Chatbots are not going anywhere, as a new report by Gartner shows that within five years they will be the primary customer service channel for close to 25 percent of organisations.
A Gartner Customer Service and Support (CSS) survey conducted earlier in 2022 revealed 54 percent of respondents are using some form of chatbot, VCA or other conversational AI platform for customer-facing applications.
Uma Challa, senior director analyst in the Gartner Customer Service & Support practice said, “Chatbots and virtual customer assistants (VCAs) have evolved over the past decade to become a critical technology component of a service organisation’s strategy.
“When designed correctly, chatbots can improve customer experience and drive positive customer emotion at a lower cost than live interactions.”
Further research from Gartner shows that in the next two years, 38 percent of organisations are planning to implement chatbots — a 40 percent increase in the adoption of chatbot technology.
Challa said CSS leaders have a positive future outlook for chatbots, but struggle to identify actionable metrics, minimising their ability to drive chatbot evolution and expansion, and limiting their ROI.
“Benchmarking chatbot performance metrics at one organisation against that of its peers is not effective and can be misleading because chatbot type, design and complexity vary widely by organisation,” he said.
According to Gartner, CSS leaders seeking to effectively deploy and measure chatbot performance as part of their service and support channel strategies should consider the following factors.
Firstly, businesses should create an appropriate chatbot deployment strategy based on use cases and the complexity of service interactions. Plan early and consider all dependencies to ensure the necessary resources are available.
Organisations need to enhance customer containment and reduce customer effort by improving chatbot usability.
Then businesses should identify the most relevant chatbot metrics (e.g., goal completion rate, abandonment rate, conversation steps, handle time, etc.) based on the organisation’s unique context.
Adapt the metrics to their desired chatbot metric performance level, or baseline, by considering the chatbot design and complexity and set up a cadence to review the chatbot metrics against the established baseline to gain insights into strengths and prioritise opportunities.