Many businesses believe that a company must be established in order to take advantage of forecasting. Although many forecasting techniques use historical data to make estimates, there are methods available to startups that do not require a collection of information.
By learning the different forecasting techniques, companies can determine which method best suits their model, inventory, and current goals.
What is Business Forecasting?
Business forecasting is a predictive model that uses historical and real-time data to estimate future outcomes based on patterns. Modern forecasting often involves software and automated tools that detect trends and anomalies, whereas traditional forecasts require manual data collection and detailed analysis.
Companies can use business forecasting on various operations, from budgeting to inventory management. For example, forecasting software can monitor a business's spending behavior over the course of a year to determine its future procurement needs and optimal budgeting strategies.
Forecasts not only enable organizations to optimize their internal processes for future events but also allows them to showcase their financial stability to stakeholders and potential investors.
Qualitative Forecasting Methods
Qualitative forecasting uses information that cannot be numerically measured to predict long-term future outcomes. This forecasting method applies to startups that have not yet gathered critical sales data. Human intervention is key in qualitative forecasting, making unbiased judgment essential.
The most common qualitative forecasting methods include-
The Delphi Method
The Delphi method requires extensive time, labor, and resources as its approach involves a board of experts answering questionnaires. These surveys can generate feedback on a variety of elements, from marketing promotions to branding.
Startups and small businesses that aren't able to afford a panel of experts can also post their surveys online and ask for feedback from experienced business professionals. For example, small retailers can post questionnaires on business social media covering a new product launch to generate feedback on the brand, customer experience, and quality.
In order to generate impactful feedback, companies should use online forums that require credentials to join to ensure only relevant professionals can submit their answers.
Businesses with experienced staff can use their in-house employees to create forecasts for hands-on operations, such as sales. For example, salespeople may be able to project the success of a new product line based on similar items they currently sell.
Market research is time-consuming and requires extensive manual labor, as companies must dive deep into their markets to define consistent trends.
Businesses can conduct market research in various ways, such as-
- Talking to loyal customers
- Monitoring social media engagement
Companies need to use discretion when compiling data from a small pool, as the feedback can be skewed or biased. Therefore, managers must try to extend their market research to include a variety of people, from consumers to business owners.
When executed properly, market research gathers accurate data for a short and medium-term forecast.
For example, retailers that continuously survey their customers can see their opinion on product changes as they make adjustments.
Quantitative Forecasting Methods
On the other hand, quantitative, also known as statistical, forecasting only uses data that can be measured and assigned a value. Working with digits allows companies to manipulate variables to define dependencies.
The quantitative forecasting method uses historical sales and performance data to anticipate emerging trends for the near future. There are two primary types of quantitative forecasting.
Time Series Analysis
Time series analysis requires multiple years' worth of sales data from a particular product line to accurately gauge its performance and fluctuations. By referencing an extensive data collection, businesses can ensure the consistency of the forecasted patterns.
Depending on the business's performance, this model either continues the pattern of past data into the future or calculates the average of historical results to make projections. However, companies can narrow down their forecasts by separating different variables, such as seasonality.
For example, an established retailer can use their years of business data to determine their slow and busy seasons. This enables them to increase or decrease their inventory orders to either drive sales or save storage costs.
The casual methods consider external factors that can affect companies. These techniques can forecast much further into the future than the time series analysis, as it relies on large data sets. In other words, casual methods combine the time series analysis and market research to provide more detailed insights.
While there are numerous casual methods, regression analysis is most often used to define cause-and-effect trends. Regression analysis considers how competition, the economic state, and other external factors can impact sales.
For example, outdoor venues must incorporate weather patterns into their forecasts to determine if the upcoming season will negatively or positively affect sales.
Advanced forecasting software can even be programmed to perform regression analysis and generate complex statistics.