Loyalty Program Analytics

My Favorite Analyses: the Recency-Frequency matrix. This simple yet powerful framework goes beyond traditional segmentation to provide actionable insights into customer behavior. By focusing on how recently and how often customers engage with your brand, you can tailor your strategies to maximize lifetime value. Why it works: - Recency: Customers who have purchased recently are more likely to purchase again. It's a strong indicator of engagement and future behavior. - Frequency: Customers who purchase more often demonstrate loyalty and satisfaction, leading to a higher customer value. Recency and Frequency are the most important indicators of customer value, exhibiting more correlation to CLV than Monetary Value which is the third component in traditional RFM analyses. The Recency-Frequency matrix helps you categorize your customers into segments based on behaviors instead of factors like demographics or psychographics that imply actions. The analysis reveals distinct customer segments that require unique marketing strategies, including your Champions, the customers who Need Attention, and those who have Already Churned. Implementing the Matrix: Depending on the size of your customer dataset, the Recency-Frequency matrix can be built in a spreadsheet or a more hefty tool like SQL or R. - Excel/Google Sheets: Use `MAXIFS`, `COUNT`, `PERCENTRANK`, and a pivot table to build the Recency-Frequency matrix, but watch out for row limits. - SQL: Leverage functions like `DATEDIFF` and `COUNT` to calculate metrics, and segment with `NTILE`. - R: The `RFM` package handles large datasets with ease, offering advanced segmentation and visualization. This approach isn’t just theory — it’s a data-backed method for ensuring your marketing dollars are spent where they’ll make the most impact. DM me if you'd like to learn more, including the marketing strategies that I most commonly recommend for each Recency-Frequency matrix customer segment. 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 #MyFavoriteAnalyses #ROI #MROI

Dear Data enthusiasts, From Data to Rewards:- How Clustering Helps Banks Personalize Offers for every customer 🎉📊 Did you Know banks are using 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 to turn data into delightful customer experiences? Let's break it down with a real-time example!🏦💡 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨:- A leading Indian bank is celebrating its 𝟏𝟓𝐭𝐡 𝐚𝐧𝐧𝐢𝐯𝐞𝐫𝐬𝐚𝐫𝐲🎂next month and wants to reward loyal credit card users. But here's the twist: 𝐧𝐨𝐭 𝐚𝐥𝐥 𝐨𝐟𝐟𝐞𝐫𝐬 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞! 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞:- Manually sorting customers for tailored offers is time-consuming and error-prone. 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐡𝐢𝐠𝐡-𝐯𝐚𝐥𝐮𝐞 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬. 𝐄𝐧𝐭𝐞𝐫 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 🤖🔍:- By analyzing customer features like:- ✅𝐏𝐚𝐲𝐦𝐞𝐧𝐭 𝐡𝐢𝐬𝐭𝐨𝐫𝐲 (on-time vs. delayed) ✅𝐂𝐈𝐁𝐈𝐋 𝐬𝐜𝐨𝐫𝐞 (excellent vs. moderate) ✅𝐂𝐫𝐞𝐝𝐢𝐭 𝐜𝐚𝐫𝐝 𝐮𝐬𝐚𝐠𝐞 𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 ( high vs. low) And More! ...the bank groups customers into clusters with 𝐬𝐢𝐦𝐢𝐥𝐚𝐫 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐬. 𝐓𝐡𝐞 𝐑𝐞𝐬𝐮𝐥𝐭 🎁:- 1️⃣🏆𝐇𝐢𝐠𝐡-𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐂𝐥𝐮𝐬𝐭𝐞𝐫:- Customers paying bills on time, high CIBIL scores, and frequent usage get 𝐩𝐫𝐞𝐦𝐢𝐮𝐦 𝐫𝐞𝐰𝐚𝐫𝐝𝐬 (e.g., luxury vouchers, bonus points). 2️⃣📉𝐌𝐨𝐝𝐞𝐫𝐚𝐭𝐞-𝐔𝐬𝐞 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬:- Those with occasional delays or medium usage receive 𝐭𝐚𝐫𝐠𝐞𝐭𝐞𝐝 𝐨𝐟𝐟𝐞𝐫𝐬 (e.g., fee waivers, cashback on essentials). 3️⃣⚠️𝐀𝐭-𝐑𝐢𝐬𝐤 𝐂𝐥𝐮𝐬𝐭𝐞𝐫:- Customers with frequent delays or low engagement get 𝐠𝐞𝐧𝐭𝐥𝐞 𝐫𝐞𝐦𝐢𝐧𝐝𝐞𝐫𝐬 and  𝐥𝐢𝐦𝐢𝐭𝐞𝐝-𝐭𝐢𝐦𝐞 𝐩𝐞𝐫𝐤𝐬 to boost loyalty. 𝐖𝐡𝐲 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠? it's 𝐟𝐚𝐢𝐫, 𝐝𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧, and ensures the right incentives go to the right people! By recognizing patterns, banks maximize ROI on promotions while strengthening customer relationships. 🤝' This is a perfect example of how  𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 and clustering are transforming customer service and loyalty programs in the banking industry!  🌐📊 [Ref : Image Generated by AI] 👉Follow Korrapati Jaswanth more insights and content on DS/ML. #MachineLearning #Clustering #BankingInnovation #DataScience #CustomerExperience #FinTech #DataAnalytics #Personalization #AIinBanking #DigitalTransformation #ML #Banking #CustomerSegmentation #DataDriven #AI #FintechSolutions #SmartBanking

👉 The Affordability Crisis Just Rendered Your Loyalty Program Obsolete. With inflation and economic uncertainty, customers are becoming ruthlessly price-sensitive. If your retention strategy still relies on generic, high-cost discount programs ("Spend $100, get $5 in points"), you are training your users to love the discount, not the brand. This transactional relationship is a financial drain and will fail under pressure. The old model of simply outspending the competition on Customer Acquisition Cost (CAC) is dead. The only way to achieve sustainable, crisis-proof growth is through an aggressive, strategic pivot to efficient retention. The Solution: AI-Powered Customer Loyalty As an expert of scaling companies like Roku and IMVU, I believe the current economic environment demands a shift from reactive loyalty to proactive, predictive retention using Lean AI. We must stop rewarding customers who would have purchased anyway and focus resources on those at risk. The AI Advantage is Clear: - Prediction over Points: Machine learning models calculate a real-time Propensity-to-Churn Score for every user. - Hyper-Personalized Value: When a user crosses the churn threshold, AI triggers a customized value proposition (e.g., exclusive access, premium service, or a targeted cash-equivalent reward)—maximizing LTV while minimizing the Cost of Retention. This approach transforms a lost customer into a highly profitable, re-engaged super-fan. A Roadmap for Growth Leaders: Four Pillars of AI Retention In my new article, I outline the non-negotiable strategy for building this efficient retention engine: 1. Build a Unified Customer Data Platform (CDP): AI is only as good as the clean, 360-degree data fueling it. 2. Product-Led Retention: Use AI to accelerate the "Aha!" moment during onboarding. 3. Continuous Automation: Automate experimentation to find the optimal reward, incentive, and timing. 4. Prioritize Exclusive Access: Build an emotional moat through community and VIP experiences, not just just price cuts. The companies that survive and dominate the next decade are the ones that strategically deploy AI to build unshakeable, hyper-personalized relationships. Read the full analysis and technical roadmap here: 👇

They burned the points. They took the reward.... But they never came back. We love bragging about high redemption rates. "70% burn!" "Record redemptions this quarter!" But here’s the uncomfortable question: 𝗔𝗿𝗲 𝘆𝗼𝘂 𝗰𝗲𝗹𝗲𝗯𝗿𝗮𝘁𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗰𝗼𝗻𝗰𝗲𝗿𝗻 𝘆𝗼𝘂? Because high burn rates aren’t always a win. Sometimes, they’re just a symptom. A symptom of: – Weak reward design (𝗡𝗼𝘁𝗵𝗶𝗻𝗴 𝗮𝘀𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗼𝗿 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝘁𝗼 𝘀𝗮𝘃𝗲 𝗳𝗼𝗿) – Poor in-brand engagement (𝗬𝗼𝘂’𝗿𝗲 𝗽𝗮𝘆𝗶𝗻𝗴 𝗿𝗲𝗮𝗹 𝗺𝗼𝗻𝗲𝘆 𝘁𝗼 𝗳𝘂𝗻𝗱 𝗼𝗳𝗳-𝗯𝗿𝗮𝗻𝗱 𝗿𝗲𝗱𝗲𝗺𝗽𝘁𝗶𝗼𝗻𝘀) – Or worse, members using up points before they churn (𝗯𝘆𝗲! 𝗯𝘆𝗲!) When burn becomes the KPI, two things happen: You confuse activity with loyalty. You cannibalise value that should have driven incremental behaviour. Not to mention the cost. Points redeemed 𝗼𝘂𝘁𝘀𝗶𝗱𝗲 your ecosystem = 𝗺𝗼𝗻𝗲𝘆 𝗼𝘂𝘁. Points redeemed 𝘄𝗶𝘁𝗵𝗶𝗻 your ecosystem = 𝘂𝗽𝘀𝗲𝗹𝗹 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆. I’ve seen this play out up close and personal, where a power brand in the region proudly quoted rising redemption rates, but behind the scenes, the redemptions are happening off platform, and the real value is leaking out. Burn was high, yes, but loyalty? Not so much. It was a reminder: High burn doesn’t mean high engagement if it’s not driving repeat spend or brand preference. So instead of chasing redemption for its own sake, ask: 👉 Is the reward driving repeat spend? 👉 Is it reinforcing the brand relationship? 👉 Or is it just a clean exit? Because loyalty isn’t about how many points get burned. 𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗼 𝗰𝗼𝗺𝗲𝘀 𝗯𝗮𝗰𝗸, 𝗮𝗻𝗱 𝘄𝗵𝘆. What do you think of burn as a success metric? Would love to hear your thoughts. #LoyaltyStrategy #CustomerEngagement #CRM #RedemptionDesign #MarketingROI #postno18

𝗖𝗵𝘂𝗿𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 – 𝗪𝗵𝘆 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗟𝗲𝗮𝘃𝗲 (𝗮𝗻𝗱 𝗛𝗼𝘄 𝘁𝗼 𝗦𝘁𝗼𝗽 𝗧𝗵𝗲𝗺) Netflix once noticed a troubling pattern—𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲𝗿𝘀 𝘄𝗲𝗿𝗲 𝗰𝗮𝗻𝗰𝗲𝗹𝗶𝗻𝗴 𝗮𝗳𝘁𝗲𝗿 𝗯𝗶𝗻𝗴𝗲-𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 show. Instead of ignoring the trend, they r𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺, keeping users engaged with new content. The result? 𝗙𝗲𝘄𝗲𝗿 𝗰𝗮𝗻𝗰𝗲𝗹𝗹𝗮𝘁𝗶𝗼𝗻𝘀, 𝗵𝗶𝗴𝗵𝗲𝗿 𝗿𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗿𝗲𝘃𝗲𝗻𝘂𝗲. Most businesses focus on acquiring new customers but neglect the most important question: 𝗪𝗵𝘆 𝗮𝗿𝗲 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗹𝗲𝗮𝘃𝗶𝗻𝗴? 𝗛𝗼𝘄 𝗶𝘀 𝗰𝗵𝘂𝗿𝗻 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗲𝗱? 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗖𝗵𝘂𝗿𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 – Analysts track purchase frequency, inactivity periods, engagement levels, and customer complaints to spot early signs of churn. 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗔𝘁-𝗥𝗶𝘀𝗸 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 – Not all churn is equal. Some customers leave due to pricing, poor service, or lack of engagement. Analysts categorize churn reasons to develop targeted retention strategies. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗻𝗴 𝗙𝘂𝘁𝘂𝗿𝗲 𝗖𝗵𝘂𝗿𝗻 – Using machine learning models, businesses can forecast which customers are most likely to leave based on historical data. 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 – Top companies implement loyalty programs, personalized offers, and proactive customer support to prevent churn before it happens. 𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗖𝗵𝘂𝗿𝗻 𝗜𝗺𝗽𝗮𝗰𝘁 – Businesses calculate the Customer Lifetime Value (CLV) to understand how churn affects long-term revenue and profitability. Companies that ignore churn analysis experience 𝗵𝗶𝗴𝗵 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗰𝗼𝘀𝘁𝘀, 𝗹𝗼𝘀𝘁 𝗿𝗲𝘃𝗲𝗻𝘂𝗲, 𝗮𝗻𝗱 𝗱𝗲𝗰𝗹𝗶𝗻𝗶𝗻𝗴 𝗯𝗿𝗮𝗻𝗱 𝗹𝗼𝘆𝗮𝗹𝘁𝘆. Meanwhile, brands that actively monitor churn—like 𝗡𝗲𝘁𝗳𝗹𝗶𝘅, 𝗔𝗺𝗮𝘇𝗼𝗻, 𝗮𝗻𝗱 𝗦𝗽𝗼𝘁𝗶𝗳𝘆—𝘂𝘀𝗲 𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝘁𝗼 𝗸𝗲𝗲𝗽 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗲𝗻𝗴𝗮𝗴𝗲𝗱 𝗮𝗻𝗱 𝗹𝗼𝘆𝗮𝗹. Do you know why your customers are leaving, or are you just guessing? 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘂𝗽𝗱𝗮𝘁𝗲𝘀 : https://lnkd.in/dAUQ5Qx7

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

✈️ Want to fly First Class on points? Better read the fine print. There's a growing trend in the airline world - and it's bad news for award travelers chasing luxury seats. More and more airlines are putting up velvet ropes around First Class redemptions, making them exclusive to their own loyalty programs - and only for elite members. Here's what we're seeing lately: 🔒 Emirates just restricted First Class award bookings to Skywards members with Silver status or higher 🔒 Singapore Airlines First Class? Only bookable with KrisFlyer miles - no partners allowed 🔒 Air France La Première? You’ll need to be a Platinum Flying Blue member to even see award availability 🕐 Lufthansa still allows partners to book First Class... but only 1-2 weeks out. Meanwhile, Miles & More members can snag it almost a year in advance. 💡 It’s a loyalty strategy shift: Airlines are hoarding their most aspirational redemptions, turning First Class into an elite-only playground. They’re not just selling luxury - they’re selling loyalty. This changes the game for points and miles strategists. 👉 What does this mean for you? 🔹 Time to rethink which programs you credit your flights to 🔹 Consider elite status with specific airlines if First Class is your goal 🔹 And always check award access before you hoard points in the wrong program ✈️ Premium travel is still possible with points - but the rules are changing fast.

RFM analysis is one of the oldest and most effective segmentation techniques. In case you don't know, RFM stands for: 1️⃣ Recency: how long ago? 2️⃣ Frequency: how often? 3️⃣ Monetary value: how much $, or was a given goal achieved? Despite its proven effectiveness, RFM analysis remains under the radar for many professionals who mistakenly believe it’s too complex or requires a team of data scientists to execute. The truth? It’s a straightforward process that can yield incredibly valuable insights with just a few steps. Here’s how I leveraged RFM analysis for a client: • Data cleansing: A client provided a dataset of 65,000 transactions. I ensured all personal information, like customer names and emails, was anonymized, focusing only on transaction data. • Initial analysis: Using ChatGPT, I conducted an RFM analysis on a sample of the data. The output included the RFM values themselves, but also quintiles "bins" (grouped by slices of 20%). • Customized segmentation: I further refined the analysis by creating original segment names tailored to the client’s industry, complete with descriptions and targeted marketing tactics. • Visual enhancements: To make the insights more actionable, I added visualizations directly into the Excel output file, making the data easier to interpret and apply. • Automated efficiency: Finally, I asked ChatGPT to generate the complete Python code for the analysis and applied it to the entire dataset of 65,000 transactions—all in just a few seconds. RFM analysis isn’t just a relic of the past—it’s a practical, powerful tool that can be executed quickly and effectively, and powerful tools like ChatGPT makes it even easier! What could have taken many hours, if not days, was done in about an hour. RFM analysis isn’t limited to sales data—it can also be applied to behavioral data, provided you have a user or customer ID. In the days of Universal Analytics, marketers had easy access to metrics like the number of days since the last visit and visit frequency. With GA4, these insights aren’t as readily available unless you implement custom tracking or utilize BigQuery.

✈️ 𝗖𝗮𝘀𝗵𝗯𝗮𝗰𝗸 𝗰𝗿𝗲𝗱𝗶𝘁 𝗰𝗮𝗿𝗱𝘀 𝗮𝗿𝗲 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗸𝗶𝗻𝗴. 𝗧𝗿𝗮𝘃𝗲𝗹 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝗮𝗿𝗲. Our latest SaveSage study, now covered widely in the media, reveals a clear shift in how Indians are using their credit card rewards beginning 2025: • 32% of all reward redemptions are now for flights & airline miles - overtaking cashback • ₹850 crore worth of rewards redeemed for travel by SaveSage users this year • Users earned up to 15.6% value back on travel bookings • The 30–35 age group emerged as the most financially savvy cohort 𝗪𝗵𝗮𝘁’𝘀 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗲? Rising travel costs + better awareness = users treating points and miles as a strategic financial asset, not just a nice-to-have perk. At SaveSage, this is exactly what we’re building for — helping Indians unlock real, outsized value from rewards they already earn, and making travel meaningfully more affordable. The future of spending isn’t about discounts. It’s about optimisation. 🚀 𝗣.𝗦. Link to read the detailed findings in the comments.

Aussies turn to loyalty programs to stretch budgets this festive season. The first Velocity Points Pulse Report from Virgin's Australia Velocity Frequent Flyer reveals: 1. Australians saved an estimated $3.5billion in the past year by redeeming airline loyalty points, with many planning a “cash-free Christmas” as cost-of-living pressures rise. 2. Members hold 783billion unused points worth nearly $2billion, and average savings reached of $400. 3. Retail redemptions are growing, led by Gen Z, as points increasingly support everyday budgets, not just travel. 4. 55% of Australians feel more financially pressured than they did a year ago, and 40% say they are struggling to afford daily necessities. As a result, millions are preparing for a “cash-free Christmas” by relying on their points balances. 5. Velocity Frequent Flyer has also seen a surge in retail redemptions since expanding its in-store partnership with Myer, with products like air fryers, coffee machines, luggage and headphones becoming popular choices. 6. Younger generations are driving this shift even further: 79% of Gen Z plan to use points for shopping 83% expect to redeem them for travel Velocity Frequent Flyer CEO Nick Rohrlach says the findings show a significant change in how Australians use reward programs. “Members aren’t just saving for flights anymore—there’s been a 40% increase in earning through non-air partners, meaning everyday spending is now turning into Christmas presents,” Rohrlach said. “As cost-of-living pressures grow, points are becoming a meaningful part of how many Australians manage their finances.” Velocity encourages members to check their balances and make use of unredeemed rewards, noting that timely redemptions can meaningfully offset seasonal spending. https://lnkd.in/gnFEZujM