Revolutionizing customer support in XaaS using GenAI | HCLTech
Digital Process Operations

Revolutionizing customer support in XaaS using GenAI

This blog focuses on improving customer support experiences, recognizing that each interaction is pivotal in influencing customer lifetime value and overall revenue growth.
5 min read
Mahesh Iyer


Mahesh Iyer
VP and Hi-tech vertical head, Digital Process Operations, HCLTech
5 min read
Revolutionizing customer support in XaaS using GenAI

Technology has ushered in a new era of connected homes with limitless potential for convenience and automation. Voice-controlled appliances, smart thermostats, lighting systems, wearables, smart home security and other interconnected devices have redefined the fabric of modern living. These devices offer convenience, automation, efficiency and increased control, making customers and their homes smarter.

Today, the prevalent trend in connected home devices revolves around subscription-based "Everything as a Service" (XaaS) models. This paradigm shift towards XaaS signifies a departure from a traditional product-centric approach, which primarily emphasizes device performance, to a more customer-centric strategy that prioritizes holistic experiences.

In this era where customer experience (CX) takes center stage, businesses recognize the profound changes in customer needs driven by technological advancements, micro-economic forces, health considerations and cultural shifts. These behavioral shifts highlight how customers evaluate products based on price, quality, service, choice and convenience. Organizations must stay abreast of these transformative shifts and actively engage in winning over customers at every touchpoint in the customer experience journey.

This blog focuses on improving customer support experiences, recognizing that each interaction is pivotal in influencing customer lifetime value and overall revenue growth.

Customer Lifetime Value (CLV) = (What customer pays for your services) x (The experience customer has on your product and services)

Or, more simply:

CLV= Average Revenue Per User (APRU) x Longevity (months)

In this equation, customer experience, a controllable factor, outweighs Average Revenue Per User (ARPU), driven by market dynamics. Enhancing customer experience at every touchpoint is crucial for attracting and retaining more customers.

In the context of customer support, these interventions would be:

  • How do I reduce wait times?
  • How to make my chatbot human-like?
  • Multi-modal to omnichannel interactions
  • How to improve first contact resolution?
  • How to improve retention/ Upsell, etc.?

Organizations face the challenge of enhancing CX while maintaining cost-efficiency and effectiveness. This involves improving agent efficiency by reducing handle times, call containment and enhancing utilization. Investments in generative AI-based technology and process-level changes aim to eliminate friction and boost efficacy across customer touchpoints to achieve these objectives.

GenAI in customer support

According to Gartner, over 70% of customers prefer self-service, a trend escalating due to improving accuracy over time. In the past 5+ years, self-service has gained significant momentum, with most companies offering 24/7 assistance. However, first-generation chatbots, based on Natural Language Processing (NLP), are limited to pre-fed information and lack broader functionality. They excel at tasks like FAQ retrieval and order tracking but lack the ability to process human emotion, behavioral intelligence and empathy.

deliver human-like experiences, offering comprehensive assistance, personalized recommendations and seamless transaction facilitation. Unlike their predecessors, GenAI bots retrieve context from past interactions, tailoring responses and recommendations based on the user's journey for more meaningful engagements. For example, if a customer searches for a "budget phone," the bots can display mid-segment phones. Alternatively, if the customer has previously mentioned a budget range in past conversations, the bots can update the search results accordingly.

These GenAI bots learn from feedback, adapt strategies and refine responses over time, ensuring a more intuitive and effective customer experience. By analyzing user behaviors, preferences and historical data, these bots can predict needs and provide personalized solutions that resonate with each customer. GenAI for customer service has ushered in a new era of connected home experiences. Here are a few GenAI-based use cases:

  • Customer acquisition: Personalized experiences boost sales as customers are more inclined to make purchases aligning with their interests. eCommerce platforms leverage GenAI to craft human-like and personalized product recommendations, generating images and text based on browsing history and past purchases.
  • Tech support: GenAI reads service tickets, analyzes customer data and automatically triages tickets to the support engineer with the highest probability of success. Automation streamlines case creation, triaging and documentation, saving time and effort. Contact centers can achieve significant ROI by boosting agent productivity and accuracy, reducing handle and call waiting times, enhancing customer satisfaction and reducing costs.
  • Retention and upsell: GenAI empowers agents with the right customer information and personalized recommendations during the conversation. This enables agents to upsell/cross-sell and delight customers via real-time assistance. Overall benefits include Improved customer lifetime value (CLV), customer satisfaction score (CSAT), lower average handle time (AHT) and improved conversion rate.

From hype to delivering outcomes

In 2023, GenAI made a significant breakthrough, generating hype ten times greater than any previous technology disruption. The latest McKinsey survey on the current state of AI confirms the rapid growth of GenAI tools. One-third of their survey respondents reported using GenAI regularly in at least one business function. This trend is expected to grow in 2024-25, with GenAI maturing and organizations beginning to benefit from its implementation.

Many use cases, mainly in marketing, sales, customer support, service development and operations, are under proofs of concept. These areas are expected to see wider use and start generating business value. Additionally, regulations are anticipated to evolve further in 2024 as the technology advances.

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