Amazon Lookout for Metrics uses machine learning to automatically detect and determine the root cause of anomalies in business metrics

DevFactory, Digitata, and Flywire among customers using Amazon Lookout for Metrics

Today, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company, announced the general availability of Amazon Lookout for Metrics, a new fully managed service that detects anomalies in metrics and helps determine their root cause. Amazon Lookout for Metrics helps customers monitor the most important metrics for their business like revenue, web page views, active users, transaction volume, and mobile app installations with greater speed and accuracy. The service also makes it easier to diagnose the root cause of anomalies like unexpected dips in revenue, high rates of abandoned shopping carts, spikes in payment transaction failures, increases in new user sign-ups, and many more—all with no machine learning experience required. With Amazon Lookout for Metrics, there is no up-front commitment or minimum fee, and customers pay only for the number of metrics analyzed per month.

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“By leveraging Amazon Lookout for Metrics, our clients will be able to unlock critical data insights quickly and accurately”

Organizations of all sizes and across industries gather and analyze metrics or key performance indicators (KPIs) to help their businesses run effectively and efficiently. Traditionally, business intelligence (BI) tools are used to manage this data across disparate sources (e.g. structured data stored in a data warehouse, customer relationship management data residing on a third party platform, or operational metrics kept in local data stores) and create dashboards that can be used to generate reports and alerts if anomalies are detected. But effectively identifying these anomalies is challenging. Traditional rule-based methods are manual and look for data that falls outside of numerical ranges that have been arbitrarily defined (e.g. provide an alert if transactions per hour fall below a certain number), which results in false alarms if the range is too narrow, or missed anomalies if the range is too broad. These ranges are also static, and don’t change based on evolving conditions like the time of the day, day of the week, seasons, or business cycles. When anomalies get detected, developers, analysts, and business owners can spend weeks trying to identify the root cause of the change before they can take action. Machine learning offers a compelling solution to the challenges posed by rule-based methods because of its ability to recognize patterns in vast amounts of information, quickly identify anomalies, and dynamically adapt to business cycles and seasonal patterns. However, developing a machine learning model from scratch requires a team of data scientists that can build, train, deploy, monitor, and fine tune a machine learning model over time. Furthermore, a single algorithm rarely serves all of the needs of a business, which causes businesses to expend meaningfully more time and expense creating and maintaining multiple algorithms to solve different use cases. Ultimately, few organizations possess the experienced data scientists and necessary resources to successfully move past rule-based methods and realize the full potential of machine learning for detecting anomalies in their metrics.

Amazon Lookout for Metrics is a new machine learning service that automatically detects anomalies in metrics and helps customers quickly identify the root cause. Lookout for Metrics puts the same technology used by Amazon internally to detect anomalies in its business metrics into the hands of every developer. Customers can connect Amazon Lookout for Metrics to 19 popular data sources, including Amazon Simple Storage Solution (S3), Amazon CloudWatch, Amazon Relational Database Service (RDS), and Amazon Redshift, as well as SaaS applications like Salesforce, Marketo, and Zendesk, to continuously monitor metrics important to the business (e.g. total revenue, gross margin, average purchase frequency, return on advertising spend, etc.). Amazon Lookout for Metrics automatically inspects and prepares the data, selects the best suited machine learning algorithm, begins detecting anomalies, groups related anomalies together, and summarizes potential root causes. For example, if a customer’s website traffic dropped suddenly, Amazon Lookout for Metrics can help them quickly determine if an unintentional deactivation of a marketing campaign is the cause. The service also ranks the anomalies by predicted severity so that customers can prioritize which issue to tackle first. Amazon Lookout for Metrics easily connects to notification and event services like Amazon Simple Notification Service (SNS), Slack, Pager Duty, and AWS Lambda, allowing customers to create customized alerts or actions like filing a trouble ticket or removing an incorrectly priced product from a retail website. As the service begins returning results, customers also have the ability to provide feedback on the relevancy of detected anomalies via the AWS console or the Application Programming Interface (API), and the service uses this input to continuously improve its accuracy over time.

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“From marketing and sales to telecom and gaming, customers in all industries have KPIs that they need to be able to monitor for potential spikes, dips, and other anomalies outside of normal bounds across their business functions. But catching and diagnosing anomalies in metrics can be challenging, and by the time a root cause has been determined, much more damage has been done than if it had been identified earlier,” said Swami Sivasubramanian, Vice President of Amazon Machine Learning for AWS. “We’re excited to deliver Amazon Lookout for Metrics to help customers monitor the metrics that are important to their business using an easy-to-use machine learning service that takes advantage of Amazon’s own experience in detecting anomalies at scale and with great accuracy and speed.”

Lookout for Metrics is available directly via the AWS console as well as through supporting partners in the AWS Partner Network to help customers implement customized solutions using the service. The service is also compatible with AWS CloudFormation and can be used in compliance with the European Union’s General Data Protection Regulation (GDPR). Lookout for Metrics is available today in US East (N. Virginia), US East (Ohio), US West (Oregon), EU (Ireland), EU (Frankfurt), EU (Stockholm), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo), with availability in additional regions in the coming months.

DevFactory is a Dubai-based provider of software and services solutions for global enterprises. “Our flagship product, Quantum Retail, offers intelligent retail-focused supply chain management and inventory optimization solutions to thousands of retail customers. Our customers have volatile sales data that is affected by millions of daily events across categories like stores, products, and departments which fluctuates according to yearly, monthly, and daily seasonality. Understanding the sales patterns and separating anomalous sales from seasonal variations is critical to accurate forecasting and downstream inventory planning,” said Rahul Subrananiam, CEO, DevFactory. “Our existing solution relied on statistical models and often failed to detect anomalous sales behaviors across stores, leading to over or under allocation of inventory to stores, which in turn significantly impacted the overall revenue and customer satisfaction. With Lookout for Metrics, we are able to automatically monitor data across all the important categories with a few clicks and identify anomalous events in nearly 40% of cases that we missed earlier. By quickly identifying such cases, we are able to adjust our inventory planning and distribution across all stores in an optimal way.”

Digitata intelligently transforms pricing and subscriber engagement for mobile operators, empowering operators to make better and more informed decisions to meet and exceed business objectives. “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 two 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.”

Marcaide founded Flywire, a startup that aims to ensure high-value international payments go through fast and friction free—both for individuals and for institutions across many industries, including healthcare, education, and travel. “At Flywire, our engineers rely on comprehensive monitoring systems, and as we grow, they have become bombarded by false positive alerts that rob them of time as they chase down these bad leads,” said Omar Lopez, Tech Lead of Infrastructure, Flywire. “By leveraging Amazon Lookout for Metrics to parse events from CloudWatch, we were able to go to production in an afternoon and reduce our false positive rate by 7x. This lets our Site Reliability Engineers focus on alerts with confidence and gives us the tools to tackle even more complex operational and business issues in the future.”

More Retail is the pioneer in omni-channel Food and Grocery Retail in India and is pursuing its mission to be Indian consumers’ most preferred choice for food and grocery needs. More has 22 hyper markets and 624 super markets across India, supported by a network of 13 distribution centers, 7 fruits and vegetables collection centers and 6 staples processing centers. “Very often, across the 4 million+ SKU-Location combinations, MRPL comes across a decline in stock which had prior indicators. These can be a specific SKU not being produced by vendors, a specific vendor facing issues across SKUs, or stress in the regional supply chain,” said Supratim Banerjee, Chief Transformational Officer, More Retail. “Our initial evaluation of Amazon Lookout for Metrics to capture these incidents looks very promising. We are able to capture 20% of incidents before they actually impact our stores and our customers. It was exciting that we were able to see the results in a matter of hours and not weeks or months. I highly appreciate how Lookout for Metrics makes it easy for my team to quickly implement AI/ML-driven workloads and allows us to dynamically support our operations people even in the most challenging times.”

Since its founding in 2001, Slalom has grown into a $1 billion company with over 5,000 employees. Its clients include more than half the Fortune 100, along with startups, nonprofits, and innovative organizations of all kinds. “By leveraging Amazon Lookout for Metrics, our clients will be able to unlock critical data insights quickly and accurately,” said David Frigeri, Senior Director of Data and Analytics, Slalom. “Giving our clients the ability to respond to near real-time anomaly detection, adapt rapidly, and anticipate future disruptions and opportunities is a key step towards embracing a modern culture of data.”

Wipro is a global IT consulting and system integration services firm that develops and implements solutions for enterprises across the globe in industries such as financial services, retail, consumer goods, and more. “For us, Amazon Lookout for Metrics is an autonomous service that provides customers with critical insights into security and business data, helping them excel in the cloud,” said Dr. Manish Govil, General Manager and Global Head, Wipro AWS Business Group. “Lookout for Metrics has not only reduced our development efforts, but also significantly lowered the time it takes to employ anomaly detection on customer workloads. It has also empowered us to analyze historical and continuous data streams in near real time, enabling us to find and eliminate anomalies from our customer’s operational and business data. We are excited to bring this AWS service to our customers to help them achieve AI driven business outcomes in the cloud at scale.”

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