Data-Driven Strategies for Enhancing Customer Engagement

Surveys can serve an important purpose. We should use them to fill holes in our understanding of the customer experience or build better models with the customer data we have. As surveys tell you what customers explicitly choose to share, you should not be using them to measure the experience. Surveys are also inherently reactive, surface level, and increasingly ignored by customers who are overwhelmed by feedback requests. This is fact. There’s a different way. Some CX leaders understand that the most critical insights come from sources customers don’t even realize they’re providing from the “exhaust” of every day life with your brand. Real-time digital behavior, social listening, conversational analytics, and predictive modeling deliver insights that surveys alone never will. Voice and sentiment analytics, for example, go beyond simply reading customer comments. They reveal how customers genuinely feel by analyzing tone, frustration, or intent embedded within interactions. Behavioral analytics, meanwhile, uncover friction points by tracking real customer actions across websites or apps, highlighting issues users might never explicitly complain about. Predictive analytics are also becoming essential for modern CX strategies. They anticipate customer needs, allowing businesses to proactively address potential churn, rather than merely reacting after the fact. The capability can also help you maximize revenue in the experiences you are delivering (a use case not discussed often enough). The most forward-looking CX teams today are blending traditional feedback with these deeper, proactive techniques, creating a comprehensive view of their customers. If you’re just beginning to move beyond a survey-only approach, prioritizing these more advanced methods will help ensure your insights are not only deeper but actionable in real time. Surveys aren’t dead (much to my chagrin), but relying solely on them means leaving crucial insights behind. While many enterprises have moved beyond surveys, the majority are still overly reliant on them. And when you get to mid-market or small businesses? The survey slapping gets exponentially worse. Now is the time to start looking beyond the questionnaire and your Likert scales. The email survey is slowly becoming digital dust. And the capabilities to get you there are readily available. How are you evolving your customer listening strategy beyond traditional surveys? #customerexperience #cxstrategy #customerinsights #surveys

Attribution has never been perfect, but for DTC brands, it has become significantly harder in the past few years. Apple’s iOS14 updates, third-party cookie deprecation, and increased privacy regulations have disrupted traditional attribution models. Brands that once relied on last-click attribution, ad platform reporting, or rule-based LTV calculations now face major blind spots in understanding which marketing efforts drive long-term value. Even those investing in first-party data strategies, post-purchase surveys, and media mix modeling (MMM) struggle to fully connect the dots. The reality is that data is still fragmented across multiple platforms such as Shopify, Klaviyo, Google Analytics, ad networks, and third-party analytics tools. Most solutions focus on aggregating data, but aggregation alone doesn’t tell the full story of how customers move through the funnel and what actually drives retention. Rob Markey - In his article, "Are You Undervaluing Your Customers?" published in the Harvard Business Review, Markey emphasizes the significance of measuring and managing the value of a company's customer base. He advocates for creating systems that prioritize customer relationships to drive sustainable growth. Chip Bell - Recognized as a pioneer in customer journey mapping, Bell has contributed significantly to the field of customer experience. In an interview titled "The father of customer journey mapping, Chip Bell, talks driving innovation through customer partnership," he discusses how organizations can co-create with customers to drive innovation and enhance the customer journey. So how do brands solve this? 1. Shift from static LTV models to predictive insights - Traditional LTV calculations are backward-looking, often based on averages that don’t account for future behavior. Predictive analytics, using real-time behavioral and transactional data, can provide a more accurate forecast of customer lifetime value at an individual level. 2. Invest in first-party data strategies that go beyond acquisition - Many brands have adapted to privacy changes by collecting more first-party data, but few are fully leveraging it. Loyalty programs, surveys, and on-site behavioral tracking can provide valuable insights into retention and repeat purchase drivers, helping brands reallocate spend more effectively. 3. Adopt AI-driven segmentation and customer equity scoring - RFM segmentation and standard cohort analysis have limitations. AI-powered models can help identify high-value customers earlier in their lifecycle, predict churn risk, and optimize acquisition based on true long-term value, not just early spend. Markey and Bell have long emphasized that customer loyalty isn’t built on transactions alone, it’s about the entire journey. Brands that can better understand and predict customer value will be the ones that thrive in a world where third-party tracking is no longer a reliable option. #CustomerJourney #Attribution #CustomerEquity

For SaaS companies, customer churn is closely tied to growth. From an industry standpoint, the average churn rate for mid-market companies is between 12% and 13%. With renewal-based revenue models, churn directly affects both topline and bottom line. At Egnyte, AI and Machine Learning have been pivotal in our journey to improving customer retention and reducing churn. We have noted a 2.5 to 3 points reduction in churn rate by deploying AI programs that are actionable for both our customers and CSM teams. AI can offer powerful capabilities to help SaaS companies significantly reduce churn by enabling proactive and data-driven customer retention strategies. Some of these strategies are: 1. Predictive Churn Analytics Machine Learning models analyze vast amounts of customer data (usage patterns, support interactions, billing history, feature adoption, login frequency, etc.) to identify subtle patterns that precede churn. They can flag customers as "at-risk" before they can explicitly signal dissatisfaction, allowing for proactive intervention. It can further assign a "churn risk score" to each customer/ user, enabling customer success teams to prioritize their efforts on the most vulnerable and valuable accounts. The actionable operational data that we received by employing ML is the essence of churn analytics. 2. Hyper-Personalized Customer Experiences AI allows SaaS companies to move beyond generic communication to highly tailored interactions based on user behavior and feature adoption. AI can suggest relevant features, integrations, or workflows that the user might find valuable but hasn't yet discovered. AI can also determine the optimal timing and channel of customer-focused content, such as help desk articles, feature awareness videos, and case studies. 3. Automated Customer Support and Engagement AI can enhance customer support, making it more efficient and impactful. AI-powered chatbots can handle common customer queries 24/7, reducing wait times and providing instant solutions. Advanced chatbots use Natural Language Processing (NLP) to understand complex queries and provide personalized responses. It also helps in online enablement, reducing onboarding costs. While these strategies are already redefining the way CSM and enablement teams service customers, their significance in the cadence of customer retention strategies is going to increase hereon. Enterprises need to use AI intelligently and efficiently and focus on gleaning actionable insights from their AI strategies. #B2BSaaS #Churn #CustomerRetention

I often say: Focus on psychographics (values, interests) Over demographics (age, gender, income) The tough part? Gathering psychographics (without being creepy or invasive.) It's easier to rely on demographics. They're: - painless to gather - straightforward - easy to analyze - quantifiable But it's a mistake to depend on them. A costly one. They're a weak data point. The role they play in purchase decisions? Smaller than many marketers think. Psychographics are much more useful. And easier to collect than you think. Here's how I do it: 👉 Customer surveys Ask direct questions about values, interests, and the purchase process. 👉 Social listening Analyze what your audience is saying in comments, reviews, and posts. Look for patterns in their language, pain points, and values. 👉 Website behavior Track which pages customers visit, what content they engage with, and how they navigate your site. 👉 Customer interviews Understand the customer buying process — from the first moment a customer noticed a problem in their life through purchasing your product (and ideally your product solving their problem). 👉 Community engagement Host webinars, engage in online groups, read and respond to customer comments. Learn your target market's pain points and how they phrase those pain points. 👉 Analyze reviews and testimonials Look for recurring themes in what people say about your product — or your competitors'. Psychographics give you: - customer behavior insights - voice-of-customer data - value props - pain points It's priceless info. Use it to hone your messaging, offers, marketing, design, and product. #marketing #customerinsights #strategy

Subscription services need strong analytics to build smarter & strategically strong plans. 🚀 Subscription models aren’t just a trend anymore—they’re shaping the future of eCommerce. 🛍 But are you leveraging data & analytics sufficiently, to iteratively build your strategy, & have your customers coming back? Here’s why you should make data analytics an integral part of your business approach: 🎯 Customer Retention Isn’t a Guessing Game Many eCommerce businesses still rely on gut feeling & high level market trends when deciding what keeps their subscribers happy. What if you could make smarter, data-driven decisions instead? Here’s how: 1️⃣ Understand User Behavior at a Granular Level Accurate analytics helps you spot patterns in how your subscribers behave. 👉 For example, a fitness app found that users who completed daily workouts stayed subscribed longer. With this insight, the app focused on features that encourage consistent engagement, boosting retention. 2️⃣ Personalize the Experience Analytics isn’t just about numbers—it’s about the people behind them. By segmenting your customers based on their behavior & psychographics, you can create personalized experiences that drive loyalty. 👉 Example: Netflix tailors its show and movie recommendations at a segment of one level, making subscribers feel seen and valued, while also making their life easier! 3️⃣ Track Key Metrics Keep an eye on crucial metrics such as Churn Rate, Average Order Value (AOV), & Customer Lifetime Value (CLTV). These metrics tell you what’s working, & where you need to pivot. 👉 For instance, a music app discovered that users who created personalized playlists were less likely to churn. Now they focus on promoting playlist creation to keep users engaged. 4️⃣ Leverage Predictive Analytics Want to predict churn before it happens? Predictive analytics can highlight warning signs of disengagement so you can take action before your subscribers leave. 👉 Takeaway: With predictive analytics you can send personalized reminders, special incentives, or tips to at-risk users, keeping them engaged. 5️⃣ Test, Learn, Optimize Don’t settle for your first plan. A/B testing helps you experiment with different subscription models, pricing, & features to arrive at the best. 👉 Example: A video streaming service can test different pricing structures & tiers, & find the best pricing plans that maximize sign-ups, market share, & retention. Bottom line: Subscription analytics give you the insights you need to understand, retain, & grow your subscriber base. Embracing smart data, & analyzing it while keeping the people behind it in your mind can create more personalized, engaging, & profitable subscription model. At Appstle Inc. there are 30,000+ eCommerce businesses that hands-on use our granular analytics to make impactful data driven customer retention strategies. The analytics are an integral part of Appstle Subscriptions. Because there is no better way to profitably scale!