What Is Amazon Forecast?
Amazon FBA business owners use Amazon Seller Tools in Amazon Web Service to scale their business and to gain a strategic edge over their competition with demand forecasting. Whether it’s Amazon Sales Reports or outside keyword ranking tools, using accurate forecast data is critical when making decisions to grow your business. One important strategy is implementing Amazon Forecast. For many seasoned sellers or data scientists, this is a must-have for forecasting and forecast accuracy. Others may be wondering, what is Amazon Forecast and how does it work?
Amazon Forecast uses machine learning software to forecast future business outcomes for FBA sellers, including resource needs, product demands, and financial performance. The built-in tools of this fully-managed service use your Amazon FBA historical data to detect patterns and make predictions for future sales. This is a meaningful part of a comprehensive strategy to consistently increase sales and cash flow for your business. The tool can also generate brand-specific sales forecasting data based on the types of products sold, length of time you have been selling on the platform, and your targeted audience.
Other factors may also alter the predictor model, such as trending items, seasonality, and customer expansion.
If you have not yet included Amazon Forecast as part of your planning, it is never too late to start. In this article, we are outlining what you need to get started, plus how to review data, and ultimately, grow your business to a profitable success. We also will introduce a new Amazon-approved application, called Crystal, which produces three separate forecasts to help you evaluate how much inventory to purchase, to help you better evaluate consumer behavior changes and adapt.
How Amazon Forecast Works
There are three main resources within the Amazon Forecast cloud computing service: datasets, predictors, and forecasts.
Datasets are collections of your business’ output and input data, which are used to create forecasting models called predictors. Predictors are either built by a pre-assigned algorithm or can be chosen through the AutoML option. The latter allows Amazon Forecast to choose which algorithm works best for your dataset. Finally, you can generate forecasts for your datasets and view them in your console or search for them using the QueryForecast API.
Though the jargon may seem overwhelming at first, once you start using the tool, you will become more familiar with how it works and how to use it to make advantageous decisions for your business.
Getting Started with Amazon Forecast
Prior to using the service, you will need to set up an Amazon Web Services (AWS) account. A free tier is available that gives you access to over 100 products, which will help you build and grow your FBA business over time. Amazon Forecast is also available as a free trial for two months to give you time to familiarize yourself with the tool before deciding to use it full-time. Once you have created an AWS account, there are three steps of the process:
- Creating and importing a dataset
- Using the predictor algorithm
- Generating a forecast
Creating and Importing Datasets
Two operations exist within the Amazon Forecast tool, CreateDataset and DescribeDataset. When creating your datasets, you specify which type of data you would like to forecast and any additional variables you want to include.
The target time-series dataset is required to perform time series forecasting, and includes the time series and target field for which you want to generate a forecast. For example, you may want to create a dataset to record weekly sales of a specific product, daily inventory levels, or monthly website visits.
Two other datasets are optional, but still useful. The first optional dataset is the related time-series dataset, which does not include a target field but uses the time series. It must include item_id and timestamp dimensions, and at least one related feature (such as price).
Related Time Series data sets are helpful in generating a demand forecast over a period of time from all domains.
Additionally, the item metadata dataset is for training data that is not time-series data. It includes metadata information about items in the target time series or related time-series data datasets. For example, if you sell blenders in multiple colors, you can track the popularity of each color of this one specific item to forecast future inventory and sales. This can help you avoid having an excess inventory of colors that don’t sell as well, taking away from your overall revenue.
Choosing Predictors and Algorithms
Once you create your datasets, machine learning guides the CreatePredictor operation. The following is required to create a predictor:
- Dataset group. The imported data chosen for training the predictor you created in the first step.
- A featurization configuration. This specifies the forecast frequency and provides information for transforming the data to make it compatible with the training algorithm.
- A forecast horizon or prediction length. The number of time-steps to make. This parameter controls how far in the future predictions can be made.
- Evaluation parameters. Splitting a dataset into training and test datasets.
- Algorithm or AutoML. The algorithm is used to train a model and set default values. Or, you can use Amazon Forecast to PerformAutoML, which chooses the algorithm for you, based on your datasets.
First, you import the data you want to train for machine learning and then set the predictors that will lead to creating a forecast.
The machine learning that generates forecasts takes into account every item you include in the datasets (created in the first step). The forecast frequency defaults to the data collection frequency you chose when you first created your datasets. This is usually weekly or monthly but can be any specified time that can be measured comparably in the future.
Once you generate the forecasts, you are able to request a specific date range within the completed forecast. You can view the results within Amazon Forecast or download the data as a CSV file to filter additionally as needed.
What Are the Benefits of Using Amazon Forecast?
Amazon Forecast and other tools are highly beneficial for deepening your knowledge of your operations at all stages of growth.
Ensuring that you make data-based decisions is essential for your company’s success. Further, using a forecasting model that has high accuracy levels (like Amazon Forecast) allows you to make accurate predictions for products and earnings.
Amazon Forecast provides highly accurate forecasts that are up to 50 percent more precise due to machine learning. Machine learning automatically detects how time-series data and variables, such as store locations and product features, affect sales growth. Best of all, you don’t have to be a machine learning expert to harness the power of Amazon Forecast. The system takes care of all of the math for you.
Similar to Amazon Forecast, Forum Brands’ application, Crystal, automatically generates accurate demand forecasts using Machine Learning. Utilizing three forecasts (P90, P70, & P50), Crystal creates a range of possible demand values which can be viewed through a custom dashboard that can be updated and accessed once a day.
As FBA businesses begin to expand to multiple locations and across several product offerings, using artificial intelligence to deliver a forecast, using increasingly complex data, is helpful when deciding how to scale in the future.
Quicker Data Review
Sellers can conduct data reviews manually, however, the advantage of Amazon Forecast is it cuts down on months of data exploration to as little as a few hours.
By importing your time-series data, Amazon Forecast does the heavy lifting of inspecting the data and identifying key attributes necessary to generate accurate forecasting. Plus, it’s not limited in the type of data you can review on a regular basis. You can set forecasts for anything, from product demand to sales growth and web traffic to resource planning.
This allows your business to scale at a pace that is manageable from an operational standpoint and delivers an enhanced experience for your customers, by meeting their demands seamlessly.
Lastly, Amazon Forecast is protected by encryption, which ensures your data is kept secure and confidential. Furthermore, you’ll always retain ownership of all data and it can only be used for machine learning with your consent. Amazon FBA sellers commonly use this service in several areas of their growth strategy, such as retail demand and resource planning. Use it as a guide for your business decisions moving forward.
Growing Your Business by Relying on Data
Running a successful e-commerce business is a complex endeavor. There are multiple data points to consider, in addition to sales, that will tell you the growth of your business and where you should focus efforts to receive the best results. Using tools and resources like Amazon Forecast will help you optimize your growth and get to your goals faster.
Reviewing all raw data line by line is time-consuming and nearly impossible to sustain. Setting target fields and reviewing specific metrics on an ongoing basis will help prioritize what’s most important in developing your business and promoting growth.
Machine learning is key to managing large-scale data. However, machine learning cannot tell the full story. You and your team are able to provide valuable context to the forecasts generated, and use the forecasts to prepare growth strategies accordingly. By making predictions using historical data, Amazon Forecast creates accurate predictions for how well your products are performing, recognizes any new patterns or trends, and ensures you are keeping everything on the operations side in good working order.
Many times, brands that experience a surge in growth are ill-prepared to meet the influx of demand. The result is delayed product and shipping availability, and consequently, unhappy customers. While growth in general is positive, you must be in a position to support rising demand in order to retain customers and sustain your business in the long-term.
Amazon Forecast, along with Forum Brands new inventory forecasting application, Crystal, is one of the many tools available to spearhead and predict business growth. This type of data is necessary for your operations and also is helpful when considering a sale to an Amazon FBA buyer. At Forum, we look for brands that have proven success on the Amazon FBA platform. Our criteria requires 70 percent or more of sales through Amazon FBA with an annual $200K+ in net profit. Using data can help set target milestones to help you reach this goal. Want to learn more about Crystal, to help your business grow and enhance your inventory forecasting? Reach out to us so we can help get you started.