This post was cowritten by Ziv Pollak, Machine Learning Team Lead, and Alex Thoreux, Web Analyst at Clearly.
A pioneer in online shopping, Clearly launched their first site in 2000. Since then, they’ve grown to become one of the biggest online eyewear retailers in the world, providing customers across Canada, the US, Australia and New Zealand with glasses, sunglasses, contact lenses, and other eye health products. Through their Mission to eliminate poor vision, Clearly strives to make eyewear affordable and accessible for everyone. Creating an optimized platform is a key part of this wider vision.
Predicting future sales is one of the biggest challenges every retail organization has – but it’s also one of the most important pieces of insight. Having a clear and reliable picture of predicted sales for the next day or week allows your company to adjust its strategy and increase the chances of meeting its sales and revenue goals.
We’ll talk about how Clearly built an automated and orchestrated forecasting pipeline using AWS Step Functions, and used Amazon Forecast APIs to train a machine learning (ML) model and predict sales on a daily basis for the upcoming weeks and months.
With a solution that also collects metrics and logs, provides auditing, and is invoked automatically, Clearly was able to create a serverless, well-architected solution in just a few weeks.
The challenge: Detailed sales forecasting
With a reliable sales forecast, we can improve our marketing strategy, decision-making process, and spend, to ensure successful operations of the business.
In addition, when a diversion between the predicted sales numbers and the actual sales number occurs, it’s a clear indicator that something is wrong, such as an issue with the website or promotions that may not be working properly. From there, we can problem solve the issues and address them in a timely manner.
For forecasting sales, our existing solution was based on senior members of the marketing team building manual predictions. Historical data was loaded into an Excel sheet and predictions were made using basic forecasting functionality and macros. These manual predictions were nearly 90% accurate and took 4–8 hours to complete, which was a good starting point, but still not accurate enough to confidently guide the marketing team’s next steps.
In addition, testing “what-if” future scenarios was difficult to implement because we could only perform the predictions for the following months, without further granularity such as weeks and days.
Having a detailed sales forecast allows us to identify situations where money is being lost due to outages or other technical or business issues. With a reliable and accurate forecast, when we see that actual sales aren’t meeting expected sales, we know there is an issue.
Another major challenge we faced was the lack of a tenured ML team – all members had been with the company less than a year when the project kicked off.
Overview of solution: Forecast
Amazon Forecast is a fully managed service that uses ML to deliver highly accurate forecasts. After we provided the data, Forecast automatically examined it, identified what was meaningful, and produced a forecasting model capable of making predictions on our different lines of products and geographical locations to deliver the most accurate daily forecasts. The following diagram illustrates our forecasting pipeline.
To operationalize the flow, we applied the following workflow:
Amazon EventBridge calls the orchestration pipeline daily to retrieve the predictions.
Step Functions help manage the orchestration pipeline.
An AWS Lambda function calls Amazon Athena APIs to retrieve and prepare the training data, stored on Amazon Simple Storage Service (Amazon S3).
An orchestrated pipeline of Lambda functions uses Forecast to create the datasets, train the predictors, and generate the forecasted revenue. The forecasted data is saved in an S3 bucket.
Amazon Simple Notification Service (Amazon SNS) notifies users when a problem occurs during the forecasting process or when the process completes successfully.
Business analysts build dashboards on Amazon QuickSight, which queries the forecast data from Amazon S3 using Athena.
We chose to work with Forecast for a few reasons:
Forecast is based on the same technology used at Amazon.com, so we have a lot of confidence in the tool’s capabilities.
The ease of use and implementation allowed us to quickly confirm we have the needed dataset to produce accurate results.
Because the Clearly ML team was less than 1 year old, a fully managed service allowed us to deliver this project without needing deep technical ML skills and knowledge.
Finding the data to use for this forecast, while making sure it was clear and reliable, was the most important element in our ability to generate accurate predictions. We ended up using the following datasets, training the model on 3 years of daily data:
Number of orders.
Average order value.
New customer revenue.
Marketing return on advertisement spend.
To create the dataset, we went through many iterations, changing the number of data sources until the predictions reach our benchmark of at least 95% accuracy.
Dashboard and results
Writing the prediction results into our existing data lake allows us to use QuickSight to build metrics and dashboards for the senior-level managers. This enables them to understand and use these results when making decisions on the next steps needed to meet our monthly marketing targets.
We were able to present the forecast results on two levels, starting with overall business performance and then going deeper into performance per each line of business (contacts and glasses). For those three cases (overall, contacts, glasses) we presented the following information:
Predicted revenue vs. target – This allows the marketing team to understand how we’re expected to perform this month, compared to our target, if they take no additional actions. For example, if we see that the projected sales don’t meet our marketing goals, we need to launch a new marketing campaign. The following screenshot shows an example analysis with a value of -17.47%, representing the expected total monthly revenue vs. the target.
Revenue performance compared to predictions over the last month – This graph shows that the predicted revenue is within the forecasted range, which means that the predictions are accurate. The following example graph shows high bound, revenue, and low bound values.
Month to date revenue compared to weekly and monthly forecasts – The following example screenshot shows text automatically generated by QuickSight that indicates revenue-related KPIs.
Thanks to Forecast, Clearly now has an automated pipeline that generates forecasts for daily and weekly scenarios, reaching or surpassing our benchmarks of 97%, which is an increase of 7.78% from a process that was done manually and was limited to longer periods.
Now, daily forecasts for weekly and monthly revenue take only 15 minutes in data gathering and preparation, with the forecasting process taking close to 15 minutes to complete on a daily basis. This is a huge improvement from 4-8 hours with the manual process, which could only perform predictions for the whole month.
With more granularity and better accuracy, our marketing team has better tools to act faster on discrepancies and create prediction scenarios on campaigns could achieve better revenue results.
Effective and accurate prediction of customer future behavior is one of the biggest challenges in ML in retail today, and having a good understanding of our customers and their behavior is vital for business success. Forecast provided a fully managed ML solution to easily create an accurate and reliable prediction with minimal overhead. The biggest benefit we get with these predictions is that we have accurate visibility of what the future will look like and can change it if it doesn’t meet our targets.
In addition, Forecast allows us to predict what-if scenarios and their impact on revenue. For example, we can project the overall revenue until the end of the month, and with some data manipulation we can also predict what will happen if we launch a BOGO (buy one, get one free) campaign next Tuesday.
“With leading ecommerce tools like Virtual Try On, combined with our unparalleled customer service, we strive to help everyone see clearly in an affordable and effortless manner—which means constantly looking for ways to innovate, improve, and streamline processes. Effective and accurate prediction of customer future behavior is one of the biggest challenges in machine learning in retail today. In just a few weeks, Amazon Forecast helped us accurately and reliably forecast sales for the upcoming week with over 97% accuracy, and with over 90% accuracy when predicting sales for the following month.”
– Dr. Ziv Pollak, Machine Learning Team Leader.
For more information about how to get started building your own MLOps pipelines with Forecast, see Building AI-powered forecasting automation with Amazon Forecast by applying MLOps, and for other use cases, visit the AWS Machine Leaning Blog.
The content and opinions in this post are those of the third-party author and AWS is not responsible for the content or accuracy of this post.
About the Author
Dr Ziv Pollak is an experienced technical leader who transforms the way organizations use machine learning to increase revenue, reduce costs, improve customer service, and ensure business success. He is currently leading the Machine Learning team at Clearly.
Fernando Rocha is a Specialist SA. As Clearly’s Solutions Architect, he helps them build analytics and machine learning solutions on AWS.
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