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 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.
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:
- 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.
- 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.
- 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.
- 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.
- 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.