Sales Forecasting Methods | 4 mins read

Best Sales Forecasting Methods for Small vs. Large Businesses

best sales forecasting methods for small vs large businesses
Serina Achall

By Serina Achall

Sales Forecasting uses historical data to help businesses estimate their future financial situations within a given time period. A reliable forecast would take into consideration external factors such as seasonality and economic conditions, as well as industry averages.

The resulting forecast allows businesses to identify crucial areas that can be improved upon. As a result, businesses can better maintain and monitor expenses, sales trends, and cash flow projections, while also procuring the most opportune venues for increasing revenue and creating customer loyalty.

While the needs of small businesses and large franchises can differ, there are different sales forecasting models available to address every need and concern. The first step is simply choosing the right forecasting method for a given situation.

Forecasting for Small or New Businesses

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Whether a company is starting a new venture or choosing to adapt an existing business, forecasting for future sales can help them make accurate and well-informed choices about how best to proceed with the next chapter of their venture by providing clear and actionable data.

Many times, forecasting as a new business means a lack of historical sales data to help predict future revenue. However, this doesn't mean creating a reliable forecasting model is impossible.

To fill in these gaps of information, new businesses can gather data by consulting experts in their field or conducting market research in their target demographic to gain a better idea of customer needs.

For quantifiable information, state and federal agencies often possess useful data open to the public. For example, the state of Washington's Department of Revenue has made useful retail sales statistics for each of their cities readily available. Other helpful government resources include the U.S Census Bureau, which provides demographic information to help estimate target markets, while the U.S Bureau of Labor Statistics offers detailed information on customer expenditure reports.

Alternatively, the Small Business Administration, the local Chamber of Commerce, and other entrepreneurial organizations are able to provide useful figures and resources regarding sales reports for small and upcoming businesses.

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Forecasting for Established Businesses

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Established businesses already have existing historical sales data from accounting summaries, making it easier to put together an accurate forecast based on existing information.

Cross-referencing this data across months and years provides a solid account of trends that can help identify market changes and formulate strategies to encourage sales growth.

1. Historical Forecasting

This is an approach that exclusively utilizes past sales data to set future goals for a specific time period. By identifying patterns from previous years' trends, businesses are able to predict sales growth or loss within the next month or quarter.

For companies that choose to automate this process, business forecasting software can instantly produce projections for future sales by integrating with the point of sale system. If the business is expecting a slight dip or increase in demand in comparison to the previous years' trends due to external factors such as economic or market changes, the forecasting techniques can be adjusted to reflect the expected shifts.

For example, if last year's Q1 revenue was $50,000 and the business expects the current year's demand to remain steady, their estimate for the upcoming quarter would be $50,000 as well. However, if the economic conditions have improved within the year and they expect a 10% increase in sales, the forecasted revenue would be $55,000.

2. Opportunity Forecasting

This technique focuses on granular information and is heavily dependent on consumer data and understanding past performance. It gauges the likelihood of closing a sale based on customer data trends and the position within the company's funnel.

Whether it's market qualified lead (MQLs) or sales qualified leads (SQLs), these qualifiers bear different probabilities based on the business' customer history. It would then be the job of the executives to move SQLs through the sales funnel and therefore create more opportunities for the company. By using these estimates in comparison to the average deal size for these kinds of leads, businesses can create a forecast for any desired time period.

3. Regression Analysis

This is perhaps the most mathematics-based quantitative method of forecasting as it is heavily based on statistics and produces in-depth sales projections.

To successfully utilize this method, businesses must understand which variables can affect the company's sales performance. To begin, a business would examine their purpose for forecasting, or what they want to learn, and then determine how much an independent variable (anything that influences sales such as weather or economic conditions) affects the dependent variable (total sales). To achieve meaningful results, a company must have extensive past data.

Tips to Keep in Mind

A helpful tip when producing accurate sales forecasts is to focus on demand rather than supply. Doing this avoids running the risk of over or underestimating the amount of stock actually needed, which can limit inventory-related costs.

Forecasting gives businesses time to prepare and work out how they intend to increase or decrease production in order to meet the estimated demand. Of course, a key part of this procedure is to keep things as up to date as possible.

Whether the business is small, established, or brand new, it is also important to account for any changes that may affect the accuracy of a forecasting report. It's always beneficial to be aware of changes in market trends, economic conditions, and weather as these factors can all affect customer demand.

For businesses that wish to simplify this process, modern sales forecasting software can instantly produce results using past data and statistical methods.

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