Why Our Customers Love Amazon Machine Learning – A Guest Post from 47Lining

Mick is CEO of 47Lining, an AWS Advanced Consulting Partner in the AWS Partner Network (APN) with the Big Data Competency designation. He holds an AWS Solutions Architect Professional certification.

Amazon Machine Learning is a service that provides predictive capabilities, the results of which can be incorporated into a wide variety of downstream applications and business processes. At 47Lining we’ve had the opportunity to partner with customers in several industry verticals to apply Amazon Machine Learning. These efforts apply predictive capabilities to optimize a wide range of operational and consumer-centric processes like establishing supply chain delivery expectations, preventing customer churn and identifying future consumer credit behaviors.

As with any relatively new service or capability, customers want to understand how it is positioned in the market and how it compares to alternative choices that are available to them. As our customers have adopted Amazon Machine Learning, we listened to what they really like about it. Here’s what we’ve heard:

Amazon Machine Learning enables widespread application of predictive analytics. Amazon Machine Learning is easy to use. It can be applied by a broader array of contributors than has ever been the case. This is driving the democratization – and commoditization – of using predictive capabilities to optimize business processes. Our customers love the “gain” that Amazon Machine Learning provides to small agile teams. The visual tools provided in the AWS Console help diverse practitioners easily assess training datasets, review model quality and iteratively refine their approach. The service makes it easier for practitioners to apply machine learning to successfully enhance key metrics for high-value activities. Because the service makes such optimizations more repeatable and cost-effective, customers can scale their efforts to improve additional business processes.

Amazon Machine Learning covers a broad array of common business processes from many industries. While it is not a fit for all categories of learning problems, the learning approaches implemented today within Amazon Machine Learning allow our customers to increase efficiency in a wide array of business processes.  For example:

• Accurately Predicting Customer Churn. 47Lining worked closely with AWS to deliver a predictive analytics engagement for a media & entertainment customer with investments in original content programming.  The engagement focused on business impact of predicting customer churn. We fused online video platform logs with 3rd party zip code demographics and social media sentiment. This data was then analyzed to extract features used to drive the learning process, such as viewing hours per quarter, daytime vs. nighttime viewing, and depth of long-tail viewing for each user. Amazon Machine Learning was used to predict customer churn with 71% accuracy.  These predictive capabilities enabled our customer to create a “sticky” conversation with their customers through relevant offers for viewer retention.  We estimate that for a typical video subscription platform, modest improvements in churn rate can yield on the order of $2-5M in annual retained subscriber revenues and avoided replacement customer acquisition costs.
• Predicting Consumer Credit Behavior. We had the opportunity to work with RevolutionCredit, a pioneering behavioral data and analytics platform that helps creditors identify upwardly mobile customers, leading to more – and higher quality – approvals, lower delinquencies, higher retention and more engaged consumers. Based on the behavioral data from the platform, we partnered with RevolutionCredit’s Chief Scientist, Hutch Carpenter and CTO, Rama Thamman to apply Amazon Machine Learning to classify consumers and identify opportunities that benefit both consumers and creditors.  This use case represents a “high-volume optimization” scenario – because volumes are high, even modest improvements can have a large impact. For example, identifying the likelihood of write-offs through behavioral signals can provide 12% better predictive power than traditional measures of credit scores and late payments alone. For financial institutions, this improvement can represent millions of dollars in profits.
• Lead Scoring for Propensity to Purchase Real Estate. One of our customers develops high-end real estate properties. We are helping them to develop a lead-scoring model using Amazon Machine Learning that predicts propensity to purchase high-end real estate based on a combined set of public and private data sources.  The goal is to increase the efficiency of the sales prospecting process, which results directly in a higher net-present-value return on their capital intensive construction projects.For a sizable capital project in this industry (on the order of $1 Billion), shrinking the average time to close for individual transactions by just 11% through improved targeting can improve the net-present-value of the project by over $50 Million.

These are just a few examples – as our customers continue to create wins through optimizing initial processes, additional opportunities to optimize their business operations or interactions with customers come into focus.

Amazon Machine Learning makes it easy to “go wide” during training.  One of the most important steps in producing quality machine learning models is engineering and generating the features used for training from the underlying raw data. It is not always apparent which combinations of features will lead to superior model quality and most practitioners brainstorm many alternatives, undertaking lots of empirical experimentation to understand which alternatives produce the best results. Amazon Machine Learning provides a set of feature engineering “recipes” that minimize data preparation requirements. Practitioners can use this capability to easily specify and build many models from source data inputs in parallel and quickly observe which ones work best. This conserves the precious time and energy of human practitioners by fanning out to relatively inexpensive compute resources instead, with all of the provisioning required for training handled seamlessly by the service.

Amazon Machine Learning has a really simple DevOps model.  As optimizations launch to production, Amazon Machine Learning’s simple DevOps model saves our customers real money because the service “just works”. It enables periodic batch predictions as well as elastically scaling highly available real-time prediction services. Some of our customers require batch predictions to be generated for hundreds of millions of samples each night. Others require a highly available real-time predictor service to be available with consistently low latency at global consumer scale. After we architect a solution with Amazon Machine Learning, our customers need not be concerned with operations and maintenance of underlying clusters or other infrastructure. Such an approach becomes even more important as predictive analytics are used in an increasing number of critical business processes.

Amazon Machine Learning’s predictable, elastic pricing model makes it easy to create a winning business case. The elastic pricing for Amazon Machine Learning scales with the operations of your business process. This makes business stakeholders very comfortable since up front implementation costs are small and the value of resulting optimizations typically far exceed the very predictable costs associated with the Machine Learning models and predictions that support them.

Whenever we talk with customers about the opportunities that they see to apply machine learning and predictive analytics, we grow more excited. This is a technology that we feel will reshape most business processes in most companies. We’re quite excited to be helping our customers play a leading role in this transformation and look forward to future releases of Amazon Machine Learning.

Want to learn more about 47Lining? Visit the company’s website here.

Note: The content and opinions in this blog are those of the third party author and AWS is not responsible for the content or accuracy of this post.