5
min read
The Old Metrics Can’t Handle the New CX
When your customers seamlessly transition between calls, chats, and tickets without interruption, why are your metrics still confined to individual channels?
Legacy dashboards weren’t built for today’s CX. They’re siloed by platform, rely on post-mortem averages, and flatten the customer journey into disconnected fragments. Worse, they assume that each channel tells a whole story, but in reality, the story lives between the modes.
For enterprise contact centers juggling voice, chat, and async tickets, “performance” isn’t just how an agent handles one call. It’s whether your system understands the whole scenario and adapts in real-time.
Redefining “Performance” in a Multimodal World
Multimodal CX performance doesn’t just mean tracking response times across chat and voice; it also encompasses the seamless integration of these channels. It means recognizing that each medium carries different expectations, cues, and friction points and then designing your measurement framework to reflect that.
Here’s what modern CX leaders are shifting toward:
1. Channel-Specific Cadence
Handle time on voice isn’t the same as chat duration. One is synchronous, and the other is often staggered. Measuring both on a flat SLA misrepresents effort and customer experience.
2. Scenario Awareness Across Channels
A customer might initiate a chat about a billing issue, be redirected to voice for identity verification, and then open a ticket to inquire about refund status. Multimodal performance means tracking resolution quality across these modes, not just per channel.
3. Behavioral Signals as Leading Indicators
Silence patterns, repeated explanations, and tone shifts are signals. Most tools miss them entirely. High-performing teams surface and act on them in real time, regardless of modality. More importantly, however, they are grounded in your business vitals to best understand their impact (positive or negative).
4. Cross-Modal Cohesion
A strong agent on voice may struggle with a multiturn chat. A knowledge gap in chat might lead to escalations in voice. 40% of consumers abandon a purchase if the experience is not seamless across channels. (source) Performance metrics need to reflect these feedback loops between channels.
A Framework for Multimodal Performance Metrics
Too many teams are trying to stretch outdated frameworks across new channels. Here’s a better way:
1. Context Is Non-Negotiable
Performance must be evaluated within the operational, customer, and conversational contexts. A long handle time may signal poor agent performance or a complex, high-value scenario that demands patience. See Vitalogy for some related CX Principles.
2. Metrics Should Follow the Scenario, Not the Channel
Instead of measuring each contact in isolation, evaluate how effectively your system navigates and resolves specific customer scenarios across all touchpoints.
3. Real-Time > Retrospective
Dashboards show what happened. Operators need to know what’s happening and what should happen next. Leading teams prioritize turn-taking patterns, tone shifts, and real-time behavioral cues over after-the-fact scores.
That doesn’t mean every insight needs to surface in the agent desktop. But if context from earlier in the scenario would have changed the outcome of a later touchpoint, it needed to be there. More importantly, gaps in scenario comprehension and agent judgment should be assessed shortly after the moment, not weeks later, if you want to drive continuous, compounding improvement.
4. Define Performance Semantically, Not Just Statistically
“Abandonment” should mean the same thing to QA, WFM, and coaching, or else performance data is meaningless. Aligning vocabulary is foundational.
How Atlas Turns Signals Into System-Level Awareness
At EndeavorCX, we’ve spent years building for the multimodal frontier.
ATLAS isn’t another dashboard. It’s a context-first intelligence system that interprets performance across every channel in real time and surfaces actions, not just analytics.
Here’s how:
Layer | What It Does |
Context Engine | Blends CRM, ticket, and operational data into every metric. No more flat averages. |
Signal Generation | Detects dozens of micro-events per interaction, from silence duration to tone shifts across chat, call, and async modes. |
Prism Schema | Transforms multimodal conversations into structured, searchable performance scenarios. |
AI Specialists | Real-time coaches, escalation managers, and QA auditors understand performance in context, not in silos. |
Scenario Intelligence | Atlas Specialist and analytics focus feedback on scenarios to make information actionable, not noise. |
While traditional tools monitor isolated calls, Atlas tracks whether your system handled the scenario effectively and identifies what needs to change when it doesn’t.
What’s Next
Multimodal CX performance isn’t a new metric. It’s a new mindset.
ATLAS gives your teams the awareness to move from reacting to calls to orchestrating outcomes across voice, chat, and beyond.
See how Atlas delivers multimodal operational awareness.
Smarter CX Starts Here
Join CX leaders using EndeavorCX to uncover what dashboards miss — and act faster.
Frequently Asked Questions (FAQs)
1. What is multimodal CX performance?
Multimodal CX performance refers to measuring and optimizing customer experience across multiple interaction types, voice, chat, email, SMS, and more, in a unified, scenario-aware way. It goes beyond tracking performance in each channel separately and focuses on how effectively the system supports the customer across modes.
2. Why are traditional call center metrics insufficient for multimodal CX?
Traditional metrics, such as AHT, FCR, or CSAT, are typically calculated per channel and lack context. They don’t account for customer journeys that span multiple modes (e.g., chat to call to ticket) or reflect real-time behavioral signals. This limits visibility into root causes and leads to reactive rather than proactive operations.
3. How can I track performance across voice, chat, and tickets?
To track performance cross-modally, CX teams require a shared semantic layer that ties each customer scenario together, regardless of the channel. This includes shared definitions (e.g., what constitutes an escalation) and a system that maps events from multiple interactions into a cohesive narrative.
4. What metrics matter most in a multimodal contact center?
Vital metrics include:
Scenario resolution time (across channels)
Customer friction points (e.g., repeated explanations, escalations)
Agent adaptability by channel
Signal patterns (silence, interruptions, tone shifts)
These metrics should be interpreted within the full conversational context rather than as isolated data points.
5. What are some examples of real-time behavioral signals?
Examples include:
Excessive silence after escalations
Abrupt tone shifts mid-call
Delays in chat reply timing
Customers repeating themselves multiple times.
These signals often precede adverse outcomes, and detecting them early is key to operational awareness.
6. How does ATLAS improve multimodal performance tracking?
ATLAS unifies voice, chat, and async interaction data into a structured context layer using our Prism schema. It identifies over 1,000 behavioral and operational signals, enabling Specialists (AI agents) to deliver coaching, escalation alerts, and real-time quality checks across any modality.
7. Can I use AI to coach agents across different channels?
Yes. With the proper data foundation, AI Specialists can adapt coaching to the nuances of voice, chat, or ticket. For instance, ATLAS Precision Coach provides whisper prompts for voice but post-turn review summaries for chat, all based on context-rich signal analysis.
8. How do I get started with multimodal performance measurement?
Start by:
Aligning your teams on shared definitions (semantic layer)
Mapping key customer scenarios across modes
Identifying signal gaps in your current metrics
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