The client is a global technology firm offering products and services across networking, security, collaboration, applications, and the cloud. With nearly 80,000 employees across the globe, the company develops, manufactures and sells telecommunications equipment, networking hardware, software, and other high-technology products and services.
The Challenge
The client was challenged by an overwhelming volume of support requests that its technical support team had to manage daily. The issue resolution time was extremely high as only 70% of customers used self-service facilities, and only 9% were able to resolve issues entirely on their own, via self-service. The lack of standalone applications capturing siloed information was a huge barrier to ensuring successful selfservice-based issue resolution.
The Solution
The client wanted to enable selfservice for its customers so that they could quickly search and find the information they needed. This would eliminate the need for raising tickets, and the client could save on support costs. Empowering the customers would also result in improved satisfaction scores and loyalty.
Persistent adopted a design thinking-led approach to meet the client’s demands of an AI-driven case deflection with self-service issue resolution. This was to ensure that all touchpoints for the end customer were recognized and aligned with the client’s business outcomes. We implemented an outside-in industry perspective, like deploying an AI feedback loop model for proactive suggestions and implicit deflections. We leveraged an ensemble modeling to build a dedicated model for each data source based on the nature of the data.
The Outcome
The client cut operational costs by 80% and reduced the physical data center footprint by a significant margin. Over 150 reusable components were built, including identity management, PaaS, and security management components, to ensure cost optimization.
In two years, over 3,500 apps, including ticketing system, financial system and media communication systems were migrated to the Azure cloud. The migration helped the client fulfill the commitment of $2 billion Azure consumption over seven years.
Technology Used
- Python
- Elastic Search
