6 minutes
November 8, 2023

Building a sales pipeline forecasting process: A step-by-step guide for B2B SaaS teams

Here are seven steps you should take to build a sales forecasting process for your sales team.
Table of Contents

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To set accurate revenue targets, allocate financial resources effectively, and manage sales performance, SaaS sales teams must have accurate sales pipeline forecasting.

In case you’re unfamiliar, sales pipeline forecasting is the process of predicting future sales revenue by analyzing potential deals and opportunities that already exist in the sales pipeline. Since SaaS revenue is typically generated through subscriptions sourced through inbound and outbound efforts, pipeline forecasting is critical for any organization that wants deeper insights and more control over its finances for budgeting and resource allocation purposes.

Additionally, since SaaS sales cycles are often longer, pipeline forecasting helps sales reps better manage the sales process, identifying bottlenecks to improve conversion rates. Plus, forecasting also helps reps identify potential expansion opportunities within existing accounts, increasing the likelihood of renewals and upsells.

The benefits of B2B sales forecasting speak for themselves. But how exactly do you get started?

To make your job easier, we’ve put together this handy pipeline forecasting guide that covers the seven steps you need to take to build a pipeline forecasting process that can supercharge your sales funnel and help your SaaS business get to the next level.

Step #1: Define your goals

When building a pipeline forecast, defining clear goals and objectives is critical. Otherwise, how would you be able to tell whether you’re making any progress?

Out of the gate, sales teams should determine what their priorities are. In most cases, you’ll want to focus on things like:

  • Revenue targets, including annual, quarterly, and monthly targets that align with business growth and financial objectives.
  • Customer acquisition, or the new customers or subscribers that reps acquire within a specific time frame. 
  • Churn reduction, or increasing customer retention by shipping exceptional products and delivering superior customer experiences in every interaction.
  • Expansion revenue, which measures how much money reps are able to bring in by upselling or cross-selling existing accounts. 
  • Sales cycle efficiency, where teams focus on shortening the sales cycle by improving lead-to-close time, reducing the time it takes to convert leads into paying customers.
  • Lead conversion rates, which can help teams assess the efficiency of the sales process by defining conversion rate goals for each stage of the pipeline.

While the above list isn’t comprehensive, it should get you thinking in the right direction. 

Step #2: Collect and prepare data

After setting goals and objectives, sales teams need to gather historical sales data to build a foundation for accurate forecasting. 

The data collection process begins with gathering tons of sales information, including sales transactions, lead sources, customer profiles, conversion rates, and sales cycle durations. Once that data has been collected, teams must ensure that it’s complete, accurate, and up-to-date. To do this, they need to clean the data, organize it, combine various sources together, and then normalize it based on factors like seasonality or market fluctuations.

Once the data is collected and prepared, it can be used for forecasting purposes. Generally speaking, the more historical data available, the more reliable the forecast will be. 

Step #3: Choose a forecasting method

At this point, it’s time to choose the sales forecasting method that makes the most sense for your unique circumstances. Some popular options include forecasting based on:

  • Historical data. By leveraging historical data, teams can improve forecasting by identifying seasonality, understanding customer behavior, and anticipating cyclical market fluctuations. This method is particularly valuable when a business has a well-established track record and can draw insights from long-term historical performance. 
  • Current sales pipeline. Forecasting based on the current sales pipeline provides a real-time snapshot of potential deals in progress. This can help teams with short-term forecasting since it reflects the most up-to-date information about potential opportunities. As a result, sales teams can prioritize and allocate resources more effectively for deals already in the pipeline.
  • Lead value. By scoring each lead or prospect based on their potential worth, sales teams can spend more energy focusing on leads with higher values. As a result, they can more accurately forecast revenue by prioritizing the most promising opportunities.
  • Opportunity stage. Categorizing opportunities into various stages — like prospecting, negotiation, and closing — makes it easier for teams to estimate how likely deals are to close and when. This method provides insights into how deals progress, which can help with resource allocation.
  • Sales cycle length. Forecasting based on the length of the sales cycle is valuable for understanding how long it takes to convert leads into customers. By considering the typical duration of each stage in the sales process, teams can estimate when deals are likely to close, which can help with managing cash flow.

Step #4: Develop your model and conduct data analysis

After you’ve chosen your method or methods, it’s time to create a forecasting model. This step often involves building an algorithm to analyze data and make predictions about future performance. Some commonly used models include time series analysis, regression analysis, and machine learning algorithms. 

Once your model is in place, it’s time to start analyzing the data to identify patterns, trends, and outliers. At the same time, teams should train the model on historical data, enabling it to learn from past patterns, which helps optimize its predictive accuracy. After the model is trained, test it to gauge its performance, and reassess from there.

Remember, the analysis and model development process is often iterative. Over time, your team may need to refine the model, fine-tune parameters, and incorporate new data as you unlock more insights from your analysis.

Step #5: Use automation and purpose-built forecasting tools

To streamline processes and improve forecasting accuracy, it’s important to invest in purpose-built tools that can automate repetitive workflows, reduce data-entry errors, and give reps more time to focus on what’s most important.

For example, tools like Gong, Salesloft Forecast, and Outreach can help teams automate data collection, track sales activities, and provide real-time insights into the sales pipeline. As a result, data accuracy improves without any heavy lifting. 

Leading solutions are designed to handle the unique complexities of SaaS sales — including subscription renewals, churn rates, and expansion revenue — and they also come equipped with advanced analytics and integrations with popular CRM systems, further improving forecasting capabilities.

Step #6: Embrace collaborative forecasting

Once your tech stack is ironed out, it’s time to encourage your team to collaborate across departments — including sales, customer support, and marketing teams  — to paint a more holistic picture of the market and better understand your customer base’s needs and wants. 

When teams work together, they bring data from multiple locations to the table, which helps improve forecasting accuracy. Additionally, it also promotes buy-in and ownership from all team members, which makes reps more motivated to achieve their forecasted goals.

Add it all up, and collaborative work helps organizations improve pipeline forecasting, which leads to better decision-making and more favorable business outcomes.

Step #7: Monitor your progress and adapt

No matter how good your pipeline forecasting game is, you can always improve.

After implementing your forecast, monitor how well it aligns with real-world outcomes and adapt accordingly. By reviewing key performance indicators (KPIs) and comparing results with forecasted figures, teams can identify discrepancies, unexpected outcomes, and areas of improvement. In the event the forecast is consistently off-target, teams may have to adjust the model, data sources, or assumptions.

By remaining agile, open to change, and committed to continuous improvement, sales teams can stay responsive to market dynamics and optimize their sales efforts, setting themselves up for long-term success.

Ready to improve the accuracy of your sales pipeline forecasting?

Accurate sales pipeline forecasting is only possible when you have reliable data — and that’s exactly where we can help.

To learn more about what makes LeadIQ’s data more accurate, reliable, and actionable, check out this post from Co-founder and CEO Mei Siauw: The elephant in the room that most B2B data providers don’t want to talk about.