Chatbots will become the primary customer service channel for roughly a quarter of organizations by 2027, according to a Gartner report.
“Chatbots and virtual customer assistants (VCAs) have evolved over the past decade to become a critical technology component of a service organization’s strategy,” said Uma Challa, Sr Director Analyst in the Gartner Customer Service & Support practice.
“When designed correctly, chatbots can improve customer experience and drive positive customer emotion at a lower cost than live interactions.”
The global chatbot market size worth $526 million in 2021 is expected to reach $3.619 billion by 2030, growing at a CAGR of 23.9 percent during the forecast period (2022–2030), says a report from Straits Research.
A Gartner customer service and support (CSS) survey of 50 respondents conducted online in January and February 2022 revealed 54 percent of respondents are using some form of chatbot, VCA or other conversational AI platform for customer-facing applications.
The report said customer service and support (CSS) leaders have a positive future outlook for chatbots, but struggle to identify actionable metrics, minimizing their ability to drive chatbot evolution and expansion, and limiting their ROI.
Benchmarking chatbot performance metrics at one organization against that of its peers is not effective and can be misleading because chatbot type, design and complexity vary widely by organization.
CSS leaders seeking to effectively deploy and measure chatbot performance as part of their service and support channel strategies 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.
Enhance customer containment and reduce customer effort by improving chatbot usability.
Identify the most relevant chatbot metrics (e.g., goal completion rate, abandonment rate, conversation steps, handle time, etc.) based on the organization’s unique context.
Adapt the metrics to their desired chatbot metric performance level, or baseline, by considering the chatbot design and complexity.
Set up a cadence to review the chatbot metrics against the established baseline to gain insights into strengths and prioritize opportunities.