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The Gist
- Exclusive news. Frame AI gives CMSWire a first look at new AI features for enterprise use cases.
- Data synergy. Combining customer interaction data across platforms is key.
- Cost management. AI can help accurately estimate costs tied to escalated service requests.
In the ever-evolving landscape of customer service, companies are leveraging artificial intelligence to pioneer preemptive solutions. Today, Frame AI, the AI-powered customer experience intelligence platform, unveiled some product enhancements exclusively to CMSWire.
Frame AI's software analyzes human interactions and harnesses AI to discern the driving factors of business outcomes within customer service ticket data. In their latest move, they are introducing additional capabilities including Cost Tiers, Score Builder and HIPAA compliance.
Frame AI Unveils Enhanced Features
Let's take a look at some of the new enhanced features Frame AI is announcing.
- Cost Tiers is designed to accurately estimate costs associated with escalated tickets, providing a more precise understanding of costs associated with specific ticket types. With the release of the dynamic cost attribution feature, the total cost of customer cases can be tracked across multiple channels — and also account for an agent’s offline time. The tech utilizes various factors, including message complexity and signals of offline work, to estimate the actual cost of customer interactions and then displays this information on a dashboard, allowing users to identify spikes in costs and investigate the underlying causes.
- The Score Builder feature will enable users to tailor custom scores that forecast business outcomes and better guide customer success teams. Utilizing AI signals, this feature detects specific factors at the sentence level in customer communications, rather than providing an overall sentiment score. George Davis, CEO and founder of Frame AI, said this level of granularity allows for a more accurate understanding of customer feedback and enables the identification of various types of complaints or issues specific to the company so scores to be categorized and assigned to different parts of the business, ensuring that relevant stakeholders can address the feedback.
- New features supporting HIPAA Compliance include a full third-party audit that allows users to partner with and serve clients in regulated healthcare industries securely under the coverage of a Business Associate Agreement.
Related Article: Level AI Debuts Generative AI Tech for Contact Centers: AgentGPT
Cost Transparency in Customer Service
Davis believes their newest feature updates will empower customer support and engineering teams to accurately ascertain the genuine costs tied to escalated service requests — and aid in optimizing actions, minimizing costs and safeguarding revenue. His excitement about the new features is palpable as Davis told CMSWire, “I feel like a grad student again, this is the best time to be working in this field.”
“In the 21st century, companies cannot choose between creating great customer experiences or staying cost-efficient — they must do both. Customer service executives, CFOs and CEOs alike need tools to help increase the speed and effectiveness of teams dedicated to addressing the challenges faced by their customer base,” Davis said. “These new features enable customer service teams to aggregate and calculate the categories and costs of service tickets specific to their business, and to create solutions for recurring challenges.”
Harnessing AI for Maximum Customer Lifetime Value
In an exclusive interview, Davis spoke about the transformative role of AI in the customer experience sector and shared that “the most effective advancements are those applied to solving specific problems.”
For Davis and his team at Frame AI, that application is customer experience optimization. They simplify their approach with a straightforward equation: data + reach = LTV (Lifetime Value).
“If you know a lot about your customers, that’s the data, and if you have a lot of ways of interacting with them, that’s the reach — then you can do things that build a relationship, and that’s the LTV,” Davis said. “That’s what connects the very personal part of customer experience to the bottom line.”
But a big challenge many organizations face is how they handle their data. Although customers interact with a brand through various channels, the company experiences that through different teams and tools, resulting in data silos. Frame AI's solution aims to combine data about customer interactions across all channels, including help desks, contact centers and CRMs. This approach requires dealing with "messy data," and according to Davis, that's where AI proves its value.
Related Article: How to Understand What Your Customers Do and Why They Do It
Mastering Messy Data: Enter Data Orchestration
While companies have prepared for data-driven actions by storing data in Customer Data Platforms (CDPs) and Customer Relationship Management systems (CRMs) and using platforms for customer outreach, Davis said they often lag behind in understanding what to do at each moment in the customer journey.
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When people hear about AI in a customer experience context, Davis said they often think of automation and replacing frontline staff with tools for automatic personalization and self-service. However, he thinks AI's goal should extend beyond augmentation and toward helping the organization as a whole do something it couldn't do before — orchestration.
Frame AI aims to solve the problem of figuring out what's important, what's changing, and who can act on it for the entire organization, regardless of where the data originates. By operating at a strategic layer, the goal is assisting executives in making real-time decisions about the customer experience. Davis said Frame AI's ultimate objective is to push data back out to where people are already working, augmenting operations rather than changing them.
“The most fundamental thing Frame AI does is combine data about customer interactions across all channels, including from your help desk, your contact center and transcripts from your CRM — and to combine that data, we have to deal with the fact that a lot of it is really messy,” Davis said. “When people call into a call center, agents can't do a perfect job of tagging and annotating exactly what happened in that conversation — and that's where AI lets us get a lot of value across it all and align learnings across all these different tools regardless of where the customer raised a particular piece of feedback.”
