How to Use Data to Improve Brand Loyalty

What we once celebrated as 'data-driven' was really just data-curious. I recall an article we wrote in 2012 about how smaller restaurant chains could compete with industry giants through what we then deemed innovative digital loyalty programs. We called it a "Moneyball" approach—using scrappy tactics to punch above your weight class. Back then, we considered it a breakthrough to connect social media engagement with loyalty rewards. When a guest tweeted about their order, a restaurant could respond not just with thanks, but with action—loading a reward directly onto their loyalty card. The focus was on program enrollment and transaction-based rewards. Success meant getting guests signed up and coming back to earn their next free item. In reality, transactional loyalty programs were just the warm-up act for today's comprehensive guest data platforms. Here’s how: From program-centric to guest-centric ⬅️ Then: Focus on loyalty program members and their point balances ➡️ Now: Focus on all guests and their individual preferences, regardless of program status From reactive rewards to proactive personalization ⬅️ Then: Rewarding guests after they engage ➡️ Now: Anticipating and offering guests’ favorite orders, based on historical data and behavioral patterns From transaction tracking to experience orchestration ⬅️ Then: tracking purchases to award points and trigger rewards ➡️ Now: Using comprehensive guest data to personalize everything from menu recommendations to ordering experiences across all channels Here’s how our earlier example would play out today: 2012: Guest tweets about their order → restaurant responds with a free reward → guest returns to redeem 2025: Guest orders → System notes preference and ordering patterns → Next time they open the app, menu item is prominently featured alongside complementary items they're likely to enjoy → If they haven't ordered in their typical timeframe, they might receive a personalized message about a limited-time offer → The experience feels curated, not automated The best restaurant brands today aren't just running loyalty programs; they're building comprehensive guest data platforms that make every interaction feel like coming home to your favorite neighborhood spot. The "Moneyball" approach has evolved, but the underlying truth remains: the scrappy operators who use data will always have a competitive edge.

Understanding your customers’ behaviors and responding accordingly is key to sustained business success. In yesterday’s post, I introduced the Recency-Frequency Matrix, a powerful tool for customer segmentation that helps businesses identify and prioritize their most valuable customers. Today, I want to take it a step further by showcasing how this analysis can inform targeted marketing strategies to drive engagement and growth. Strategic Actions Based on the Recency-Frequency Matrix: Champions: These are your top-tier customers who purchase frequently and recently. To maintain their loyalty, consider offering early access to new products or services, implementing a robust loyalty rewards program, and sending highly personalized communications. Loyal Customers: Customers in this segment are consistent buyers but with slightly less frequency. Encourage more frequent purchases through special incentives, reminders of your product or service benefits, and targeted re-engagement campaigns. Needs Attention: These customers have shown steady engagement but may need a prompt to stay active. Reactivation campaigns with tailored offers, requesting feedback, and exclusive deals can help prevent potential churn. Churn Risk: These customers are at risk of disengagement. Win them back with significant incentives, reminders of positive past experiences, and personalized offers designed to reignite their interest in your brand. Already Churned: For customers who have not engaged for a while, occasional check-ins or updates, targeted ads for reintroduction, and a focus on acquiring new customers might be the most efficient use of resources. Leveraging a Recency-Frequency Matrix not only provides a clear view of where your customers stand but also empowers you to implement highly tailored strategies that maximize both engagement and ROI. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

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!

In today's competitive high street retail landscape, staying relevant to new generations and shopping trends is key. Partnering with brands and retailers daily, I witness the exciting changes taking place to drive increased share, customer retention, and acquisition through effective cross-channel personalization strategies. 1. Harnessing the Power of AI for Predictive Insights. By leveraging AI to analyze customer behavior, businesses can identify trends and preferences, enabling personalized messaging and tailored offers. This data-driven approach fosters loyalty among existing customers and attracts new ones. 2. Adopting Personalized Product Discovery (PDP). Implementing PDP customizes the shopping experience based on individual preferences. Dynamic search features suggest products aligned with past interactions online, while in-store digital kiosks enhance personalized recommendations, merging online and offline experiences seamlessly. 3. Creating a Unified Customer View. Integrating data from various channels provides a comprehensive understanding of the customer journey. This unified view enables consistent communication, real-time personalization, and effective tracking of customer engagement. 4. Cultivating Customer Loyalty through Personalized Rewards. Tailoring loyalty programs to individual spending habits and preferences using AI and customer data enhances customer loyalty. Exclusive events, early collection access, and personalized discounts resonate more with customers, fostering long-term loyalty. 5. Elevating Creativity Across All Channels. Creative excellence enhances personalized strategies. Compelling visuals, authentic storytelling, and innovative campaigns across email marketing, social media, and in-store promotions captivate customers and drive engagement. Creative design elements play a crucial role in building loyalty. By embracing these strategies, high street retailers can navigate personalization successfully, creating engaging customer experiences that nurture loyalty and attract new clientele. For further insights, feel free to reach out directly!

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