Much like weather forecasts, sales forecasts are notorious for being inaccurate. In a 2021 State of Sales Forecasting study of over 400 companies, a mere 15% of leaders¹ said that they were very satisfied with their revenue forecasts, while a staggering 91% of participants admitted that their forecasts are off from actuals by at least 6%.

Irrespective of the forecasting methods used, most organizations are missing their sales forecasts² by a wide berth—and this comes at a big opportunity cost. As someone who has built a career in sales and business development over two decades, let me share my take on how your business can be the exception.

Why is accurate sales forecasting important?

“Forecasting determines how much operating expense you have to invest in the business. When people try to under-forecast and over-deliver, that simply causes an underinvestment in the company,” says Carl Eschenbach³, venture partner at Sequoia and CEO of Workday, about the importance of accurate forecasts. “On the other hand, when people forecast too high, the company spends accordingly, expecting a certain number of bookings to come in. Then, when you miss the number, the expenses get the company in a bad position.”

Poor sales planning and forecasting has a detrimental impact on the functioning of every team. Your sales team, for one, can achieve their goals much better if they can examine their sales pipeline for early warning signals and course-correct before it’s too late. Without an accurate forecast, they’re journeying with a wonky compass.

Product development is another team that’s affected. Businesses often plan product development cycles based on sales forecasts. If projected sales figures are inaccurate, it can lead to delays in releasing new features or products, affecting your company's competitiveness in the market.

It can also make your marketing efforts ineffective. Overestimating demand may lead to overspending on marketing, while underestimating demand may result in missed opportunities for customer acquisition.

Customer support bears the brunt of poor revenue projections too. Because of an inaccurate forecast, you may end up underestimating the demand for customer support and service. This can lead to chaos in the support teams, longer response times, and a decline in customer satisfaction.

Why is Sales Forecasting Important for SaaS businesses?

Forecasting is important for companies of all kinds, but especially for SaaS businesses. Let me list out the main reasons.

• Since the SaaS market is very competitive, a good forecast helps you stay ahead of the competition. It allows you to respond swiftly to market changes and capitalize on emerging trends.
• It also helps you get a handle on your key metrics—project CLV accurately and set achievable MRR and ARR targets, thus effectively guiding your business growth and customer acquisition strategies.

If you are a SaaS startup seeking funding, your investors are likely watching your sales forecasts carefully. Inaccurate sales projections and revenue forecasts in a business plan can erode investor confidence, potentially impacting your funding opportunities and the overall valuation of your company.

SaaS Sales Forecasting Challenges

The first of many hurdles in the path of effective forecasting is poor data. If your CRM data itself is incomplete/low in quality, it doesn’t matter what sales forecasting process you use.

Even when quality CRM data exists, many sales reps default to relying on their gut feel while projecting future sales figures. This leads to wide gaps between actuals and forecasts. I will talk more about the specific role of sales reps later in this piece.

To add to this, many sales teams work with multiple tools to forecast numbers, and these tools don’t always work together. The more you can connect software through integrations, the better your CRM and sales data will be. While sales forecasting on Excel is definitely doable and is what many young businesses rely on, it is not the best way to bring all relevant organizational data together. This is one of the reasons we’re building DataviCloud: to help fast-growing businesses do sales forecasting and revenue projections better.

Okay, so the challenges I’ve spoken about so far hold true for all businesses. But there are a few more that matter specifically to SaaS sales forecasts.

1. The SaaS business model is heavily influenced by external factors that directly affect its revenues and future projections. It is often one of the first industries to be impacted by the performance of the economy.
2. The SaaS landscape is highly competitive. This introduces an additional layer of uncertainty. Even a small price drop from a competitor can directly affect your revenues, rendering your revenue projections inaccurate. So, what can you do?

Strategies to forecast your SaaS revenues better

While there’s much outside your control, here are four measures you can take that will bring your forecasts much closer to actuals.

1. Improve your data quality.

The first and foremost necessity for sales forecasting: Clean data. This means many things.  Begin by emphasizing CRM discipline. i.e. consistent use of your CRM system with every team member promptly logging activities, deals, and customer interactions. Training and regular refreshers can be instrumental in maintaining this discipline.

As they enter data, they should be following standardized protocols, so that data across all your teams is comparable and ready for accurate analysis. When introducing new tools or processes, make sure there's a standardized onboarding process. This helps in seamless integration with existing systems and minimizes disruption to the sales cycle.

Expend as much energy and focus as required on creating a culture that treats CRM logging as a sacred activity. Then, when you hire new members, they are far more likely to follow the already-prevalent culture of disciplined updation and tracking.  

Schedule periodic reviews of the CRM data to rectify any inaccuracies or outdated information, to the health of your sales pipeline. Also implement mechanisms to detect and eliminate duplicate deal entries in the CRM. This is important since duplication can lead to skewed data and misinformed decisions.

Implement a cross-verification method for sales data such as triangulation of numbers. By comparing data from different sources (like sales reports, CRM data, and customer feedback), you can identify discrepancies and gain a more accurate understanding of the sales pipeline.

2. Make your sales team more involved.

As a young sales rep in the early 2000s, if I had walked up to my manager and said I haven't closed any deals this week because I haven't got any good leads from the marketing team, he would have given me an earful. That's not the case today.

Hubspot CEO Brian Halligan coined the term 'inbound marketing' in 2005. But the idea that you could run campaigns and write blogs and send newsletters, all to get potential customers to come to you, really took off around 2011-12. In many ways, inbound marketing is a gift that keeps on giving—a healthy pipeline of leads, brand awareness and recognition, lower costs. But sometimes, SaaS businesses over-rely on inbound marketing to bring in leads.

My take is that the key lies in balance—inbound efforts must be complemented by strong, sales-driven efforts. As the sales team holds more ownership, revenue forecasting will automatically become more accurate.

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In the survey I spoke about earlier, only 25 percent of companies reported that their sales reps, those most involved with the deals, were involved in the forecasting process. As a matter of fact, lack of sales rep accountability was identified as the number one reason for poor forecasts.

Is this the case in your organization? Then you must revisit what stops your sales reps from being part of the forecasting process and take active measures to include them. If you can create a psychologically safe environment, in which your sales team feels comfortable discussing challenges and failures, you are much more likely to get them closely involved, leading to better forecasts.

Besides this, incentive models for sales reps need to be designed such that they feel motivated to not just achieve targets but exceed them. You need to do your best to align their goals with the company’s growth objectives.

Aside from the people aspect, you also need to examine your deal review process: are there gaps? Are there opportunities?

Here are four sales forecasting initiatives I highly recommend for SaaS businesses.

Deal Scoring: While many SaaS businesses assess deals, implementing a comprehensive scoring system is less often practiced. Develop a scoring system that assigns values to various factors such as deal size, customer fit, competition, and timeline. These scores can help you allocate resources more strategically and focus on high-value opportunities.

Risk Assessment: Are you conducting a thorough risk assessment for each deal? Potential risk factors include budget constraints, competitive pressures, or technical challenges. Assess how these risks may impact the likelihood of closing the deal. This will help you take informed early action and ultimately improve forecasting accuracy.

Deal Health Indicators: This approach goes beyond traditional metrics and helps in predicting deal outcomes more accurately. Define key indicators relevant to your industry/vertical that signal the health of a deal, such as engagement levels, response times, and quality of prospect interactions. Monitoring these can provide valuable insights into the likelihood of closing a deal.

Decision Framework: Establishing a clear decision-making framework for deal progression may not be standard practice but it’s well worth adopting. Determine and document specific criteria for advancing or pausing deals. A well-defined framework like this ensures that decisions are based on data and strategy rather than gut feelings, promoting consistency and better forecasting.

3. Get product and marketing to support the process.

Accurate sales forecasting is primarily the job of the sales team. But underlying it, there needs to be a solid commitment from the product and marketing teams.

The product team needs to align closely with the sales team to understand customer needs and priorities, and use this information to guide product improvements. If there are upcoming features or changes in the product that may affect sales timelines and customer interest, this must be discussed with the sales team well in advance. By sharing product release schedules and expected customer adoption rates, the product team can help Sales make more informed predictions about their pipeline and revenue projections.

Meanwhile, the marketing team needs to provide steady, high-quality leads to the sales team. It should work closely with the sales team to define and refine lead qualification criteria. The team should also work on marketing channel optimization to ensure a consistent flow of leads, which can reduce variability in lead generation and stabilize the sales pipeline and forecasting.

It also helps if sales and marketing can collaborate on targeted marketing campaigns aligned with the sales strategy. By focusing marketing efforts on specific customer segments or industries, the sales team can benefit from a more qualified and interested pool of prospects.

4. Simplify it all with automation.

Automation can work wonders to simplify forecasting and make it more accurate. Here are just a few opportunities:

• Use automation to sync data between your CRM and other tools, so your sales team has access to the most up-to-date information.
• Implement machine learning algorithms to analyze historical data and predict future sales trends.
• Automate lead score assignment based on predefined criteria, helping your sales team prioritize high-value opportunities over others.

As I wrap up this deep dive into sales forecasting, I want to reiterate that the journey to accurate sales forecasts is multifaceted, requiring a blend of people, processes, and technology.

What challenges does your SaaS business face with forecasting? I’d love to know what has been working (or not) for you.