What is Quantitative Forecasting? Quantitative Forecasting Methods

Forecasting is one of the most frequent activities in Marketing. In addition to Qualitative forecasting, Quantitative forecasting can be also a very useful tool for marketers as it allows them to make predictions about the future based off of past data and current events. In this article, we will find out the definition, characteristics of Quantitative forecasting ways to use it in Marketing cases.

What is Quantitative Forecasting?

Quantitative forecasting is a method which is data-based mathematical when making a forecast. In other words, is a kind of method that uses numerical techniques in order to predict future activity. This includes things such as sales, costs, demand etc.

Quantitative forecasting is often used in many marketing activities such as:

- Forecast the total sales of the company in the next quarter.

- Forecast the percentage of chance to defeat a competitor in the next year.

- Forecast the budget could be used in the next advertising campaign.

- Forecast time needed to develop a new product.

- Forecast the success rate of launching a new product.

Short history of Quantitative Forecasting

Quantitative forecasting was first mentioned by a French mathematician and physicist Blaise Pascal in 1654. He was using regularly averages to forecast upcoming events about gambling. Later, many scholars have worked on this method.

In 1950s, the founder of modern quantitative forecasting methods Box and Jenkins conducted a pioneer work in this field. They used Time Series Analysis in order to apply quantitative forecasting on business. During this time, there were many debates between the scholars about whether it is possible to use statistical forecasts for decision making without considering expert judgements or not.

Nowadays, many organizations use both qualitative and quantitative forecasting techniques, and they are using them as a complement in their marketing activities.

Differences between Quantitative forecasting & Qualitative forecasting

There are many differences between qualitative and quantitative forecasting. Below is a brief list of these:

- Quantitative forecasts tend to be objective, while qualitative forecasts tend to subjective.

- Quantitative use the avantages of statistics when Qualitative forecast take the advantages of experience.

- Quantitative forecasting has certain rules and models which are considered standard. Qualitative forecast does not have any well known model or rule.

Quantitative Forecasting Methods

Below, you will find a list of some Quantitative forecast methods which are used by many businesses and organizations.

Naive Forecast

Naive forecast is based on the assumption that past activity will repeat in future. Therefore, using Naive forecast to predict the future performance of a product, service or marketing activities can be highly inaccurate because marketers do not take into consideration other variables such as competitors behavior and market conditions.

For example, a company's sales were $10 million in December of the last year. To predict sales of this December using Naive forecast, the company could assume that it will repeat this past performance as $10 million or maybe less if there is an economic recession etc.

Linear Regression

Linear regression is one of the most basic forecasting methods that has been used for decades. With this method, you are basically drawing a straight line between two points in time. Predicting the future performance based on Linear regression is effective when there is a pattern in previous data.

For example, if a company's sales were increasing over 10 years, Linear regression can be used to predict future sales based on the trending line based on the company sales history.

Moving Average

Moving average is a method that uses the most recent data points of time series in order to estimate future performance. Using Moving average alone without considering other forecasting methods may result in less accurate forecasts because there are not many other variables considered such as competitor's behavior and market conditions.

Exponential Smoothing

Exponential smoothing method is similar to Moving average method, however, this method makes an adjustment in the previous forecast by considering the error between projected result and actual performance. It also takes into consideration trend, seasonality and current conditions in order to produce more accurate forecasts.

Seasonal Decomposition

Seasonal decomposition is a forecasting method that identifies data patterns which are repeating over different periods of time. For example, the number of surfers on the web varies depending on the season.

Seasonal decomposition can be used in order to adjust forecasts for each period so that organizations have more accurate performance assessments based on actual market conditions.

Revenue Run Rate

Revenue run rate method is a forecasting technique that combines past actual performance with future projections in order to provide an estimate of the total revenue expected at some point.

For example, if the company has started a new service and it expects to complete a certain amount of sales in one year, it can calculate its Revenue run rate by multiplying its present total sale with the number of sales it expects to achieve over one year.

Historical Growth Rate

Historical growth rate is a forecasting method that uses actual historical performance to estimate future growth. This method becomes less accurate as time goes on because it doesn't take into consideration any new events which may affect the business performance.

Below is the formula:

(Present Value - Past Value) / Past Value x 100

Here’s an example using words that may make it easier to understand:

(Quarter 3 Revenue - Quarter 1 Revenue) / Quarter 1 Revenue x 100

Advantages and Disadvantages of Quantitative forecasting

There are several advantages and disadvantages of quantitative forecasting methods.

Advantages

Quantitative forecasting usually produces more accurate results

Thanks to the calculation of data, quantitative forecasts are usually more accurate than qualitative forecasts.

Quantitative forecasting provide better interpretation

Since quantitative forecasting methods are based on numbers, it is easier to interpret results.

Quantitative forecasting can be used in long run

If a business picks a right method and there are not many changes in the marketing environment, quantitative forecasting can be used for a long time without having to change it.

Quantitative forecasting can be automated

Because of the repetition of steps in using quantiative forecasting methods, many company have developed specialized software to carry out the work in a short time. The company can consider spending money to save its time and human resource.

Disadvantages

Quantitative forecasting takes longer time of implementation than Qualitative forecasting does.

Because all quantitative forecasting methods need processing a lot of data, it takes a longer time than qualitative forecasting. If you are using only one method and don't have enough data, this will result in inaccurate forecasts.

Quantitative forecasting is difficult to implement in organizations that have not been using it

Since quantitative forecasting requires data gathering and processing, it will be difficult for small companies that are not used to this method. For example, many small businesses don't have their own sales data because they use distributors. Even if the business has its own sales information, it may take a lot of time to organize it.

Quantitative forecasting requires lot of tools and equipments.

All quantitative forecasting methods require some tools and equipments. For example, companies using econometric models may need computers to calculate the coefficients needed for these models. Also, organizations using statistical forecast method may require software like SPSS or SAS in order to process data.

Quantitative forecasting can't be used for every type of business.

Quantitative forecasting mainly includes reporting methods and projection methods, which must have data available to apply them. But there are some businesses that don't have any data or information about their customers (e.g. service companies). In this case, quantitative forecasting methods cannot be used.

Conclusion

All in all, quantitative forecasting is a powerful business tool that can be used to predict future performance with accuracy. We hope this article provides you with a good understanding of quantitative forecasting.

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