Accurate Sales Forecasting

Deconstructing the art of sales forecasting to increase accuracy and make better decisions.

Planning for the future has never felt so daunting as it does right now. In these uncertain times, businesses are throwing out their trusted practices and protocols. They find themselves in a desperate scramble to reorient themselves to a new “normal.”

Adaptation is key, but how can you make the right decisions for the future when so much feels unknown?

To improve the accuracy of your organization’s sales forecasting strategy, it can be helpful to go back to the basics.

We need to revisit exactly why sales forecasting is such a pivotal part of any business plan. In this post, we’ll review what accurate forecasting entails. We’ll explore forecasting philosophies and actionable best practices. And we’ll arm you with foundational concepts you need to ensure your own projections reflect reality.

What is sales forecasting?

Sales forecasting is an umbrella term for a variety of prediction methodologies. They can range from the statistical to the anecdotal. The end goal is the same for all sales forecasting. And that goal is to help you generate as realistic a picture as possible of your business’s sales at a designated point in the future (say, six months from now).

If you generate an accurate sales forecast, you’ll be able to integrate that information into your business plan. With that, you’ll be able to make sound, timely decisions in the present.

Because most of your organization’s budget is determined by sales numbers, forecasts empower all departments to prepare for and respond well to spikes or dips in buying trends.

Is there evidence that accurate sales forecasting makes a difference?

Academic studies conducted by entities such as Harvard Business Review have found say yes. They defined a formal sales forecasting method as reps having thorough training. But additionally, as reps setting aside several hours a month to work on their sales forecasts. They found that companies with that did this perform better and have greater longevity than competitors who don’t.

This shouldn’t come as a surprise. Experienced business leaders know that making the future less uncertain always predicts success.

Qualitative vs. quantitative forecasting

The science (or art, depending on who you ask!) of sales forecasting can be broken down into two different categories. Those are a) qualitative sales forecasting and b) quantitative sales forecasting.

As the word “qualitative” implies, this first approach relies on anecdotal evidence. As such, it’s recommended for new ventures that haven’t been in business long enough to accrue sales data.

The quantitative approach uses actual numbers. From the numbers it creates a more realistic prediction for future performance.

But a qualitative sales forecast is limited by personal and subjective viewpoints. So, businesses that have accumulated sales data should weight quantitative sales forecasting greater. Especially in decision-making.

Both approaches are two sides of the same coin, and qualitative insights should never be outright discounted.

In fact, qualitative insights can sometimes be a helpful thread to pull on. It can be investigated and substantiated by hard numbers.

For example, say a rep hypothesizes that certain deals will be affected by seasonal variables. You can then use that insight as a basis for quantitative research to either prove or disprove the hypothesis.

Perhaps without that insight, you never would’ve thought to investigate the data. Forward-thinking sales teams lean on both quantitative and qualitative sales data for decision-making.

Common sales forecasting models

Let’s get into the nuts and bolts of how to actually apply sales forecasting within your organization.

Below are the most common forecasting models used by modern sales teams.

We’ve written a deeper dive into each of these forecasting models. So be sure to check out our comprehensive introductory post on sales forecasting.

Sales Stage Forecasting

Sales Stage forecasting, also referred to as Opportunity Stage forecasting, is the most straightforward forecasting model. The premise is simple: your revenue forecast is based on what stage a particular deal is in.

Let’s say you have the following subsequent stages in your sales cycle:

• Prospecting
• Qualified
• Proposal/Quote
• Negotiation/Review
• Closed Won
• Closed Lost

You would give each of these stages a percentage associated with their likelihood to close. The more a deal progresses down the funnel, the more likely it is to result in a Closed Won sale.

Sales Stage forecasting is great if you’re looking for a quick, simple, “back-of-the-napkin” sales forecast formula. But, its simplicity comes with a cost to accuracy and accounting for complex market factors.

Opportunity Age Forecasting

The Opportunity Age Forecasting model is closely related to the Sales Stage Forecasting model and equally as simple.

Rather than forecasting a deal based on its pipeline or funnel stage, the Opportunity Age forecasting model forecasts revenue according to the opportunity’s age in your pipeline.

The best way to set up an Opportunity Age forecast is to use historical CRM data to calculate your average sales cycle length. In other words, you need to know how much time elapses from the moment a prospect enters your CRM to the moment payment is received.

Again, if you’re looking for simplicity, this model is a great approach. But for truly accurate forecasting numbers you can stake your reputation on, you’ll want something more robust.

Here’s why: age is not a consistently reliable indicator of a deal’s viability. Some opportunities ripen in the pipeline, others grow rotten with age.

Whether it’s due to lack of follow-up, poorly qualified leads, or dirty data, a languishing deal in the pipeline can completely throw off accuracy when projecting revenue.

Historical Forecasting

The Historical Forecasting model looks at your past data (wins, losses, etc.) and identifies trends in success indicators. For years this practice was only feasible if you had an immaculate CRM data set going back several years.

However, with innovations in sales AI technology, teams are now able to sift through their most adopted sales tools (email, phone calls, calendars, CRM) and extract relevant buyer/seller communications and activity records.

With this capability, more teams are migrating to a historical forecast approach. Why? Because they are able to harvest a reliable data set to get started.

But while using historical sales data is a great way to account for seasonal market factors, it isn’t very dynamic. As we’ve written before, B2B sales is an indiscernible knot of complex variables.

Without some sort of AI-driven sales enablement tool, trying to untie the knot manually is simply impossible.

While historical forecasting is more accurate, it doesn’t consider important variables. Variables like individual buyer pacing, new products, or new employees. It also doesn’t account for market segments, competitors, or growth goals.

Multivariate Forecasting

Here’s the reality: any forecasting model that only relies on one single factor will always fall short.

Consider all the variables that affect your revenue forecast:

• Sales stage
• Opportunity age
• Sales content
• Rep win rate
• Seasonal/historical factors
• Demographic close-rate
• Deal size

Each of these hold varying levels of importance and interdependence.

It is possible to build out a monstrous Excel spreadsheet that calculates how these variables correlate to success. But, which of your sales reps would want to maintain that? Or who on your sales team has the time (or knowledge) to devote to such a huge undertaking?

Bottom line: If you don’t have AI sales software that can collect this data and automate the calculations, you’re in for a lot of headaches.

Choosing the best sales forecasting model for your business

So what type of sales forecasting is right for you?

A given company’s ideal sales forecasting method is going to depend on its size, age, and complexity. It’s naive to think that a four-person startup can immediately jump to complex multivariate forecasting.

For a small business starting out, keeping overhead low is mission-critical. Furthermore, historical data isn’t a luxury most new businesses enjoy.

If that’s your reality, one of the simple models noted above might be your best bet.

You’ll just need to make sure you set up a crystal-clear process for your reps. You’ll also want to keep them accountable to documenting deals, as all models rely on accurate CRM data.

I It’s never a bad idea to train your reps to track their deals—but as your organization grows, you’ll want your forecasting model to evolve as well. Staying with a simple model might be suitable for now, but for truly accurate insights, you’ll want to move to a multivariate model. More specifically, you’ll want to look into implementing a dedicated Sales AI platform that can do all the computing and heavy lifting for you.

Solutions like our Supercharger AI platform even offer predictive insights natively in your CRM. So, you can make business decisions with confidence.

Supercharger features at-a-glance opportunity scorecards. AI-driven visualizations that score the activity, energy, and health of a deal in comparison to your company’s standard model.

Forecasting accuracy depends on data accuracy

Many know the term “CRM” as Customer Relationship Manager. Or software that aggregates all relevant information on customers, opportunities, and revenue.

But it’s more than a software tool. CRM (customer relationship management) is a series of disciplines.

It entails collecting data on your consumers. This can be via social media, interactions on the phone, through your website, past orders, and more. Then storing it in the appropriate place.

If you’re a mid-sized company or bigger, you know how much of a headache dirty data can be, and how maintaining data hygiene is an essential skill.

The most basic discipline of CRM is manually entering accurate and timely sales data in spreadsheets or a CRM tool. While practiced by tens of thousands of sales teams across the world, it’s incredibly tedious. And it burns through way too many man hours to be efficient (not to mention the increased opportunity for human error).

Automating CRM processes via AI-driven solutions is a cleaner, more cost-effective, and growth-minded way forward.

For instance, Accent’s AI-powered CRM Supercharger automatically collects data from multiple sources. It does it in real time and enters it into your CRM (or preferred central platform).

Sales reps don’t have to sweat inputting all that information themselves. And sales managers get a snapshot visualization of the sales pipeline at a moment’s notice. This makes the sales forecasting process simpler and helps mitigate humor error or negligence.

The future of forecasting is AI

There’s a reason we build our software solutions to rely on artificial intelligence. It’s the same reason we write about it often in our blog posts. The future of sales forecasting is in AI-driven insights.

The implications of data science for business grow more impactful by the day. AI-driven tools like Supercharger CRM or our Sales Manager Dashboard tool rely on algorithms to translate reams of data into prescriptive business decisions.

By its very definition, machine learning technologies will continue to absorb data and learn from their mistakes. AI will continue to become smarter, more accurate, and more efficient.

And we’ve built our software with this vision in mind. If 2020 has taught us anything, it’s that the future is not going to roll out in a steady continuation of historical trends.

Five Best Practices for Sales Forecasting Accuracy

Now we’ve talked about the implications of accurate sales forecasts and some common models. Let’s talk about some best practices in sales forecasting.

As we review these, consider if they reflect your current sales forecasting process. Furthermore, if they don’t, start making a plan for how you’ll implement them in your organization.

The key here is to break this seemingly-complex discipline down into action items. This ensures that sales teams don’t get overwhelmed or frustrated by the workload. The below presents a step-by-step schema of the best practices for maximizing sales forecasting accuracy.

Best practice 1: Conduct an in-depth audit of your current sales forecasting methods

Taking an honest look at your current practices is the first step to growth, as unflattering as that process may be.

Here are some key questions to ask during your internal review:

• What type or types of sales forecasting have you been using to date?
• Have you been hedging your bets on qualitative data alone, allowing gut instincts to have an outsized influence on your forecasts?
• Have sales reps been gathering and entering accurate CRM data? And if not, do they need more time in their work day to do so? Or even need specific training in this area?

Using these questions as a guide, identify any weak areas in your current sales forecasting process that will need special attention.

Best practice 2: Don’t be shy about consulting industry experts

It’s crucial that you do your research on other organizations in your field and get a feel for their past and current sales forecasting processes. This is one of the best ways to learn how you need to augment your own practices.

Try to dedicate time to forging relationships with leaders at those organizations. And seek out counsel from industry experts.

Whether you network through industry events, LinkedIn, or meetups, there’s no substitution for actual human conversation and collaboration.

Oftentimes, just hearing out how other businesses manage their sales forecasting processes will reveal gaps in your own strategy.

These “aha” moments will serve as the basis for improving (or even completely overhauling) your current practices.

Best practice 3: Identify tools to modernize your sales forecasting techniques

After speaking to other sales reps, business owners, and/or expert consultants, you’ll realize that trying to manually manage sales forecasts costs more in opportunity cost and man hours than using an automated sales solution.

Fifteen years ago, organizations had to make do with spreadsheet formulas to determine their forecasts. They weren’t as laser-accurate as sales leaders would’ve liked, but they got the job done.

Today, there’s a range of sophisticated software to help you with everything. From gathering CRM data, to analyzing and visualizing that data, to extrapolating that information for sales forecasts.

The most accurate sales forecasts take into account multiple weighted variables. Due to that, the calculations required to generate a future sales prediction based on historical data is very complex.

Tools like Accent’s Sales Management AI and CRM Supercharger take care of those calculations for more accurate forecasts. They use AI to fill in missing contact information and gather phone calls, emails, and other communications with the prospect.

Meanwhile, the Sales Management AI dashboard visualizes your entire pipeline at-a-glance, using AI to score seller performance.

The two tools used in tandem provide sales leaders with accessible sales insights. And leading teams to more consistent and accurate sales forecasts.

Best practice 4: Get your team invested

So now you’ve got expert insights to guide you and the up-to-date tools you need to modernize your practices—but there’s another hurdle to clear before you’re home-free.

Those gains you’ve made so far will prove. Unless you’ve got a team that thoroughly understands why accurate sales forecasting is so crucial for the organization.

You need reps that are committed to implementing best practices and held accountable for their performance.

To start with, it’s worth it in the long run to invest the time and money in professional training for your sales reps. Perhaps even your finance teams, especially if you’re going to be implementing the new software tools described above.

Once your team has completed training, draft up a series of documents and service-level agreements. These internal documents should have your best practices and expectations in crystal-clear language.

The documents should include expectations on things like:

• Data entering procedures and best practices
• Criteria for sales stage progression
• A standardized language for CRM notes
• Due dates for entering in prospects and leads (i.e., all business cards from an event must be entered within 48 hours of the event)

Again, much of this tedium can be automated by a tool like Supercharger CRM, which takes a lot of the manual work out of data entry and collection so sales reps can get back revenue-generating tasks.

Once these processes are in place, sales leaders need a way to monitor how closely reps abide by them.

Tools like Accent’s sales performance dashboard offer sales managers immediate insight into their reps’ engagement, performance, and the real-time health of their deals, which can help team leaders evaluate employee performance to date.

To improve sales forecasting performance, sales leaders can then set quotas not just for actual sales, but for the degree of accuracy of a rep’s sales forecasts—and keep increasing those quotas over time.

Some companies have also found success encouraging friendly competition by linking KPIs to how well reps maintain accurate, up-to-date data, as well as to their forecasts’ accuracy. In turn, those performance indicators can influence compensation, which of course is the ultimate accountability buddy.

Best practice 5: Stay up to date

There’s no point in modernizing your sales forecasting processes if you don’t adapt those processes to the ground-level reality of your reps’ daily reality.

It’s essential, first and foremost, to keep CRM and sales data up to date (hence the need for employee accountability mentioned above) so you can, in turn, keep honing those forecasts for future performance.

But it’s also imperative to stay abreast of developments in sales forecasting techniques and technologies via industry publications, workshops, and relationships with other sales leaders.

As we’ve written before on our blog, AI is the future of sales forecasting. As the technology continues to advance, forecasting processes will be ever-changing as a result.

Putting the pieces together to make accurate decisions

Sales forecasting does not happen in a void. It is, of course, a crucial factor in a company’s decision-making process.

You should always consider your sales forecasts alongside your marketing funnel (or the system that models the lifecycle of a consumer’s relationship with your organization, from the moment they first become aware of your product or service until at last the deal is closed).

Understanding the variables that shape every step of that relationship will give you a detailed, step-by-step picture of how a prospect evolves into a buyer.

This information can augment the accuracy of a sales forecast. Moreover, considering marketing funnels and sales forecasts together will give company leaders robust insights into how to set up the most effective sales and marketing strategies.

Implementing the methods mentioned in this post will set you up for optimal sales forecasting success. No matter your organization’s specific forecasting needs, these techniques in combination with a powerful sales enablement platform leveraging AI will give you the insight and confidence you need to take most of the uncertainty out of your future success.