Why You’re Doing Sales Forecasting All Wrong

Organizations use sales forecasting to predict the amount of revenue they can expect over a period of time. To make a sales forecast, organizations rely on historical data, market research, and sales team performance estimates. 

Accurate sales forecasting helps organizations maintain healthy growth and can improve financial models and enable smarter decisions when budgeting, hiring, running marketing campaigns, setting goals, and more. 

There’s just one big problem…

Most sales forecasts aren’t accurate. Some research has even estimated 59% of sales forecasts are wrong. 

That begs the question: Why are sales forecasts so inaccurate? 

That’s what we’re going to find out in this article. We’ll also show you how you can fix the issue and reap all the benefits of accurate sales forecasting.

Why accurate sales forecasting matters

First and foremost, sales forecasting can motivate your team. If your business is trending 30% below quota, you can analyze what’s going wrong and take action to correct it. 

Simply put, sales forecasting makes your organization more proactive. A good forecast can help you get ahead of problems before they become, well, a big problem. 

This is why your sales forecast must be at least somewhat accurate. If a sales forecast is too optimistic or too pessimistic, you won’t have a good reference point for analyzing your team’s successes and failures. 

An inaccurate sales forecast can also lead to bad decisions, as that data will affect other financial modeling and analysis. For better understanding, let’s do a quick example.

How a bad sales forecast could harm a university’s finances

A university’s sales forecast helps make financial and strategic plans for the coming academic year. Since tuition is the main revenue generator for the university, new student enrollments are a significant part of the sales forecasts. 

To predict the number of new student enrollments, analysts use the total projected number of student applications, market research, and historical admissions data. After calculations, they predict between 2,800 to 3,200 new student enrollments for the coming academic year. 

That sales forecast data is plugged into an overall enrollment forecast (which considers retention rate, graduates, students taking time off, etc.). The analysis may look like this: 

Again, since tuition makes up the bulk of the university’s revenue, this forecast must be accurate because this data will go into the university’s financial models. And these models influence strategic decision-making. 

Yet, given the number of data points that go into sales forecasts, the chance of making mistakes is high. If a data point is wrong, it can lead to erroneously high or low predictions. This can then lead to significant errors across the organization, from poor budgeting decisions to untimely capital investments that can negatively impact its financial sustainability.

Where sales forecasting goes wrong

Most sales forecasts do an excellent job of including internal and external factors that could impact revenue generation, such as:

• Hires and fires, policy changes, and territory shifts (internal factors)
• Competitive changes, economic conditions, market conditions, industry trends, seasonality, and legislative changes (external factors)

This is important to ensure you capture the whole range of future scenarios. But this also involves making the right assumptions — something many organizations fail to do well. 

Research shows that organizations aren’t structured enough with how they formulate their assumptions. A 2018 CSO Insights study found that 35.6% of companies used a subjective or casual approach to sales forecasting. Such an approach causes you to make random assumptions. This can be very problematic if you’re way off about, say, deal size or the likelihood of closing a deal.

Over time, a lack of a comprehensive, data-driven approach hurts win rates. That same 2018 CSO Insights study found that companies with formal, structured sales forecasts had a 12.6% higher win rate on average. 

Now, sales managers and stakeholders may wonder if their sales forecasting approach needs to be changed. For instance, a telecom company in a growing small city may wonder if their historical forecasting approach has affected their forecast accuracy? After all, the local population has been increasing faster than expected (historical data isn’t as relevant).  

While the telecom company’s forecast model may need updating, keep in mind, there’s no one-size-fits-all approach. Each organization is unique, and you have to find what suits you best. The key is to be aware of the shortcomings of each sales forecasting approach.

The pitfalls of each sales forecasting method

For more clarity, let’s go over what could go wrong with any sales forecasting method you may use: 

1. Historical forecasting: You may not factor in market trends, seasonality, and changes in buyer demand. 
2. Opportunity stage forecasting: You may not factor in the size and age of each deal. 
3. Intuitive forecasting: Your forecast may lack consistency and be too optimistic. That 2018 study from CSO Insights found that 47% of salespeople are too subjective with their estimates. 
4. Pipeline forecasting: A lot of data goes into pipeline forecasting calculations, such as potential deal value and sales representative win rate. Without an advanced sales forecasting tool, you can have inaccurate data and get projections that provide zero value. 

Multivariable analysis forecasting: Although this is the most sophisticated approach, this requires advanced tools, and mistakes are common.

The danger of spreadsheets

With all these sales forecasting methods, there’s a whole other issue that causes issues: Spreadsheets! 

Over-reliance on spreadsheets leads to inaccurate data. Nearly 90% of spreadsheets contain an error. Remember the saying, “garbage in, garbage out.”

While a powerful tool, Excel isn’t built for sales forecasting. Not only can mistakes lead to bad data, but also spreadsheets have numerous inefficiencies:

• Too manual: Building sales forecasting models can use up a lot of time and resources. 
• Static: Your model won’t be able to update in real-time and adapt to the ever-changing situation your organization and industry face. 
• Black box: Spreadsheets are usually only understood by the analysts who build them. This hinders communication with stakeholders and leaders.
• One-dimensional: Spreadsheets cannot analyze multiple scenarios at once. This forces analysts to create multiple spreadsheets, which is inefficient and time-consuming. 

Given the limits of spreadsheets, they also leave you unable to effectively build scenarios for your sales projections (base case, best case, worst case, and unexpected case). That can leave you unprepared and unable to envision the whole spectrum of future possibilities.

What your sales forecasting needs

Of course, you need a structured sales forecasting approach that relies on objective data and thorough research and analysis. You also must be aware of the common pitfalls of sales forecasting and take steps to prevent such issues. Additionally, you must keep your team informed and hold them accountable. 

Furthermore, you need to use the right tools. It’s time to ditch the spreadsheets and tighten up that sales forecast. 

That means having a tool that can:

• Build your sales forecasting models quickly and without mistakes 
• Update in real-time so you can adapt wisely and efficiently
• Be used across the organization with ease
• Analyze multiple scenarios at once

With such a sales forecasting tool, you can take an agile approach to sales forecasting. With accurate, updated data, you’ll be able to make better projections. That will not only ensure realistic goal-setting and boost team confidence, but it will also ensure your financial models provide accurate insights and your leadership has the right info to make better decisions.

The only sales forecasting tool you’ll need

At Synario, we’ve built a tool to take your sales forecasting to the next level. Out multidimensional modeling solution does away with outdated spreadsheets by enabling analysts to build models quickly with features like:

• Fast data import
• Automated object orientation
• Integrated financial statements
• Patented layering technology (test unlimited sets of assumptions at once)

Our solution also enables you to analyze your sales forecast intelligently with advanced scenario analysis, sensitivity analysis, and what-if analysis. You can explore all the possibilities within one model. 

Furthermore, with powerful visualizations, an intuitive “explain” feature, simple toggle functionality, and much more, our sales forecasting tool can be easily used and understood. This brings clarity to board meetings and helps achieve consensus at the highest levels. 

Ultimately, Synario’s solution can help you create better sales forecasts. And your sales team and your organization will reap the benefits. 

So, why not see what Synario can do for you?