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The Role of Data Sources in your Sales Forecasting

Apr 28, 2023
4 min read
Sales Forecasting 101, Vol. 3

Forecasting is, by its nature, an uncertain practice—part art, part science. In the past, it was more of a sales leaders’ art, based on experience, intuition and assumptions. But as the need for more precise forecasts has grown, and as sales leaders have gained greater access to key data, the science of forecasting has moved to the forefront.

That science depends on the collection of the right kind of data—delivered in a timely way and drawn from the right data sources, then unified to provide context and granular detail about your organization.

10 Data Sources Essential to Your Sales Forecasting Strategy

However, collecting this data from different systems can be difficult and result in blind spots. And, where there are data blind spots, sales leaders use intuition to fill the gaps, which can put them at a disadvantage against the competition.

While every company can fine-tune its forecasting process by introducing data that’s unique to your company, there are some vital data points that every forecast needs to include to drive revenue and profitability. Let’s look at 10 important data sources you should be using in your sales forecasting strategy.

1. Individual sales rep performance

Every sales rep is responsible for reporting their sales numbers in your CRM system and to sales managers. These numbers can be compared to past performance data for each rep to determine the probable accuracy of their reporting and coach them to make accurate individual sales forecasts.

2. Close rates

Close rates are important at a basic level because they show the number of prospects in your pipeline that become paying customers. Beyond that, however, close rates can provide important insights about factors that impact your sales team’s ability to close deals. They’re a useful starting point for performing a sales data analysis on things like lead quality and external market conditions that impact sales potential.

3. Average contract value

Average contract value measures the average revenue generated by a single deal in your pipeline. It’s a critical metric for understanding the total revenue potential of your current sales pipeline and creating accurate sales forecasts based on that information.

Measuring average contract value also helps you analyze whether you’re pursuing the right customers and/or if your targeting strategy needs adjusting to meet revenue goals.

4. Win rates by stage

Win rates by stage is a metric that tells you what’s happening to prospects at each stage of the pipeline. You can gather important information such as points in the sales process where most prospects convert or, conversely, where you’re losing the most deals to competitors.

When it comes time to make sales forecasts, you can look at your current pipeline holistically and use win rates by stage to understand how—as a whole—it will translate to revenue.

5. Length of sales cycle

Your length of sales cycle refers to the amount of time deals spent in the pipeline between initial touchpoint to final close. Knowing the duration of your sales cycle enables you to make an accurate forecast for sales revenue based on a particular time frame. For example, does your sales cycle lengthen when deals are bigger, or does it appear to be arbitrary?

Just like sales numbers and overall revenue, your sales cycle length should be a predictable metric. An unpredictable sales cycle means an inability to pinpoint sales forecasts based on a given time period.

6. Seasonality

Seasonal buying trends are a natural part of any sales cycle. It’s important to track seasonality trends and incorporate them into your sales forecast to ensure proper budgeting and resource allocation.

7. Upselling rates

Selling to existing customers is more lucrative, cost-effective, and time-efficient than winning new customers in almost any market. Particularly as subscription-based and “as-a-service” business models grow in prevalence, companies must shift their sales strategies (and forecasts) to include upselling potential.

8. Rate of CRM adoption

Your CRM is the central point of visibility for sales data provided by your sales team — but it is only as valuable as the rate at which your reps use it. It’s critical, then, to both understand and continually facilitate high levels of CRM adoption to ensure the data you pull from it is accurate and complete.

Low CRM adoption rates leave gaps in your sales data that make it impossible to create accurate forecasts.

9. Customer lifetime value

Customer lifetime value (CLV) is one of the most important revenue-related metrics that companies measure. As it relates to sales forecasts, understanding CLV is critical to the ability to target the right prospects and predict the ROI you’ll earn from each customer segment.

Using CLV as a sales forecasting metric helps you allocate sales resources at the right stage of the sales cycle (i.e. initial interest vs. retention) and forecast the revenue you’ll earn based on your current pipeline.

10. Unique sales variables

Of course, there is no one-size-fits-all approach to sales forecasting. It’s essential to consider the data sources and other factors that influence your unique organization and industry. For example: have you had a period of particularly high sales turnover? If larger portions of your staff are ramping at the moment, you may experience a temporary dip in sales revenue as they get up to speed. It is important to be able to understand the impact of these reps on overall organizational success.

If your industry took a hit based on external factors beyond your control (for example, the restaurant industry during the initial months of the pandemic), you must adjust your sales forecasts—and your strategy—accordingly.

Utilize Key Sales Data Sources With Xactly Forecasting

It’s critical to build a revenue forecast that your stakeholders can trust. It doesn’t have to be a painful process when using the appropriate sales data resources. Through sales pipelines analysis, if you can trust what you see in the pipeline and that your pipeline is filling at the appropriate rate with qualified leads and opportunities that fit your ideal customer profile, you can have the confidence that you’ll hit your number.

In our next blog, titled Sales Forecasting 101, Vol. 4: Why Use Sales Forecasting Software, we’ll break down current manual forecasting problem areas and how they stack up against an automated forecasting solution.

Grab our ebook on the Six Strategies to Building an Accurate Revenue Forecast to learn more!

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Pooja Bhatt Headshot
Pooja Bhatt
Business Consultant

Pooja Bhatt is a Business Consultant at Intangent. She is an experienced Technical Business Consultant with a demonstrated history of working in the management consulting industry. Strong information technology professional skilled in Oracle Database, Databases, Java, Networking, and HTML.