Why Sales Forecasting Fails—and How to Fix It

If I had a dollar for every time I heard a CEO, CRO, or VP of Sales say, “We need better forecasting,” I’d have retired long ago.

The truth is, sales forecasting is notoriously difficult. Many companies struggle to generate accurate forecasts, leaving their revenue leaders in constant firefighting mode.

So, why is forecasting so challenging? It comes down to two primary reasons:

1. Lack of Sufficient Data Points for Modeling

Accurate forecasting requires robust models, and robust models require lots of data points. Without sufficient historical data to analyze, it’s nearly impossible to achieve predictability.

Take weather forecasting as an example. Over the past few decades, meteorologists have drastically improved their predictions, thanks to better underlying models powered by vast amounts of data.

But many companies, especially early-stage or enterprise-focused ones, don’t generate enough data points to build reliable models. With limited leads, meetings, pipeline, and deal flow, the sample size is simply too small to make accurate predictions.

In these cases, expecting traditional sales forecasting is counterproductive. Boards and leadership teams would be better served by adjusting their expectations. Instead of relying on speculative forecasts, revenue leaders should focus on providing qualitative assessments of key deals in the pipeline.

2. Time Horizon Too Short for Course Correction

Forecasting is only useful when it allows enough lead time to take corrective action.

Many companies limit themselves by forecasting with bottom-of-funnel data alone—e.g., deals close to the finish line. While these forecasts may seem more grounded, they leave little to no time for intervention if things go off track.

Imagine New York City’s weather forecast was based solely on what’s happening in Chicago. This approach would give NYC only 12–24 hours of visibility to prepare for an incoming storm. Now compare that to a forecast based on weather patterns forming off the West Coast, which gives 3–5 days of warning.

The same principle applies to sales. By incorporating top-of-funnel data—leads, meetings, and pipeline creation—companies can extend their forecasting horizon and gain time to course-correct before it’s too late.

How Forecasting Drives Misalignment

Forecasting challenges often lead to misalignment between key teams. The root cause? A lack of full-funnel models that connect marketing, sales, and customer success.

Here’s how it typically plays out:

• Marketing operates with a model focused on leads or pipeline opportunities created.
• Sales operates with a model focused on pipeline deals progressing to closed-won.

When these models don’t align, it limits the company’s ability to forecast with enough foresight to take meaningful action.

Red Flags That Forecasting Challenges Are Driving Misalignment

Here are some signs your organization’s forecasting problems are creating misalignment across teams:

🚩 Siloed Measurement Systems: Marketing, sales, and customer success each have their own ways of capturing, measuring, and reporting performance—none of which connect.

🚩 Reactionary Tactics: When sales targets are missed, leadership’s first instinct is to push outbound reps to “hit the phones harder” in a last-ditch effort to save the quarter.

🚩 Constant Scrambling: Sales leaders are under pressure to “call their number” weekly, often resorting to overly optimistic projections to maintain confidence.

The Solution: Fixing Sales Forecasting with a Full-Funnel Approach

Improving sales forecasting isn’t just about better tools or more frequent reporting—it requires a fundamental shift in how organizations approach data, processes, and planning. Here are the two key steps to get it right:

1. Implement a Custom Full-Funnel Tracking Object

For companies using Salesforce, a common challenge is the disconnect between top-of-funnel Leads and bottom-of-funnel Opportunity objects. This structural gap makes it difficult to track the entire sales cycle and calculate accurate conversion rates.

The solution? Install a Custom Full-Funnel Tracking Object that seamlessly integrates these data points. This allows organizations to:

• Measure how leads progress through each stage of the funnel.
• Analyze end-to-end conversion rates with greater precision.
• Identify bottlenecks or inefficiencies anywhere in the sales cycle.

By bridging this gap, businesses gain the clarity they need to forecast more accurately and make informed decisions.

2. Build Bottoms-Up, Full-Funnel GTM Plans and Models

Forecasting becomes significantly more reliable when businesses create a unified plan that connects marketing, sales, and post-sale efforts. A bottoms-up, full-funnel Go-To-Market (GTM) model achieves this by:

• Aligning the entire funnel from lead generation through closed-won deals and customer success.
• Setting measurable targets for each stage of the funnel, ensuring accountability across teams.
• Creating visibility into how top-of-funnel activity impacts bottom-of-funnel outcomes.

When these plans are in place, companies can measure and analyze full-funnel performance data against their GTM models. This creates a feedback loop that enables accurate and predictable forecasting.

Why These Steps Matter

By installing a Custom Full-Funnel Tracking Object and building comprehensive GTM models, companies address the core problems that undermine forecasting:

• Disconnected data between funnel stages.
• Lack of a unified framework for measuring and improving performance.

With these solutions, organizations can:

• Forecast with greater precision and predictability.
• Gain enough lead time to identify risks and course-correct before it’s too late.
• Align marketing, sales, and customer success teams under a shared set of metrics and goals.
  
  

The End Result

When you’re capturing and measuring full-funnel performance data—and have GTM plans and models in place to guide your efforts—you’ll finally achieve the kind of accurate and actionable forecasting that drives predictable growth.

Sales forecasting doesn’t have to be a guessing game. With the right systems and strategies, it becomes a powerful tool for aligning teams, making data-driven decisions, and staying on track toward revenue goals.