Revenue Intelligence
Operationalize the data models we architected. Dashboards, forecasting frameworks, pipeline analytics, and performance reporting—turning strategy into insights.
Data without insights is just noise. After designing your GTM architecture, we build the reporting layer. Forecasting models based on your conversion funnels. Dashboards that track what actually matters. Analytics that show what's working.
We don't build generic dashboards. We build reporting based on the data models and KPIs we defined in your GTM architecture. Pipeline health scoring based on your sales stages. Attribution that maps back to your GTM motion.
We use modern BI tools (Tableau, Looker) and native CRM analytics. This works with GTM Architecture (which defines what to measure) and Infrastructure & Systems (which ensures data quality).
Our Revenue Intelligence Services
Pipeline Health & Forecasting Frameworks
We implement structured forecasting models based on deal stages, conversion data, and historical trends. This provides more reliable visibility into future revenue and risk - and eliminates guesswork at quarter-end.
Full-Funnel Analytics Dashboards
We build dashboards that track performance across marketing, sales, and customer success. This enables real-time insights, faster course correction, and a single source of truth for team alignment.
Attribution & ROI Analysis
We analyze which channels, campaigns, or plays are actually driving revenue - and which aren't. This helps you invest in what works and answers the question: "What's driving growth?"
Cohort & Segment Reporting
We analyze customer group behaviors over time to reveal patterns in retention, expansion, and churn. This identifies high-performing segments and improvement opportunities.
Sales Productivity & Activity Insights
We track rep-level metrics across calls, emails, meetings, and opportunities. This reveals what drives individual performance, where coaching is needed, and how to drive consistent, scalable seller productivity.
CS & Retention Analytics
We create dashboards and models to monitor customer health, track Net Revenue Retention (NRR), and understand churn drivers. This transforms Customer Success from reactive to strategic.
Frequently Asked Questions
What is revenue intelligence and how does it differ from reporting?
Revenue intelligence goes beyond basic reporting by providing predictive insights, trend analysis, and actionable recommendations rather than just historical data. While traditional reports show what happened, revenue intelligence explains why it happened, what it means for your business, and what actions you should take. It combines data from multiple sources across the full revenue lifecycle to provide a complete picture of pipeline health, forecasting accuracy, and performance drivers.
What tools do you use for revenue analytics and dashboards?
We typically build dashboards and analytics using native CRM reporting (HubSpot or Salesforce), business intelligence platforms like Tableau or Looker, and specialized RevOps tools depending on your needs and existing tech stack. Our focus is on creating actionable insights rather than tool implementation. We prioritize dashboards that answer critical business questions, update in real-time, and are accessible to the teams who need them.
How do you improve forecast accuracy?
We improve forecast accuracy by implementing structured forecasting frameworks based on deal stages, historical conversion rates, deal velocity, and rep performance trends. This includes defining clear stage exit criteria, standardizing pipeline hygiene practices, analyzing win/loss patterns, and building predictive models that account for seasonality and market conditions. The result is more reliable revenue projections and fewer surprises at quarter-end.
Can you help us understand why our conversion rates are declining?
Absolutely. We conduct deep-dive analysis to identify where prospects are dropping off, which segments or channels show the steepest decline, how conversion patterns differ across rep performance tiers, and what external or internal factors may be contributing. This diagnostic work informs targeted interventions such as process adjustments, enablement programs, messaging changes, or lead quality improvements to reverse negative trends.
How quickly can we expect to see results from revenue intelligence work?
Initial insights typically surface within 2-4 weeks as we conduct discovery and analyze your existing data. Implementing forecasting frameworks and dashboards takes 6-8 weeks. However, the real value compounds over time as we refine models based on actuals, identify trends, and build historical baselines. Most clients see measurable improvements in forecast accuracy within one full sales cycle (typically one quarter) of implementing new intelligence frameworks.
What data sources do you typically integrate for revenue intelligence?
We integrate data from your CRM (deals, contacts, activities), marketing automation platform (campaign performance, lead sources), sales engagement tools (outreach metrics, email response rates), customer success platforms (health scores, NRR), product analytics (usage data for PLG motions), and financial systems (bookings, revenue recognition). The goal is a unified view of the entire customer lifecycle from first touch to renewal.
Can revenue intelligence help with sales rep performance management?
Yes. We build rep scorecards and performance dashboards that track leading indicators (activity levels, pipeline generation, conversion rates) and lagging indicators (quota attainment, win rates, deal size). This provides objective data for coaching conversations, helps identify top performers to model best practices, flags underperformers early, and ensures performance reviews are based on data rather than subjective opinions.
Do we need a data analyst on staff to maintain revenue intelligence systems?
Not necessarily. We design dashboards and reports to be self-service and maintainable by your RevOps or sales leadership team. For basic reporting and dashboard monitoring, you do not need a dedicated analyst. However, for advanced predictive modeling, cohort analysis, or complex custom reporting, having analytical resources (either in-house or fractional) becomes valuable as your data needs scale.