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How to Drive Sales Success through Quantitative Forecasting

Optimize your sales process with quantitative forecasting. Learn more about this forecasting method and its benefits here.

Predicting future sales can help your business succeed in a crowded marketplace. Unfortunately, business owners don't have a crystal ball that can tell them what sales will be like down the line, especially after making changes to their business.

Quantitative forecasting is the closest thing to a crystal ball for business owners, allowing them to foresee future sales using data-based AI predictions. Forecasting is crucial for every business, helping you determine sales after changes to your business occur and before implementing those changes. This makes it a valuable tool that enhances decision-making.

By accurately forecasting sales, you can adjust inventory, labor levels, and other aspects of your business to increase your return on investment (ROI). But what is quantitative sales forecasting, and how does it work? Keep reading to learn more.

What is quantitative forecasting?

Quantitative forecasting relies on existing data to predict future sales. It's essentially predictive analytics in that it uses historical data to anticipate future outcomes. For instance, will your sales increase or decrease if you update your pricing? Quantitative forecasting models include past sales data to determine whether your sales will grow or decline.

A business owner can use quantitative forecasting methods to track sales trends and make predictions using data to support their business decisions. For instance, they can predict sales during the peak holiday season based on past data, helping them adjust their strategy accordingly. A company can also view sales and inventory turnover ratios for past peak seasons, so they might need to increase inventory by 50% based on the quantitative forecasting data obtained.

Quantitative forecasting can also help decision-makers determine whether they should adjust their business strategy. For instance, these AI predictions allow them to determine the market demand curve, providing a better understanding of the relationship between pricing and demand for products and services.

Quantitative vs. qualitative forecasting

There are generally 2 types of forecasting: quantitative and qualitative. Quantitative forecasting relies on data and numbers to predict future demand and sales.

On the other hand, qualitative forecasting makes predictions based on the opinions of others. For instance, you might work with a business consultant who can tell you how changing your prices will affect demand or whether there's a product-market fit for your offerings.

Qualitative forecasting commonly uses focus groups, customer and employee surveys, and polls to gather expert insight or opinions into future trends.

The main difference between quantitative and qualitative forecasting is the data. With qualitative forecasting, the data is numerical, while qualitative forecasting relies on opinions rather than hard numbers.

Neither forecasting method is considered better than the other. Instead, we recommend using a good balance of both when making important business decisions. Qualitative forecasting methods are based on data or fact, but both are up to interpretation based on who is reading the analytics report.

Unfortunately, quantitative methods don't have the same insight as expert opinions, but expert opinions and surveys don't yield numerical data that can support decision-making. While you should always value expert and customer feedback, finding actionable insights can be challenging.

This is why you should use both quantitative and qualitative forecasting to predict and measure the success of your business and understand how various variables affect it.

What are the advantages of using quantitative forecasting?

Quantitative forecasting methods use real, historical data to help business owners make important decisions, ensuring the data is always objective. The more data you have, the more reliable your results will be.

The benefits of using quantitative methods of forecasting include the following:

  • Accuracy. Quantitative forecasting is accurate as long as the data used is reliable. Since it relies on existing numbers your business already has, you won't have to worry about inaccuracies as long as your system is up to date.
  • Consistency and reliability. Because quantitative forecasting is accurate, it's consistent and reliable since the historical data is already established. Your business collects this data and information, so you'll know how many sales you have at any given time. This reliability makes identifying and predicting trends easier while driving better business decisions.
  • Objectivity. You can't argue with data. While qualitative forecasting relies on opinions business owners can choose to ignore, you can't overlook numerical data. Numbers are inherently more objective and less up to interpretation than opinions. Reviewing data objectively makes it easier to rely on it for decisions that can affect the performance and health of your business, making forecasting easier to understand.
  • Replicable. Quantitative forecasting is replicable because it's based on historical data. Therefore, you can replicate various types of predictions, including sales and demand forecasts. In addition, since you're using data rather than opinions, you can repeat the forecast with new information, adjusting goals and insights depending on your needs.

Unfortunately, quantitative forecasting isn't perfect. Historical data can help you make more accurate predictions, but many experts recommend a mix of both quantitative and qualitative data for forecasting because the former lacks detail.

Quantitative forecasting models are simple and easy to understand, but they're based on numbers rather than the opinions of your customers or industry experts. Many businesses also don't have enough data, and a small sample size can lead to inaccurate results.

Additionally, quantitative forecasting can be challenging to interpret because it yields numerical results. Therefore, you'll need to understand the data available to you. Data doesn't have context, whereas qualitative information can help you fully understand why certain trends occur and the actions you need to take.

Quantitative forecasting can also be expensive because you need to collect massive amounts of data to improve accuracy. Collecting this data can be a significant undertaking that many small businesses may not be able to afford. However, you can reduce expenses by using automated reporting software to simplify the process.

What are the techniques used for quantitative forecasting?

There are several different quantitative forecasting methods you can use. Which one is right for you will ultimately depend on the results you're looking for and the data you have available.

Some of the most common quantitative forecasting methods include the following:

Moving average

The moving average is a forecast for short-term trends like sales, demand, or revenue increases. The moving average is dynamic and can consist of several past periods to provide a more reasonable sales forecast. This method can help you predict sales based on past sales by dividing the number of time periods by the sum of sales.

For instance, if you want to determine sales over the next 30 days, you'll look at sales over the previous cycles, noting the number of time periods or 30-day periods used to calculate the sum of prior sales.

Let's say you have 90 days' worth of historical sales data. You'd simply add the sales during that time period and divide the number by 3 since you're using 3 30-day periods. The result is your moving average, which tells you the average sales for every 30 days.

You can repeat the process by pushing a 30-day period forward to keep the average "moving." Then, repeat the process for however many periods you have.

The moving average ultimately calculates forecasted sales for the final period and can be used for various periods, depending on how much data you have available.


The straight-line quantitative forecasting method calculates future sales with growth, providing you with a historical growth rate.

To find the next period's revenue, you'll multiply the percentage rate of growth plus 1 by the previous period's sales revenue.

For instance, if you're trying to predict the sales for next quarter and you have an average revenue this quarter of $50,000 with a growth rate of 10%, the formula would look like this:

$50,000 x (1 + 0.10) = $55,000.

This tells you that your predicted sales for next quarter is $5,000 more compared to this period.


The naive forecasting method assumes that your business continues to perform the same without increasing or decreasing sales, using only past data to predict sales. For instance, if you had $20,000 in sales last month, naive quantitative forecasting assumes you'll make $20,000 this month.

It's the easiest type of quantitative forecasting, but it's not the most accurate because sales can fluctuate. For instance, an e-commerce company wouldn't use naive forecasting to predict winter holiday sales based on summer data.

However, it can make it easier to determine what it takes to help your business succeed. Since you're assuming you'll earn the same revenue this month as you did last month, you can start to make decisions about how to increase sales or decrease costs to earn more in the future.

Unfortunately, naive forecasts won't help you determine sales based on external or internal factors like trends or changes within your business, so you'll need to use another type of forecasting.

Revenue run rate

The revenue run rate forecasts the end-of-year revenue based on performance metrics. You'll need a fairly large data set to calculate it, but it can help you determine how much your business earned in sales or revenue over the last several months.

To calculate the revenue run rate, you'll collect revenue data over a specific time period and multiply it by the number of periods within the year. For instance, you can use months, weeks, quarters, days, or any other time period.

Let's say your total revenue in Q1 is $100,000, and you want to forecast end-of-year totals. Your equation would look like this:

$100,000 x 4 = $400,000

In this example, the number 4 comes from the number of quarters in a year. If you measured the total revenue for a month, you would use 12 for the total number of months in a year.

Unfortunately, the revenue run rate is less accurate for businesses in more volatile markets. If you have a seasonal business, for example, the run rate may give you an inaccurate depiction of your end-of-year totals.

Linear regression

Linear regression calculates sales, revenue, and other important business variables based on how they're affected by independent variables. For instance, if you want to understand whether a price increase or decrease would affect sales, you could use linear regression. In this case, you would look at the number of total sales for specific time periods, such as months or quarters, and compare it to the change in price.

You can also use linear regression to determine how other independent variables affect sales. For example, you might want to know if better lead generation results in higher sales. In this case, you would track the number of leads and sales in various time periods to determine if they correlate.

Seasonal index

The seasonal index, or seasonal forecasting method, looks at seasonal historical data to determine sales or revenue for the season rather than any other time period. For instance, you can use historical data from over the last five years to determine what this year's season will look like. You can also use it to understand what seasons are most profitable. For example, you can look at sales during peak seasons over the last several years to define a benchmark or average.

This quantitative forecasting method can help you prepare for seasonal peaks and slowdowns, making it easier to understand how your business performs during those periods. To conduct seasonal indexing, you should have data specific to various seasons over the last several years. Then, you'll add that data and divide it by the number of seasons being compared.

For instance, if you're measuring peak holiday season over the last 3 years, you'll add the sales from the last 3 years and divide it by 3. In other words, let's say you had $50,000 in sales in year 1, $75,000 in year 2, and $100,000 in year 3. The equation would look like this:

($50,000 + $75,000 + $100,000)/3 = $75,000.

This tells you that you can expect sales of around $75,000 this peak season, so you should prepare your inventory and other strategies.

How to implement quantitative forecasting methods

Implementing quantitative methods requires you to review previous sales data. It doesn't have to be a massive undertaking and can give you a general idea about demand trends and expected sales during a certain period.

There are only a few steps you need to take to develop an effective quantitative prediction:

  1. Determine what you're measuring. Before you start plugging data into the various equations mentioned above, you should determine exactly what you're measuring. With quantitative forecasting, you can measure future demand, sales, expenses required, and so forth.
  2. Find historical sales data. You'll need historical data to help you calculate potential future revenue. For instance, if you want to know how much you'll earn this month, you'll need the previous period's sales revenue.
  3. Identify the right method. Since several forecasting techniques are available, you should determine which is best to help you accurately predict future performance. For instance, if you want to forecast seasonal sales, you can use the seasonal index. Meanwhile, you can use the moving average to calculate sales over the short term.
  4. Perform an analysis. Once you have enough data, you can perform an analysis using one of the forecasting techniques we discussed above. You can use forecasting software or a spreadsheet with formulas to make accurate calculations and visual representations of data like growth charts and graphs.
  5. Take action. Quantitative forecasting provides insight into your business, its sales, revenue, expenses, and other factors that can affect performance. After performing your analysis, you should be able to predict future sales. This information can also help you determine whether you need to change your business strategy. For instance, you might find that your sales from the same period last year have decreased. While quantitative forecasting won't tell you why this happened, you can use data from other sources to help you determine what affected your numbers and find ways to improve them.

Find success with quantitative forecasting

Quantitative forecasting can help you reach your business goals by helping you understand sales and revenue based on past performance. With data, you can paint an accurate picture of your business and find forecasting results that enable better business decision-making.

To make accurate forecasts, you'll first need data. Mailchimp's suite of marketing tools collects data on your marketing campaigns and sales to help you accurately forecast using different methods to find everything from seasonal patterns to projections based on growth. Try Mailchimp today.

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