If you're an FBA seller, you know that accurate sales forecasting and inventory management are key to staying competitive and profitable on Amazon. But with so many products and variables to consider, it can be overwhelming to try to predict future demand on your own. That's where Amazon Forecast comes in.
This powerful machine learning service can help you make sense of your sales data and give you more accurate demand forecasts for your products. This guide will walk you through the ins and outs of Amazon Forecast and show you how to use it to make better decisions for your business. Whether you're just starting or looking to take your sales forecasting to the next level, this guide is a must-read for any FBA seller. Let's dive in.
What is the Amazon Forecast?
Amazon Forecast is a fully managed machine-learning service by Amazon Web Services (AWS) designed to help users to produce accurate forecasts from time series data. Since 2000, Amazon has used machine learning and the same technology Amazon Forecast uses to address challenging forecasting challenges, increasing accuracy 15X in the past two decades.
The Amazon Forecast tool is great for a range of corporate use cases, including resource and financial planning as well as forecasting future performance and product demand across a broad range of industries, from retail to healthcare.
The machine learning algorithms that power Amazon Forecast automatically identify how forecasting results are influenced by interactions between time-series data that evolves and independent variables like product attributes and shop locations.
The accuracy of predictions is increased, and analysis produces commercial insights. As a component of the AWS Machine Learning Suite of services, Amazon Forecast benefits from AWS's extensive cloud platform, which is highly secure, dependable, and provides the greatest possible combination of computation, storage, security, and analytics capabilities.
What are the Features of Amazon Forecast?
Amazon Forecast automates most of the time series forecasting process which allows you to focus on data preparation and prediction interpretation. The forecast comes with some capabilities such as:
- Automate machine learning- By deciding which machine learning algorithms work best together for your datasets, Forecast automates challenging machine learning processes.
- State-of-the-art algorithms- Forecast offers a wide range of training algorithms from basic statistical techniques to deep neural networks
- Missing value support- To handle missing values in your datasets automatically, Forecast offers several filling methods.
- Additional built-in datasets- Forecast can automatically improve your forecasting model using built-in datasets. These datasets don't need any additional setup because they have already been feature engineered.
How Does Amazon Forecast Work?
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The Amazon Forecast cloud computing service has three main resources including datasets, predictors, and forecasts.
- Datasets- These are collections of output and input data from your business that are used to build forecasting models called predictors.
- Predictors- These are either created using a predetermined algorithm or can be selected using the AutoML feature. With the latter, Amazon Forecast can determine which algorithm is optimal for your dataset.
- Forecasts- You may create forecasts for your datasets, view them in your console, and search for them using the QueryForecast API.
Although the language may initially appear overwhelming, once you begin to start using the tool, you will become more familiar with how it functions and how to use it to make informed decisions that are helpful for your business. Today, Amazon's forecasting team has benefited from developments in areas such as deep learning, image recognition, and natural language processing to create a forecasting model that reliably predicts future trends in a variety of product categories.
How to Get Started with Amazon Forecast
Before using your service, you will need to set up an Amazon Web Services (AWS) account. With a free tier, you will get access to over 100 products that will help you build and grow your FBA business over time.
Once you create an AWS account, you will need to take three steps:
a). Create and Import Datasets
There are two operations in the Amazon Forecast tool, CreateDataset, and DescribeDataset. When creating datasets, you should specify the type of data you would like to forecast and any additional variables you would want to include. To perform time series forecasting, you will need a target time series dataset that includes the time series and target field for which you want to produce a forecast.
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Two other optional datasets could also be useful. The first one is a related time series dataset which doesn't come with a target field but it uses a time series. It has to include item_id, timestamp dimensions, and at least one related feature like the price. Related Time Series data sets are suitable for generating accurate demand forecasts over some time from all domains.
In addition, the item metadata dataset is meant for training data which is not time series data. It comes with metadata information about the items in the target time series or the related time series data datasets.
b). Choose Predictors and Algorithms
Once you have created your datasets, machine learning (ML) guides the CreatePredictor operation. There are several things you will need to create a predictor including:
- Dataset group- This is the imported data picked to train the predictor you came up with in the first step
- A featurization configuration- This will specify the forecast frequency and offer information to transform data and make it compatible with the training algorithm
- A forecast horizon or prediction length- This is the number of time steps to create and it controls how far in the future accurate predictions can be made
- Evaluation parameters- This is splitting the dataset into training and test datasets
- Algorithm or AutoML- This algorithm is used to train forecasting models and set default values. You can use the fully managed service PerformAutoML, which chooses the algorithm for you depending on your datasets.
You must first import the data for machine learning, after which you set the predictors that will result in generating forecasts.
c). Generate Forecasts
The machine learning for generating forecasts accounts for every item you will include in the datasets which you created in the first step. When you initially generated your datasets, you were given the option to choose the data collection frequency as the forecast frequency. While typically weekly or monthly, this can be any specific period that can be monitored similarly in the future.
You can request a specific date range within the final forecast after the highly accurate forecasts have been generated. You may view the forecast results in Amazon Forecast or download the information as a CSV file to further filter it as required.
What are the Benefits of Amazon Forecast?
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When it comes to improving your understanding of your operations at all phases of growth, tools like Amazon Forecast and others are quite helpful. It is important to make data-based decisions necessary for your company's success. Additionally, you can predict revenue and product sales accurately by employing a forecasting model with high accuracy levels like Amazon Forecast.
1. Forecasting Accuracy
Thanks to machine learning, Amazon Forecast can produce forecasts that improve forecast accuracy by up to 50% more. Using time-series data and other variables, including store locations and product attributes, machine learning can automatically identify the effects of these factors on sales growth. Luckily, the best part is that anyone can use Amazon Forecast because machine learning is not a complex field and your entire math is handled by the forecasting system.
When considering how to scale in the future, employing artificial intelligence to create a forecast using increasingly complex data is helpful as FBA businesses start to expand to several locations and across a wide range of product offerings. It also helps businesses to gain a strategic edge over their competition with demand forecasting.
2. Quicker Data Review
Although Amazon sellers can conduct their data reviews manually, using Amazon Forecast will offer an advantage as it cuts down months of data exploration to a few hours. By integrating your time-series data, Amazon Forecast handles the labor-intensive task of evaluating historical data and identifying key characteristics needed to generate accurate forecasting.
Additionally, there are no limitations on the kinds of data you can regularly review with this fully managed service. Forecasts can be made for a variety of things, including product demand, sales growth, online traffic, and resource allocation.
In addition to providing a better client experience by seamlessly addressing their expectations, this enables your business to grow at a rate that is manageable from an operational perspective.
3. Data Security
Finally, encryption is used to safeguard your data in Amazon Forecast, ensuring its security and privacy. Additionally, all information will always remain in your ownership, and the forecast uses machine learning only with your permission to get accurate demand forecasts.
This forecasting service is often used by Amazon FBA sellers in a variety of growth-related areas, including resource planning and retail demand. Future business decisions should be made using this fully managed service as a guide.
Limitations of Amazon Forecast
While the Amazon Forecast tool is a great one, it comes with its limitations. We have listed the most common limitations you may encounter:
- For the target, metadata, and related time series datasets, only 13, 10, and 25 features are allowed. If there are more, you might have to choose between features.
- The Forecast horizon should be the lesser value of between 500 and ⅓ the size of the target dataset.
- Hyperparameter tuning is supported only by CNN-QR and DeepAR+.
- DeepAR+ which offers the best estimates will only work when the number of observations is > 300.
Types of Costs in the Amazon Forecast
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Like with many AWS services, you will only pay for what you use and there is no upfront commitment. Although there is a minimal cost of service to build a proof of concept (PoC) especially if you have the chance to benefit from the Free Tier. There are four dimensions you can consider to operationalize a forecasting pipeline with Amazon Forecast and they include:
a). Storage
To enable the quickest training time for Amazon Forecast, the data is ingested and prepared. Each gigabit (GB) that is consumed by the service is charged for data storage.
b). Training Hours
You get charged for the number of training hours each time you create a new custom model using your historical data. The amount of time needed for training includes the time it takes to clean your data, run multiple algorithms in parallel, select the best combination of algorithms, calculate forecast accuracy metrics, produce explainability insights, track predictor performance, and use the infrastructure for forecasting.
c). Generated Forecast Data Points
Each unit of 1,000 forecast data points produced by the forecasting service is billed separately. A forecast data point is made up of the total number of unique time series, quantiles, and time points inside the forecast horizon.
d). Forecast Explanations
You will also be charged for any explanation the service produces to explain the effects of various dataset features or related variables on your forecasts. Explainability makes it easier for you to understand how the characteristics of your datasets affect the values of your forecasts.
The price is determined by the quantity of forecast data points and features (such as price, holidays, and weather index) that are described.
Pricing of Amazon Forecast
Free Tier
If this is your first time using Amazon Forecast with any particular account, you will get access to a Free Tier that entitles you to 2 months of free subscription. You won't be billed if you utilize any of the following during this time:
- Storage of less than 10GB every month
- Less than 10 training hours per month
- Less than 100,000 forecast data points per month
When it comes to pricing, AWS service developers work hard to minimize the operation cost of services, and price reductions usually happen regularly. Below are the prices:
- Storage- $0.088 per GB
- Training Hours- $24 per hour
- Generated Forecasts- $2.00 per 1,000 forecasts for the first 100,000 data points. this pricing decreases as per the tiered pricing table
Conclusion on Amazon Forecast
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In conclusion, Amazon Forecast is a powerful tool that can help data scientists or FBA sellers like you make more informed decisions about your inventory management and sales forecasting. By understanding the key features of the forecasting service and how to implement them in your own business, you can gain a competitive edge and optimize your inventory levels to increase profitability.
Remember, the key is to start small, set realistic goals, and monitor the results. By following the top Amazon strategies outlined in this guide, you'll be well on your way to making data-driven decisions and taking your sales forecasting to the next level. And that's what it's all about - making smart business decisions to increase your sales and grow your business.
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