The history of sales forecasting is incredibly deep and rich. It dates back to the 20th century when businesses used it to strategize and plan their operations. Also, they used it to make informed decisions in areas such as marketing, production, and inventory.
So, how has the sales forecast process revolutionized from ancient times? Read on to learn more.
Ancient Methods Of Sales Forecasting
There are several ancient methods that businesses have used for sales forecasting. They include the following:
Jury of Executive Opinion
The jury of executive opinion is one of the oldest methods of sales forecasting. In this method, experienced executives predicted the sales forecast figures. They used their experience and knowledge of market factors to make the estimates. Note that the jury of executive opinion relies on the executives’ judgment and experience to make sales forecasts.
The Delphi Method
The Delphi Method involves a process of surveying opinions from a panel of experts. In this method, experts answer several rounds of questionnaires. Then, the responses are aggregated. The main goal of the Delphi method was to get aggregated opinions on sales from a panel of experts.
Statistical forecasting began to gain popularity in the 20th century. It involved the use of statistics or quantitative data to make forecasts. This statistical forecasting helped businesses to use historical sales numbers to predict future sales.
The Rise Of Statistical Models
Statistical models help businesses to determine relationships between variables. They also help businesses in making financial predictions. The origin of these statistical models is from the 19th century. However, it was not until the 20th century that modern statistical innovation was introduced.
Today, statistical models play a crucial role in sales forecasts. They help businesses to:
- Identify patterns
- Identify seasonality
- Identify trends
Technological Advancements In Sales Forecasting
Technological advancements ensure that the sales prediction process is sophisticated and accurate. This accuracy in sales prediction occurs due to technologies, such as Artificial Intelligence (AI). The invention of AI has transformed how companies approach their sales forecasting, as it allows them to analyze a substantial amount of data.
Also, it ensures that businesses can derive benefits, such as;
AI allows you to analyze data in real-time. It means you can quickly adjust your sales forecasts as the market conditions change. For instance, if there is a change in consumer behavior, AI will quickly analyze the data. Then, use the analyzed data to adjust the sales forecasts. Note that this technology ensures that you can stay ahead of your competition.
AI uses natural language processing methods and machine learning to correct and detect errors. In short, it ensures that the collected sales data does not have any inconsistencies, missing values, duplicates, or typos. Therefore, it facilitates increased sales’ data accuracy.
Provides a holistic view
Artificial Intelligence has the ability to provide businesses with a deeper insight into sales data. It analyzes a large amount of data at a relatively fast pace. Thus, it allows you to delve deep into the latest consumer trends, which can be incredibly helpful in sales budgeting and forecasting.
AI has algorithms that can help you identify trends and patterns in the data. Thus, you will be able to have an accurate picture of the buying habits of your customers.
Artificial intelligence can automate many of the sales forecast tasks. This automation enhances the efficiency of business operations. It also ensures that human analysts can focus more on other activities.
As stated above, AI will help you to analyze customer behaviors and preferences. It can also offer you relevant sales forecasts for each customer. This method will help businesses to target and engage with their customers effectively.
Challenges and Limitations in Sales Forecasting
Businesses use sales forecasts to predict the future of sales generation. Thus, it is essential that you understand the following challenges and limitations involved in the forecasting process.
Uncertainty in the predictions
There are several natural and economic events that businesses cannot predict. For instance, predicting accurate sales increase rates can be challenging. In addition, it can be difficult to forecast;
- Competitor behavior
- Economic conditions
- Global pandemics
In adequate sales history
Forecasting relies on past sales data to predict future sales. However, in a situation where there is inadequate sales history, the results of the forecasting can be inaccurate. Also, if there is no sales history, it can be challenging to predict the future.
Sales forecasts rely on the judgment of experts, who may be subjected to personal bias. Thus, if an expert is too optimistic or pessimistic, they can skew the forecast data, leading to inaccurate predictions.
Sales forecasts have undergone a significant revolution. Today, businesses use advanced technologies, such as artificial intelligence, to help in sales budgeting and forecasts. The best thing is that these technologies are more efficient and accurate than human analytics.
Does your business use any advanced technology to predict the future of sales? What is your experience with the forecasting process?