Liveperson Eliminates Friction Between Brands and Consumers With a Breakthrough AI-Powered, Consumer-Centered Bot Scoring System
Meaningful Automated Conversation Score (MACS) provides insights at every turn of a conversation, setting a new standard for automation-based sales, marketing, and care
LivePerson, Inc., a global leader in conversational AI, today announced the debut of its proprietary Meaningful Automated Conversation Score (MACS), the only way to measure conversations with bots from the customer’s point of view.
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MACS is the world’s first measurement of how much friction customers experience in automated conversations.
Powered by state-of-the-art machine learning, MACS is the world’s first measurement of how much friction customers experience in automated conversations. MACS evaluates every turn of a conversation, providing a level of granularity and precision that helps brands transition to and improve automated sales, marketing, and customer care by making it simple to:
- Understand how well bots are helping with requests, questions, issues, and purchases
- Highlight points of failure in bot-customer conversations, then drill in to identify fixes
- Empower bot builders to quickly adapt and measure impact
How MACS works
MACS produces a quality score between 1 and 3 for every interaction with an automation. The score combines two critical factors: the customer’s response and the level of effort they have to put into the conversation.
In pinpointing which specific interactions have failed, MACS makes it clear what actions bot-builders should take next to improve conversations with the automation going forward.
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How MACS compares to other metrics
Due to its easy-to-understand, in-the-moment insights, MACS is a more scalable metric than other commonly used KPIs for bot building and management. Relying solely on metrics like containment rate can lead to suboptimal outcomes, including conversation flows that make it harder for customers to exit conversations and reach live agents.
MACS can also help brands bridge gaps that other solutions leave uncovered. CSAT and NPS, for example, don’t account for some of the most important conversations of all: escalated and abandoned ones.
In addition, MACS is unique in that it identifies specific failure types within conversations that determine whether a customer experience is positive or negative. Other metrics, like CSAT, only measure conversations in their entirety and don’t distinguish which agent or bot contributed to the customer’s satisfaction.
Finally, MACS is an explainable model, meaning it gives bot-builders insight into what changes to their automation will have the most impact on customer experience.
“MACS is not just a monitoring product. To begin with, it addresses the shortcomings of existing approaches by measuring conversations from the customer’s point of view. But perhaps even more importantly, its interpretable insights make transforming to automation-based sales, marketing, and customer care more practicable than ever,” said LivePerson CTO Alex Spinelli.
How MACS will transform the automated conversations of the future
As AI-assisted conversations become the prevailing line of communication between brands and consumers, MACS’ AI-first approach is poised to set the new standard for measuring and improving customer care and commerce.
Long-term, MACS also lays the groundwork for automating the entire process of bot building and management by identifying the penalties and rewards needed to train AI to manage dialogue. This opens the door to new self-learning loops that eliminate the long hours employees spend on mundane tuning tasks, meaning they can focus on critical moments that need the human touch, plus gain experience with AI tools and systems.
“As automation-driven conversations increasingly dominate commerce and care, the impact of MACS will only become more profound,” said Spinelli. “Brands that adopt MACS as their new standard will be best positioned to truly understand and improve the primary way they’ll be engaging with customers in an AI-first world.”
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