Our latest Revinar on topic: Revving Up Revenue: Conversion Rate Confusions + Digital Revenue Twins  was a fruitful discussion featuring Marc Friend the CEO at Rapid API, Bill Kantor managing partner at FunnelCast, and Johannes Hoech the CEO & founder of Premonio. The conversation centered around the future of sales forecasting, revenue optimization, and the innovative concept of a Digital Revenue Twin.

The session provided insights into how businesses can:

• Refine their forecasting approaches
• Optimize spending
• Improve decision-making processes

To achieve predictive accuracy and optimize your budget, you need to answer two key questions: 

• How do you set a target that you can hit? 
• How do you reach that target without overspending your budget? 

Johannes kicked off the conversation with a forecasting topic: Traditional forecasting relies on historical data, but this doesn’t work if you’re starting a new company with no past performance. Johannes introduced the idea of the digital revenue twin approach. Instead of a “left-to-right” forecasting approach that uses past data, the “right-to-left” method starts with your revenue goal and works backward to determine the necessary actions and assumptions. By simulating your go-to-market operations with a digital facsimile, you can test, compare, and optimize different strategies to ensure efficient spending. With a digital twin, you can experiment and make informed decisions without committing real money upfront, giving you a powerful tool for more accurate growth planning and budget optimization. Planning for growth means setting accurate revenue targets without overspending. 

Next Bill explains that many businesses fall into the trap of tracking conversion rates from one stage to the next. The issue? Conversions alone don’t pay the bills—wins do. Instead of just measuring how many deals move from one stage to another, Bill argues that you need to focus on the win rate, which is inherently a function of time. For example, in the demo POC stage, only about 31% of deals are marked as closed-won after 50 days, even though waiting for all deals to settle might show a 50% win rate. Relying on that terminal win rate can lead to significant forecasting errors—especially if you’re mid-quarter and need to make adjustments.

To tackle this, Bill introduces the use of Monte Carlo simulations. By applying time-sensitive win rates to each deal, you can simulate a range of outcomes for the quarter, rather than banking on a single average number. Imagine knowing there’s a 50% chance of reaching $2.5 million, but also a 90% chance of exceeding $2.03 million. This nuanced view allows you to communicate more effectively with your board and make smarter, data-driven decisions.

Beyond forecasting, Bill emphasizes the value of optimizing your sales pipeline. Rather than just prioritizing the biggest or latest-stage deals, his focus ranking approach can boost productivity by up to 60%. This method helps you build an optimized portfolio of deals, ensuring that resources are allocated to opportunities with the highest potential impact.

Q&A Highlights

Marc: “What I see in the two approaches is that, the Premonio approach is where I should spend my resources or what resources provide results and the Funnelcast approach is where I should spend my time. So given the fact that most companies have a forecast and they have probabilities attached to leads and they have stages. And dollar amounts. What is it that they seem to be getting wrong consistently?”

Johannes: “Here are two key points. First, many organizations fail to connect their sales and marketing funnels. Marketing might track website traffic, Google Ads, or events, but without a unified handoff to sales, there’s no clear path from lead to revenue. Second, without linking budgets to these activities, you lose sight of your forward-looking budget efficiency. In short, if sales and marketing aren’t integrated and tied to budget tracking, it’s difficult to understand which activities drive revenue and how effectively your budget is being spent.”

Bill: “When forecasting short-term outcomes, relying on historical probabilities can be misleading—they represent long-term win rates, not the current performance, which leads to overly optimistic and unachievable forecasts. This misalignment frustrates stakeholders and boards. I suggest prioritizing deals based on actuarial probabilities rather than just chasing the biggest deals. Focusing solely on conversion rates is problematic because changes in one stage affect others in complex ways, often shifting bottlenecks downstream and creating inefficiencies instead of driving real sales.”

Johannes: “A key element of Premonio’s approach is tracking from the very beginning—measuring win rates from the initial lead all the way to a closed deal, rather than just between individual stages. This holistic view ensures no step is missed as leads move through the funnel. By linking each contact to its associated spend, you get a clear picture of ROI. For example, if 100 emails sent on Tuesday generate $100K in revenue at a $10K cost three months later, that’s a 10x ROI. Without this continuous tracking from origin to close, forecasting inaccuracies can quickly arise.”

Bill: “Sales is inherently a high variance process, and crude predictive models often miss the mark because so many factors—like customer acquisitions or economic shifts—are beyond your control. While conversion rates are attractive due to their quick measurability, they encourage siloed management and focus on short-term metrics. Ultimately, accurate forecasting requires waiting until the entire sales cycle is complete to truly assess improvements in win rates.”

Marc: “Based on your work, how do you account for attribution along the entire process? I can measure eventual win rates from lead sources, but how do your computations capture what Shelly eloquently describes as the “alchemy” of these programs working together?”

Johannes: “Many struggle with attributing sales success across multiple touchpoints. The traditional models—tracking through 10 stages with a multi-attribute approach—often spark debates over which touchpoint actually “won” the deal, making it hard to allocate weights accurately. On the positive side, especially in new initiatives, the key is to monitor the very first touch, whether it’s a blog, a call, or a meeting. Over time, by accumulating data with tools like a revenue digital twin and Funnelcast, you can gain clear insights into which interactions deliver sales success. This tracking helps demystify the so-called “alchemy” of effective sales strategies.”

Bill: “Attribution is one of the toughest challenges in sales analytics—there’s no perfect solution yet. The approach is to start with basic segmentation (by lead source, geography, business type, etc.) and focus on core CRM data like stages, deal amounts, and close dates to cut through the analytic noise. Once those basics are solid, you can introduce more nuanced attribution models to better allocate resources.”

Marc: “People have traditionally relied on classic human interaction methods—doing things the way they’ve always been done, regardless of whether it’s effective. Especially in the United States, people tend to defend the status quo simply because it’s familiar. How do we gently guide them toward a new approach, even when they might claim that it doesn’t work? In other words, how do we help them evolve and embrace this new path?”

Johannes: “The success of switching from legacy systems depends on three key factors. First, the company must recognize they have a problem, such as missed forecasts or revenue discrepancies. Second, the motivation for change must be genuine—whether driven by a desire to uncover analytical truths or simply to appease the board. If it’s the latter, progress is limited. Lastly, the ability to think analytically is important.”

Bill: “With measurable business data, you can retroactively demonstrate significant gains—like a 60% improvement—from using a systematic focus ranking model instead of relying on ad hoc processes. By analyzing historical performance on a regular basis, you can clearly show how following the model would have improved outcomes, which serves as a strong motivator for change. Even though sales managers are adept at prioritizing, this systematic approach can further optimize the portfolio of deals for better overall performance.”

The Revinar highlighted a major shift in sales forecasting—moving from outdated, gut-feel estimates to sophisticated, data-driven methodologies. 

For organizations looking to refine their approach, the message is clear: 

• Implement data-driven forecasting: Start with your revenue target and work backward.
• Focus on efficiency over volume: Use time-based win rates rather than simple conversion metrics.
• Continuously iterate: Use tools like a digital revenue twin and Monte Carlo simulations to refine your strategy over time.

Whether you’re a startup with no historical data or an established company looking to improve forecasting accuracy, these methodologies offer a powerful roadmap to revenue growth.

Interested in learning more? Visit Premonio and FunnelCast websites or watch the entire revinar here.