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Sales forecasting is, by nature, inexact.

When a Sales VP works with inaccurate sales forecasts, he or she will see their teams turn in poor performances in attaining sales quota (which may be too high or too low to begin with) and their organizations fail to generate predictable revenue.

The key, then, is to take as much of the guesswork out of your sales forecasts as possible. Identifying and eliminating these barriers allows managers to create data-driven sales forecasts that use advanced sales analytics while allowing for added input from sales reps.

(For more detailed information about sales forecasting, check out our FREE eBook: The Definitive Guide to Data-Driven Sales Forecasting.)

Smart Forecast - InsightSquared

Here are some of the major barriers to effective, data-driven sales forecasting:

1. Poor data quality

Working with the wrong information is akin to analyzing with no information at all – in these scenarios, sales managers and VPs are essentially stabbing in the dark and adopting a finger-in-the-wind approach. Bad data can be summed up by comparing qualitative forecasting that relies on sales forecasting stages to quantitative forecasting that depends on opportunity stages.

Many organizations use the former, where sales reps are asked to analyze their own pipeline opportunities and prescribe their personal judgments to them – are these opportunities in a commit, upside or worst case forecast stage? These subjective stages can lead to reps developing “happy ears” or disingenuously sandbagging deals to pad their own numbers.

Comparatively, opportunity stage forecasting is much more quantitative, with an established set of milestones or benchmarks that each opportunity must reach before being qualified for the next opportunity stage. Since reps are working with objective data, the information will be more consistent and accurate across the board. With concrete definitions associated with each stage, sales reps will not be able to blur stages or manipulate data.

It is incumbent upon management to:

a) Create a culture that stresses the importance of measurable data. All aspects of all sales processes must be measured and monitored. Such an emphasis must take on a top-down approach in order to gain full buy-in from all sales reps.

b) Stipulate a set of required fields in your CRM system. Each of these fields must be filled in on every opportunity, with the relevant information entered at each individual stage of the sales process before opportunities can progress to the next phase.

c) Establish consistency. Ensure that your team is working with crystal clear definitions of sales pipeline stages, the same benchmarks for qualifying leads, the same milestones for progressing through the sales funnel stages and the same processes for entering and monitoring data.

2. Lack of personal accountability among sales reps

If sales reps aren’t involved in the sales forecasting process, they won’t believe that the information can help them or, worse, they might manipulate the data to make themselves and their own forecasts look good. Therefore, perhaps the most important barrier to effective sales forecasting is that reps are often not held accountable or treated as a critical component to the overall process.

Sales Analytics - Opportunity Pipeline This goes beyond simply making sure that every rep is entering the right information in the right places. There has to be full-scale adoption of data-driven sales forecasting by reps. Focus specifically on important metrics – pipeline stages, age of opportunities, engagement momentum, slippage –  and explain how that data is being used, and where. If reps don’t know what their managers are reporting, who is using it and what they’re using it for, they won’t be able to appreciate the importance of the data in the first place. Sharing this information with your reps – as well as how they stand to benefit from having this information – will result in higher and more accurate compliance with day-to-day data entry.

Overconfidence and sandbagging among reps are examples of what happens when reps run amok and are not held accountable. As mentioned above, a migration to more objective opportunity stage forecasting from qualitative and subjective sales forecasting stages can help eliminate these problems. Management can also eliminate sandbagging incentives and penalize such scenarios to help establish an overt company culture of accountability and accuracy over brash overconfidence.

3. Don’t forget about intuition / intangibles / the human element

It might seem ironic that a data-driven sales forecasting method relies on such intangibles as a rep’s intuition, but the truth is that the most accurate sales forecasts demands that the two disparate concepts not be mutually exclusive. For starters, opportunities must be qualified and re-qualified at every single stage. Qualification largely rests on the abilities of sales reps to draw out important information by asking the right questions at the right stages. Among all the various data-based efforts that go into producing an accurate sales forecast, the qualification of opportunities remains largely based on the intuition of reps. This puts the onus on you as a sales coach to help your reps develop this intuition.

There are other intangible factors and external dynamics that contribute to the likelihood of an opportunity being closed – outside competition, changing market factors, and the nuances of individual opportunities can all play pivotal roles. Overlaying your sales analytics for a data-driven forecast with your reps’ intuition and external dynamics will create a more accurate sales forecast.


All sales managers depend on accurate forecasts to more effectively manage their team, plan and budget more realistically, and set achievable and aggressive expectations for the organization, ultimately growing revenue. Data-driven methodologies that take advantage of predictive analytics, are consistent, receive the full buy-in from all sales reps and contain insights from the human element will set you on your way toward more accurate forecasts and improved performances.

What are some other difficult obstacles you have encountered on your path toward accurate data-driven sales forecasting? Share your cautionary tales below.

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