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6 Barriers to Sales Forecasting Precision

Did you know that 80% of sales teams’ forecasts are less than 75% accurate? 

Because it is difficult to produce a consistently reliable forecast, sales leaders need to be aware of common roadblocks that get in the way. Six of these barriers are below. 

  1. Unformalized forecasting processes.
    Let’s start with the basics: Defining what sales forecasting looks like at your company. Over 67% of organizations lack a formalized approach to sales forecasting altogether, according to CSO Insights. 

Two-thirds of organizations lack a formalized approach to forecasting altogether.

— CSO Insights 

2. Inconsistent leadership visibility.
Leaders use different methods to produce sales forecasts, which obscures pipeline and forecast health. This siloed approach also gives different leaders different views of what the future looks like, and often causes them to develop unaligned plans.

Want to make sure you’re using the right sales forecasting method? We’ll explore the pros and cons of different forecasting models in our next blog. You can also access them now by downloading our guide to Sales Forecasting in the Next Normal.

3. Departmental silos.
Flawed sales forecasts result from a lack of collaboration across product, sales, marketing and finance. Sales isn’t the only department involved in sales forecasting precision! For example, if customer success doesn’t provide a forecast around upsell and cross-sell opportunities, the sales forecast is likely to exclude a significant portion of potential revenue.

4. Insufficient expertise.
Most professionals lack the expertise to calculate realistic figures and pragmatically estimate closing dates. Without sufficient training and enforcement of a team’s sales process and lifecycle, forecasts suffer. 

5. Poor data quality.
Forecasting’s foundation is built on sales data, which is only reliable when comprehensive and accurate. Poor data mainly results from inconsistent or subpar sales process execution. Sellers often fail to enter information into their customer relationship management (CRM) or forecasting systems. When they do, many sellers over-rely on gut feelings about an opportunity, instead of objective data. While some sellers are overconfident, others tend to be too conservative. Individuals and managers alike tend to sandbag figures.

6. Poor execution of the sales process.
Poor data quality can be expected when sales management fails to define or enforce strict stage definitions and milestones for sales cycles across marketing, sales, and customer success. Unfortunately, this reality is common at most companies. 

If you want to sidestep these common barriers, stay tuned for our next blogs.

Using an effective mix of macro and micro forecast models is key to formulating accurate sales projections, provided that the exercise incorporates best practices and fits into your overall forecasting and revenue operations motions. We’ll cover these topics in upcoming posts.

 

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