Discover how LLMs analyze support calls to extract insights, improve customer service, and predict churn with AI-driven transcription and NLP
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Customer support calls are a treasure chest of untapped potential. They reveal what customers love, loathe, and long for—insights that can sharpen service, boost retention, and drive revenue. But with contact centers juggling thousands of calls daily, manually decoding them is a pipe dream. That’s where Large Language Models (LLMs) step in, paired with Glyph AI’s Speech-to-Text (STT) technology, to turn audio into a goldmine of actionable data.
This isn’t just about automation—it’s about empowerment. Users can build custom workflows to extract exactly what they need from a single call, presented in a workflow-style table. Beyond that, LLMs can dig deeper: identifying issues, checking policy adherence by uploading company policies, spotting upselling opportunities, flagging complaints, and even offering feedback for agents. Add in the ability to predict churn, and you’ve got a powerhouse tool transforming customer service into a strategic asset. Let’s dive into how this works, step by step, and explore the real-world impact.
Support calls are a raw, unfiltered pulse of customer sentiment, but they’re tough nuts to crack. Volume is a beast—thousands of interactions daily swamp traditional analysis. Language is messy, with slang, dialects, and emotional undertones complicating the mix. Context is king; the interplay between customer and agent demands nuance that keyword searches can’t touch.
Legacy approaches—manual notes or basic text tools—stumble here. They’re slow, shallow, and don’t scale. LLMs flip the script, offering speed, depth, and flexibility. With added capabilities like policy checks and upselling detection, they’re not just analyzing calls—they’re supercharging them.
How do LLMs unlock this potential? It’s a multi-layered process blending tech and customization. Here’s the rundown:
It all starts with transcription. We lean on Glyph AI’s Speech-to-Text tech to convert audio into text, the raw material for LLM analysis. Glyph AI tackles conversational chaos—accents, crosstalk, background noise—with precision. Speaker diarization splits customer and agent voices, laying a foundation for granular insights.
Fresh transcripts can be rough—think “ums,” “uhs,” or incomplete thoughts. We scrub this noise out, ensuring the LLM works with clean, focused data. This step sharpens the accuracy of everything that follows.
Now the LLM shines. Users craft workflows to extract what matters most—summaries, sentiments, or specific metrics—displayed in a table. But it doesn’t stop there. By uploading company policies, the LLM can check adherence, spot upselling cues, flag complaints, and evaluate agents. Here’s what it can do:
For instance, a workflow might analyze a call and produce this table:
This table, from one audio, hands users a roadmap for action, tailored to their priorities.
Let’s unpack how these features deliver value, with examples grounded in practice.
A 15-minute call becomes: “Customer upset over late shipment; agent offered expedited delivery.” A workflow could add “reason for delay” to the table, pinpointing supply chain hiccups.
If 30% of calls are about payment issues, LLMs tag them. Your workflow might break it down further—say, “overcharges” vs. “payment failures”—for targeted fixes.
A customer snaps, “This wait is unbearable!” The LLM marks it negative. Pair it with “wait time logged” in your table, and you’ve got data to streamline operations.
“Competitor X’s app is smoother,” a customer says. The LLM flags it; your workflow adds “feature mentioned” to compare strengths and weaknesses.
In a retail energy case, LLMs boosted at-risk customer detection by 23%. A workflow table might list “churn signals” like repeated billing gripes, prompting retention moves.
A customer grumbles, “My order never arrived.” The LLM isolates it as a delivery issue, and your workflow ties it to “order number” for quick resolution.
Upload your refund policy, and the LLM checks if agents stick to it. A table might show “Policy Met” or “Deviation Detected,” ensuring consistency.
“Will this plan save me money?” a customer asks. The LLM spots the cue; your workflow flags it as an upsell chance, suggesting a higher-tier pitch.
“This is the third time I’ve called!” The LLM tags it as a complaint. Your table could track “complaint frequency” to escalate chronic issues.
An agent calmly explains a policy—LLM scores it positive. Your workflow might add “tone” and “clarity” columns, guiding training.
Here’s an expanded workflow table:
This level of detail turns calls into a multi-dimensional toolset.
The payoff is massive. Time savings are immediate—automation and workflows slash analysis time. Precision is unmatched—custom outputs zero in on what you care about. Scalability is built-in—handle a handful or a flood of calls effortlessly.
The extras elevate it further. Policy adherence ensures compliance without micromanaging. Upselling detection boosts revenue—imagine a 10% bump from seized opportunities. Complaints and agent feedback refine service quality, while churn prediction (that 23% lift) protects the bottom line. It’s customer service reborn as a growth engine.
To nail this, we follow these steps:
The horizon sparkles with possibility. Multimodal LLMs could soon blend audio and text analysis, skipping STT for real-time insights. Workflows might sync with CRMs, piping tables into dashboards. Continuous training will keep models fresh—new policies, products, or trends won’t trip them up. Imagine an agent getting upselling tips mid-call or a manager seeing churn risks as they emerge.
Support calls aren’t just chatter—they’re a strategic goldmine. With LLMs and Glyph AI, you’re not just listening; you’re dissecting, customizing, and acting. Build a workflow to extract issues, ensure policy adherence, spot sales chances, address complaints, and coach agents—all from one call, laid out in a table that drives decisions.
This is customer service reimagined: efficient, insightful, and revenue-savvy. Ready to turn your calls into a superpower? Start crafting your workflow, upload your policies, and watch the insights—and opportunities—roll in.
Most recorded calls are never analyzed, leaving actionable insights untapped.
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