A RevOps Command Center for Real-time Revenue Analytics


  • Revenue Operations is a centralized business development team that orchestrates revenue strategy, go-to-market motions and enterprise-wide revenue execution.
  • The team unifies revenue processes and technologies across departments to manage a more cohesive and effective pursuit of the company's financial goals.
  • To be effective, the team requires information visibility and predictability. A RevOps Command Center delivers real-time revenue analytics to measure and forecast results, and swiftly respond to variances. A RevOps command center should include dashboards, benchmarks, predictive analytics and insights derived from artificial intelligence.
Johnny Grow Revenue Growth Consulting

Revenue Operations designs and oversees the company's revenue execution. That requires revenue analytics to know what's working and what's not.

Rather than a random collection of information reports, a RevOps Command Center delivers the right information to the right person at the right time. It uses dashboards to display real-time deviations for real-time course corrections. It shifts information reporting from hindsight to foresight with predictive analytics. And it uses artificial intelligence to derive insights that would otherwise remain hidden.

This post will share the 3 components needed to create a RevOps Command Center.


Data Transformation

Many companies are data rich and information poor. They understand the value of data but struggle to transform it into actionable insights.

Many times, it is because the data resides in fragmented, disparate systems and is difficult to access. Other times, it is that companies don't know how to use the data they have. Most of the time, it's a combination of both.

But the challenge is more than just data and systems. In many cases, management hasn't created a culture that emphasizes data-driven decision making. Information is just as important to the business as products, services and processes. But this is a foreign concept for some managers.

Fortunately, more business leaders are clearly recognizing the benefits of advancing data from a byproduct to an information asset. In fact, many leaders will tell you their data is their most valuable asset.

But converting raw data into actionable intelligence requires a data transformation process. Below is an example of how this can occur.

Data Transformation Pipeline

The data transformation pipeline is built on a RevOps tech stack or information architecture that shows how to connect data, insights, action, and outcomes. This includes defining the methods to acquire, integrate, store, secure, deliver, manage, monitor and measure the value of data. It also includes data rules such as structured formats, naming conventions and data properties.


3 RevOps Reporting Tools

Using the right tool for the job is something my father taught me as a young man.

The three most important revenue analytics tools needed to deliver execution visibility and predictability are dashboards, predictive analytics and artificial intelligence.


A Revenue Operations dashboard displays the most important key performance indicators (KPI) in an easy to consume visual interface.

Real-time revenue performance reporting gives Revenue Operations teams the visibility to focus their limited time to achieve the most important results.

But here's the challenge with dashboards. While the source data is typically available in your marketing automation platform (MAP) and CRM system, very few of these systems calculate and report the most important KPIs that drive revenue growth.

For example, very few MAP or CRM systems report revenue uplift opportunities such as lead leakage, forward looking measures such as customer lifetime value, or essential revenue operations metrics such as account health scores, sales win rate, forecast accuracy, customer churn and many more.

And because these systems also don't capture costs, they cannot calculate ROI for processes and programs.

So, one of the RevOps best practices is to identify the most important KPIs, define their calculations in the data transformation pipeline, and append your dashboards to surface the most impactful information. Only then can you proactively manage execution, quickly identify trouble spots and swiftly intervene with course corrections.

Sales Dashboard

The best Rev Ops dashboards take a less is more approach. They focus on fewer metrics to drive more action. Experienced revenue architects know that adding more measures clouds what's most important and quickly results in diminishing returns.

When implementing the Command Center, I often get asked how many metrics should be included on a dashboard. The answer is always the same, as many as will get acted upon.

And an interesting thing about Rev Ops dashboards is that staff spend less time accessing information reporting and more time improving or making changes to revenue processes. That's the sign of successful reporting. If the information is causing operational changes to be made, it's working.

Performance Benchmarks

Revenue analytics with performance benchmarks create additional intelligence.

Benchmarks provide a relative comparison to identify where the company stands and most needs to improve. They also enable predictive analytics.

For example, sales performance research shares sales win rates. When filtered by industry and extrapolated, revenue architects can calculate how even small improvements to sales win rates deliver a significant financial uplift.

Sales Win Rate Benchmark

Many managers like to apply pro forma models to show how a 1 percent improvement in any revenue process impacts total revenues.

Others with KPIs below the industry median may prefer to see the financial impact by improving their performance to the median level. Knowing the financial upside impact allows managers to know how much they should invest to achieve that upside.

Predictive Analytics

Without predictive analytics, the view and information for every person in your company is entirely backward looking.

Predictive analytics analyze historical and real-time data to show patterns, relationships, trends and anomalies. They create simulation, propensity and predictive models. They extend the trajectory of data to deliver forecasts.

The ability to apply data for what-if scenarios or pro forma modeling is a powerful lever to compare competing alternatives and allocate limited budgets.

When KPIs can model future performance, they become more actionable. They shift your visibility from where you have been to where you are going. It's the difference between looking in your car's rearview mirror or through the windshield.

A powerful example we routinely deploy for clients is the bottom-to-top revenue roll-up that we call the Predictive Pyramid. It's an interactive dashboard that shows how lower-level operational analytics align and feed higher level revenue analytics to satisfy the company's top priorities.

Revenue Growth Predictive Analytics

The pyramid is needed because no revenue process, program, tactic or best practice lives in isolation. Each has cascading effects that impact other areas and must be considered when making tradeoffs. This visualization is helpful in determining where to invest your limited time to achieve the biggest uplift.

The data that drives the calculations is sourced from company history if it's available or industry benchmarks if it's not.

In working with clients to populate the predictive pyramid, we find that many managers believe they don't have the needed data. But quite often they have more data than they think, it's just disorganized and decentralized in siloes.

And like the dashboards, this measurement system should induce action. Remember, if your information reporting is not causing course corrections and shifting tactics, you're doing it wrong.

Artificial Intelligence

AI is the final tool in the revenue analytics toolchain. It's also the tool that can deliver the most significant boost to a RevOps ROI program.

And the good news here is that popular CRM systems such as Microsoft Dynamics 365 and Salesforce make this technology readily available.

In fact, when combined with artificial intelligence, CRM software shifts from a customer data repository to a predictor of customer behaviors, creator of customer insights and facilitator of customer and company objectives.

AI within CRM software can surface problems or opportunities and notify the right resources in real-time. These alerts may include things like resolving revenue leakage, following up on neglected leads or following through on sale opportunities.

Or they may surface business process breakdowns such as a quoting or order entry problem. AI can recognize that a high value customer is dissatisfied and at risk of churn. Swift course corrections then prevent the loss of short-term and long-term revenue.

AI can predict customer behaviors, recommend offers, content, products or next best actions, and perform other capabilities just not possible without this technology.

Data is the fuel, AI is the engine, and actionable insights are the destination.


Data Driven Operating Model

No RevOps implementation is complete without a management culture that embraces and insists upon insight driven decision making. Decisions that impact the customer experience or revenue objectives must be made with insights, and not intuition or guesswork.

A Data Driven Operating Model (DDOM) is a decision model which operationalizes data to produce insights for decision support. A DDOM leverages data to build better products or services, deliver greater customer value, improve staff productivity, outflank competitors, lower costs and earn more revenues and profits.

However, many companies find data both abstract and overwhelming. They struggle to operationalize the process of converting data to actionable insights that yield measurable and predicted business outcomes. That's where a framework can help.

Below is a summary of the Johnny Grow DDOM framework and essential components to make a data driven operating model a reality.

Data Driven Operating Model

Data transformation must be operationalized into a streamlined process with clear objectives, operational use cases, supporting metrics and the delivery of actionable insights to customer facing staff and decision makers. Only then will revenue analytics empower staff and managers to make better and faster decisions that more directly contribute to the company's performance objectives and priorities.

However, taking advantage of a DDOM is more about company culture than data.

Replacing intuition with data for decision making starts at the top with executives and managers that lead by example. They should require all financial and customer facing decisions to be anchored in data. They should make data-based decisions the norm and not the exception.

As legendary detective Sherlock Holmes told his assistant, "It is a capital mistake to theorize before one has data."

Only when the company culture reinforces data-driven, fact-based and objective decision making and discourages gut-based, trial and error and subjective decision making will users and managers deliver the last mile of revenue analytics.

About a hundred years ago, a smart guy named William Edwards Deming mandated to his staff, "In God we trust, all others must provide data." That perfectly describes the culture to support a data driven operation model.

The Point is This

Revenue analytics are critical to performance measurements and the achievement of company objectives.

A RevOps Command Center delivers a birds eye view of real-time revenue execution across the company. That permits managers to detect and intervene when outcomes are at risk and drive financial objectives to their slated destination.

Implementing a RevOps Command Center takes serious thought and time. That's why those who succeed will achieve competitive advantage over those who don't.