Explaining the benefits, procedures, and methods of sales forecasting using machine learning

Sales forecasts are an important indicator when planning sales and marketing for your company's products and services. Using machine learning to forecast sales allows you to perform objective analysis based on data, and can also help you predict contributions to your company's profits.

Therefore, many people may want to know more about sales forecasting using machine learning.

In this article, we will explain the overview, benefits, procedures, and points to note about using machine learning for sales forecasting. Please read to the end.

What is machine learning sales forecasting?

Sales forecasting and budgeting are essential tasks for companies to manage production and inventory levels and to develop sales and marketing strategies. By introducing machine learning, sales forecasts can be made on your behalf based on vast amounts of past data and market trends.

Machine learning is a technology that allows computers to automatically learn patterns and rules related to data, enabling them to make predictions and decisions. Since it is an objective method, rather than relying on the experience and intuition of sales forecasters, the demand for it in predicting your company's sales is likely to increase in the future.

Especially in recent years, as consumer needs have become more diverse and trends have become more volatile, companies are more likely to take on inventory risks. By making sales forecasts and budgets that are close to the actual values, it becomes easier to prevent pressure on management due to excess inventory and opportunity loss due to stock shortages, which can ultimately lead to cost reductions.

5 Benefits of Using Machine Learning for Sales Forecasting

Here are five benefits of using machine learning for sales forecasting:

Reduce inventory risk and manage it

By utilizing sales forecasts based on machine learning, you can manage your products by predicting future demand, minimizing inventory risk. For example, if your sales forecasts are inaccurate, you may end up with a lot of excess inventory and have to sell it at a lower price.

In addition, if there is insufficient inventory relative to demand, this will result in lost opportunities and a drop in sales. By forecasting sales, you can minimize these risks and aim for the appropriate amount of purchases.

Optimize staffing

Another benefit is that it makes it easier to understand busy and slow seasons, allowing for more efficient staffing. If you quantitatively express sales forecasts and share them within the company, it becomes easier to review the balance of each person's work responsibilities.

Not only can this improve operational efficiency, but it also has the advantage of reducing human resources costs. For example, by using machine learning models to replace procurement planning, which was previously performed by multiple people, human resources can be allocated to other tasks, such as creating new products or improving sales floors.

Prevents personalization

With machine learning, the task of forecasting sales of products and services is not assigned to a specific person, which helps prevent the task from becoming dependent on a specific individual. A major advantage is that the company's know-how can be continuously accumulated without relying on a specific person in charge.

In recent years, the rate of job turnover in Japan has been on the rise, so by introducing machine learning, the risk of losing know-how due to human resource outflow can be reduced.

Ad budget can be allocated appropriately

You can allocate the appropriate advertising expenses to achieve the predicted sales. In order to increase the sales of a product or service, it is common to place advertisements in mass media and on the Internet and to develop promotions.

However, if you misread demand and allocate too much budget, the project may end up in the red.

In this regard, sales forecasts using machine learning can be used as an objective basis for determining advertising and promotion costs.

Accuracy improves with continued use

The accuracy of machine learning improves by continuously accumulating data such as past sales and customer attributes. For example, sales forecast models for three or five years from now tend to be more accurate than models for this year, which may further reduce inventory risk.

By building a model that meets your company's needs, you will be able to make more accurate sales forecasts, which will make it easier to use in promotional and sales activities.

[4 Steps] Steps for sales forecasting using machine learning

Next, we will explain the procedure for using machine learning to forecast sales in four steps.

Step 1. Identify the problem

First, let's clarify the problem you want to solve with the sales forecast model. If you don't clarify the problem at the beginning, you may end up with additional features or requests to be implemented during the process, which may result in you significantly exceeding your initial target budget.

Specific examples include the following issues:

• Increased labor required for inventory management
• Increased waste caused by excess inventory
• Lost sales opportunities due to stock shortages

In this way, by setting tasks that meet your company's needs, you can increase the effectiveness of your machine learning implementation.

Step 2. Prepare your data

Next, we will collect the following data to train the AI:

• Sales of applicable products
• Weather information
• Retailer geographic data
• Costs and volume of advertising spent on marketing initiatives

If the data is collected in a variety of formats or has missing values, it will need to be preprocessed to be reworked. For example, if the data is in a variety of formats, it will need to be converted into a format that the model can read.

In addition, to improve the accuracy of the model, it is important to perform careful preprocessing, such as inputting average values ​​if there are missing data or outliers.

Step 3. Input data

Input the data collected in step 2 into the tool to generate a sales forecast.

It is easier to forecast sales by using external tools and systems than by developing a predictive model in-house. This is because, when developing a predictive model in-house, you need to secure AI personnel with programming knowledge and input them into each process, such as data collection, model construction, and implementation. Furthermore, model construction and implementation are not always successful.

Using pre-packaged external tools and systems can reduce the risk of failure and implementation costs.

Step 4. Verify and improve

A sales plan is created based on the sales forecast and order quantities are adjusted. In this step, it is important to thoroughly verify the actual effects of introducing machine learning, such as improved profit margins and reduced labor hours for inventory management.

For example, if your company has an existing method for sales forecasting, you can confirm the effectiveness of introducing it by verifying the difference with the predictions generated by machine learning.

As mentioned above, the accuracy of machine learning improves with the amount of accumulated data, so it is necessary to continually input the latest data to continue improving it.

Things to keep in mind when using machine learning for sales forecasting

From here, I will explain two points to keep in mind when using machine learning to forecast sales.

Be sure to check the quality and quantity of your data

When preparing data to be used for machine learning, be sure to carefully check the quality and quantity.

First, regarding quality, poor quality data, such as data collected over a short period of time or data that contains unnecessary information, can lead to a decrease in the accuracy of the sales forecasting model, so be careful.

And in terms of quantity, you need to prepare a sufficient amount of data on sales, number of visitors, whether or not you place ads, etc. Even with machine learning, you can't just leave it all to automate, so you need to prepare in advance.

Build a system of collaboration within the company

It is important to build a cooperative system within the company to ensure the quality and quantity of data, because having the relevant parties collect the necessary data will lead to improved accuracy of the predictive model.

Specific examples of data include sales figures for each product, renewal and cancellation rates for services, number of days it takes to process an order, etc. In order to ensure smooth data sharing across departments, be sure to ask for the cooperation of relevant parties in advance.

Let's use "MMM", a sales forecasting method that uses machine learning

As a sales forecast using machine learning,MMM (Marketing Mix Modeling)MMM is a method for quantifying and analyzing the effects of marketing measures, and is being adopted by an increasing number of companies in Japan.

The unique feature of MMM is that it analyzes past data spanning several months to several years in chronological order to look at factors such as the following that affect sales and unit sales:

• Advertising and promotional measures
• Price cut
• Delivery rate
• climate

At MMM, we use machine learning to build mathematical models that unravel the relationships between the above internal and external factors. Using these models, we can simulate marketing initiatives that are within the company's control, such as advertising volume, promotion implementation, and sales prices, enabling highly accurate sales forecasts.

A major advantage is that you can appropriately allocate your company's resources, including budget allocation and inventory production, based on such sales forecasts. Even if the situation suddenly changes due to an increase or decrease in the advertising budget, you can run a new simulation, allowing you to respond quickly and appropriately.

Free download of case study

Gain knowledge of "statistics" and accelerate your marketing
- Introducing the basics and "MMM," ​​a statistical analysis method that has recently been gaining attention again -

Download Now

My Feelings, Then and Now

By introducing machine learning into sales forecasting, you can expect benefits such as reduced inventory risk, prevention of dependency on individual skills, and proper allocation of advertising budgets. However, when conducting sales forecasting, it is important to thoroughly verify the effectiveness of each method and make improvements.

In addition, since it is difficult to secure the human resources and budget to develop a sales forecasting model in-house, it is recommended to use external tools and systems.MMM Product "MAGELLAN"You can simulate optimal budget allocation to maximize sales.

In addition to the analytical model design function, it also has a data bulk import function, external system integration function, and navigation function, so you can work appropriately to increase your company's sales.

If you would like to implement sales forecasting using the MMM method, why not consider introducing "MAGELLAN"?

Author information

XICA Co., Ltd. Marketing Team