Success in enterprise sales is a function of making smarter, data-driven decisions that efficiently allocate your sales efforts to the right prospects and pipeline deals. Whether that’s in how you prospect for new enterprise leads or in how you prioritize your customer support stream, knowing which activity will yield the greatest ROI helps you avoid wasted effort on the wrong tasks.

One such crucial stream is your sales cycle - knowing which leads stand a better chance of converting at what stage of the funnel. This post will explore four ways to forecast your enterprise sales cycle performance - and steps you can take to improve your odds. Let’s dive in!

What is sales forecasting?

Sales forecasting, at its core, is about estimating future revenue from current sales activity. Through a series of ever-changing calculations, sales forecasting predicts how much revenue a sales professional or team will bring in during a certain period. This prediction is typically based on historical data, industry averages, and the current status of your sales pipeline.

This distinction is important, as different businesses may forecast earnings in wildly different ways. For example, a small startup that sells an email management tool may simply predict future revenue based on the previous year’s results, but a company that sells audit and compliance software might forecast a much higher revenue stream upon the introduction of new privacy laws in their region. In either case, the forecasts are used as a planning mechanism for salespeople and not as a firm guarantee of future revenue.

But why do you need sales forecasting?

Every sales leader grapples with mission-critical questions such as:

• "What’s our sales revenue target this quarter?
• “How many leads do we need to hit that sales quota?”
• “What’s our margin on this new product?”
• “How should we scale our sales team to handle new business?”
• And so on.

The right sales forecasting method helps answer these questions by painting a tentative picture of the future to help answer today’s questions. Let’s now look at a few sales forecasting models you can use to predict revenue.

Sales forecasting technique #1: Relying on gut feeling

This is a common, albeit inconsistent way to predict sales revenue. The method is simple: you gauge your prospects’ likelihood of converting and extrapolate your potential revenue from that. In the absence of any other concrete data, gut feeling predictions might suffice - but don’t rely on them too much. One reason is that your revenue forecasts might be understandably optimistic and cause you to predict a sale where there is none. It’s also not entirely repeatable, as different sales professionals and prospects will bring wildly different odds to the calculation.

Sales forecasting technique #2: Using opportunity stage forecasting

With robust sales cycle management, you can use the deal stages in your pipeline to predict the likelihood of a prospect converting and extrapolate sales revenue from that. This conversion probability is determined beforehand in your sales playbook and standardized across all clients.

For example, let’s say you have the following opportunity stage forecasting model:

When a prospect responds to your LinkedIn InMail or cold email, you can peg the probability of converting them at a tiny 5% (they’ve shown interest by responding but are far from converting). When you send them your sales docs (product sheets, competitor comparisons, etc.) and get notified that they’ve downloaded them from your sales enablement platform, that pushes up their conversion probability to 10%.

When they agree to sign up for your email list and enter your nurture funnel, that raises their likelihood of buying to 20% - making progress, but still far off the mark. It’s when the prospect signs up for a demo that the closing probability goes up to a meaningful 40% - but they may still be comparing other options at that moment so it’s not a sure bet. When they agree to a post-demo sales call to chat about their experience and the way forward, it pushes their odds of closing up to 60%.

When you deliver the contract and they’re deliberating over it, your chances shoot up again - this time because you’re more involved in their internal approval processes. It’s even easier if you’re using a tool like Momentum that lets you sync all deal data directly from your Salesforce CRM to Slack with other members of your deal desk.

Once the deal is finalized, it becomes a sure 100% - and you can confidently add the revenue from the deal to your sales reports. Repeating this process across different accounts gives you a more accurate view of how much you can expect to make in any given period.

The one drawback to this sales forecasting method is that it doesn’t account for time passage. For example, a customer who signed up for a demo four weeks ago and one who signed up for a demo four days ago are on two different timelines - but the most recent demo prospect has a higher chance of converting than the older one. You can alleviate this problem by baking the age of the sales opportunity into the sales forecasting process.

Sales forecasting tool #3: Reviewing historical data

How you’ve performed in the past is usually a good sign of how you might perform in a similar position or period in the present or future. Reviewing historical data is an easy way for businesses to predict how much revenue they can earn around a certain period of the year. Business is typically cyclical, and you’ll get a feel for what to expect after a few years in business.

For example, if you know that each year for the past five years, your Q2 revenue has hovered around $25,000 MRR and jumped to $40,000 in Q3, you can use historical forecasting to confidently predict that the same will happen this year as well - assuming no other conditions have changed.

Small businesses use intuitive forecasting to predict sales revenue all the time. A florist knows that their revenue will rise around Valentine’s Day, while fashion retailers know that winter-wear sales skyrocket in the colder months and drop in the warmer months. This is no different in enterprise sales, where you can get a feel for when organizations start preparing their annual budgets and are more willing to switch vendors or invest in new products and services.

Of course, the biggest drawback to this sales forecasting method is that your business context is always changing each year. For example, many venue owners looked forward to the seasonal influx of patrons to their bars and restaurants, but none of them could’ve predicted COVID-19 and its devastating impact on their businesses. Likewise, real estate agents would’ve counted on a seasonal ebb and flow of companies leasing out office space, but the rise of remote working threw a wrench in the works and ruined their sales forecasts. As with the previous methods, use historical data as a starting point for revenue forecasting and not as a definite portrait of expected revenue.

Sales forecasting tool #4: Performing multivariate analysis

Singular data points can be helpful when it comes to forecasting enterprise sales revenue. But the best approach is to combine all your data points to get a more accurate view of expected income. Your custom sales forecasting model will depend largely on your business and business model, your pricing and conversion rates, your customer personas, and their typical sales cycle lengths.

For example, let’s say your sales reps are working on three different deals:

1. An enterprise customer worth $20,000 per year who has just completed a demo (40% close probability). Tentatively, this deal is worth: $20,000 * 0.4 = $8,000.
2. A customer who signed up for a 30-day free trial 2 weeks ago on a $5,000 per year deal (~50% close probability). Since they’re halfway through their trial, you can estimate this deal to be worth: $5,000 * 0.5 = $2,500.
3. A deal worth $10,000 per year that your deal desk team is hashing out (80% close probability). On paper, this deal is currently worth: $10,000 * 0.8 = $8,000.

Adding up the figures gives you a total or $8,000 + $2,500 + $8,000 = $18,500 in forecasted sales revenue.

Sales data does matter

Sales forecasting relies heavily on relevant, accurate sales data. This is much easier to attain if you’re a data-driven sales organization to begin with, but data can always be sourced, analyzed, and mined for insights. The more historical data you have on sales performance - such as YoY, QoQ, or MoM data - the better your sales planning and forecast accuracy.

There are other data points you can use to build a more accurate sales forecast. These include:

1. Historical customer sales data: For instance, you might determine that customers with 200+ employees close at a 30% rate compared to customers with 50-100 customers, who close at a 20% rate.
2. Marketing spend: The more money you spend on marketing, the more leads and conversions (MQLs) you should be getting - so your historical and current marketing spend plays a role in your sales forecasting activities.
3. Economic conditions: Things like viral pandemics and natural disasters can affect your sales forecast in a given period. For example, event management software sales might become affected by sweeping lockdowns that shut down all events.
4. Political/legal conditions: Depending on your niche, political and legal conditions might affect your sales forecasts for the foreseeable future. For example, the introduction of privacy laws like GDPR might (positively) affect your compliance software business.

Increase your sales forecasting accuracy

All the above sales forecasting models are designed to give you a more accurate view of your revenue potential - but that’s all they do. You still need to put in the effort to make the forecasts come true. To make it happen, start with our 5-D approach to enterprise sales or learn more about managing your sales cycle effectively. You can also lean on tools like Momentum, which helps salespeople hit quota by streamlining the deal closing process. Sign up for a demo today.