What is Sales Forecasting?

Sales forecasting is the process of estimating future sales. It is a projection of your share of the market over a specific period. Sales forecasts are essential for the efficient allocation of resources. They help sales managers set goals and plan ahead.

Forecasting your sales involves combining your expert knowledge of your business, current sales and historical data with predictive analysis to project your future sales figures.

You can use forecasting for any period of time, whether it’s weeks from now or years into the future.

Since sales forecasts determine your short-term spending decisions, so it’s important to take active steps to ensure they’re accurate.

Why is Sales Forecasting So Important?

Forecasting sales is a crucial step for any business.

The main reason for tracking sales is to see whether you will meet your quota. It shows how your company is performing and whether it meets the required standards for sales growth.

Sales forecasting is important as it allows businesses to make the necessary changes to improve their sales. 

For example, if a sales forecast indicates that you are likely to exceed your sales target for a certain quarter, you can purchase additional inventory in advance. This helps you prepare for increased demand and ensures you are able to meet the needs of your customers.

Sales forecasts can also help determine whether your business should hire more staff to meet an expected surge in demand.

If your sales team is forecasted to miss its targets, you can take proactive action by increasing your lead generation budget. This will help ensure that your business does not slow down during periods of low activity.

If you can’t generate more leads, you can also decide to incentivize your existing customers to buy more and make up for a lack of new sales. For example, you can offer them discounts for exceeding a specific order value.

When forecasting sales, it’s important to not rely too heavily on your instincts or use incorrect information. That can severely impact your ability to plan for the future. It’s essential to use proven sales forecasting models. Speaking of that:

Sales Forecasting Models – How to Forecast Sales

Sales forecasting models are mathematical models that are used to predict future sales. These models take into account historical sales data, as well as other factors that may affect future sales, such as economic trends, seasonality, and customer behavior. 

The goal of a sales forecasting model is to provide the best estimate of future sales so that businesses can make informed decisions about inventory, staffing, and other planning needs.

What are the three main ways to forecast sales?

Before getting into the various types of forecasting, let’s discuss the three main categories. This will help us determine which sales forecasting methods are best.

1Qualitative Methods 

One way to get started with your sales forecast, especially for a product that’s new to the market, is through qualitative research. This includes things like conducting interviews and running polls.

When you are launching a new product, you often have to rely on your own judgment and intuition. This is because you lack the hard data that quantitative methods provide. However, qualitative research can be extremely helpful in sales forecasting.

When developing your forecasting method, use factors that are objective, systematic, and impartial. This is difficult, as you’ll be starting from nothing, so you’ll need to define your approach as part of an entire family of methods.

2 Time Series Analysis

If you have data already available, a time-series analysis of that data may be worthwhile. A time series analysis uses statistics and takes into account sales information collected over several years for a specific product or service to predict future sales. 

A time-series analysis can be helpful when you already have data, for example, from the last three years from which you can draw patterns and predict future sales. 

Start by figuring out your sales velocity. Is it increasing or declining? Based on that, you can then start to forecast your sales.

Analyzing time series data can be difficult because it requires identifying trends and patterns. This can be tricky because you need to project the future from raw numbers. 

With careful planning, however, you can accurately predict how customers will behave in the future.

Data is raw and useless until it has been converted and put into a form that is useful. The raw data of a series is the actual data points themselves. This could include sales figures for a particular product or several years worth of the same type of information.

A time-series analysis of your sales data can help you identify seasonality, long-term cycles, and sales growth rate. This can be useful for making decisions regarding your inventory and your marketing campaigns.

3 Causal Models

If you have already done some historical analysis and looked into how certain factors such as the economy, and the business climate affect each other, you can construct a predictive model that expresses these relationships. 

Predictive sales forecasting models, which are the most complicated, use precise numbers to express how one variable affects another.

The most advanced way to forecast your sales is by using causal models. These use hard numbers and mathematical formulae to identify relationships between the different factors that can affect your sales figures.

A causal model uses results from a time-series analysis and a market survey. It accounts for everything that influences a business, including business-related events, the actions of competitors, and any related economic effects that directly or indirectly affect it.

But if you don’t have all the data, you can still use a simple, basic model of forecasting. Instead of making assumptions, you can instead make an educated guess about the relationship between two variables.

Companies often use predictive modeling to forecast future sales and predict when certain events will occur.

Now that we have laid the groundwork, let’s look at the different methods of sales forecasting:

8 Types of Sales Forecasting Methods

Forecasting your sales can be a tricky business. There is a myriad of different forecasting methods to choose from.

That said, choosing the right sales forecasting software is the first step to using your data more effectively.

Here are 8 different ways to forecast your sales, their pros, and cons, and when to use them.

1 Gut-feeling method

The gut-feeling sales forecasting method requires your sales reps to provide feedback about the quality of existing customers. It also requires you to monitor the activity of these accounts for signs of churn.

The gut feeling method is one of the least reliable of all the forecast methods because it relies on so much input from humans. It is commonly used by smaller companies and those who have longer, more complex processes.

While your gut feeling might be right sometimes, it’s important to realize that they’re often wrong. This is because these predictions are based on your personal opinion, which can often be too optimistic. As such, it’s best not to put too much stock in the results of your gut feeling.

Let’s say you’re nearing the end of the third quarter and your manager asks you for sales projections for the fourth quarter. Based on your model, you predict a 10 percent increase in overall sales.

This forecast strategy assumes that the last quarter was bang on trend when it could just have been an exceptional one. Just because the company met its goals in that time period, doesn’t necessarily mean it will again.

While this method isn’t perfect, there are times when your gut feeling is more reliable than the more qualitative sales forecasts.

2 Almanac method

The almanac revenue forecasting method uses historical data to forecast sales. While it is a more reliable method for forecasting sales, it shouldn’t be your only method. Yes, the method relies on facts and removes any subjective opinions, but it’s limited because it only looks at historical data.

Relying solely on your past data to predict future sales assumes that your industry and business will stay the same in the months and years ahead. But we know the business environment is rarely that stable.

Another weakness of almanac-based forecasts is that they don’t take economic factors and industry trends into account. A good example is the coronavirus outbreak that rendered many sales forecasts totally meaningless.

Firms that were relying on their 2019 forecasts from the 2020 Almanac were sorely disappointed by the pandemic and the resulting shutdowns.

While it does come with its perks, the almanac sales forecasting method is only useful if you’re certain that your industry won’t change drastically in the near future. Most business owners would agree, however, that this is an optimistic assumption.

Analyzing past performance can help predict how successful your salespeople will be in the future. By identifying which team members are most successful with which accounts and assigning them accordingly, you can optimize their abilities.

Companies with an established history and large amounts of data can use the Almanac forecasting technique. The more data available, the more accurate the forecast will be.

3 Funnel forecasting method

Forecasting your sales funnels can help you predict future sales. By looking at your performance metrics, you can figure out if your team is meeting goals.

This forecasting method requires you to look at your current performance across different key performance metrics. These include your win rate or the percentage of deals you close, the average time it takes to close a deal, and your rate of up-selling existing customers.

If you know your average sale takes 2 months and your close rate is 40%, you can project how many deals you can complete in a certain period of time.

If a prospect has 10 opportunities that each have a $1 million dollar value, they predict that they could complete $4 million dollars worth of sales in one quarter if they were being very conservative. This is because their typical success rate is 40%.

The funnel forecasting method is more effective for companies that have long, drawn-out sales processes than for those with short, simple ones.

4 Portfolio forecasting

Forecasting a portfolio’s value involves looking at past performance, current market trends, and expected future returns. This data is then used by portfolio managers to make investment decisions.

This hybrid method uses a combination of the techniques above. It combines historical and predictive data, which most sales managers contend produces the most reliable sales forecasts.

With this method, sales managers use a combination of their experience and expertise along with forecast data from their funnels and gut feelings when making decisions.

So, how will it work in practice?

Your sales manager will first dig into your historical data, then look at your pipeline. They will consult their sales reps, go over the information they’ve gathered, and then analyze all that intelligence using their own knowledge and expertise to create a forecast for the company.

Portfolio forecasting helps management make more informed business decisions that reduce risks. 

For instance, If a sales manager predicts that their business is going to decline, they may choose to focus their resources on selling more to their existing customer base. This is because income from an existing customer is more stable and, thus, more predictable.

5 Multivariate regression analysis

Multivariate Regression Analysis is a statistical tool used to predict a company’s future sales. It assumes that by changing certain inputs, you can get different results.

Multivariate regression analysis is only as good as the data that is fed into it. Inaccurate data can lead to incorrect conclusions being drawn from the analysis. It is therefore essential that any data used in a multivariate regression analysis is complete and accurate.

As machine learning becomes more prevalent in sales forecasting, it is important to understand how multivariate regression works in order to make the most accurate predictions possible.

Let’s say that your business has decided that it needs an analytic specialist who can run regression analysis. These calculations use historical data about sales and calculate the probability of future successes.

Using this information, they can learn more about the current sales process and then predict the probability of winning sales deals.

6 The length of sales cycle forecasting

Understanding your sales cycles and how long a lead takes to become a paying client can help you make accurate sales forecasts. By knowing how different marketing strategies affect this, you can better allocate your resources and adjust your numbers.

If you know the typical length of your sales cycles, you can more accurately estimate when a lead will turn into a sale. For instance, if your average prospecting process lasts 5 months, and you’ve already been working with a prospect for 2.5 of those 5, there is a 50% chance they will turn into a client.

Forecasting your sales process allows you to predict how long it’ll take for a prospect to become a customer. This helps you plan your resources accordingly and is more accurate than the estimates given by your sales team.

This model isn’t about someone feeling they have a hot prospect.

While a shorter sales process may yield more revenue, companies should pay more attention to the individual value of each channel. By looking at the value of each part of the sales funnel, you can make better decisions about where to invest your time and resources.

You can measure the sales cycle of each of your different marketing channels and determine which one converts the most leads to paying customers.

You have so much valuable information to use by combining your sales cycles with forecasts.

But tracking the length of the sales process is only possible when marketing and sales work together closely. If these two departments are disconnected, this method may not be as effective.

Firms with departments that are too segmented will struggle with this forecast.

7 Lead-driven forecasting

For companies with lots of campaigns, forecasting by lead produces more accurate forecasts. This predictive analysis technique works by assessing each of your lead generation channels and assigns a value to each based on the behaviors of past customers.

Forecasting your revenue by factoring in your predicted conversion rates will help you estimate how much your business will make. These predictions can be further integrated into other predictive models for your sales.

This model allows business owners to better allocate their time and resources by prioritizing which customers are most likely to convert.

To produce lead-driven forecasts, you’ll need to know three important pieces of information:

  • the average sale price by each marketing channel, 
  • the number of monthly inbound and outbound qualified leads to each of your sales reps, and 
  • the percentage of those lead that convert into customers.

While this data-driven sales forecasting method is usually quite reliable, it has a few downsides.

For example, if your marketing strategy or customer base shifts, your forecast may be off. Also, if something happens in your industry, your predictions may no longer be accurate.

If your industry is constantly changing, your forecasts can also become out of date and unreliable.

If your team’s lead generation strategies change, then so will your number of customers. Unfortunately, this will also change your conversion rate

8 Sales forecasting with machine learning and AI

The demand is growing for faster and more accurate sales forecasts that are easily accessible.

Machine learning and artificial intelligence are powerful tools that can identify trends that human analysts might overlook.

While it may take time to train these AI systems, once they are trained, they can become very accurate. This improved efficiency can better help companies make important decisions about sales and other areas of their business.

Models that utilize machine learning algorithms are highly accurate, producing forecasts that are easy to explain to management.

A sales training model could be created that looks at key metrics such as average deal size, days it takes to close a sale, and success rate.

The AI can then analyze what factors influence these numbers, such as seasonality.

By analyzing your sales data and discovering that the best times for lead generation and customer onboarding are during spring and fall, you can ensure that you’re prepared to maximize sales and revenue during these peak times of the year.

With machine learning, decision-makers can trust that the models are highly accurate. This confidence boost will help to increase employee and stakeholder trust in your projections.

Factors to Be Aware of When Forecasting Sales

Sales forecasting is a tricky process, and there will always be some deviation between actual sales and forecasted ones. The higher this variance, the more impact it will have on the company.

As a sales forecaster, it’s important to consider various outcomes that could impact your business. By thinking ahead, you can better prepare for any unexpected events that could significantly impact the operation of your company.

Such examples could include sudden changes in the economy, such as the stock market, or new legislation or policies.

Sales forecasts are extremely important, but they should not be the only factor considered when setting targets. If a company’s forecast is way off, it could lose a significant amount of money.

A holistic postmortem is required to determine how all business elements contributed to a disappointing quarter.

Quantitative and qualitative forecasts each have unique properties that must be considered.

When using qualitative forecasting methods, it is important to keep in mind that these techniques are subjective. This means that you will need to create a comprehensive view and then use additional research to support your claims. In order to do this effectively, it is essential to use credible sources.

Quantitative forecasts can uncover patterns, but could still fall victim to the bias of correlation and causation.

Sales Forecasting FAQs

What are forecasting models?

A forecasting model is a predictive analysis used to project future events. Forecasting models are often used in business and economics to predict future demand for goods and services.

What are the three main sales forecasting models?

The main methods of forecasting sales are trend analysis, regression analysis, and time-series analysis. Trend analysis involves looking at past sales data to identify trends and using those trends to predict future sales. Regression analysis is a statistical technique that identifies relationships between different variables. Time-series analysis involves analyzing data over time to identify patterns and trends.

Which model is best for sales forecasting?

The causal sales forecasting model produces the most reliable forecasts. The causal model looks at the relationship between factors that may affect sales and uses the correlation to draw a picture of what future sales may look like. It may also incorporate time-series analysis.


Final Word on Sales Forecasting

There are different methods and approaches to forecasting in sales. The main models are trend analysis, regression analysis, and causal analysis. These are different methods that you should review for their fit with your specific circumstances.

Each method has its own strengths and weaknesses, so it’s important to choose the right one based on your specific needs. Whichever method you use, it is crucial that the information you are basing your assumptions on is accurate and up-to-date. Otherwise, your forecasts will lead to the wrong business decisions.

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Editors Note:

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Justin McGill
About Author: Justin McGill
This post was generated for LeadFuze and attributed to Justin McGill, the Founder of LeadFuze.