Companies need a data-processing solution that increases the speed of business agility, not one that is complicated by too many technology requirements. This requires a system that delivers continuous/real-time data-processing capabilities for the new business reality.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing?
In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
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As one of the largest providers of internet, voice, and media products and services to business and residential users in the US, this media conglomerate’s employees are responsible for handling of over one million customer interactions per day.
Its customers can access support services via many different channels – call center, website or from a mobile device using self-service support; all of these channels roll up into the same customer support organization. The challenge was their ability to handle the large volume of events that were system generated (from within their own infrastructure) or from users interacting with their hardware or software applications.
A major objective for the business was to be able to automate as much of the support process as possible and to reduce support response times for their customers. Having the ability to access up-to-date customer account information such as who the customer is, what services they have, where they live, what’s their history, what’s the current state of the devices in their home, etc. would be key to improving the current event-based support model. By having this data available in near real-time, support can quickly identify and analyze areas where there may be problems with a service or product.
The business use-case was for a large-scale distributed data grid that would have access to real-time device telemetry data from routers, set-top boxes, mobile devices and applications in addition to all the customer account information from their back-office business systems. Having this data available shortens query times by keeping the data in a near-cache to support a more event-based model.
This was achieved with the internal development and deployment of a proprietary support platform that leverages artificial intelligence (AI) and machine learning (ML) technologies to augment natural language processing capabilities
(NLP). It relies on Hazelcast IMDG (In-Memory Data Grid) to access large amounts of stored, unstructured data, to deliver customers, support agents, and AI chatbots near real-time information to improve the self-service or support
The support platform is a hive-mind that uses telemetry data that captures who the customer is, what services they have, where they live, what’s their history, what’s the current state of all the devices in their home, etc. When a customer contacts the support organization with an issue, all relevant information is processed by the support platform to offer the right recommendation to resolve their issue in real-time.
The system is expanding steadily and usage and volume have increased significantly across the organization. The forecast is for tens of millions of accounts on the system, with dozens of data sources per account. Presently, they
are using Hazelcast IMDG to store transient data in AWS related to their services and products and they are handling about 300K customers a day. As they expand their reach across all of their customer service properties, the platform
and Hazelcast IMDG are capable of delivering the scalability to handle the increased transaction volumes.
Hazelcast IMDG enables all this information to be instantly available, reducing the interaction time with support, which allows them to handle more interactions with a higher problem resolution rate. This significantly reduces the instances of having to send a technician on-site and enables the service provider to offer the best customer service experience for its customers across the varied customer-facing service channels.
Hazelcast IMDG working with in-house developed technology is viewed by the company as a silver bullet for the goal of improving the customer experience.
The organization relies on Net Promoter Scores (NPS) to track customer sentiment and has reported a dramatic improvement from a negative to a positive score in this important service-centric KPI, as well as reduced operational costs.
The company prides itself on providing the best possible customer experience. Digital self-service is viewed as the first line of defense to reduce support hold times and to offer a more personalized customer interaction.
The support teams have seen huge benefits since using the in-house developed technology for identifying and resolving set-top box errors and high-speed data internet issues. Tracking the kinds of errors that come up most often is one of the main sources of data used to train the AI/ML engine to identify major support issues.
The development of this platform and the integration with Hazelcast IMDG have enabled them to drive continuous
service improvements for their customers.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.