Editorial
The Gist
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Expanded CX metrics. Traditional CX metrics like NPS don't capture the full customer journey. A 360-degree view, combining sentiment, product usage and satisfaction, is necessary.
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AI-driven insights. AI tools can analyze customer interactions to predict satisfaction and sentiment, and it can offer proactive insights beyond surveys.
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Predictive analytics. Predicting customer behavior through predictive analytics helps companies take proactive steps to prevent churn and enhance customer experience.
Editor’s Note: This article is Part 1 of a two-part CMSWire series on the landscape of customer experience (CX) measurement in the enterprise. In this first installment, we explore how leading technology companies are moving beyond outdated metrics like NPS and CSAT toward more dynamic, holistic and integrated approaches to understanding customer sentiment and behavior.
Technology companies pride themselves on innovation, yet many have long measured customer experience using outdated, one-dimensional metrics. For years, scores like the archaic net promoter score (a simplistic, transactional, and limited view of reality) and basic satisfaction surveys were the go-to barometers of customer sentiment.
With the continued progress of AI and the evolution of technologies such as customer data platforms (CDPs), we are slowly advancing toward a more sophisticated level of metrics and data gathering.
Response rates to surveys are low, feedback is lagging and superficial, and complex B2B client relationships can’t be reduced to a single number or two. In fact, Gartner predicted years ago that many organizations would phase out NPS as a primary service metric by 2025, a shift that is now gradually taking place.
Leading enterprise firms and SaaS providers now recognize that measuring CX requires a 360° lens and real-time insight. They are using artificial intelligence and richer data to move beyond vanity scores and truly understand whether customers are successful, engaged and getting value.
The following points outline how metrics and measurement of customer experience are changing, with concrete examples of global tech companies making these transformations today.
Table of Contents
- Expanding CX Metrics to a 360-Degree View
- Integrating Customer Success Data with CX Feedback
- Continuous Listening Replacing Traditional Surveys
- AI-Driven Sentiment Analysis
- Proactively Managing CX with Predictive Analytics
Expanding CX Metrics to a 360-Degree View
Relying on one metric to judge customer experience is increasingly viewed as naïve. NPS, CSAT or any single score offers only a snapshot, and it often lacks context, data and root causes.
Today, we’re shifting toward a “dashboard” of metrics that together give a full picture. For example, SAP still checks NPS for a pulse on loyalty, but it complements that with multiple data points like customer satisfaction on support cases, product usage analytics and renewal rates. This 360° approach gives SAP a reality check beyond any momentary score.
Similarly, consumer tech giants like Samsung learned in the hard way that a lone metric can’t capture evolving expectations. Samsung combines survey results, data and real time information with online sentiment and support data to detect emerging issues.
The importance of any one metric is limited because, for example, a high NPS might mask dissatisfaction in certain areas or a low score might be an outlier rather than a reason to panic. By tracking a balanced set of CX indicators, companies get a nuanced, longitudinal understanding of customer sentiment. This multi-metric mindset is becoming the norm at leading firms, and it’s replacing the old “manage to a number” mentality with a holistic view of the customer’s reality.
Integrating Customer Success Data with CX Feedback
Traditional CX metrics often lived in a vacuum, separate from “hard” business metrics. But now there’s been a major change in tying customer experience measurement directly to customer success and outcomes.
Tech companies are blending product usage and success data with experience feedback to see the whole picture. ServiceNow exemplifies this by looking beyond satisfaction surveys; they monitor how effectively customers adopt their software and achieve desired outcomes. If a customer isn’t fully using key features and also reports low satisfaction, that combo alerts ServiceNow to intervene.
This integration of operational metrics (i.e., adoption rate, time-to-value, and churn rate) with experience metrics (i.e., ease and satisfaction) creates actionable insight.
One concrete practice is the use of customer health scores in the SaaS world. For instance, HubSpot uses a health score that combines product usage frequency, feature adoption, support ticket volume and feedback ratings. This composite score gives HubSpot’s team an early warning if an account is trending “unhealthy,” even if the customer hasn’t voiced a complaint.
Samsung saw this when it blended usage analytics with net promoter score; a drop in usage preceded lower loyalty score and signaled a product adoption issue. In short, integrating success metrics with experience data makes sure that CX measurement isn’t happening in isolation. Instead it’s linked to whether customers are actually achieving value.
Continuous Listening Replacing Traditional Surveys
Another significant shift is from occasional, after-the-fact surveys to continuous, real-time listening. Rather than waiting for a quarterly feedback form, companies are capturing signals 24/7 across digital touchpoints.
Cisco provides a great example. Historically, Cisco would email large customers an NPS survey each year. Now, Cisco’s approach is to monitor experience continually through its cloud platforms and support channels. They track real-time product telemetry, support interactions and even social media mentions for each account. If a critical issue occurs (i.e., a network outage or a spike in support calls), Cisco knows immediately from the data, instead of waiting to find a poor survey response weeks later.
Many companies are following suit. Telecom providers, for instance, watch X (formerly known as Twitter) for outage complaints; a surge of angry tweets alerts them to a service problem long before any formal survey results come in. In enterprise software, Adobe has in-app feedback prompts and usage monitoring in its Creative Cloud; they see which features frustrate users (via click patterns or feedback widgets) and respond in near real time with tips or fixes.
This always-on listening is allowed by AI and cloud data streams. It marks a departure from the era of “send a survey and hope they answer.” Instead, every customer touchpoint becomes a feedback source, and measurement is continuous. The benefit is obvious. Companies can catch and address pain points as they happen, not after customers have already churned or gone unhappy for months. Continuous listening turns CX measurement from reactive to proactive.
Modernizing Customer Experience Measurement
This table summarizes how leading organizations are redefining CX measurement by tying it to business outcomes and shifting from episodic surveys to continuous, real-time feedback.
| Theme | Key Practices | Example Companies | Why It Matters |
|---|---|---|---|
| Integrating success and CX metrics | Blend product usage, adoption rate, churn and support data with satisfaction and ease-of-use scores | ServiceNow, HubSpot, Samsung | Links CX insights to actual business outcomes and creates early warning signals when customers struggle |
| Customer health scoring | Create composite scores using data like usage frequency, support tickets and feedback trends | HubSpot, Samsung | Enables proactive intervention with accounts before dissatisfaction escalates or churn occurs |
| Continuous listening | Shift from scheduled surveys to real-time signals across digital, in-app, social and support channels | Cisco, Adobe, telecom providers | Allows immediate detection of issues and enables faster response, moving CX from reactive to proactive |
| AI-enabled signal capture | Use AI to monitor sentiment, usage anomalies and feedback without needing to prompt users manually | Adobe, Cisco | Scales listening efforts and ensures no critical insight is missed due to low survey participation |
Related Article: Top Customer Experience Metrics That Matter Today
AI-Driven Sentiment Analysis
Artificial intelligence is allowing companies to measure customer sentiment without relying solely on asking survey questions. Modern AI can analyze text, voice and behavior to infer how customers feel.
For example, Zendesk has deployed an AI feature that scans support tickets and chats to predict customer satisfaction. If a conversation’s tone and keywords resemble past cases that led to bad ratings, the system flags it as a likely negative experience before the ticket is even closed.
Similarly, enterprise IT firms like IBM use natural language processing to mine hundreds of open-ended survey comments and call transcripts. IBM’s AI can churn through what customers have written or said and score the sentiment as positive, neutral or negative.
Even on social media, AI-powered sentiment tools let companies gauge brand sentiment by analyzing tweets and posts for emotional tone. The advantage of AI sentiment analysis is scale and objectivity; it can cover virtually 100% of customer interactions, not just the few customers who respond to surveys.
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Rakuten, for instance, analyzes millions of e-commerce reviews and support chats with AI to continuously measure customer mood toward its marketplace. This “listening without asking” enriches the CX dashboard with real feelings derived from unstructured data.
Proactively Managing CX with Predictive Analytics
Leading tech companies are both looking at past CX metrics and using predictive analytics to forecast customer experience outcomes. The question has shifted from “How did we do last quarter?” to “Who is likely to be unhappy or to churn next, and why?”
A clear example comes from the SaaS domain. Platforms like Gainsight produce a health score that both reflects current status and predicts future risk. If a client’s login frequency has dropped 50%, support tickets have increased, and their executive sponsor left, then the model might predict a high churn risk.
Many SaaS providers, such as Microsoft or Salesforce, use such predictive health scoring to alert their customer success teams to reach out before the client complains. In the telecom world, this approach has also been significant. Major operators now analyze network performance per individual customer and feed it into churn prediction models. And in enterprise tech, Cisco similarly examines equipment logs and usage metrics to foresee issues.
Predictive CX metrics turn measurement into a forward-looking exercise. Rather than just scoring the past, companies forecast the future. They identify which accounts need attention now to prevent negative outcomes later.
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