Insights / Digital Customer Experience

Plotting the future of customer care through an effective Virtual Agent (VA) rollout strategy

Improve the VA’s ability to engage with customers confidently and more accurately

The Virtual Agent’s (VA) market is at an all-time high and is garnering more and more interest with each passing day. It is beginning to establish as “the must-have” solution for the businesses in the connectedness industry, seeking to improve customer experience, reduce call center costs, optimize time to serve, etc.

But are these virtual agents living up to the hype?

Gartner has placed them in a “trough of disillusionment” in its hype cycle, meaning the technology is struggling to meet the envisioned expectations. When faced with complex and unknown scenarios, VAs tend to react in an unexpected way. One often comes across instances on social media where VAs are humiliated for their out-of-context interactions.

The primary reason for this shortcoming is that many VAs are launched without the right implementation strategy. As a result, they don’t reach the required confidence levels and cannot capture the right customer intent.

Virtual Assistants (VAs) use semantic and deep learning (such as Deep Neural Networks (DNNs), natural language processing, prediction models, recommendations, and personalization to assist people or automate tasks.

To prevent your VA from humiliation, adopt a robust VA implementation strategy encompassing the top 10 considerations that can help service providers to ensure their customers engage in the VA interaction, increasing overall customer satisfaction. This strategy provides key recommendations on the most important focus areas that are imperative for a successful rollout. Some of these include:

  • Choosing the right use case: Group the inbound calls into different categories like customer service enquiries, technical troubleshooting, sales etc. Based on these categories, different use cases can be invoked. For instance, kickoff the least complex rollout with self-service flows.
  • Analyzing the complexity of intents: Analyze the length of conversation and time taken by the agent to complete the conversation. Further, build a hierarchy of intents and sub-intents to identify high-volume intents and complex intents.
  • Considering variations in intent: Analyze the scope, lifecycle, and precursor of intents to improve engagement by increasing precision or recall.
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