What does a RevOps analytics engagement look like?
We start by mapping your revenue data sources: CRM, billing, marketing platforms, support tools, and finance systems. Then we build extraction pipelines, a dimensional model in your warehouse, and reporting that covers pipeline health, forecast accuracy, conversion analysis, and customer metrics. Most clients have pipeline-to-cash reporting within 8 weeks. Deeper models like customer health scoring, cohort analysis, and predictive forecasting typically follow in subsequent phases.
Do you replace our existing RevOps tools?
No. We connect them. Your CRM, billing system, marketing platform, and support tools stay in place. We build the data layer that unifies their data into a shared revenue model. Most RevOps problems are not tool problems. They are data architecture problems.
How is this different from hiring a RevOps person?
A RevOps hire configures your CRM, defines processes, and manages workflows. The analytical infrastructure, the data warehouse, the pipelines that sync systems, the dimensional models that make cross-system reporting possible, is data engineering. We build the foundation that makes your RevOps team effective. Many of our clients have internal RevOps people who use the infrastructure we build every day.
Revenue data scattered across too many systems?
We’ll unify your CRM, billing, and pipeline data so your revenue team can stop reconciling spreadsheets and start closing gaps.
Book an Intro CallOur Other Services
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Operations Analytics
Track production efficiency, warehouse performance, and workforce productivity with data infrastructure built for operations teams.
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Data Engineering
Build reliable, cost-effective data pipelines on AWS, GCP, and Azure. CorrDyn designs and implements data infrastructure that scales.
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