Revenue forecasting holds a central place in shaping the strategy and operations of an organization. Accurate sales forecasts help you make confident investments. When forecasts go wrong, it can cause chaos across departments.
But, if you think forecasting is solely about getting the numbers right, think again.
The true essence lies beyond forecast accuracy – it's all about gaining visibility into your pipeline and improving visibility.
Picture this: you're the ship's captain, navigating through turbulent waters, your eyes fixed on the horizon. Your goal is simple - reaching your destination safely and efficiently. Just like the captain, SaaS companies, too, are navigating through a constantly changing market landscape, where precise decision-making is essential to reach their growth targets.
Therefore, while the accuracy of forecasting is important, it’s better to wager on building a culture of forecasting which could make a difference in reaching your goals in the long term.
In this blog, we'll discuss the different stages of maturity every organization goes through to master effective revenue forecasting and the impact it can have on your bottom line.
The 5 stages of revenue forecasting maturity
In the modern B2B SaaS environment, most revenue teams know the importance of revenue forecasting. Still, not everyone has a streamlined forecasting process.
What makes it more difficult, especially given the current economy, is that you probably have fewer people on your GTM team than a year ago.
Yet to stay afloat, you cannot afford to slow down your revenue growth. That means as more opportunities come into your ecosystem, it gets harder to manage them and ensure that every single deal gets the attention it deserves.
At the early stages of organizational maturity, we all face our fair share of challenges to forecast revenue reliably.
But, over the years, we’ve also understood that revenue forecasting at organizations follows a maturity curve. Depending on the stage, it may vary from having no forecasting practice to being flow to being completely predictive.
Here are the five stages of forecasting maturity:
Stage 1: No forecasting
Most young organizations don’t have a forecasting process, i.e., they don't commit the potential revenue for a month or quarter. And that’s not because they don’t want a reliable revenue forecast but because they don’t have historical data to predict future revenue and growth.
These companies don't have a VP of Sales or Customer Success at this stage. They are pretty much playing it by ear instead of relying on a pipeline of deals.
As a result, companies at this stage are more dynamic.
They may face unexpected hurdles, thus making it difficult to confidently make long-term commitments.
Stage 2: Spreadsheet based manual forecasting
Forecasting becomes an integral part of the organization's operating rhythm in this stage. Weekly or biweekly check-ins are established as a formal process where sales leaders meet with their respective teams to review the status of deals and gauge the team's confidence in hitting their quotas. These meetings serve as an essential forum for exchanging information, addressing concerns, and aligning strategies.
The organs in this stage realize the importance of forecasting but don't use any tools yet. Instead, they rely on spreadsheets and regular check-ins as their primary method of gathering insights for forecasts.
Without automated tools that provide data-driven insights, sales leaders heavily rely on the representatives' subjective judgments during these check-ins. They assess each deal's status, asking probing questions to gauge the sales representatives' confidence in meeting their quotas.
Recognizing the need for more robust solutions, companies often explore potential forecasting software and CRM platforms that streamline their processes and provide greater visibility into their pipeline.
Stage 3: Forecasting based on CRM data and deal progression
The orgs in this stage begin to rely on CRM as a central hub to get better visibility into the sales pipeline. With CRM in place, reps can regularly update deal statuses, providing real-time information on the progress of individual deals.
This dynamic view of the sales pipeline empowers revenue leaders to make more informed decisions and create reliable forecasts.
While using a CRM for forecasting is significant progress compared to not doing forecasting at all or managing deal updates on a spreadsheet, the key challenge is—the revenue forecast entirely relies on the subjective inputs of sales and customer success reps.
Stage 4: Connecting Conversation Intelligence and CRM to remove subjectivity in forecasting
More mature orgs realize that keeping prejudices out of your forecasting is tough—especially because personal biases dampen our judgment at the subconscious level.
The forecasts shared by sales and customer success reps are often ad hoc and based on individual hunches. Sales and customer success reps are just as likely to underestimate their forecasts as they might overestimate them.
To combat this problem, these teams integrate conversation intelligence with the CRM to enable accuracy and data-driven decision-making in revenue forecasting. By combining the deal progression data from CRM with the qualitative insights from conversation intelligence platforms, organizations eliminate guesswork and uncertainty from their forecasts.
Here’s how it works:
As always, the starting of your forecasting process is the summation of the deal values of all the deals in your pipeline.
But what a conversation intelligence platform with forecasting capabilities like Avoma brings to the table is—at any given time, you get instant insights into the deals won, deals lost, existing pipeline value, and the current gap between revenue attained and revenue committed.
It also gives you insights into the risk status of every deal, thereby proactively ensuring no deal slips through the cracks.
For example, it captures key positive sentiments such as:
- Competition: If your prospect mentions 2 or more competitors during your conversation displaying high buying intent.
- Positive Moments: If there are 5 or more Positive Moments during your conversation.
- Upcoming Meeting: To nudge you to prepare for a meeting with a prospect in advance if it's coming up in the next 48 hours or less.
Examples of negative deal health alerts include:
- Single Threading: If you have only one stakeholder at the prospect account, Avoma flags an alert nudging you to add more stakeholders.
- Email sentiment: If the sentiment of the latest email from your prospect account is negative, Avoma will alert you so that you take appropriate action.
- No Recent Activity: If a deal has been stalled for 20 days or more and there’s no email, call, or meeting recorded.
- Positive Moments: This one is also a risk alert in cases where your deal conversations don’t have even a single Positive Moment.
- Time Since Last Meeting: If there has been no meetings for more than 15 days, indicating that the deal is not progressing.
Organizations at this level of forecasting maturity save a lot of time, avoid manual errors otherwise resulting in spreadsheets, and more importantly manage potential deal risks in time so that they don’t let deals slip through the cracks.
The forecasting at this stage is reliable and unbiased, but is not predictive.
Stage 5: Predictive forecasting
Organizations at this stage don’t stop at analyzing the health of deals based on how the deal progresses. They leverage the power of AI to learn from historical data and make accurate predictions. This is a significant leap from earlier stages, as AI takes the driver's seat in generating reliable forecasts.
The orgs leverage AI algorithms to dive deep into the organization's historical data, analyzing all your deals, customer interactions, and sales performance. By processing this data, AI uncovers patterns, correlations, and insights that human analysis alone cannot easily discern.
For instance, AI recognizes which of your deals require multithreading with multiple key stakeholders to secure success. It identifies optimal deal strategies based on deal size, industry, and customer preferences.
And that means, AI would also by default analyze the conversion rates at both the managerial and individual rep levels to generate tailored forecasts based on their strengths and weaknesses.
For revenue leaders, this means gaining insights into their team's performance and identifying areas for improvement. For reps, it provides a roadmap for enhancing their skills and optimizing their sales strategies.
The result is a dynamic and collaborative GTM environment that continually evolves and improves.
Building a culture of revenue forecasting across your organization
While culture may start at the top, it has to permeate the entire organization. Regardless of where you are in the forecasting maturity curve, to build a culture of revenue forecasting you need to educate your team about its importance.
Ensure that everyone understands the concept of revenue forecasting, its purpose, and the role it plays in the company's success. This can be achieved through regular training sessions, workshops, or online courses.
Moreover, share success stories and case studies where revenue forecasting has markedly contributed to a company's success. This will not only help your team understand the concept better but also inspire them to adopt it in their day-to-day operations.
Make forecasting an inclusive exercise
The next step is to make everyone a part of the forecasting process. Instead of keeping it restricted to sales or customer success, involve people across your go-to-market function. This will result in diversified inputs, leading to more accurate and effective revenue forecasts.
Additionally, inclusivity instills a sense of ownership amongst the team members. When they know that their inputs are valued and that their contributions can significantly impact the company's financial success, they are likely to be more committed and engaged.
Set up a review cadence
Revenue forecasts should not be a one-time activity. They must be regularly re-evaluated and adjusted based on new data, market changes, and business growth. By doing so, your team will always have an accurate picture of the company's financial health, enabling them to make informed and timely decisions.
Establish a regular cadence for reviewing and updating revenue forecasts. This could be monthly, quarterly, or on-demand, depending on the dynamics of your business and market.
Invest in the right tools based on your forecasting maturity stage
Lastly, leverage technology and tools to streamline and automate the forecasting process. They also save time and resources by automating routine tasks, allowing your team to focus more on strategic planning and decision making.
They can also help in making the forecasting process more transparent and accessible, further promoting a culture of revenue forecasting in your company.
Persistence is the key
Building a culture of revenue forecasting is no easy task and can come with its share of challenges. It involves changing attitudes and habits, which will not happen overnight. It's important to be patient and persistent.
Even if the results are not immediately visible, stick to the process. The long-term benefits of a revenue forecasting culture far outweigh the initial challenges and efforts.
That said, no forecast can be 100% accurate, and there will always be some degree of uncertainty. So it’s better to focus on improving pipeline visibility than accuracy. Accurate forecasting is a byproduct of a transparent pipeline.
Revenue forecasting is not just about predicting future revenues. It's about building a culture where everyone understands the importance of revenue forecasting and actively contributes to it. It involves educating your team, promoting inclusivity, regularly reviewing and updating forecasts, leveraging technology, and overcoming challenges.
A culture of revenue forecasting can be a game-changer especially for SMB SaaS. It encourages proactive and informed decision-making, fosters effective communication, and drives the entire team towards hitting financial targets.