Engagement Metrics and Behavioral Analysis

Certainly, while wishlists have emerged as a valuable tool for gauging consumer interest, there are several other methods and metrics that e-commerce platforms can use to measure consumer interest: 1. Cart Abandonment Rate: Observing how many customers add products to their carts but don't complete the purchase can provide insights into potential hesitations or barriers. 2. Product Views: The number of times a product is viewed can indicate its popularity or interest level. 3. Time Spent on Page: Monitoring the average time consumers spend on product pages can hint at their level of interest. 4. Product Reviews and Ratings: A high number of reviews or ratings, even if mixed, can signify strong interest or engagement with a product. 5. Search Query Analysis: Observing which products or categories users are searching for on the platform can indicate trending interests. 6. Social Media Engagement: Shares, likes, comments, and mentions related to products can provide insights into consumer preferences. 7. Referral Traffic: Analyzing traffic from external sites or social media can show where the interest is coming from and which products are driving it. 8. Customer Surveys and Feedback: Directly asking customers about their preferences or interests can yield detailed insights. 9. Sales Data: A straightforward metric, but analyzing which products are selling the most can clearly indicate consumer interest. 10. Click-Through Rate (CTR): Observing how often people click on a product after seeing it in a recommendation or advertisement can be a strong indicator. 11. User-Generated Content: If consumers are posting pictures, videos, or blogs about a product, it showcases genuine interest and engagement. 12. Repeat Purchases: Products that are frequently repurchased can indicate high levels of satisfaction and interest. 13. Customer Service Inquiries: The number and nature of questions related to a product can offer insights into areas of curiosity or concern. 14. Heatmaps: Tools that show where users most frequently click, move, or hover on a page can help in understanding which products or sections grab their attention. 15. Newsletter and Email Open Rates: If consumers are frequently opening emails about specific products or categories, it can be an indication of their interest areas. 16. Retargeting Campaign Success: The conversion rate of retargeting campaigns can provide insights into the residual interest of consumers after their initial interaction. By leveraging a combination of these methods, brands can gain a comprehensive understanding of consumer interest, helping them to tailor their offerings and marketing strategies more effectively. #ecommerce #LinkedInNewsIndia

Ever tried to demonstrate something in the social sciences without a metric? It’s like trying to bake without measuring cups. You might pull something out of the oven, but no one will trust what’s in it. Metrics are the backbone of research. They help us quantify abstract ideas like satisfaction, trust, or attention. In UX, they serve the same role: giving us concrete signals to understand how users feel, think, and behave. Without them, we’re just guessing. And guessing doesn’t scale. So, what exactly is a UX metric? It’s a quantitative measure that captures some aspect of a user’s experience. That might be how long it takes to complete a task, how satisfied someone feels after using a product, or how often they return. But it’s not enough to measure what’s convenient. We have to measure what matters. UX metrics typically fall into a few broad categories. Behavioral metrics capture what people do, such as task completion rates, time on task, or drop-off points. Attitudinal metrics reflect what people think or feel, often through surveys measuring satisfaction, trust, or perceived ease of use. Business metrics connect UX to broader outcomes like conversion or retention. And in more advanced research, physiological metrics like eye movements, galvanic skin response, or EEG data provide insight into cognitive load, attention, or emotional engagement. But here’s the thing. Not all metrics are good metrics. A number is only useful if it validly represents the concept you’re trying to understand. That’s where validation comes in. Face and content validity make sure a metric makes intuitive and theoretical sense. Construct validity checks if the metric behaves as expected in relation to other psychological concepts. Criterion validity looks at whether it can predict relevant outcomes. Known-groups validity asks whether it can distinguish between populations that should, logically, perform differently. Good metrics don’t just seem right. They work right. To help structure UX measurement, researchers often turn to frameworks. The HEART framework is a popular one, capturing Happiness, Engagement, Adoption, Retention, and Task success. AARRR is another, focusing on Acquisition, Activation, Retention, Referral, and Revenue. Other tools like SUS, SUPR-Q, UMUX, and Quality of Experience models are helpful for usability testing, benchmarking, and evaluating perceived product quality. Still, existing frameworks aren’t always enough. As new technologies emerge, voice interfaces, mixed reality, adaptive systems, old metrics can fall short. In those cases, UX researchers often need to define new metrics from scratch. We might develop a new way to quantify trust in an AI assistant, or invent a task success measure for a hands-free interface. It’s not about throwing out scientific rigor. It’s about extending it to new contexts. #UXMetrics #UXResearch #UserExperience

User behavior is more than what they say - it’s what they do. While surveys and usability tests provide valuable insights, log analysis reveals real interaction patterns, helping UX researchers make informed decisions based on data, not just assumptions. By analyzing interactions - clicks, page views, and session times - teams move beyond assumptions to data-driven decisions. Here are five key log analysis methods every UX researcher should know: 1. Clickstream Analysis - Mapping User Journeys Tracks how users navigate a product, highlighting where they drop off or backtrack. Helps refine navigation and improve user flows. 2. Session Analysis - Seeing UX Through the User’s Eyes Session replays reveal hesitation, rage clicks, and abandoned tasks. Helps pinpoint where and why users struggle. 3. Funnel Analysis - Identifying Drop-Off Points Tracks user progression through key workflows like onboarding or checkout, pinpointing exact steps causing drop-offs. 4. Anomaly Detection - Catching UX Issues Early Flags unexpected changes in user behavior, like sudden drops in engagement or error spikes, signaling potential UX problems. 5. Time-on-Task Analysis - Measuring Efficiency Tracks how long users take to complete actions. Longer times may indicate confusion, while shorter times can suggest disengagement.

Most brands analyze creative tests by looking at ROAS and CPA. That's like judging a restaurant by the bill instead of the food. ↳ Here's how to actually find winning patterns: Looking at performance metrics alone tells you IF something works. But it doesn't tell you WHY it works or how to replicate it. The Framework That Actually Works: 𝟭. 𝗦𝗽𝗹𝗶𝘁 𝗬𝗼𝘂𝗿 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗜𝗻𝘁𝗼 𝗧𝘄𝗼 𝗕𝘂𝗰𝗸𝗲𝘁𝘀 Primary metrics = Performance (tells you IF it works) - Spend, Purchases, CPA Secondary metrics = Storytelling (tells you WHY it works) - Scroll Stop Rate (hook strength) - Hold Rate (narrative engagement) - Outbound CTR (offer appeal) Why this matters: Performance metrics help you scale winners. Behavioral metrics help you create more winners. 𝟮. 𝗨𝘀𝗲 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿 𝘁𝗼 𝗙𝗶𝘅 𝗨𝗻𝗱𝗲𝗿𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗲𝗿𝘀 Don't change offers randomly. Let the data guide you: Low Scroll Stop Rate = Weak hook → Test bold claims, fast motion, pattern breaks Poor Hold Rate = Boring narrative → Improve pacing, cut slow parts Low Outbound CTR = Weak CTA/offer → Test different positioning Why this works: You're fixing the actual problem, not guessing at solutions. 𝟯. 𝗙𝗶𝗻𝗱 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗶𝗻 𝗬𝗼𝘂𝗿 𝗪𝗶𝗻𝗻𝗲𝗿𝘀 Stop looking at winning ads in isolation. Find common threads: Do they use specific hook styles? Similar pacing structures? Particular testimonial formats? Build a Creative Optimization Library documenting what works. Why this matters: Patterns create predictable processes. Processes eliminate guesswork. 𝟰. 𝗧𝗲𝘀𝘁 𝗪𝗶𝘁𝗵 𝗣𝘂𝗿𝗽𝗼𝘀𝗲 Most brands test random variations. Instead: If Scroll Stop Rate is bad → Test new hooks If Hold Rate is weak → Adjust storytelling If CTR is low → Optimize offer positioning Why this works: Every test has a clear objective and higher success probability. What You Can Expect: Fewer failed creative tests → Faster winner identification → Predictable creative production process → Higher overall ROAS from better optimization The Psychology: → Behavior data reveals true audience preferences. → Patterns show what actually drives action. → Purpose-driven testing eliminates waste. Next Steps: Week 1: Set up behavioral metric tracking Week 2: Analyze your last 10 winners for patterns Week 3: Build your Creative Optimization Library Week 4: Implement purpose-driven testing Be honest... Are you iterating creatives based on data, or gut instinct?

How do you retain people? "Make few promises. Keep them all." That's the simplest rule I always recommend. How do you keep your promises when they're a WIP? You send comms to keep people updated. But... How do you know if they're landing? Enterprise orgs should track these metrics: 1) Did they even get it? Measure: Delivery rate or % of employees who got this message. If people never see the message, nothing else matters. You'd be surprised how many internal IT protocols actually end up blocking your internal comms. Happens more than you think. 2) Did they see it? Measure: views or video plays. It helps show you initial engagement which is a good start. It doesn't quite tell you comprehension yet but that comes later. P.S. I've seen open rates below 10% before at Fortune 100 companies who initially thought their comms were landing well until they started benchmarking open rate by cohort & department 3) Did they understand it? Measure: Quick poll: “Was this clear?” (Yes/No. Short quizzes help too btw) People may see the message but not understand what to do with the info. That's why I'm a big fan of the BLUF technique the Navy Seals used for comms. It stands for "Bottom Line Up Front" where the most important information is presented at the beginning of your message. 4) Did they act on it? Measure: Did employee complete the action in question. Ex: what's your completion rate on the actions you sent? THIS is your ultimate indicator that comms are working. 5) Did they feel heard? Measure: Weekly or monthly pulse surveys This gives you qualitative data that helps improve tone, clarity, and trust over time. What you say is one thing but the TONE that your frontline feel from it is what really matters from a culture perspective. Friendly reminder: All of these performance analytics SHOULD be segmentable by department, location, team and region to validate where your areas of high and low engagement are so that you can address this with operations and local leadership teams. P.S. What else would you add?