Travel Loyalty Program Insights

Inflation often forces businesses into a dilemma—raise prices and risk losing customers, or keep prices stable and shrink margins. But what if data could help strike the perfect balance? 🚀 Challenge: Flipkart, one of India’s largest e-commerce platforms, noticed fluctuating customer retention rates and declining repeat purchases, especially during inflationary periods. Traditional deep-discount campaigns led to short-term sales spikes but failed to build long-term customer loyalty. 🔎 Solution: Data-Driven Discounting Strategy Flipkart’s analytics team uncovered a key insight: Small, frequent discounts (e.g., 5-10% on repeat purchases) led to higher engagement. Personalized offers based on purchase history encouraged repeat buys. A/B testing revealed that customers preferred consistency over occasional deep discounts. 💡 Implementation: Using AI-driven dynamic pricing, Flipkart rolled out: ✅ Tiered discounts for loyal customers. ✅ AI-powered coupon recommendations. ✅ Targeted email campaigns promoting small, time-sensitive discounts. 📈 Results: After three months of testing, Flipkart saw: ✔️ 17% increase in repeat purchases ✔️ 12% uplift in customer retention ✔️ Higher profit margins vs. deep discounting 🎯 Key Takeaway: In an inflationary environment, data-driven pricing isn't just about maximizing revenue—it’s about customer psychology. Businesses that personalize their offers and optimize discounts intelligently can boost retention while protecting margins. 𝑾𝒉𝒂𝒕 𝒑𝒓𝒊𝒄𝒊𝒏𝒈 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒊𝒆𝒔 𝒉𝒂𝒗𝒆 𝒘𝒐𝒓𝒌𝒆𝒅 𝒇𝒐𝒓 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒊𝒏 𝒄𝒉𝒂𝒍𝒍𝒆𝒏𝒈𝒊𝒏𝒈 𝒕𝒊𝒎𝒆𝒔? #datadrivendecisionmaking #DataAnalytics #DiscountStrategy #BusinessStrategies

82.6% of Click's sales came from one thing. Not paid ads. Not foot traffic. Not even their pharmacy offering. ClubCard! (Source: Eyewitness News, 23 Oct 2025) Let me break down what just happened, because this is a masterclass in loyalty economics that every exec should be studying. The numbers that matter: → 14% profit growth in a year where most retailers are in survival mode → 12.6 million active ClubCard members (up from 12.1M just 6 months ago) → 82.6% of total sales driven by loyalty members → 30 years of compounding customer lifetime value That last one; That's the insight everyone's missing. Here's what Clicks actually built: Most brands think loyalty = discount. Clicks built something different: a behavioral data moat wrapped in everyday utility. They didn't just give points. They studied purchase patterns, personalized offers, and created an Affinity programme with partners that actually matter to their customers. The result; Members who've been scanning that card since 1995. Think about that ROI curve. CEO Bertina Engelbrecht said it perfectly: "When you have the kind of loyal customer base that we have, that augurs very well for your continued growth." Translation: Predictable revenue. Lower acquisition costs. Premium customer intelligence. The kind of moat that makes competitors scramble to "upgrade" their own programmes. Why this matters now: In a market where consumers are squeezed, brands that own the customer relationship win. Not the loudest. Not the cheapest. The most trusted. ClubCard isn't a discount card. It's a 30-year trust deposit that's now paying compound interest. What's replicable here: ✓ Make value immediate, not aspirational ✓ Use data to personalize, not just segment ✓ Pick partners strategically (their Affinity model is brilliant) ✓ Play the long game — 30 years of iteration beats copying competitors Massive respect to Bertina Engelbrecht , Melanie Van Rooy Craig Small , Mamusa Stulweni , and your colleagues You've built the kind of loyalty architecture that finance teams love, and marketing teams wish they had. The real question: If 82.6% revenue concentration from a loyalty programme is your STRENGTH and not a risk — what does that tell you about the power of owning customer behavior?

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

What Retail's Obsession with Customer Data Can Teach Hospitals About Patient Loyalty The world's best retailers know their customers with extraordinary precision. They know what you browse, what you buy, when you buy it, and what might convince you to come back. They use this understanding not to manipulate, but to serve better. To anticipate needs. To personalise experiences. To build relationships that last. Hospitals sit on an equally rich treasure of patient data. But most of it remains locked in electronic medical records, used primarily for clinical documentation rather than for understanding and serving patients better. Three ideas from retail's data driven approach that healthcare can adapt with powerful results: 1) Understand patient journeys, not just patient episodes. Retail tracks the entire customer lifecycle. First visit, repeat purchase, loyalty programme, advocacy. Hospitals can adopt the same thinking. A patient is not a single OPD visit or a single admission. They are a relationship that spans years, sometimes generations. When hospitals start tracking the complete patient lifecycle, they discover opportunities to serve proactively. A patient who visited for a cardiac check up a year ago might benefit from a wellness follow up. A family that trusted the hospital for a delivery might welcome paediatric care guidance. Each touchpoint deepens the relationship. 2) Segment and personalise communication. Retail abandoned one size fits all marketing years ago. The best retailers send you information that is relevant to you, based on your history and preferences. Hospitals can do the same, ethically and with care. A diabetic patient receives seasonal health tips relevant to their condition. A new mother receives age appropriate child wellness reminders. A cardiac patient receives lifestyle guidance tailored to their treatment plan. This is not marketing. This is care delivered through communication. 3) Measure loyalty, not just satisfaction. Retail distinguishes between a satisfied customer and a loyal one. Satisfaction means the experience was adequate. Loyalty means the customer chooses you again and recommends you to others. Hospitals that start measuring loyalty metrics, return visit rates, referral patterns, and patient lifetime value alongside clinical satisfaction scores gain a much richer understanding of how well they are truly serving their community. The ethical considerations are important. Patient data must always be handled with the highest standards of privacy and consent. But within those boundaries, there is an enormous opportunity to use data the way the best retailers do: not to sell more, but to serve better. Is your hospital using patient data to build longer, deeper relationships? I would love to hear innovative examples from your experience. #PatientEngagement #HealthcareData #HospitalMarketing #PatientLoyalty #HealthcareInnovation #IndianHealthcare

𝗞𝗲𝗻𝘆𝗮'𝘀 𝗥𝗲𝘁𝗮𝗶𝗹 𝗚𝗶𝗮𝗻𝘁𝘀 𝗔𝗿𝗲 𝗦𝗶𝘁𝘁𝗶𝗻𝗴 𝗼𝗻 𝗮 $𝟭𝟬𝟬𝗠+ 𝗗𝗮𝘁𝗮 𝗚𝗼𝗹𝗱𝗺𝗶𝗻𝗲 – 𝗔𝗻𝗱 𝗗𝗼𝗶𝗻𝗴 𝗡𝗼𝘁𝗵𝗶𝗻𝗴 𝗪𝗶𝘁𝗵 𝗜𝘁! 💎📊 After deep-diving into #Kenya's Big 3 supermarket loyalty programs (Naivas Limited, Carrefour, Quickmart Supermarket), I discovered something shocking: We're witnessing the greatest missed opportunity in African retail history. 🤯 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 📈 🔹 Naivas: 2+ million customers, 5-year purchase histories, yet still relies on MANUAL point capture by cashiers 🔹 Carrefour: Digital-first approach, but basic utilization of customer intelligence   🔹 Quickmart: Traditional program with ZERO data sophistication 𝗧𝗵𝗲 𝗧𝗿𝗶𝗹𝗹𝗶𝗼𝗻-𝗦𝗵𝗶𝗹𝗹𝗶𝗻𝗴 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 𝗧𝗵𝗲𝘆'𝗿𝗲 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 💰 Kenyan supermarkets are missing out on a trillion-shilling opportunity to leverage their loyalty data for hyper-targeted offers such as personalized discounts and product suggestions based on individual shopping habits. Mass customization at scale through predictive replenishment, personalized lists and subscriptions, and advanced revenue optimization strategies like dynamic pricing, waste reduction, cross-selling, and churn prediction, all of which could dramatically boost profitability and transform customer experience through true personalization. 𝗪𝗵𝗮𝘁'𝘀 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗛𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗜𝗻𝘀𝘁𝗲𝗮𝗱? 🤦🏾♂️ - Naivas: Customers manually tell cashiers their phone numbers to earn 1 point per KES 100 - Carrefour: Has the tech but uses it like a digital receipt system - Quickmart: Prayer, Vibes & Inshaallah 🙏🏾 𝗧𝗵𝗲 𝗣𝗮𝘁𝗵 𝗙𝗼𝗿𝘄𝗮𝗿𝗱: 𝗪𝗵𝗮𝘁 𝗜𝘁 𝗪𝗼𝘂𝗹𝗱 𝗧𝗮𝗸𝗲 🚀 To truly unlock the value of loyalty programs in Kenya’s retail sector, supermarkets must invest in real-time customer data platforms, AI-powered analytics, mobile money integration, and omnichannel journey mapping, while strategically building teams for data science, segmentation, and personalization; above all, a cultural shift is needed - from simply running 'points programs' to building intelligent customer relationship platforms, allowing for dynamic offers, relationship-driven engagement, and individualized experiences that will drive loyalty and long-term profitability. 𝗧𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗰𝗮𝘀𝗲 𝗶𝘀 𝗠𝗔𝗦𝗦𝗜𝗩𝗘 📈: proper loyalty data utilization could deliver 20-30% higher customer lifetime value, 15-25% larger transactions, 40-50% better retention, and 10-15% marketing cost reduction. 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻❓ 𝗪𝗵𝘆 𝗮𝗿𝗲 𝗞𝗲𝗻𝘆𝗮'𝘀 𝗿𝗲𝘁𝗮𝗶𝗹 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗝𝘂𝗺𝗶𝗮, 𝗔𝗺𝗮𝘇𝗼𝗻, 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗲-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 to master customer intelligence while they collect dust-gathering phone numbers? 🤔 The data is there. The customers are willing. The technology exists. What's missing is vision and execution. 💪🏾 How do we unlock this goldmine? 🔓 #RetailInnovation #CustomerData #AI

Three years back, One of my friend faced a drop in repeat purchases in a fast-growing online marketplace. Instead of blindly increasing discounts, the team turned to SQL and data analytics to uncover the real reasons behind customer churn. SQL-Driven Approach 1. Identifying Lapsed Customers SELECT customer_id, COUNT(order_id) AS total_orders, MAX(order_date) AS last_order_date FROM orders GROUP BY customer_id HAVING COUNT(order_id) > 1 AND DATEDIFF(day, MAX(order_date), GETDATE()) > 60; 🔹 Insight: Target customers who haven’t ordered in 60+ days. 2. Discounts vs. Organic Purchases SELECT customer_id, COUNT(CASE WHEN discount_used = 'Yes' THEN 1 END) AS discount_purchases, COUNT(CASE WHEN discount_used = 'No' THEN 1 END) AS organic_purchases FROM orders GROUP BY customer_id; 🔹 Insight: Identify if customers only buy with discounts—these may not be loyal customers. 3. High-Value Customers Who Stopped Ordering SELECT customer_id, SUM(order_value) AS total_spent, MAX(order_date) AS last_order_date FROM orders GROUP BY customer_id HAVING total_spent > 500 AND DATEDIFF(day, MAX(order_date), GETDATE()) > 90; 🔹 Insight: Focus retention efforts on high-value customers. Challenges & Solutions Slow Queries? ✅ Added indexes on customer_id & order_date. Who to target? ✅ Used cohort analysis to find optimal re-engagement timing. Retention vs. Profitability? ✅ Ran A/B tests—loyalty perks worked better than heavy discounts. Business Impact ✔ 18% increase in repeat purchases with targeted campaigns. ✔ Optimized loyalty program to reward engagement, not just discounts. ✔ Reduced churn by identifying & acting on key retention signals. 💡 Key Takeaway: SQL isn’t just for reporting—it’s a powerful tool for understanding customer behavior and making smarter business decisions. What data-driven strategies have you used to boost retention? Let’s discuss!

Ever wonder why some brands seem to read your mind? It's RFM. Let me show you how. Recency, Frequency, Monetary value - the trifecta behind the curtain. By analyzing how recently and how often you engage with a brand, plus how much you spend, companies can predict your next move. Or try to persuade you to do something they want. 1️⃣𝗥𝗲𝗰𝗲𝗻𝗰𝘆: 𝗛𝗼𝘄 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗱𝗶𝗱 𝘁𝗵𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗺𝗮𝗸𝗲 𝗮 𝗽𝘂𝗿𝗰𝗵𝗮𝘀𝗲? Imagine a customer who just purchased last week. They’re still excited about their new find. Capitalize on this enthusiasm with timely communications that thank them for their purchase or offer a complementary product as a follow-up. For instance, an online fashion retailer noticed a 30% higher email open rate from customers who had made purchases within the last month. ➟ Armed with this insight, they launched tailored email campaigns offering a "Welcome Back" discount to recent buyers and a "We Miss You" campaign to reactivate dormant shoppers. 2️⃣ 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆: 𝗛𝗼𝘄 𝗼𝗳𝘁𝗲𝗻 𝗱𝗼 𝘁𝗵𝗲𝘆 𝗯𝘂𝘆? Considered your regulars, the lifeblood of your business. A subscription-based meal delivery service found that customers who ordered more than twice a month were prime candidates for an upsell to a premium plan with more choices and exclusives. ➟ By targeting these frequent diners with personalized offers to enhance their plan, they not only boosted the average LTV but also reinforced customer loyalty. 3️⃣ 𝗠𝗼𝗻𝗲𝘁𝗮𝗿𝘆: 𝗛𝗼𝘄 𝗺𝘂𝗰𝗵 𝗱𝗼 𝘁𝗵𝗲𝘆 𝘀𝗽𝗲𝗻𝗱? High spenders are your VIPs. They expect—and deserve—a level of service commensurate with their expenditure. An online goods retailer used their data to identify customers spending over $500 per transaction. ➟ These high rollers were then offered access to an exclusive VIP program that included personal stylist consultations and early access to new products, enhancing their buying experience and encouraging even higher spends. By breaking down your customer base using these three metrics, you can tailor your marketing strategies to target specific groups more effectively. *************** I am Alvin Huang I'm an e-commerce veteran with over $189 million in sales, specializing in scalable growth and resilient leadership. I deliver no-nonsense, actionable insights for serious business growth. Follow me for real-world strategies and case studies that drive success. #RFMStrategies #customercentric #alwaysbeselling

Why Loyalty Programs Have a Data Problem…and How PAR Fixes It. Loyalty programs live and die by their data. Yet the moment a shopper switches from swiping a card in-store to tapping Apple Pay online, that single customer suddenly looks like two (or three, or four) different people in your database. As a Data Scientist, I’ve tried stitching those identities together with everything from hashed emails to device fingerprints, when working with major brands like Shell, Nike and others, only to watch the whole approach crumble the second any one of those signals goes missing. The root issue is simple: traditional loyalty systems depend on sign-ups. If a customer doesn’t type in an email, scan a barcode, or swipe the exact same card each time, you’re blind to their full spending pattern. That means broken rewards, frustrated customers, and marketing campaigns that miss the mark. Payment Account Reference (PAR) changes the game. Each card gets a single, non-sensitive 29-character reference that travels with it everywhere, chip, swipe, tap, or token. Because the value is non-financial, you can store and query it without pulling sensitive data back into scope. Suddenly, every purchase across in-store POS, mobile app, and e-commerce checkout rolls up to one universal ID, no sign-up required, and no PCI headache. Here’s why that matters for loyalty teams: Auto-enrollment, zero friction – Rewards accrue when a customer pays, even on a brand-new device or a freshly issued card. No more “register to earn points” pop-ups. True 360° spend view – Whether it’s lattes on Monday or a bulk-bean order online Friday night, the same PAR ties it all together, giving you rock-solid lifetime value metrics and smarter segmentation. Cleaner data → better offers – With duplicates gone, your models stop double-counting customers and start surfacing the products they actually care about. Security without sacrifice – Because PAR can’t be used to transact, you slash the volume of sensitive fields peppered across your tech stack, shrinking compliance scope while expanding insight. IXOPAY bakes PAR directly into its payment orchestration layer, so merchants get the reference value in every authorization response, no custom build, no extra API calls. The heavy lifting stays behind the scenes; your loyalty engine simply keys off a single identifier and goes to work. From my perspective, this is the first realistic path to a loyalty program that works. Earning, burning, and personalizing in the background, exactly the way customers expect in 2025. What do you think? Could PAR finally unlock the seamless, card-linked loyalty experience we’ve been chasing for years? Let me know in the comments. P.S. For more in-depth Payments Strategy Breakdowns, check out my newsletter https://lnkd.in/e6eXZrF9

💥 “𝐘𝐨𝐮’𝐫𝐞 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐫𝐞𝐚𝐜𝐭𝐢𝐧𝐠 𝐭𝐨 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫. 𝐘𝐨𝐮’𝐫𝐞 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐢𝐭.” That’s one of many insights in a must-read article by Mark Sage, where he reframes the role of price, loyalty, and perception through the lens of 𝐌𝐨𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐄𝐜𝐨𝐧𝐨𝐦𝐢𝐜𝐬. Mark illustrates how 𝑑𝑒𝑠𝑖𝑔𝑛𝑖𝑛𝑔 𝑖𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒𝑠 𝑤𝑖𝑡ℎ 𝑖𝑛𝑡𝑒𝑛𝑡 can create lasting behavior, or bad habits. Whether it’s Tesco’s tiered challenges or mass coupons acting as signals (not discounts), his core message is clear: 🧠 Loyalty isn’t built with bigger deals. It’s built with better design. 🎯 Motivation matters more than mechanics. 📈 And perceived value often beats actual value. 🔍 𝐌𝐲 𝐥𝐞𝐧𝐬: 𝐇𝐨𝐰 𝐝𝐚𝐭𝐚 𝐚𝐧𝐝 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐛𝐫𝐢𝐧𝐠 𝐌𝐨𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐄𝐜𝐨𝐧𝐨𝐦𝐢𝐜𝐬 𝐭𝐨 𝐥𝐢𝐟𝐞 To apply this thinking in practice - take loyalty programs in grocery retail as an example - we need to ask better questions of our data. Measuring campaign ROI isn't enough. We need to understand what behaviour we're shaping and which customer segments we're reinforcing. This is where pricing analytics and customer insight come in. 📊 𝟓 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬 𝐭𝐡𝐚𝐭 𝐛𝐫𝐢𝐧𝐠 𝐦𝐨𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 𝐭𝐨 𝐥𝐢𝐟𝐞: 𝟏. 𝐏𝐫𝐢𝐜𝐞 𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐢𝐭𝐲 𝐛𝐲 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐲 𝐚𝐧𝐝 𝐡𝐨𝐮𝐬𝐞𝐡𝐨𝐥𝐝 → Who chooses low-, mid-, or high-priced items? → What price role do these categories play in the basket? 𝟐. 𝐀𝐧𝐜𝐡𝐨𝐫 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐩𝐞𝐫𝐜𝐞𝐩𝐭𝐢𝐨𝐧 → Which items drive price fairness, even if rarely purchased? → These are your price “symbols.” 𝟑. 𝐄𝐥𝐚𝐬𝐭𝐢𝐜𝐢𝐭𝐲 𝐛𝐲 𝐬𝐞𝐠𝐦𝐞𝐧𝐭 → Where does price movement change volume? → Who is discount-reliant vs. value-driven? 𝟒. 𝐏𝐮𝐫𝐜𝐡𝐚𝐬𝐞 𝐭𝐢𝐦𝐢𝐧𝐠 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 → Are customers trained to wait for discounts? → Does your promo calendar reinforce delay? 𝟓. 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐯𝐬. 𝐝𝐢𝐬𝐜𝐨𝐮𝐧𝐭 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐯𝐞𝐧𝐞𝐬𝐬 → Who stretches for goals? Who only bites on price? → What motivational profiles emerge? These dimensions help distinguish price-driven purchases from motivated participation and tell you if you're building habits or eroding margins. 🧠 𝐀 𝐬𝐭𝐫𝐨𝐧𝐠 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥 𝐬𝐡𝐨𝐮𝐥𝐝 𝐚𝐧𝐬𝐰𝐞𝐫: Who buys at full price, and why? Which discounts erode margin vs. build loyalty? Where does perceived value live, and how can we reinforce it? 💡 𝐁𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: We’ve trained customers to wait for deals. Now, we can train them to feel rewarded through progress, stretch, and participation. But only if we use our data to design for 𝐦𝐨𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧, not just discount. 👏 Thanks again, Mark, for the inspiration, and for reminding us that in loyalty, just like in Berk, the biggest dragon is often the one we created ourselves. 🔗 Link to the article in the comments. Curious to hear how others are using data to drive better behaviour. 🐉

"Points for purchases" is killing your brand. That's what Phil C., CEO of Upzelo, told me during our recent Chew On This episode. And after seeing the data from 4,000+ brands, I believe him. Here's what's actually working in loyalty and retention → Phil's journey is fascinating. Before Upzelo, he built the world's largest fitness platform with a 1.45% churn rate. Now he's helping brands reimagine loyalty programs. What he taught us: While most DTC brands are still playing the points game, they're bleeding customer value and watching CAC skyrocket. Instead, here are 3 strategies to ensure your loyalty program brings value to your customers and your brand: 1. Stop Chasing Transactions Traditional approach: Points for purchases Modern approach: Reward customer success Phil shared how one UK brand connected health data to their loyalty program. Every workout became a reason to engage, not just every purchase. 2. Meet Customers in Real Life Your customers don't live inside your Shopify store. One of Phil's clients, a motorcycle gear company, built their entire program around Saturday group rides. The result? 3,500 new program members in 3 weeks. No email blasts. No ads. Just organic sharing between riders. 3. Measure Real Impact Drop these vanity metrics: - Program signups - Points earned - Reward redemptions Instead, track what drives growth: - Purchase frequency - Category adoption - Real-world sharing 4. Goal achievement At Obvi, we're already seeing the impact of this approach. When we shifted from points-based rewards to focusing on customer fitness goals and results, our retention impact transformed. The Big Revelation → The best loyalty programs don't feel like programs at all. They feel like a natural extension of why customers chose you in the first place. Want to build real loyalty in 2024? Stop trying to buy it with points.  Start earning it by helping customers succeed. Huge thanks to Phil Carr for sharing these insights from his work with over 4,000 brands. Want the full playbook? Check out our Chewonthis DTC episode where we break down: - Moving beyond transactional loyalty - Building retention through real-life connections - Measuring what actually drives growth