Analytics for Community Engagement

The One Metric I Trust Most on LinkedIn Over three years on LinkedIn, I’ve tracked every community metric I could: week to week, month to month, year over year. I’ve analyzed trends, looked for forward vs. lagging indicators, and tried to understand what truly drives growth. At first, I focused on top-line metrics - like impressions. Then engagements. But the best predictor of long-term success - the one metric I now trust most - is something few people even check: Members Reached (formerly Unique Impressions). If you go into your post analytics, LinkedIn shows you not just impressions but how many unique people saw your content. And I’ve found that growth in this number is the strongest signal that I’m on the right path. Why? Engagements fluctuate. A viral post, a trending topic, or a high-emotion moment can skew the numbers. Many people who value my content don’t engage. Senior professionals, in particular, often prefer to observe rather than publicly interact. Some folks just, increasingly, value anonymity and will discuss seeing my posts but never engage. Members Reached can’t be hidden. Unlike engagements, which depend on visible likes or comments, this metric quietly tracks how many real people are seeing what you share. Metrics should never drive your content - you should create what matters to you. But if you’re looking for a true measure of reach and impact, start paying attention to Members Reached. For me, it’s been the clearest predictor of whether the community will grow - or not - down the road.

As a foresight professional, one of the most important things I can do is continuously nurture and expand my network of fellow futurists, strategists, researchers, and innovators. Not just to “collect contacts” — but to genuinely understand: what topics people are exploring, what signals they are noticing, what challenges they are living through, and how the global foresight conversation is evolving in real time. This is why I found GetViktor AI incredibly useful. Recently, I asked it to analyze the LinkedIn activity of the 100 most influential futurists and foresight professionals worldwide and identify: → who shapes the conversation, → what themes dominate, → and what drives visibility and engagement in the foresight space today. What impressed me most was not only the depth of the analysis, but also the delivery format: all insights, rankings, benchmarks, and engagement statistics were compiled into a beautifully structured and professionally formatted report that was genuinely easy to explore and use. Some key takeaways stood out immediately: 🔹 AI now dominates the foresight discourse 42% of top futurists primarily post about AI and intelligent automation. 🔹 The most influential futurists are highly focused The strongest voices consistently “own” 2–3 topics instead of speaking broadly about everything. 🔹 Carousel posts dominate Carousel/document posts achieve ~6× higher engagement than plain text posts on LinkedIn. 🔹 Community engagement matters more than follower count Comments are weighted far more heavily than likes by the LinkedIn algorithm. What I appreciated most is that this wasn’t just a list of “top influencers.” It became a map of the current foresight ecosystem and emerging narratives. For someone working in scientific foresight and strategic futures, this kind of intelligence is extremely valuable. Networking in foresight is not only about staying connected. It is about staying cognitively diverse. #Foresight #BigData #DataAnalysis #Futurist #ArtificialIntelligence #Innovation #FutureOfWork #ScenarioPlanning #EmergingTech #AI #FutureStudies #GetViktor Sandhya Mishra

Can you prove the value of your community? Many can’t. And that’s the problem. Unless you have the time, expertise, and resources to run a controlled trial, all you can do is establish relationships—without fully knowing the direction of causality. But what if you could estimate the value of your community in a statistically valid way? That’s precisely what FeverBee did for Pragmatic Institute. They wanted to know: 1️⃣ Does community participation increase course enrollments? 2️⃣ How does community activity impact revenue? 3️⃣ What behaviours should be encouraged to drive these metrics? Here’s what we found: ✅ Active members (>1 post) generated 21% more revenue than inactive members. ✅ Members who posted at least once contributed 44% more revenue. ✅ Logging in at least once correlated with a 21% revenue increase. ✅ Replying to a direct message increased the probability of taking an additional course by 16%. ✅ Creating a new thread? A 201% increase in course enrollments. The takeaway? Not all engagement is equal. The most valuable actions weren’t just any interactions. They were starting discussions, sharing resources, and responding to messages. This insight led to a clear strategy: → Encourage thread creation. → Provide more downloadable documents. → Increase engagement in direct messaging. Most community managers KNOW their communities drive value… but struggle to prove it. With the right methodology, you can show leadership exactly what’s working—and secure more support.

A few years ago, I worked with an online education platform facing challenges with student engagement. While they had a significant number of users enrolling in courses, they struggled with low participation rates in course discussions and activities, leading to a decline in course completion rates. The platform needed to identify the causes behind low engagement and implement strategies to encourage more active participation. Improving Student Engagement Using Data Analytics 1️⃣ Analyzing Engagement Data We began by analyzing user interaction data, focusing on metrics such as time spent on the platform, participation in discussions, video completion rates, and quiz scores. Using SQL, we aggregated the data to identify patterns and pinpoint where students were losing interest. SELECT student_id, course_id, AVG(time_spent) AS avg_time_spent, COUNT(discussion_post_id) AS posts_made, AVG(quiz_score) AS avg_quiz_score FROM student_activity GROUP BY student_id, course_id; 🔹 Insight: We identified that students who interacted with course discussions and quizzes had higher completion rates, while others dropped off quickly. 2️⃣ Building a Predictive Model We then created a predictive model to determine which students were at risk of disengaging based on their activity patterns. The model incorporated features such as time spent on the platform, participation in discussions, and progress through the course material. # Pseudocode for Predictive Model def predict_student_engagement(student_data): model = train_engagement_model(student_data) predictions = model.predict(student_data) return predictions 🔹 Insight: This model helped us flag students who were likely to disengage early, allowing for timely interventions. 3️⃣ Implementing Engagement Strategies Based on insights from the model, we implemented strategies such as sending personalized emails with reminders, offering incentives for completing activities, and increasing interaction opportunities through live Q&A sessions. # Pseudocode for Engagement Follow-Up def send_engagement_reminder(student_data): if model.predict(student_data) == 'at_risk': send_email_reminder(student_data) 🔹 Insight: Personalized engagement and incentives led to an increase in student participation. Challenges Faced Identifying meaningful engagement metrics that were predictive of success. Finding the right balance between engaging students without overwhelming them. Business Impact ✔ Student engagement improved, leading to higher completion rates. ✔ Retention rates increased, as more students continued with courses. ✔ Revenue grew, driven by more active and satisfied students. Key Takeaway: By analyzing user activity and leveraging predictive analytics, businesses can identify disengaged customers early and implement strategies to improve engagement and retention.

❓Question: What's the impact of Community on Customer Success? 🔢 Data: Community Members w/ Activity + CRM Accounts w/ Churn/Renew Event Details for Q2. Match on email domain as UID. ✅ Analysis: “In Q2, customer accounts with at least 1 active community user were 3x more likely to renew their contract with us.” 📢 Narrative: “Customers that are active in the community tend to learn the product quicker, adopt new functionality at a high rate, increase their spend, have higher satisfaction, and remain customers longer than those that do not engage in the community. As a result, the Customer Success organization is scaling self-service resources at a faster pace, seeing increased satisfaction across all accounts, and is steadily lowering the overall cost to retain customers.” =============== The numbers have changed, but the story is true. Nearly all measurements for community business outcomes KPIs and ROI are attributions based on cohort analysis, or comparisons of customer accounts with and without active community users. Having this level of insights flips the conversations you're likely having right now about next year's budget from "what does community cost?" to "if we invest more in community, can we raise our retention rates?"

Customer Lifetime Value 2.0 After analyzing 500+ customer accounts, I've discovered that traditional CLV calculations miss up to 60% of actual customer value. Here's an enhanced framework for 2025: 1. Direct Revenue + Referral Value 📈 Most companies track: - Base subscription revenue - Feature upgrades - Seat expansions - Service fees But they miss the hidden revenue multipliers: - Referred leads convert 3x better - Referred deals are 20% larger - Some customers generate 5+ referrals yearly - Case study & reference call impact For example, Acme Corp's (Wile E. Coyote, CEO) $100K ARR becomes $400K, including their referral impact. Traditional CLV misses 75% of its value. 2. Implementation Resource Investment 🎯 Innovative companies track both costs and value signals: - Technical onboarding hours - Integration complexity - Data migration scope - Training investment - Success planning effort Key finding: Higher initial investment often yields better retention. One enterprise client reduced time-to-value by 40% after we increased implementation support. 3. Support Ticket Investment 💡 Support interactions create measurable value: - Product feedback quality - Feature adoption correlation - Customer expertise growth - Expansion opportunities Data point: Customers engaging support 3-5 times in the first 90 days show 40% higher retention rates than non-engagers. 4. Product Feedback Impact 🔍 Value creators: - Beta testing participation - Feature request quality - Bug report impact - Advisory board input - API usage insights Case study: Mid-market customer feedback led to UI improvements, reducing overall churn by 15%. 5. Community Engagement ROI 🌟 Measuring network effects: - Knowledge base contributions - Forum participation value - User group leadership - Brand advocacy reach - Peer support impact Success metric: Top community contributors save our support team 200+ hours annually through documentation and peer assistance. New CLV Formula: CLV = (Direct Revenue + Referral Value) × Expected Lifetime - Implementation Investment - Support Investment + Product Feedback Value + Community Impact Value Results from companies using this framework: - 35% more accurate retention predictions - 25% higher expansion revenue - 40% increase in referrals - 50% more valuable product feedback - 30% growth in community engagement Implementation Tips: 1. Start small - Pick one new value dimension - Test with a pilot group - Gather baseline data - Scale what works 2. Cross-functional alignment - Connect Success, Product & Support data - Create shared value metrics - Build automated tracking - Set review cadence 3. Measure impact - Track prediction accuracy - Monitor retention correlation - Document value stories - Share learnings How does your organization measure hidden customer value? What metrics beyond direct revenue have you found most insightful?

Many organizations are sitting on a treasure trove of insights they're barely using. 🗝️💡 It's not just about collecting data; it's about actively engaging with it. Your existing data holds the power to keep your donors engaged but also predict and disengagement. How? By: 1. 𝐔𝐭𝐢𝐥𝐢𝐳𝐢𝐧𝐠 𝐄𝐱𝐢𝐬𝐭𝐢𝐧𝐠 𝐃𝐚𝐭𝐚: Dive into the data you already have. Patterns of past behaviors, interactions, and preferences are waiting to be discovered and acted upon. 2. 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞 𝐄𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Analyze engagement metrics and communication responses to identify early signs of donor withdrawal. Tailor your outreach to rekindle their interest before they consider leaving. 3. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬: Implement segmentation and predictive analytics to customize your communications. Show your donors they're not just another name in the database but a valued member of your community. 4. 𝐌𝐚𝐱𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Leverage tools and techniques like RFM (Recency, Frequency, Monetary value) analysis and machine learning to turn raw data into actionable strategies for retaining your donors. The reality is, you already possess a wealth of data that can transform your approach to donor stewardship. The challenge lies in effectively mining and applying these insights to foster deeper, more meaningful relationships with your supporters. By harnessing the power of the data at our fingertips, we can make every supporter feel like a hero to our cause. 🙌

We're not in an attention economy. We're in an engagement economy, In 2026 the KPI to measure is ROE Return On Engagement. That's about tracking the impact brands get back when people actually interact with them, not just buy or convert but interact with them. It measures attention, participation, trust, and loyalty. You know, it's the things that make the upper and mid funnel work. It's the things that drive discovery. It's the things that also drive retention and makes revenue possible later. In essence, it measures how deeply people interact with your brand and how that interaction translates into awareness, sentiment, loyalty, and ultimately revenue or pipeline.  The stuff that shows up before revenue and makes revenue possible later. A simple example Say you run a small apparel brand on Instagram or TikTok. You spend time creating content, replying to comments, and showing up consistently. Your ROE shows up as: --Growing followers who actually engage --Posts that get shared, not ignored --People joining your email list --Brand recall when they’re finally ready to buy Not measuring sales but the momentum. How businesses use ROE Strong ROE usually shows up as: --Deeper brand affinity --More word-of-mouth --Higher repeat engagement --Customers who stick around longer -- The ever elusive LTV It’s especially useful when you’re building brand, when GEO is a concern, launching something new, in long sales cycles, when culture is a brand moat, or playing a longer game where trust matters. A practical way to think about it ROE isn’t a perfect formula, but the idea is simple: ROE ≈ Value of engagement (attention, loyalty, future intent) ÷ Time and money invested Low spend + meaningful interaction = strong ROE. High spend + empty impressions = weak ROE. Metrics Used Inside ROE Teams usually define ROE as a composite of several engagement indicators. Typical components: --Social / digital: likes, comments, shares, saves, mentions, UGC volume, click‑throughs, time on page, repeat visits, content depth consumed. --Community: active members, posts per member, response time, peer‑to‑peer replies, retention of community members. --Events / experiences: session attendance, dwell time, repeat attendance, booth visits, chats, demos, survey scores, NPS, emotional response indicators. --Business impact proxies: increase in branded search, email opt‑ins, MQLs from engaged audiences, pipeline influenced, renewal/upsell rates for highly engaged customers. ROE will be the important KPI in 2026 ---- Join 2,000 marketers as we discuss ROE and other topics in this new marketing era -  https://lnkd.in/dYyRQnur

👁️ 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗿𝗮𝘁𝗲𝘀 𝗮𝗿𝗲 𝗶𝗻 𝗳𝗿𝗲𝗲 𝗳𝗮𝗹𝗹 … 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗲𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝘀 𝗴𝗼𝗶𝗻𝗴 𝗱𝗮𝗿𝗸. Because what if the public metrics aren’t telling the full story? Engagement might appear to be down. But content is still being shared. Just in places your dashboards don’t track: DMs, WhatsApp threads, private communities. Let's look at the data: 🔺 Around 70% of content shares happen through private channels like messaging apps or DMs, making traditional analytics blind to them. 🔺 Dark social — content that gets shared but leaves no visible referral — now accounts for a major chunk of traffic labelled as “direct.” 🔺 With the average social media user on nearly 7 platforms per month, the public feed is no longer the only active space. But what does this mean for brand measurement? ❗ 𝗪𝗲𝗹𝗹, 𝗽𝗲𝗼𝗽𝗹𝗲 𝗵𝗮𝘃𝗲𝗻'𝘁 𝘀𝘁𝗼𝗽𝗽𝗲𝗱 𝗲𝗻𝗴𝗮𝗴𝗶𝗻𝗴. 𝗧𝗵𝗲𝘆’𝗿𝗲 𝗷𝘂𝘀𝘁 𝗲𝗻𝗴𝗮𝗴𝗶𝗻𝗴 𝗽𝗿𝗶𝘃𝗮𝘁𝗲𝗹𝘆. And our measurement frameworks haven’t caught up. We’re still reporting on the public conversations, while the richest ones happen behind closed doors. ❗ “𝗥𝗲𝗮𝗰𝗵” 𝗵𝗮𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝗱. 𝗜𝘁’𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝘀𝗼𝗹𝗲𝗹𝘆 𝗮𝗯𝗼𝘂𝘁 𝗳𝗼𝗹𝗹𝗼𝘄𝗲𝗿𝘀 𝗮𝗻𝗱 𝗽𝘂𝗯𝗹𝗶𝗰 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀, 𝗯𝘂𝘁 𝗮𝗯𝗼𝘂𝘁 𝗳𝗼𝗿𝘄𝗮𝗿𝗱𝗲𝗱 𝗹𝗶𝗻𝗸𝘀, 𝘀𝗰𝗿𝗲𝗲𝗻𝘀𝗵𝗼𝘁 𝘁𝗵𝗿𝗲𝗮𝗱𝘀, 𝘄𝗵𝗶𝘀𝗽𝗲𝗿𝗲𝗱 𝗿𝗮𝘁𝗶𝗻𝗴𝘀 𝗶𝗻 𝗴𝗿𝗼𝘂𝗽 𝗰𝗵𝗮𝘁𝘀. So here are some immediate actions for your brand: 🔺 Add “How did you hear about this?” fields in checkout or sign-up forms. 🔺 Monitor direct and ‘unknown’ traffic spikes, not just referral sources. 🔺 Develop content designed to be shared privately (brief, screenshot-friendly, mobile-first). 🔺 Build micro-communities (WhatsApp/Telegram groups, broadcast channels) where trust and sharing thrive. 🔺 Adjust KPIs: measure search lift, share of “direct” traffic, sentiment shifts, and not just likes. Remember, engagement isn’t dead, it’s darkening. So if your reports show a drop, your audience hasn’t disappeared. They’ve just moved into private rooms. Thoughts? Drop them below! 😍 #BeautyIndustry #BeautyBusiness #BeautyTrends #BeautyTrends2025 #BeautyMarketing #MarketingStrategy #MarketingTrends #BrandStrategy

🌟 Health Scores Without Telemetry: Yes, It’s Still Possible! 🌟 In this week's newsletter, I waxed poetic about health scores without product telemetry. Here's a short digest of that article. If you want the full version of these weekly musings, sign up for the newsletter which also includes show notes from The Digital CX Podcast (formerly Digital Customer Success Podcast). https://lnkd.in/gh7H-356 Enjoy! +++++++ As a CS professional supporting on-premise software, I often face the challenge of having no direct visibility into how customers are using the product. Despite this, it’s still possible to judge customer health effectively. Here’s how: 🏥 First - A Word on Health Scores in General Before designing a scorecard, clarify its purpose. Is it predictive to identify churn risk or reactive to provide insights into customer health? Ideally, it should serve both purposes, with predictive measures to get ahead of churn risk and reactive measures to diagnose issues. Key Tips: • Avoid overloading your scorecard with metrics. • Ensure measures are backed by accurate data. • Conduct regression testing against churned accounts. • Consider different scorecard versions for various audiences. 🔍 Besides Telemetry - Where Else Can You Look? 👥 Community Engagement If your customers interact within a community, metrics like logins, upvotes, comments, and posts are invaluable. Aggregating these metrics at the company level provides an overall view of customer activity. Compare each company to a customer average to gauge their community usage effectively. 📚 Learning/Education/LMS Track course enrollments, completions, and assessment scores in your Academy/University and offer user-level recommendations on next courses to maintain engagement. 🆘 Support Interaction Support case volume is a classic health indicator. However, it’s nuanced: • Zero tickets may signal disengagement, a potential churn risk. • Use a bell curve for ticket volumes: zero tickets (red), average count (green), high count (red) Bonus Metric: • Escalation to Case Ratio: The proportion of escalated tickets to total tickets is a significant churn indicator. 🎟️ Marketing / Event Attendance Track customer participation in events like webinars, AMAs, and CAB meetings. Attendance, especially by champions and executives, signals positive engagement. 🤝 CS Engagement For accounts with assigned CSMs, measure engagement through Meetings, QBRs & Calls. Long gaps in these interactions should turn the score red. 🔄 Champion / Executive Change Champions or Executive Buyers leaving, are immediate red flags. This loss of rapport poses inherent risks. By focusing on these alternative metrics, you can create a comprehensive view of your customer’s health even without telemetry. 🌐✨ #CustomerSuccess #HealthScores #Telemetry #CommunityEngagement #CustomerEducation #SupportMetrics #CustomerEngagement #DigitalCS