This case study illustrates how WNS leveraged its industry expertise and next-generation capabilities in customer experience to power business outcomes for a leading telecommunications conglomerate.

As we know…

Telecommunications is an intensely competitive space, where high customer churn rate and cost constraints loom large. Given the limited differentiation in technological product offerings, companies face the onerous challenge of creating an exceptional customer experience by resolving customer issues at the very first instance.

Therefore, the use of analytics in the telecom industry assumes paramount importance. By leveraging analytical insights to understand the intent of customer contact, telecom companies can gain a distinct advantage in the market.

The challenge for our client was…

In nearly 45 percent of cases, customers who initially contacted the company through chat subsequently made voice calls within 48 hours for similar reasons. This pattern proved expensive for the client and frustrating for customers, as their issues remained unresolved.

Identifying the granular issues that caused customers to leave the chat instance and switch to a voice call was challenging, because a significant amount of customer contact data was unstructured.

Stepping in as a co-creation partner…

WNS deployed its customer interaction analytics platform, ElevateEX, to ingest and analyze structured and unstructured data across chats and calls. Platform-agnostic and an important point solution of our digital customer experience framework WNS EXPIRIUS, ElevateEX used deep learning neural networks to allow our proprietary analytics taxonomies and algorithms to deliver the highest accuracy.

Key features of ElevateEX that were vital to this engagement included:

  1. Interactive dashboards displaying trending topics to jumpstart further analysis

  2. A highly relatable multi-collinear study across matrices to identify overlaps and intensity of the impact of one metric over the other

  3. Enhanced sentiment analysis to sort calls based on positive or negative sentiment

  4. Robust 'related phrases' functionality showing relationships between topics and providing the context for Root Cause Analysis (RCA)

Critical aspects of the overall solution included:

Reducing Cost To Serve With Interaction Analytics

WNS' extensive library of over 5,000 pre-built taxonomies gave us an advantage in this engagement, canceling the need to build taxonomies from scratch. Experientially, a taxonomy can take anywhere between three to eight hours to come into effect.

Actionable insights from ElevateEX helped the client…

Create a robust and prioritized roadmap for implementing the derived recommendations based on the control, impact and cost principles. This roadmap helped the client's IT and business units take decisive steps toward preventing customers from switching to voice (from chat interactions), reducing cost-to-serve and improving customer experience.

Our prioritized recommendation roadmap enabled the client to make several rapid and specific interventions without undergoing comprehensive digital transformation. For instance, something as simple as chat agents using a mandatory phrase after introduction led to 95 percent of customers remaining on the chat channel and avoiding getting disconnected.



 basis points

improvement in FCR (from 56 to 69.3 percent)




potential productivity gains across the business

While the scope of this engagement was confined to repair-related queries, insights from ElevateEX can potentially impact the entirety of the client's contact center landscape, including one million chats / calls a month.

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