Organizations rely on historical data to determine where they are coming from, where they are, and where they are headed to. Forecasting helps marketing and sales teams get answers to questions that can guide them to implement strategies for scaling the organization.
Historical data is used to know the revenue that was achieved and the new targets to fix for the year ahead. The teams can determine the total leads required to help them hit the sales target. Forecasting is an important factor that directors and sales teams require when making crucial decisions.
- RELATED – Inbound sales guide – process features
- 6 Best Chromebook Accounting Software To Increase Sales In Business
Table of Contents
Understand important forecasting components
Effective forecasting requires large volumes of data obtained from multiple sources. It can come from social media channels, customer service, sales, marketing, and accounts departments. The organization needs to implement the right forecasting processes to ensure no detail is missed. A forecasting tool or platform is required to automate the process.
The organization needs to look at its historical sales forecast examples to help estimate its annual revenue growth. The organization might have generated $2,000,000 in revenue in year A and $3,000,000 in year B. The revenue difference between years A and B is 50%. The organization can forecast to generate 50% more in year C, which will be about $4,500,000.
Using this historical data, the organization can make decisions about how it will achieve a minimum of 50% revenue growth. The sales forecast platform by Revenue Grid helps organizations collect and analyze data to achieve an accurate forecast. The platform is powered by AI to provide real-time insights into business operations and performance.
How to use historical data to forecast
An organization can use multiple forecasting strategies using historical data. Each selected strategy is determined by the type of business activities performed by the organization. It matters the type of forecasting tool available to the organization.
The organization uses sales data from a certain period and analyzes it to develop tangible revenue trends. The data is used to generate reports that show monthly, quarterly, semi-annual, and annual growth. The report helps the organization determine accurate estimates of future performance. There are sales forecasting strategies by organizations.
1. Forecasting based on based leads:
- This forecasting can be done using CRM data to determine the total leads achieved. Leads help the sales teams determine the value of each. Data helps them determine if the lead is likely to convert or not. The team uses math to calculate total convertible leads multiplied by their value.
2. Forecasting based on opportunities
The organization looks at the total sales opportunities it has. It is determined by:
- Total prospects
- Total deals in the pipeline
- Price negotiation
- Power to close deals
- Number of people who have shown interest in product purchase
To forecast effectively based on opportunities, an organization needs to have accurate data for past performance. The sales and marketing teams analyze success rates in past deals. If data shows a 60% success rate, it is likely it will achieve the same rate or more in the future.
3. Forecasting based on market tests
This strategy is used by organizations seeking to launch new products into the market. The product is taken through market testing to determine if it will record success once launched. If the product recorded a 70% success probability, the organization could use this rate to determine the performance of the product once launched.
4. Forecasting using sales cycle
An organization might have recorded a 50% lead success rate. However, it needs to determine how long it takes for one lead to convert. If it takes two months for a lead to convert, this data can be used to forecast future leads.
5. Forecasting based on past sales
Successes in past sales can help an organization forecast future sales. The sales team looks at the balance sheets of the past few years to see performance patterns. The team uses it to determine future sales performance.
- RELATED – SASE vs. Zero Trust Security For Enterprises
- How Businesses Can Utilize Market Insights to Get the Maximum Advantage
Historical data helps an organization determine where it is coming from and where it is headed to. Different strategies help sales teams determine future performance. They include forecasting using the sales cycle, past sales, market tests, and available opportunities.
I hope this tutorial helped you to know about How to Forecast Sales Using Historical Data. If you want to say anything, let us know through the comment sections. If you like this article, please share it and follow WhatVwant on Facebook, Twitter, and YouTube for more Technical tips.
How to Forecast Sales using Historical Data – FAQs
How do you calculate forecast sales using historical data?
The formula used to calculate forecast sales using historical data is: previous month’s sales x Velocity = additional sales; and then: additional sales + previous month’s rate = forecasted sales for next month.
What is historical sales forecasting?
Historical sales forecasting uses previous sales data to determine future sales numbers. You’ll need the previous sale numbers for the timeframe in question. Historical forecasting always assumes future sales will be equal to or greater than that number.
Which method of forecasting uses historical data only?
Straight-line Method is one of the simplest and easy-to-follow forecasting methods. A financial analyst uses historical figures and trends to predict future revenue growth.
Which test is used for forecasting based on historical data?
Backtesting involves applying a strategy or predictive model to historical data to determine its accuracy. It allows traders to test trading strategies without the need to risk capital.
What are the four sales forecasting methods?
The four forecasting techniques are trend analysis, regression analysis, time series analysis, and casual analysis.