Executives nowadays evaluate some form of forecasting in almost every move they make. If managers are to deal with seasonality, rapid changes in demand, price-cutting maneuvers by competitors, strikes, and big swings in the economy, accurate demand and trend predictions are no longer a luxury item, but a necessity. 

Learning how to forecast sales can assist with these issues; but, the more a manager understands the fundamentals of forecasting, including what it can and can’t do for them right now, and which methodologies are most suited to their current needs, the better. That’s what we’re covering in this article, by the end you should be able to choose a forecasting technique that suits your team’s needs and find tools that will improve and simplify your results.

What is sales forecasting

A sales forecast is a statement of predicted revenue; how much your organization expects to sell in a given time frame, such as a year or a quarter.  The strongest sales estimates are extremely accurate in this regard.

Sales projections differ in terms of where they receive their data from, for example, some organizations rely on staff’s intuition while others utilize artificial intelligence (AI). More on this in the section on sales revenue forecasting tools. However, all sales estimates must respond to two fundamental questions:

HOW MUCH?  Each potential transaction has an anticipated amount of money it will bring in. Sales teams must come up with a single number to symbolize that new business, whether it is $500 or $5 million.

WHEN? It’s important to focus on a specific period of time such as a month, or year. 

It’s not an easy task to come up with those two projections. To build their forecasts, salespeople consider the following factors: 

WHO? Forecasts are made by sales teams, around who their prospects are. The projection will be more or less accurate depending on whether their prospects are true decision-makers or just influences.

WHAT? Ideally, forecasts will be based on the specific solutions you want to offer. As a result, this should be based on issues that your prospects have raised and that your organization can answer in a unique way.

WHERE includes “where will they make the purchase?” As well as “where will they use the product”.  Sales teams can predict more accurately when they’re close to the center of the action.

WHY? You should know why the prospect or existing client is interested in your company in the first place? Is there a compelling occurrence that has prompted them to think about it now? I should be clear why they need you at this moment

HOW? What factors do you think this prospect considers when making a purchase? Without knowing, it’s likely your predictions will be wrong. You have to be able to account for why they’re here today and how they’ve done in the past.

Why Is Sales Forecasting Important?

Consider two scenarios: one with a car factory and the other with an eCommerce store.

Cars take a long time to build.  The manufacturer has a complicated supply chain in place to ensure that every car part is available when they need it to produce cars, ensuring there are enough vehicles to meet customer demand. 

In our other example, however, when you buy something online, you get a delivery estimate. This is true regardless of coming from a major marketplace or a little boutique. If your item arrives a day or a week after it was promised, you’ll be annoyed and it could cause a customer to leave a bad review and not shop with you anymore. 

In both circumstances, sales forecasting is comparable. The sales predictions assist the entire company in planning resources for shipping products, paying for marketing, hiring workers, and more. Accurate sales forecasting results in a well-oiled machine that fulfills current and future consumer demand. Internally, sales income that arrives inside the predicted time frame makes managers happy, just as a package that arrives on time makes the customer happy.

If the company’s estimates are incorrect, many aspects of the organization will be impacted including pricing and product delivery.  Meanwhile, if estimates are accurate, the corporation can make more strategic investments, such as recruiting 20 new developers instead of 10 or opening a new sales office in a new emerging market.

Sales Forecast Vs. Sales Target

 

Simply put, forecasts are calculated to be accurate, while targets are meant to be exceeded. Confusing these two terms is a mistake many professionals have made. 

Sales forecasting is an educated outlook on expected sales. Sales targeting, however, is a goal often defined for motivation. The numbers may be the same, or, in a proactive team, they may forecast sales next month to be $30,000. However, their goal may be to sell $35,000.

Sales Forecasting Techniques

Forecasting isn’t new, we’re just getting better at it. Many techniques have been used over the years, for the purpose of this article, we’re going to focus on more current and relevant techniques, so, by learning how to forecast sales, you can discover the best method for your organization.

INTUITIVE FORECASTING METHOD

Some managers simply ask their sales reps to assess the odds of a deal being finalized. “I’m convinced they’ll buy at the end of next week, and the deal will be worth X$2,800,” is an example response  This is a good example of intuitive sales forecasting.

On the one hand, this strategy takes into account the views of the individuals who are closest to prospects: your salespeople. On the other hand, sales teams are inherently positive and frequently provide overly optimistic predictions.

Additionally, there’s no scalable means to confirm their findings. The sales manager would need to listen to all phone calls, observe meetings, etc. to discover if a prospect is as likely to close as the salesperson claims.

EXAMPLE OF INTUITIVE FORECASTING

Imagine you’re forecasting for a brand new organization; you’ve only been in business for four months and have no prior experience. Since you have two salesmen on your team, you ask them to anticipate sales for the next six months using their intuition.

Each salesperson reviews the prospects of their sales pipeline as well as any potential calls they have scheduled for the months ahead. They estimate $35,000 in sales for the next six months based on their findings.

PROS OF INTUITIVE FORECASTING

• Dependent on your sales team’s opinions, who are closest to your customers
• No historical data is required

CONS OF INTUITIVE FORECASTING

• Calculations are entirely subjective
• This method can’t be scaled or replicated

OPPORTUNITY STAGE FORECASTING

This forecasting method takes into account the various stages of the sales process in which each offer is currently located. The further along a deal is in the pipeline, the more likely it is to be completed.

Once you’ve decided on a reporting period, which is often based around your sales cycle and your teams’ quota, you can multiply each deal’s potential value by the probability of it closing.

Add up the totals for each transaction in the pipeline to reach your overall projection.

Although creating a sales prediction in this manner is reasonably simple, the results aren’t as reliable as other forecasting techniques. The age of an opportunity is not taken into account in this strategy.

So, if a customer has been in the pipeline for two months, they’ll be treated just like one that has been in the pipeline for a week (as long as the close dates match).   You need to count on your sales team to clean up their pipelines on a regular basis, which isn’t always possible.

A sales prediction for the opportunity stage may potentially be overly reliant on prior data. Your offers will close at various percentages at each stage if you change your messaging, items, sales procedure, or any other element.

EXAMPLE OF OPPORTUNITY STAGE FORECASTING

With this forecasting model, you’re establishing the “likely to close” percentages based on potential accounts in your pipeline. Let’s consider the following projections based on your funnel:

• • Initial Call 5% likely to close
  • Qualified 15% likely to close
  • Product Demo 40% likely to close
  • Product Trial 65% likely to close
  • Final Call 85% likely to close
  • Deal closed 100%

With the data above, using opportunity stage forecasting you’ll determine that a customer that’s made it to the Product Trial phase is 65% likely to close. That means, the forecasted amount for a $1000 deal that’s made it this far would be $650

PROS OF OPPORTUNITY STAGE FORECASTING

• It’s very easy to use
• Calculations are objective

CONS OF OPPORTUNITY STAGE FORECASTING

• Data isn’t very accurate which leads to misleading forecasts
• Calculations don’t consider important factors such as the age or size of the opportunity

LENGTH OF SALES CYCLE FORECASTING METHOD

This method looks at the age of individual opportunities to anticipate when they’ll close.

You’re less likely to get an overly generous prediction because this technique depends purely on objective facts rather than the rep’s comments.

Let’s say a salesperson schedules a demo with a lead before they’re ready. They may tell you that the prospect is likely to make a purchase, however, this method will determine that they are unlikely to make a purchase because they have only been speaking with the salesperson for a few weeks.

Additionally, this technique can be applied to a variety of sales cycles. An average lead can take up to six months to buy, but referrals can take as little as one month, and leads from trade exhibitions can take up to eight months. Each deal can be grouped by average sales length

You’ll need to keep track of how and when leads join your salespeople’s pipelines to receive accurate results. Your reps will spend a lot of time manually inputting data if your CRM doesn’t interface with your marketing software and doesn’t automatically log interactions.

 EXAMPLE OF LENGTH OF SALES CYCLE FORECASTING EXAMPLES

Assume your typical sales cycle is four months. If a sales rep has been pursuing an account for two months, your prediction will indicate they have a 50% chance of closing the deal. 

 PROS OF LENGTH OF SALES CYCLE FORECASTING EXAMPLES

• Calculations are objective
• Lead sources can be better integrated into more accurate opportunities

 CONS OF LENGTH OF SALES CYCLE FORECASTING EXAMPLES

• It needs carefully tracked data to work
• The calculation don’t always consider the type of opportunity or its size

HISTORICAL FORECASTING METHOD

Looking at the matching time period and assuming your results will be equal to or larger than those results is a quick and dirty technique to anticipate the amount you’ll sell in a month, quarter, or year. This is a projection based on previous sales.

It’s important to keep in mind, this technique does not account for seasonality. It’s also predicated on the assumption that customer demand is continuous. However, if something unusual occurs, your model will not hold up.

Finally, rather than being the cornerstone of your sales forecast, historical demand is best when used as a standard.

EXAMPLE OF FORECASTING METHOD

Let’s suppose your team made $90,000 in monthly recurring revenue (MRR) in July. According to this theory, they should sell for $90,000 or more in August.

By including your previous growth, you can make this projection more sophisticated. A realistic figure for August would be $095,400 if you continually raise sales by 6% each month.

PROS OF FORECASTING METHOD

• It’s quick and easy
• The information is historically accurate which is helpful in stead markets

CONS OF FORECASTING METHOD

• Does not consider buyer demand
• It doesn’t account for the seasonality of market change

PIPELINE FORECASTING TECHNIQUE

If you don’t have software to perform your calculations, the pipeline sales forecasting method can take a long time, sometimes too long. It analyses each opportunity in your pipeline and evaluates its odds of closing based on company-specific criteria such as the rep’s personal success rate and opportunity value.

This form of predicting is based on your capacity to supply high-quality data. If you make a mistake with the numbers or employ skewed data, your predictions will be worthless.

To get the most out of this strategy, make sure your reps are consistently entering correct, current data into their CRM.

EXAMPLE OF PIPELINE FORECASTING

All current deals in your sales team’s pipeline would receive a high possibility of closing if your sales team routinely closes deals worth $5,000 to $8,000 within 60 days.

This information can then be used to calculate your monthly or quarterly projection.

PROS OF PIPELINE FORECASTING

• Accounts for unique factors in each opportunity
• Data reliant making it very accurate

CONS OF PIPELINE FORECASTING

• Results are easily skewed if data is wrong
• Works best with a sales forecasting tool (discussed in the next section)

MULTIVARIABLE ANALYSIS FORECASTING METHOD

Known as being one of the most advanced sales forecasting methods, multivariable analysis integrates several of the criteria mentioned earlier, including average sales cycle duration, the likelihood of closing according to opportunity type, and each rep’s performance.

This is usually the most accurate forecast. It does, however, necessitate an advanced statistical solution, which isn’t always practical if you’re on a tight budget.

You’ll also need clean data – no matter how good your software is, if your salespeople aren’t dedicated to documenting their deal progress and efforts, your statistics will be misleading.

EXAMPLE OF MULTIVARIABLE ANALYSIS FORECASTING

Assume you have two salespeople, each of whom is responsible for a single account. Your first salesperson has a procurement meeting scheduled for Friday, while your second rep has finished her first briefing the buying committee.

Based on your first rep’s win percentage for this point of the sales process, the relatively big predicted transaction size, and the amount of time left in the quarter, he’s 40% likely to close in this time frame. This gives you a figure of $9,600 as a projection.

Your second rep is in the middle of the sales pipeline, but the deal isn’t as big and she closes quickly. She also has a 40% chance of closing, resulting in a prediction of $6,800. These two deals combined forecast $16,400 in quarterly sales

PROS OF MULTIVARIABLE ANALYSIS FORECASTING

• Heavily reliant on data so the most accurate
• It provides clear information

CONS OF MULTIVARIABLE ANALYSIS FORECASTING

• Reps need to track and clean data consistently
• Requires a lot of analytics which take time and money

Sales Forecasting Tools

Learning how to forecast sales is a lot easier when you have the right tools. Many forecasting strategies have been created in recent years to deal with the increasing diversity and complexity of organizational forecasting concerns. Each has a specific use, and it’s important to choose the right approach for the job. Both the manager and the forecaster play a part in technique selection, and the better they grasp the range of the forecasting options mentioned above, the more likely a company’s forecasting efforts will be successful.

Tools are available, however, to take some of the hard work, math, and guessing out of the process. When it comes to sales forecasting, technology has made great strides since the days when Excel spreadsheets were the only option. There are now a plethora of companies that enable businesses to gain more in-depth insights.

However, with so many possibilities, deciding which is the best can be difficult. When deciding which sales forecasting tools are best for you, keep the following points in mind:

• • Dynamic VS Static: While some software provides static data, more solutions are beginning to use dynamic projections that are constantly tested to ensure that they accurately predict sales.
  • Import Capabilities:  The accuracy of the forecasts you obtain is limited by the data that your software can collect. Import capabilities ensure your tools have all of the information they require without having to manually enter it.
  • Collaboration:  Forecasting sales is a collaborative effort. The easier it is to communicate data, the more insights and consistency there will be in the organization.
  • Simplicity: Some teams are hesitant to take on additional responsibilities. The software’s simplicity will determine how well your team uses it and how often leadership consults it for information.

Aviso Insights connects with most CRMs to provide real-time data and historical information that can help you get new insights and make better decisions. Aviso can create forecasts based on sales and pipeline performance using AI and big data.

Gong.io aims to give executives complete visibility into the buyer’s journey. Sales are often conducted through a variety of channels, including phone, email, text, and meetings. Gong lets organizations arrange everything in one place so they can see where each lead is in the funnel. It can also help you recognize whether a deal is going well or not, and put a stop to sales forecast surprises.

HubSpot focuses on providing conversation intelligence, pulling valuable details from recorded sales calls for you. There’s also sales automation that frees up your reps time, as well as sales reporting and analytics to track your success and show aspects of your performance that can be improved. 

Map My Customers assists leadership in identifying crucial growth regions. With the use of maps, you can have a better understanding of the difficult areas along with the most significant growth potential. We assist firms in better understanding their areas, allowing them to make more informed decisions.

Salesforce can help unite customers and companies by creating more engaging marketing such as target messaging. You’re able to connect all your B2B and B2C channels, supporting your customer in new ways.

Improve Your Sales Forecasting Regardless Of Technique And Tools

It’s important your organization takes its time while deciding on which forecasting technique and tools to utilize. Fortunately, there are some steps you and your team can take to improve your forecasting in the meantime, and these tips will also be essential when you enlist in your permanent forecasting process.

1. START SIMPLE: When you begin using new software such as CRM tools, it’s tempting to try to incorporate each of the model types we discussed previously, but avoid the impulse. If you’re utilizing a quantitative forecasting model to project sales for the coming year for the first time, don’t be scared to start small and develop your model over time.

A model that integrates seasons, time series, and demand forecasting into one is better than one that uses a regression forecasting platform for five of the most typical sales tasks your team conducts. Why? Because the less variables you have to track, the easier it will be to achieve your sales goals.

2. USE HISTORICAL DATA: Many large corporations have historical data on which to base credible sales projections. If your organization hasn’t already established analytics and other tracking methods that can be linked to goals and your conversion rates, now is the time to do so. It’s true, after all, to effectively estimate where you’re going, you need to recognize where you’ve been.

Some get hung up on the idea that sales aren’t always reliable indicators of future results. You might release new items this year, grow into new markets, encounter increased competition, and so on.

However, past data provides a solid basis on which to build when you consider other, unforeseen elements that may affect sales in the following year.

3. ENSURE YOU KEEP CLEAN RECORDS: 

Salespeople can sometimes come up with their own personal definitions and use cases if no clear rules are conveyed to the team. This results in uneven data entry. Reps may also neglect to use a feature if they are unaware of its importance.

You can’t make effective judgments with stale data, so make sure everyone is on the same page about any numbers that aren’t as tangible as sales and revenue, such as current opportunities in the pipeline or the number of deals per customer segment. You can do this by providing ongoing training, especially with your CRM software and by checking up on deals during one on one meetings.