How To Interpret Customer Behavior Data Effectively

I’m not asking my CSMs to resolve support tickets. I’m asking them to leverage them. Support tickets aren’t just a backlog of problems; they’re customer truth bombs waiting to explode. If you’re not mining them for insights, you’re flying blind—and that’s exactly how churn sneaks up on you. Every Customer Success team I’ve ever led has been trained to use Support tickets strategically. Why? Because they’re packed with insights that make us better at our jobs. ✅ We learn more about the product. ✅ We spot trends before they become problems. ✅ We understand our customers’ use cases more deeply. If you’re not tapping into support data, here’s what you’re missing: 🔥 Emerging Pain Points Recurring issues expose friction in the customer journey. Ignore them, and those minor frustrations turn into churn-worthy headaches. 🔥 Product Gaps Customers vote with their tickets. If the same feature requests or usability complaints keep surfacing, your roadmap is practically writing itself. 🔥 Engagement Risks A spike in tickets isn’t just noise—it’s a flare. Users don’t submit tickets when they’re thriving; they do it when they’re stuck, frustrated, or in need of more enablement. Here are a few ways my team and I are using these insights: ✅ Spot & Engage Struggling Users A surge in ticket volume? Proactively reach out before frustration turns into a cancellation. ✅ Create Targeted Content If the same questions keep coming up, turn those insights into help docs, webinars, or office hours. ✅ Surface Expansion Opportunities Seeing frequent feature requests? Build them—or better yet, use them to tee up expansion conversations. ✅ Map Out User Behavior Support tickets tell you who’s onboarding, who’s adopting new features, and who’s stuck. Use that data to drive deeper engagement. ✅ Collaborate with Product Your product team needs this intel. Share support trends regularly to influence meaningful fixes and features. High ticket volume isn’t necessarily a bad thing—but you need to know how to use it to your advantage. Bottom line? CSMs don’t need to fix support tickets. But the best ones know how to use them to drive retention, expansion, and adoption. _____________________________ 📣 If you liked my post, you’ll love my newsletter. Every week I share learnings, advice and strategies from my experience going from CSM to CCO. Join 12k+ subscribers of The Journey and turn insights into action. Sign up on my profile.

Ever looked at a UX survey and thought: “Okay… but what’s really going on here?” Same. I’ve been digging into how factor analysis can turn messy survey responses into meaningful insights. Not just to clean up the data - but to actually uncover the deeper psychological patterns underneath the numbers. Instead of just asking “Is this usable?”, we can ask: What makes it feel usable? Which moments in the experience build trust? Are we measuring the same idea in slightly different ways? These are the kinds of questions that factor analysis helps answer - by identifying latent constructs like satisfaction, ease, or emotional clarity that sit beneath the surface of our metrics. You don’t need hundreds of responses or a big-budget team to get started. With the right methods, even small UX teams can design sharper surveys and uncover deeper insights. EFA (exploratory factor analysis) helps uncover patterns you didn’t know to look for - great for new or evolving research. CFA (confirmatory factor analysis) lets you test whether your idea of a UX concept (say, trust or usability) holds up in the real data. And SEM (structural equation modeling) maps how those factors connect - like how ease of use builds trust, which in turn drives satisfaction and intent to return. What makes this even more accessible now are modern techniques like Bayesian CFA (ideal when you’re working with small datasets or want to include expert assumptions), non-linear modeling (to better capture how people actually behave), and robust estimation (to keep results stable even when the data’s messy or skewed). These methods aren’t just for academics - they’re practical, powerful tools that help UX teams design better experiences, grounded in real data.

In a sea of possible insights, how do you know which are worth reporting? As a data analyst, there are two types of insights you will report: 1) Ones that are directly aligned to a business question or priority 2) Ones that nobody is asking for… but should be 90% of the time, you should be focusing on the first one. But when done right, the second can be very powerful. So… how do you find those hidden insights? How do you know which ones truly matter? ➤ Explore high-level trends Scan dashboards, reports, or raw data for unexpected patterns. Look for sudden spikes, dips, or emerging trends that don’t have an obvious explanation. ➤ Slice the data by different dimensions Break data down by different categories (customer segments, time periods, product lines, etc.). Where are things changing the most? Which groups are behaving unlike the others? ➤  Identify outliers Look at the extremes. What’s happening with your best customers? Worst-performing regions? Most productive employees? Outliers often reveal inefficiencies or hidden opportunities. ➤ Tie insights to business impact Before reporting, ask: Would knowing this change a decision? If it doesn’t, it’s probably not worth surfacing. ➤ Pressure-test with stakeholders Run your findings by a manager or friendly stakeholder. Ask them if the finding resonates with other trends they've seen, whether they see potential value, and whether it could influence strategy. In other words: - Start broad - Dig deep - Sense-check —-— 👋🏼 I’m Morgan. I share my favorite data viz and data storytelling tips to help other analysts (and academics) better communicate their work.

Track customer UX metrics during design to improve business results. Relying only on analytics to guide your design decisions is a missed opportunity to truly understand your customers. Analytics only show what customers did, not why they did it. Tracking customer interactions throughout the product lifecycle helps businesses measure and understand how customers engage with their products before and after launch. The goal is to ensure the design meets customer needs and achieves desired outcomes before building. By dividing the process into three key stages—customer understanding (attitudinal metrics), customer behavior (behavioral metrics), and customer activity (performance metrics)—you get a clearer picture of customer needs and how your design addresses them. → Customer Understanding In the pre-market phase, gathering insights about how well customers get your product’s value guides your design decisions. Attitudinal metrics collected through surveys or interviews help gauge preferences, needs, and expectations. The goal is to understand how potential customers feel about the product concept. → Customer Behavior Tracking how customers interact with prototype screens or products shows whether the design is effective. Behavioral metrics like click-through rates and session times provide insights into how users engage with the design. This phase bridges the pre-market and post-market stages and helps identify any friction points in the design. →  Customer Activity After launch, post-market performance metrics like task completion and error rates measure how customers use the product in real-world scenarios. These insights help determine if the product meets its goals and how well it supports user needs. Designers should take a data-informed approach by collecting and analyzing data at each stage to make sure the product continues evolving to meet customer needs and business goals. #productdesign #productdiscovery #userresearch #uxresearch

Your churned customers will show 5 warning signs months before leaving. While writing latest book, Behind The Click, I analyzed 15+ years of optimization efforts at The Good for Fortune 500 brands like Adobe, Nike, and The Economist. It pointed to one key theme: → Most companies look at conversion rates and revenue *after* the damage is done. But digital experience issues often show warning signs long before customers leave. Here are the 5 key metrics that signal customer dissatisfaction: 1. Path Efficiency Issues When customers take longer paths to complete basic tasks, it increases cognitive load and frustration. Don't make customers hunt through your navigation to find basic product information. 2. Search Behavior Changes Large volumes of search queries for basic information indicate a broken digital journey. Easy wins are often found in your on-site search data. 3. Mobile Experience Friction Only 34% of US customers prefer shopping on mobile. But 62% are less likely to purchase again after a negative mobile experience. So, focus your mobile experience around product research tasks, knowing they'll likely convert later on desktop. 4. Cart Abandonment Patterns 17% of visitors abandon due to lack of trust. Trust signals also impact retention. Security badges are too often used as a bandaid for trust issues. Research and fix the underlying issues. 5. Customer Service Escalations Digital experience issues create support burden. Is your customer service flooded with questions your site isn't answering? Surface those questions, then provide the answers in your site content. 🪄 Boom! More conversions, less support overhead killing your margins. The most successful enterprise brands don't wait for churn. They proactively optimize their digital experience using customer behavior data and research-backed improvements. Don't let your customers slip away.