Triangulating sales forecasting methods for a forecast you can actually trust

Sherlock Holmes never solved a mystery by relying on a single clue. If he only considered a witness statement but ignored footprints, fingerprints, or motives, he’d risk reaching the wrong conclusion. He cross-checked different pieces of evidence to expose the truth.

Similarly, business leaders are risking a lot when they rely on just one method to forecast future revenue. Forecasting in isolation by using a single method can lead to inaccurate, unreliable, and biased projections. But by triangulating sales forecasting methods, leaders can reduce uncertainty to get a complete and more accurate picture of what’s ahead. 

This article explores the power of triangulation in sales forecasting, breaking down different forecasting methods and how combining them can lead to more accurate predictions. We’ll also discuss how startups can use triangulation to tackle common sales forecasting challenges.

The sales forecasting approaches and methods every business needs to know

Revenue forecasting in SaaS involves estimating and predicting your future revenue. There are multiple sales forecasting methods, each catering to different business models and growth rates. And they all fall into one of four common approaches based on:

1. Judgement-based forecasting
2. Probability-weighted forecasting
3. Driver-based forecasting
4. Statistical forecasting

Methods that use these approaches can be further characterized as “top-down” or “bottom-up” forecasting. 

Top-down forecasting starts with broad company goals or market potential and breaks these down into smaller targets. For example, a company might start with a $100M annual revenue goal and divide it into quarterly and regional targets.

Bottom-up forecasting works in reverse, building projections from specific sales activities and data. This approach combines elements like current pipeline opportunities, average deal sizes, and conversion rates to create an overall forecast. For instance, a sales manager might aggregate all the pipelines for individual sales reps and look at historical close rates to predict the total expected revenue.

1. Judgement-based forecasting

As the name implies, judgement-based forecasting is a bottom-up approach that relies on a qualitative but informed assessment of different pieces of information to predict sales. 

Rep-level or manager-level forecast

This is the most common judgement-based sales forecasting method. It builds forecasts from individual sales reps’ projections. Each rep evaluates their pipeline, assigns probabilities to deals based on factors like customer interest and competition, and estimates potential revenue. 

2. Probability-weighted forecasting

Probability-weighted forecasting is another bottom-up approach that assigns probabilities to some aspect of the pipeline.

Stage-weighted forecast

Also known as opportunity stage forecasting, this method assigns probability weights to each stage of the sales pipeline. Deals in the early stages get lower probabilities, while those nearing closure receive higher ones. 

Forecast category-weighted

This method is similar to stage-weighted forecasting but groups deals into forecast categories like “Commit,” “Best Case,” or “Pipeline” with predefined probability weights. It provides a more structured way to assess forecast confidence.

Historical pipeline closure forecast 

This is a more top-down approach to forecasting based on past pipeline data. This method assumes past trends will continue, using historical win rates to predict the number of deals that will close in the future.

3. Driver-based forecasting

Driver-based forecasting can include both bottom-up methods and top-down methods. In either case, the method relies on evaluating the information and data for different drivers that can impact sales. 

Sales capacity (aka quota-based) forecast

This method focuses on the sales team’s capacity to hit revenue targets based on factors like rep performance, available selling time, and quota attainment rates. It is a bottom-up forecasting method that can also help companies in their sales capacity planning. 

Holistic (all-drivers) forecast

This method considers multiple drivers, including high-level factors like market trends and  economic conditions, calibrated with internal sales data for a more comprehensive projection.

Demand-gen funnel forecast

This is another top-down forecasting method that estimates the number of leads needed to meet high-level revenue targets. It focuses on the expected performance of marketing-driven demand generation.

Incremental forecast

An incremental forecast is also a top-down approach, which starts with past sales performance and adds or subtracts the potential or expected impact of high-level growth factors, such as market expansion or new product launches.

4. Statistical forecasting

Time series forecast

This top-down forecasting method analyzes historical sales patterns, including seasonality and trends, to predict future performance. 

Regression forecast

Regression forecasting is another top-down approach that uses statistical models to identify relationships between sales and influencing factors like ad spend or industry growth. 

How does triangulation improve the sales forecasting process?

Triangulation means using multiple forecasting methods to improve the accuracy of sales forecasts. Instead of relying on a single method, you compare results from different models to get a more balanced and reliable prediction.

The main benefits of triangulation include:

• Improved accuracy: By combining methods, triangulation balances strengths and weaknesses, leading to more reliable forecasts.
• Reduced risk: Identifying differences in your results alerts you to potential problems with your forecast and an opportunity to address them before they lead to unpleasant surprises down the road.  
• Less deal shifting: Triangulating different data points helps to identify excessive deal shifting and provides an opportunity to address the issues driving it, such as unrealistic close date predictions, poor pipeline qualification, or challenges in the sales process.

Most companies start their forecasting journey with either a top-down or bottom-up approach. Then they use the alternative method (opposite to the one picked initially) as their first step into triangulation.

As organizations mature and gather historical data, they typically expand their triangulation toolkit to include other methods we discussed in the previous section. 

Triangulation works best when paired with techniques like inspection and collaboration:

• Inspection: It involves analyzing recent sales activity and buyer interest to identify trends and patterns that help fine-tune forecasts.
• Collaboration: The collaboration between different stakeholders like sales reps and business leaders bridges the gap between different perspectives. It helps develop a more data-driven middle ground, ensuring that forecasts are aligned with both market realities and business objectives.

Collaboration between CXOs and sales teams is key to using triangulation effectively 

Triangulation works well when C-suite executives and sales teams work together. Sales teams bring ground-level insights, and leadership adds strategic context to ensure forecasts align with broader business objectives. 

• Data sourcing and reconciliation: The foundation of effective triangulation starts with data. CXOs and sales teams must speak the same language when it comes to data standards, definitions, and key metrics. This makes it easier to compare and reconcile forecasts generated from multiple sources, such as sales pipelines, CRM systems, and external market data. 
• Risk mitigation: Sales teams are typically very good at identifying deal-specific risks, and the leadership perspective adds awareness of broader market risks and strategic challenges. When these viewpoints combine during the forecasting process, it helps identify potential risks or roadblocks early on in the forecasting process. 
• Decision-making: Collaboration helps to ensure that the triangulated forecast is not only accurate but also actionable. For instance, if the forecast suggests aggressive growth, but resources (such as headcount or marketing budget) are not aligned, stakeholders working together can make more informed decisions on how to address the gap.
• Real-time adjustments: In an ideal scenario, forecasts should reflect the most current data. However, this can be difficult when forecasts are based on data aggregated from multiple stakeholders, each with their own spreadsheets which may or may not be up to date. It’s only when teams use collaborative FP&A software that they are able to see and make adjustments to their forecasts in real-time.

Triangulation methods and examples for effective sales forecasting

Combining different sales forecasting methods to cross-verify projections reduces biases and improves accuracy. Below are some triangulation methods with examples of how they can be combined.

1. Combining judgmental methods with statistical methods

Judgmental forecasting approaches and methods rely on subject matter expert insights, and statistical methods use historical data and predictive forecasting models. Combining the two methods makes sure that forecasts are not just data-driven but also account for market changes, sales team sentiment, and external factors like economic changes.

‍Example: A financial services company planning its AOP (annual operating plan) combines statistical models using historical revenue trends and pipeline data, with executive judgment to account for interest rate changes, regulatory shifts, and macroeconomic conditions, resulting in a more realistic forecast.

2. Combining top-down with bottom-up methods

Top-down sales forecasting methods start with revenue targets, breaking them down into sales goals. On the other hand, bottom-up forecasting builds projections from individual rep performance and pipeline data. Combining different methods that use these two approaches help finance leaders create forecasts that are realistic and grounded in actual sales activity. 

Let’s look at a few examples.

Combine bottom-up, pipeline-based, and top-down approaches

In this triangulation, bottom-up forecasting gets a detailed, deal-by-deal view from the sales team. Pipeline analysis examines the actual opportunities in play, their values, and movement. Top-down forecasting provides context on what's possible in your market based on size, growth, and your company's position. When used together, these approaches validate each other and highlight potential gaps in your forecast.

Combining bottom-up with stage-weighted forecasting

Deals at different stages have different likelihood of closing. By applying historical conversion rates to each pipeline stage, leaders get a more realistic view of the pipeline. Combined with bottom-up forecasting, leaders can quickly spot if sales teams are being too optimistic about early-stage deals or too conservative with late-stage opportunities. 

Combining bottom-up with funnel forecasting

Funnel forecasting assesses the volume and conversion rates at each funnel stage. It tracks how many opportunities sales teams need at each stage to hit the revenue targets. When paired with bottom-up forecasting, it helps validate whether you have enough pipeline coverage to achieve the forecasted numbers.

Triangulation can remedy many of the common pain points in sales forecasting

From selecting the best-suited sales forecasting methods to dealing with bad data and unexpected market shifts, startups often struggle to build reliable forecasts. Here’s how triangulation helps solve the most pressing forecasting issues faced by startups. 

1. Overreliance on a single method

Problem: Most startups depend heavily on one approach like historical data or pipeline analysis, missing important signals and market dynamics.

Solution: Triangulation combines multiple forecasting methods to create a more complete and accurate picture.

2. Subjective bias

Problem: One of the major problems in sales forecasting is that teams often swing between overly optimistic or conservative forecasts.

Solution: Cross-referencing multiple data points through triangulation creates natural guardrails, helping teams land on realistic projections backed by diverse evidence.

3. Siloed collaboration

Problem: Different departments often forecast in isolation. This leads to missed valuable insights from other teams, creating misaligned predictions.

Solution: The triangulation process requires input from sales, marketing, and operations, fostering cross-functional collaboration and shared ownership.

4. Poor data quality

Problem: Inconsistent or inaccurate data from various sources can undermine forecast reliability.

Solution: Triangulation cross-validates data points from multiple sources, highlighting discrepancies and ensuring higher data quality.

5. Market volatility

Problem: Traditional forecasting methods struggle to adapt to rapid market changes and economic shifts.

Solution: By incorporating real-time signals alongside historical data, triangulation helps teams stay responsive to changing market conditions.

‍Tip: In addition to triangulation, consider creating rolling forecasts to keep your sales projections dynamic. Unlike static quarterly/annual forecasts, rolling forecasts continuously update based on real-time data. This makes sure your revenue forecasts stay aligned with actual pipeline movement, market shifts, and evolving business priorities.

Start forecasting smarter with Drivetrain

Don’t put all your eggs in one basket. This wisdom, though simple, is fundamental when it comes to sales forecasting for SaaS businesses. By incorporating triangulation, startups can reduce risks, improve forecast accuracy, and make more informed financial decisions.

For new businesses in the early stages, forecasting in Excel templates should suffice. But for growing businesses, planning on Excel doesn't cut it. You need to consider not just historical data but also parameters like conversion rates, deal size, and other key metrics. Modern sales forecasting software helps SaaS teams create forecasts that drive predictable revenue and achieve sales goals. 

Drivetrain makes cross-functional financial planning easy. Teams can slice and dice finance data in real time and get more accurate forecasts. It also offers a wide range of versatile built-in financial modeling templates and scenario analysis capabilities to help leaders tackle challenges like poor data quality, market changes, and siloed collaboration. 

‍Explore Drivetrain today to see how it can transform your sales forecasting process.