Forecast future values and detect anomalies in your business metrics
Tracking, monitoring, and analyzing the right business metrics are integral to the success of any business. Effective business data analysis lets you to learn from the past, monitor the present, and better plan for the future. But analyzing large amounts of business data to forecast their future values, or to detect outliers and understand the root cause, is complex, time-consuming, and not always accurate. Amazon’s Business Metrics Analysis ML solution leverages Amazon Lookout for Metrics and Amazon Forecast to solve these problems by using machine learning to analyze large volumes of data while dynamically adapting to changing business requirements.
Intelligence that adapts to change
Amazon’s Business Metrics Analysis ML solution leverages machine learning to analyze large volumes of data while dynamically adapting to changing business requirements.
Faster, easier, and no ML experience required
By using pre-trained models that solve the most common use cases, Amazon’s Business Metrics Analysis ML solution saves you time and money, without needing ML experts to create your own models.
Amazon Forecast and Amazon Lookout for Metrics use machine learning to process large sets of data producing more accurate forecasts and anomaly detections than traditional non-ML solutions. AWS ML solutions for business metrics analysis are perfected based on more than 20 years of experience at Amazon.
Business Metrics Analysis on your terms
Alternatively to Amazon Forecast and Amazon Lookout for Metrics pre-trained AI services, you can use Amazon SageMaker to create and maintain your own forecasting and anomaly detection models.
“I was very impressed with the world class machine learning team at AWS. My team worked closely with the Amazon Machine Learning Solutions Lab to develop a demand forecast model using Amazon Forecast within a few weeks. Our solution increased our forecasting accuracy by 8%. We project $553K annual savings using this solution for our factory in Mexico. As a bonus, it will be easy to integrate this solution into our cloud workflow once we migrate our data infrastructure to AWS. This collaboration with AWS helped minimize wasted labor costs and maximize customer satisfaction.”
Azim Siddique, Technical Advisor and CoE Architect - Foxconn
“Using Amazon Forecast, we have been able to increase our forecasting accuracy from 27% to 76% reducing wastage by 20% for the fresh produce category. Amazon Forecast provides a distribution of forecasts which helped us optimize our under and over forecasting costs leading to stock-outs at 3% and improved gross margins. This makes it easier for our store managers to place more accurate purchases orders by looking at the daily forecasts. We are now expanding the model to other categories, iterating with additional related datasets, and adding newer data to Amazon Forecast to continuously improve the model accuracy.”
Supratim Banerjee, Chief Transformation Officer - More Retail
“At Digitata, what really matters is getting everyone connected at an affordable price. This requires a deep understanding of economics, specifically supply and demand and customer behavior according to changes in either,” said Nico Kruger, Chief Technology Officer, Digitata. “Using Lookout for Metrics we were able to discover an issue that was negatively impacting pricing for a Mobile Network Operator customer within minutes. We were able to instantly identify the culprit and roll out a fix within 2 hours. Without Lookout for Metrics, it would have taken us approximately a day to identify and triage the issue, and would have led to a 7.5% drop in customer revenue. Lookout for Metrics allows us to act quickly and ensure the optimal performance of our pricing models, leaving us to focus on what really matters—getting everyone connected.”
Nico Kruger, Chief Technology Officer - Digitata
Advanced Microgrid Solutions (AMS) is an energy platform and services company that aims to accelerate the worldwide transformation to a clean energy economy by facilitating the deployment and optimization of clean energy assets. NEM uses a spot market where all parties bid to consume/supply energy every 5 minutes. This requires predicting demand forecasts and coming up with dynamic bids in minutes, while processing massive amounts of market data. To solve this challenge, AMS built a deep learning model using TensorFlow on Amazon SageMaker. They took advantage of Amazon SageMaker's automatic model tuning to discover the best model parameters and build their model in just weeks. Their model demonstrated improvement in market forecasts across all energy products in net energy metering, which will translate into significant efficiencies.
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Plan for sales and top-line revenue, and effectively manage cash flows.
Analytics platform integration
For organizations that have business intelligence and analytics applications in place, AI for Data Analytics (AIDA) partner solutions offers ways to leverage ML within the analytics tools they already use.
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Contact us for more information on machine learning solutions for Business Metrics Analysis
Leverage Amazon AI Services or Amazon SageMaker to develop your own business metrics monitoring and forecasting solution
Do it yourself
Amazon offers the following AI and ML services that can be used to implement your own business metrics analysis machine learning solution.
For organizations with no machine learning experience or with time-to-market constraints, Amazon Forecast and Amazon Lookout for Metrics are fully managed AI services that can be easily integrated into your applications to address the most common business metrics monitoring and forecasting use cases.
Amazon Forecast provides accurate time-series forecasting based on the same technology used at Amazon.
Amazon Lookout for Metrics uses machine learning to automatically detect and diagnose anomalies in business and operational data.
You can use the built-in algorithms and pre-trained models of Amazon SageMaker available through AWS Marketplace to build your own forecasting and anomaly detection models.
Amazon SageMaker is a fully managed service that provides every ML developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
You can use the following AWS Solutions Reference Architectures as a reference.
AWS Solutions Reference Architectures are a collection of architecture diagrams created by AWS. They provide prescriptive guidance for applications, as well as other instructions for replicating the workload in your AWS account.
Deploy a solution designed to help organizations generate accurate forecasts from diverse datasets.
Monitor streaming data and compare it in near real time to a machine-learned forecast, raising an incident or alarm if actual performance deviates significantly from the forecast.
This end-to-end ML pipeline detects anomalies by ingesting real-time, streaming data from various network edge field devices, performing transformation jobs to continuously run daily predictions/inferences, and retraining the ML models based on the incoming newer time series data on a daily basis.