Identifying Key Customer Experience Metrics

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

So, how much did being genuinely nice to our customers earn us this quarter? Now imagine asking this question to your CFO. Today we are well aware and sometimes even obsessed with metrics: NPS, CSAT, churn rates…all perfectly calculated. But translating the warmth of customer happiness into cold, hard financial results? Well, that's not so simple. After all, it is not easy to connect a ‘smiling support rep’ to ‘higher EBIT’. However, the truth bomb here - Top CX performers consistently outperform their competitors. But the magic they create is not just in making customers smile. It is about connecting every delighted customer with revenue, retention, and even willingness to pay a little extra. The question for us to answer is - Are we connecting dots, or just coloring the margins? As business leaders, are we digging deep enough? What would happen if CX was tagged to every financial review, not just a customary part of the annual presentation? You could be walking into your next review, armed with not just satisfaction scores, but a clear graph of what those scores added to the bottom line. If you think ROI from customer experience is not just fairy dust, then here are 4 metrics to add gravitas to your next board meeting: ☘️ C - Customer Retention Track repeat purchase rate/ renewal rate. Know how many customers come back. Even a 5% increase in retention can boost profits considerably. ☘️ T - Ticket Size Happier customers spend more. We all do that. Measure if your CX improvements lead to higher average order value. ☘️ S - Share of Voice Delighted customers talk. Track organic referrals, online reviews and social media mentions. Don't forget - word of mouth reduces marketing costs. ☘️ S - Service Cost Zero-effort experiences reduce complaints and rework. When customers don't need to call back, your cost to serve drops. Measure cost per support ticket and first contact resolution rate. These may not happen in a day, but start somewhere. One step of transition a day leads to transformation over a quarter or a year. Let’s get past the vanity metrics and start making CX pay its own bills. About time no? #cx #customerexperience #serviceexcellence

A critical part of journey management in any large organisation is measuring how your journeys perform. 📊 By setting clear goals, monitoring performance, identifying gaps, and measuring improvement impact, you create a continuous cycle of management and enhancement. Measurement surfaces opportunities and kickstarts improvements. 🚀 Yet many organisations struggle: data sits in silos, teams measure inconsistently, and dashboards report numbers without a coherent story. Product, marketing, sales, service, and digital teams collect valuable insights, but without a common language, they never combine into a unified performance view. The result? Plenty of activity, little clarity on what actually improves customer experience and business performance. Measuring performance along specific journeys—rather than isolated KPIs—provides the right context: the journey itself. 🗺️ This approach transforms your journey framework into an engine for improving both customer experience and business performance holistically, creating a shared structure and language where different KPIs unite. 🧭 Inspired by the Balanced Scorecard, this pragmatic 3x3 Matrix structures performance measurement across two dimensions: 👉 First, it distinguishes 3 performance metric categories: - Customer performance (behavior and sentiment) - Commercial performance (conversion, customer base, revenue) - Operational performance (cost, efficiency, reliability) 👉 Second, it distinct three journey hierachy levels: - Overall customer lifecycle - End-to-end product or service journey - Individual customer tasks These intersecting dimensions ensure each metric sits logically within a complete, coherent view. The visual below shows example metrics for all nine sections, helping you build a balanced measurement framework for journeys. This matrix delivers three immediate benefits: ✨ 1. It aligns siloed KPIs and contextualizes them into a shared journey 2. It enables drill-down and aggregation through connected KPIs across journey levels 3. It surfaces trade-offs and synergies between performance metrics A few quick tips to take into account when drafting or structuring your own journey-driven measurement framework 👇👇👇 🐌 Consider both leading and lagging indicators for a robust measurement approach that balances early warning signs with outcome metrics.  🤲 Don’t collect everything. Start with a North Star KPI for each journey, and add a small set of supporting metrics. Less is more. 💬 Always mix performance metrics with more qualitative feedback and insights that will help you determine why performance is down and how to fix it. Happy measuring! 🎉

Crowning a New Term: “Iceberg Metrics” 🧊 ✨ I’m calling it: Iceberg Metrics represent KPIs that only reveal the tip of what’s really happening below the surface. Metrics like abandoned carts seem simple but often mask much more—checkout friction, hidden costs, trust issues, and more. To truly understand and optimize, we need to dig deeper. Here’s how to dive into the “iceberg” of abandoned cart rates: 1. Establish Baseline Metrics: Start by gathering data on current abandoned cart rates, session times, and bounce rates using heat maps and session recordings to see where users drop off. 2. Segment the Audience: Analyze users by behavior (first-time vs. repeat visitors, mobile vs. desktop) and traffic source (organic, paid, email). 3. Experiment Hypotheses: Develop hypotheses for abandonment reasons—shipping costs, checkout friction, distractions, or lack of trust signals—and test them. 4. Run A/B Tests: Test variations like simplifying the checkout process, showing shipping costs earlier, adding trust badges, or retargeting abandoned cart emails. 5. Use Heat Maps & Session Recordings: Examine user behavior in real time. Look for confusion or hesitation, where users hover, and whether they engage with key information. 6. Contextualize Results: Analyze how changes impact overall user flow. Did simplifying checkout help, or did other metrics like bounce rate increase? 7. Ecosystem Approach: Examine how tweaks affect the full journey—from product discovery to checkout—balancing short-term improvements with long-term goals like lifetime value. 8. Iterate: Refine solutions based on experiment findings and continuously optimize the customer journey. This one’s mine, folks! #IcebergMetrics #OwnIt #DataDriven #EcommerceOptimization #NewMetricAlert Cheers, Your cross-legged CAC and CLV buddy 🤗

A few weeks ago, I was in a conversation with a VP at a major fintech in India. We were talking about an incident which lead to downtime and impacted few thousands of dollars in a matter of minutes. As we discussed how to handle these situations better, we kept coming back to one question: What’s the story of that incident, and how can data help us uncover it? At DevDynamics, this question is at the core of what we do. Engineering visibility doesnt end at metrics, infact it begins from them to tell the right story, the kind that leads to real solutions. Here is what we came up with. Imagine an outage hits your platform. Everyone’s asking what went wrong. Where do you start? You begin by gathering the pieces of the story: 1️⃣ 𝐓𝐡𝐞 𝐎𝐩𝐞𝐧𝐢𝐧𝐠 𝐂𝐡𝐚𝐩𝐭𝐞𝐫: 𝐇𝐨𝐰 𝐐𝐮𝐢𝐜𝐤𝐥𝐲 𝐃𝐢𝐝 𝐖𝐞 𝐀𝐜𝐭? - 𝐌𝐞𝐚𝐧 𝐓𝐢𝐦𝐞 𝐭𝐨 𝐑𝐞𝐜𝐨𝐯𝐞𝐫𝐲 (𝐌𝐓𝐓𝐑): This tells how fast your team resolved the issue. If it took too long, what slowed you down? - 𝐓𝐢𝐦𝐞 𝐭𝐨 𝐃𝐞𝐭𝐞𝐜𝐭 (𝐓𝐓𝐃): Did you spot the problem quickly, or was there a gap in monitoring? 2️⃣ 𝐓𝐡𝐞 𝐂𝐨𝐧𝐟𝐥𝐢𝐜𝐭: 𝐖𝐡𝐚𝐭 𝐂𝐚𝐮𝐬𝐞𝐝 𝐭𝐡𝐞 𝐈𝐧𝐜𝐢𝐝𝐞𝐧𝐭? - 𝐂𝐡𝐚𝐧𝐠𝐞 𝐅𝐚𝐢𝐥𝐮𝐫𝐞 𝐑𝐚𝐭𝐞 (𝐂𝐅𝐑): Was this the result of a bad deployment? - 𝐂𝐨𝐝𝐞 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐌𝐞𝐭𝐫𝐢𝐜𝐬: Were there bugs, vulnerabilities, or technical debt that contributed to the failure? 3️⃣ 𝐓𝐡𝐞 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: 𝐈𝐬 𝐓𝐡𝐢𝐬 𝐏𝐚𝐫𝐭 𝐨𝐟 𝐚 𝐁𝐢𝐠𝐠𝐞𝐫 𝐏𝐥𝐨𝐭? - 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐅𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲: Are you shipping too fast without enough safeguards? - 𝐈𝐧𝐜𝐢𝐝𝐞𝐧𝐭 𝐑𝐞𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐞 𝐑𝐚𝐭𝐞: Has this happened before, and if so, why wasn’t it addressed? 4️⃣ 𝐓𝐡𝐞 𝐑𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧: 𝐇𝐨𝐰 𝐃𝐢𝐝 𝐖𝐞 𝐇𝐚𝐧𝐝𝐥𝐞 𝐈𝐭? - 𝐄𝐬𝐜𝐚𝐥𝐚𝐭𝐢𝐨𝐧 𝐓𝐢𝐦𝐞: Did the issue reach the right people quickly? Or were there delays in ownership? - 𝐈𝐧𝐜𝐢𝐝𝐞𝐧𝐭 𝐋𝐨𝐠𝐬: Was the root cause and response process documented clearly, ensuring future teams can learn from it? 5️⃣ 𝐓𝐡𝐞 𝐈𝐦𝐩𝐚𝐜𝐭: 𝐖𝐡𝐨 𝐅𝐞𝐥𝐭 𝐭𝐡𝐞 𝐄𝐟𝐟𝐞𝐜𝐭𝐬? - 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐈𝐦𝐩𝐚𝐜𝐭: How many users were affected, and how severely? When we piece together these metrics, a clearer story emerges. We understand, where things went wrong, how the team responded, and what needs to change to prevent a repeat. “Every incident,” he said, “is like a detective story. And these metrics? They’re the clues.” I couldn’t agree more.  

🔎 UX Metrics: How to Measure and Optimize User Experience? When we talk about UX, we know that good decisions must be data-driven. But how can we measure something as subjective as user experience? 🤔 Here are some of the key UX metrics that help turn perceptions into actionable insights: 📌 Experience Metrics: Evaluate user satisfaction and perception. Examples: ✅ NPS (Net Promoter Score) – Measures user loyalty to the brand. ✅ CSAT (Customer Satisfaction Score) – Captures user satisfaction at key moments. ✅ CES (Customer Effort Score) – Assesses the effort needed to complete an action. 📌 Behavioral Metrics: Analyze how users interact with the product. Examples: 📊 Conversion Rate – How many users complete the desired action? 📊 Drop-off Rate – At what stage do users give up? 📊 Average Task Time – How long does it take to complete an action? 📌 Adoption and Retention Metrics: Show engagement over time. Examples: 📈 Active Users – How many people use the product regularly? 📈 Churn Rate – How many users stop using the service? 📈 Cohort Retention – What percentage of users remain engaged after a certain period? UX metrics are more than just numbers – they tell the story of how users experience a product. With them, we can identify problems, test hypotheses, and create better experiences! 💡🚀 📢 What UX metrics do you use in your daily work? Let’s exchange ideas in the comments! 👇 #UX #UserExperience #UXMetrics #Design #Research #Product

Don’t measure ITSM by ticket count. Measure by delay time eliminated. Too many IT teams brag about volume. 🗂️ “We closed 5,000 tickets this month.” But here’s the reality: Speed doesn’t equal value. If it took 6 days to resolve a P1 — Or 12 emails to approve one laptop — You didn’t deliver ITSM. You delivered frustration. The best ITSM teams don’t chase numbers. They chase impact. Here’s what they track instead: ✅ Total wait time reduced ✅ Approval cycles shortened ✅ Mean time to resolution (MTTR) ✅ First-contact resolution rate ✅ % of requests resolved before escalation 📊 Volume tells you how much you’re doing. ⏱️ Time tells you how well you’re doing it. Stop measuring output. Start measuring flow. Because your users don’t care how many tickets you close. They care how fast they can get back to work. 👉 Follow for more: https://lnkd.in/e4ekDV4C #ITSM #ServiceNow #DigitalWorkflows #WorkflowAutomation #ProcessImprovement #PlatformStrategy #DelayTime #ITOperations #IncidentManagement #ServiceNowConsulting #MTTR #CustomerExperience #EmployeeExperience #ITSupport

Most churn analysis in digital products focuses on a simple yes or no - did the user leave or not. But churn is not just about if, it is about when. The timing matters. That is where survival analysis, or time-to-event analysis, comes in. It is a set of statistical methods designed to answer questions like: How long does the average user stay? How does the risk of churn change over time? Which user groups leave sooner and which ones stick around longer? Survival analysis works especially well in digital product research because it can handle censored data - users who are still active when your observation period ends. Instead of ignoring them or making arbitrary assumptions, the method uses all available information. This means you can work with incomplete churn outcomes without throwing away valuable data. It also adapts naturally to real-world product behavior. Many products have usage in fixed cycles like weekly logins or monthly subscriptions. User behavior can change during their journey, such as upgrading to a premium plan or decreasing engagement after a poor experience. Some users churn and later return, sometimes multiple times. Survival analysis methods have extensions that can account for all of these realities. If you are only using classification models to predict churn, you are leaving insights on the table. Classification tells you who might leave. Survival analysis tells you when they are most at risk, how risk changes over their lifetime, and what factors influence that timing. That knowledge is critical for designing targeted interventions, personalizing retention strategies, and understanding long-term engagement patterns. Modern best practices blend classical survival models like Kaplan–Meier curves and Cox regression with adaptations for digital products, such as discrete-time survival for interval-based data, time-varying covariates to reflect evolving behavior, competing risks models to separate different churn types, and recurrent events models to track leave-return cycles. For small datasets, robust techniques like penalized estimation, bootstrapping, or Bayesian survival can stabilize results.

As CX programs are being cut, it’s becoming clear that those focused solely on survey scores are at risk. To truly drive value, B2B CX programs must tie their efforts to financial outcomes—a critical connection many programs miss. One simple but powerful metric to consider is order velocity—the frequency of customer orders, regardless of size or type. By combining the order data with good survey questions, you can track how improved customer experiences lead to faster order velocity. While it’s not the final financial metric, it gives you an early indication of CX impact. Order velocity works especially well in industries with less frequent transactions, like B2B insurance. For example, if brokers typically average six policies yearly, an improved experience should lead to more orders the following year. If not, it could signal that your surveys aren’t targeting the right issues or that other factors, like pricing, are having a larger impact. Remember, there’s often a delay between shifts in customer attitudes and changes in behavior. In industries like health insurance, a boost in CX scores during mid-year could drive more orders by Q4. In manufacturing, the timeline might vary—tactical orders may rise quickly, while long-term sales like turbines could take years to reflect the change. For a more holistic view, pair order velocity with client-specific metrics like margin per client or number of categories ordered. Order velocity is relatively easy to track and is a great entry point for deeper insights. Reporting on this invites questions from leadership—and when the right questions are asked, it paves the way for gathering more valuable data. #CX #CXROI #Customerexperience

Churn isn't just a customer success problem. Here's how PMs & PMMs can enter the chat: First, 3 things we do to lay down the groundwork: 1) Track churn reasons at the point of exit, even if you have to put down money. Without knowing why customers are leaving, it's hard to figure out whether you need to adapt the product, your messaging or target audience. The biggest mistake is attempting to fix something that's not broken. Use exit interviews or win/loss analysis. 2) Tracking BOTH logo churn & revenue churn gives you a better picture. If you just measure logo churn, you won't be able to differentiate between the weight of those lost accounts. It could be a high-value segment or a barely lucrative one. Similarly, tracking just revenue churn hides how your revenue is distributed across customers. You can have low revenue churn simply because a few high-value accounts are still intact, which implies a high-risk portfolio. 3) Churn analysis is a vital input into your acquisition strategy. Understanding churn not only helps you understand complaints but also poor-fit customers. Looping this intel into your GTM allows you to refine messaging and pursue better-fit segments. -- So, how do PMs and PMMs act here? Get ready for a lot of ifs: If there is high logo churn + low revenue churn: - If small customers struggle with activation, PMs can seek to simplify onboarding and add more value to entry-level tiers. - If certain low-value segments struggle, PMMs can adjust messaging to laser-focus on stronger personas and use cases. If there is low logo churn, high revenue churn: - If enterprise needs are unmet, PMs should prioritize filling gaps like security, scalability, advanced modules. - PMMs can partner with marketing & customer success teams to develop educational programs and personalized customer marketing campaigns. If the product has stable growth (low logo & revenue churn): - PMs can focus on expansion features and growth loops. - PMMs can help build customer advocacy and referral programs. If the company is burning down (high logo & revenue churn): - Both PMs and PMMs will have to partner together to conduct discovery, audit the product experience, do a gap analysis etc. - Figure out where the product falls short and/or where the GTM is flawed. Customer success can orchestrate some of this for sure. But PMs and PMMs can deliver a lot of concrete value faster. -- Does your team discuss churn regularly?