RingCentral has committed to exceeding its target for annual recurring revenue (ARR) in AI tools following a string of product releases earlier this week.
The cloud-based software company announced its third-quarter earnings report on Monday, revealing strong results across the board.
This has led to ongoing investments into current and future AI products and initiatives.
“We remain on track to exceed the $100MN in ARR from new products by the end of 2025," said RingCentral CEO, Vladimir Shmunis, adding:
“Adoption of our new AI-led products is broad-based across various customer cohorts, from small businesses to large enterprises.
“Our GSP partners are also beginning to sell these new offerings, expanding our reach and accelerating adoption.”
This expansion has led the company to roll out multiple AI product releases this week, increasing its customer base by branching out into emerging AI trends. The launches are aimed at enhancing communication experiences for both customers and agents.
This has resulted from an R&D spend of $125MN into its new AI portfolio. The company is already seeing consistent profitable growth, in the hopes of exceeding the year-end ARR target.
RingWEM Adds AI Workforce Tools to Cloud Contact Center
On Monday, RingCentral released its latest AI product, RingWEM, an AI-powered workforce engagement management suite designed to enhance its native cloud contact center, RingCX.
The suite offers four capabilities to strengthen customer experience across agent performance, customer satisfaction, and operational efficiencies by using AI-powered insights:
AI Quality Management: Designed to give human agents the skills to improve their overall performance, the quality management tool will use personalized customer quality criteria to evaluate and provide extensive insight and feedback on customer calls.
The tool is used to analyze and observe full customer interactions and agent workflows, delivering focused guidance for reskilling.
Furthermore, the tool offers AI-based coaching recommendations to improve agent expertise, allowing enterprises to improve their workforce by viewing their agents’ performance analytics, common communication themes and advise them on next steps using the provided data-driven advice.
AI Workforce Management: Used to improve quality in customer service, planning for potential challenges, and overall efficiency, this tool combines precise data forecasting and resourceful scheduling to align staff with probable tasks to tackle targeted demand.
By using precise data forecasting, the AI workforce management tool can use algorithms to analyze past and current data trends and business drivers to predict the likelihood of contact quantities, allowing time for agents to tackle abnormal spikes before they occur.
With intelligent scheduling, the workforce tool can generate actionable schedules for agents to follow that include agent preferences, adjustable service level actions, and business requirements to keep agents on track with tasks.
These can be modified to fit irregular changes in working conditions to ensure that service levels stay the same to avoid customer friction.
Additionally, customer agent supervisors can run probable scenarios to analyze the effectiveness of staff models and company changes before they are implemented.
AI Interaction Analytics: This tool provides enterprises with high-level insights into customer satisfaction with interactions, compiling data taken from surveys and summaries from the interactions themselves to address negative customer experiences.
AI Interaction Analytics can dissect customer conversations through voice tone, language preferences, and patterns in speech to assess satisfaction.
The tool can use this and other conversations to further analyze key customer trends and issues as a whole, allowing businesses to proactively address these concerns before escalation.
Screen Recording: Similar to the AI Quality Management capability, this tool allows supervisors to evaluate customer-agent interactions by collectively linking calls and screen recordings for a wider range of information into quality of conversation and workflow efficiency.