Sales Forecasting Processes To Achieve 95% Forecast Accuracy

When it comes to your sales forecast, the process by which you create it is one of the largest determiners of accuracy.

Without a well-structured, consistent, and visible process, you cannot guarantee that the right data is making it into your forecast. 

However, you also must ensure that salespeople still have time to sell, and their managers still have time to manage. That's why your forecasting process must be robust, but also simple.

We were recently joined by revenue experts Elyse Iovino (Sitecore), Eric Teuscher (Pluralsight), and Brandon Bussey (Lucid) as they covered their forecasting process best practices for accuracy and productivity. 

Want to watch the roundtable in full? View the recording or download the slides here.

Forecasting processes across different size companies

One of the most difficult aspects of sales forecasting is that no two companies are the same.

Brandon Bussey points out that a one size fits all approach will most likely lead to inconsistencies and inaccuracies within your forecast. He says that one of the largest problems in forecasting is new leaders who bring their previous forecasting approaches to new roles. Those old processes likely will not function well within the new business.

How accurate does your forecast need to be?

When it comes to forecasting within your organization, it is important to think about the level of accuracy that really matters. Eric Teuscher says that the amount of accuracy required will vary by company size and stage. For example, a Series A company may only need to land within 20 or 30 percent of their forecast in order to please investors. Yet a late-stage org or public company likely needs to land within the coveted +/- 5 percent.

Forecast Process Considerations in High Growth Companies

What happens when your company growth suddenly outpaces your forecasting processes?

When it’s time to catch your processes up to your company size, you need to be mindful of your teams’ capacity, how other teams outside of sales impact the accuracy of your forecast and the taxes of a new process, says Elyse Iovino. If revenue generation is a focus - how much time do you want salespeople to now spend on forecasting? Further, how do processes such as legal, accounting, and implementation impact your forecast?

Especially in rapidly growing companies, teams must ensure that new processes are a part of onboarding so new reps are proficient in processes before they begin. Companies in hypergrowth likely have different business types with different sales cycles and inorganic growth (through acquisitions), which can both cause problems with creating a consistent forecast.

Forecast Process Best Practices

When it comes to building the most accurate sales forecasting process, there are several best practices that all experts agreed on:

Focus

• Define your key areas... Eric says to ask yourself, “What is the outcome that the process needs to create.”
• In the case of a high-growth company, Elyse says to find the biggest risk to forecast accuracy and start there.
• For other companies, Eric recommends prioritizing processes to first ensure data hygiene.

Standardization

• To maximize accuracy, you must first standardize everything.
  Make sure your entire company is speaking the same language, and that definitions are upheld globally
• Clearly define:
  • Sales stages, how to progress between them, the functions of each role in the forecast, how they do it.
• Consistency is key. Although it’s not ideal, Brandon says that a team that is consistently inconsistent is more likely to be accurate.

Quality Data

• If you’re using the wrong numbers to make your forecast, your whole forecast will be wrong.
• Put extra effort into creating the most accurate data possible.

Deal Inspection

• Every team needs to be inspecting deals on at least a weekly basis.
• This is not only to ensure that your most important deals close, but also to guarantee forecast accuracy. Look for inconsistencies in amounts, close dates, activity levels, and any red flags that would constitute a risk in your forecast.

Involvement from Key Players

• Forecasting is a company-wide activity and therefore should involve key stakeholders from all departments.
• Don’t bring in every person though, says Brandon. Focus only on the key players and who is most impacted.
• Work with senior leaders to build processes and workflows that fit with their teams and capacities.
• Ops and IT should build the actual architecture behind a forecast, what tools and functions are necessary?

Transparency

• “Transparency drives rigor,” says Brandon.
• The visibility goes both ways. Management needs full insight into every deal, while salespeople need to know the calls, what goes into them, and why.
• A mutual understanding allows everyone to work for the same goals.

Robustness

• Brandon adds that teams should avoid a single model and that different perspectives lead to more accurate forecasts because of the angles that they provide. “Every process has different assumptions, risks, and inaccuracies, so by taking multiple approaches it really helps to triangulate your number,” he says.
• Top-down, bottom-up, data-driven, gut-driven forecasts are all more accurate when used in conjunction with one another.

Automation

• You hired your sellers to sell and your managers to manage, not forecast. Use automation to increase their productivity and reduce forecasting workload.
• Automation removes the possibility for human error and increases data accuracy.

Sales Forecasting Pitfalls to Avoid

When it comes to common mistakes, Brandon points out a few of the most common.

Too Much Complexity

Far too many people overdo their forecasting process out of the gate. As previously mentioned, a best practice is to start simple. Too much complexity out of the gate only makes it more difficult to get started. Additional complexity also makes it more difficult to pinpoint problems and areas of improvement as your forecasting program expands.

Mismanaged Expectations

Many teams make the mistake of not considering the taxes they are levying on individual roles. For example, your sales managers are not data scientists, and you probably don’t want them to be. Your salespeople are not data entry specialists, they should be spending time selling.

Make effort to improve the workflows of different roles, and involve operations and IT to give them the tools they need.

Thinking of forecasting as a singular process

Forecasting is not as simple as data in, data out. Your forecast is likely made up of individual forecasts, contributions from all different teams, roll-ups, overrides, and so on. Therefore, when asked about a forecast, you must understand all the different types of forecasting and how each of them impacts your business.

To view the full roundtable recording, or download the slides, go here.