Loyalty Program Analytics

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

It was a pleasure to talk to Paul Morrison at WNS about the impact of AI on retail. We discussed a wide range of topics, from the impact of GenAI on retailers operations, to how it could impact the customer journey. It's such a fascinating area which is changing at pace. Here are a few areas that I think will see the largest impact. ➡ Personalisation at Every Stage GenAI crafts individual experiences, from targeted product recommendations based on past purchases to custom promotions that hit right when a customer is most receptive. It builds customer loyalty by making each interaction feel tailor-made. ➡ Intelligent CX Support (WISMO) Solving the most common customer concern, “Where’s my order?” GenAI-powered chatbots handle this and other frequent queries instantly, freeing up staff and providing seamless, reliable support—no human intervention needed. ➡ Predictive Inventory Management By analysing sales patterns and seasonal demand (and thousands of other inputs such as weather, supply chain disruptions, social media buzz), GenAI forecasts precisely what stock to have on hand, minimising costly overstocking or disappointing stockouts. This ensures products are ready when customers want them. ➡ Dynamic Pricing, Rewards, and Promotions for Real-Time Relevance GenAI empowers retailers to adjust prices, rewards, and promotions in real-time based on demand, competitor trends, and customer profiles. This approach ensures every deal feels personalised, offering customers relevant discounts or loyalty rewards right when they’re most likely to engage. It’s a seamless way to stay competitive, maximise margins, and increase customer satisfaction—all while driving repeat business through tailored offers that adapt to each shopper's unique journey. ➡ Enhanced Loyalty Through Personalised Rewards GenAI helps personalise loyalty programme rewards, delivering offers that resonate based on individual behaviour, increasing retention and turning one-time buyers into repeat customers. Please do have a listen, I really enjoyed the conversation. Apple: https://bit.ly/AP3-L Spotify: https://bit.ly/SO3_L Amazon Music: https://bit.ly/AZ3_L

Retailers I spoke with at CRMC kept telling me they’re strategically all-in on identifying and leveraging “key moments”—critical interactions indicating that someone will become a high-value, loyal customer. They don’t want to chase customers with offers, and they don’t want to just track loyalty. They want to architect it by building marketing programs that drive toward these key moments. AI decisioning agents excel at this. They understand the unique behavioral patterns that lead to high lifetime value for each customer. Instead of waiting for someone to hit an arbitrary purchase threshold to “become loyal,” they create personalized experiences that guide customers toward defining moments. Maybe that’s the third purchase, the first use of a premium feature, or referring a friend. AI Decisioning optimizes for these moments by learning the interactions, timing, and channels that create lasting customer relationships, not just immediate conversions. It helps retailers decisively shift from hoping for loyalty to cultivating it. I wrote more broadly about how AI Decisioning can impact retail and loyalty programs recently in Total Retail. Read it here: https://lnkd.in/d_Y9DnkP

We're heading toward a “hidden recession” in credit-card rewards. I used to love the perks from my Chase Sapphire Reserve card, however the perks we’ve all come to treat like a second currency are poised for a reset. Issuers are applying higher annual fees, tighter redemption windows, and rotating bonus categories - classic early signs that points are getting pricier to earn and harder to burn. What's going on? 🚩 $1.2 TRILLION in card debt. Outstanding balances hit an all-time high in Q4 2024, even as rejection rates for new credit rise. 🚩 Gen Z & young millennials are most exposed. They’re the heaviest rewards gamers and the least likely to pay in full each month - creating a perfect storm if perks get devalued. 🚩 Downturn playbook = quiet devaluation. Post-2008, issuers raised redemption thresholds and dialed back 0% promos. Expect déjà vu if a “normal” recession lands. Rewards programs aren’t going away - but the easy wins are. When points lose punch, issuers will shift loyalty economics toward behavioral engagement (think cash-back for everyday spend) and embedded experiences that keep customers inside their ecosystem. For merchants and fintechs, this is an opening to rethink value props: replace one-size-fits-all credit perks with real-time, data-driven loyalty incentives that meet customers where they are...online, in-app, or at a biometric-enabled checkout. #fintech #payments

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!

Exciting news! My first solo-authored paper has just been published — and it’s full of insights for anyone designing or managing a loyalty program. Here are the big takeaways: 👉 When customers redeem loyalty points instead of paying with money, they feel more satisfied — and that leads to greater loyalty. 👉 Redemption = satisfaction = return visits. BUT there's a catch… 🚫 If your program includes bonus incentives that customers give up by redeeming points (e.g., “save your points and get a bonus”), it can actually backfire. So what should you do? 🔹 Encourage redemption. Don’t make your points so “valuable later” that people never want to use them. 🔹 Be careful with conditional bonuses. Incentives that punish redemption can unintentionally reduce the effectiveness of your loyalty program. If you're in marketing, hospitality, retail, or fintech — and you’re thinking about how to get the most from your loyalty program — this paper is for you. DM me if you'd like a copy or want to talk more about how these findings apply to your business. #CustomerLoyalty #LoyaltyPrograms #ConsumerBehavior #MarketingResearch #BehavioralScience #PublishedResearch #MarketingStrategy https://lnkd.in/gUmKF2Tf

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

𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗟𝗲𝗮𝗱/𝗟𝗮𝗴 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: 𝗧𝗶𝗺𝗲-𝗧𝗿𝗮𝘃𝗲𝗹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 Ever wanted to slip through the folds of time in your data to revisit a customer’s last purchase—or even forecast their next move? With PySpark’s 𝗹𝗮𝗴(), 𝗹𝗲𝗮𝗱(), 𝗮𝗻𝗱 𝗳𝗶𝗿𝘀𝘁_𝘃𝗮𝗹𝘂𝗲(), you’re equipped to navigate both past and future transactions like a true analytics explorer. 𝗧𝗵𝗲 𝗧𝗶𝗺𝗲‐𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗧𝗼𝗼𝗹𝗸𝗶𝘁 𝗹𝗮𝗴(): Peek into the past (“What was their previous order?”) 𝗹𝗲𝗮𝗱(): Sneak a glimpse at the future (“What might they buy next?”) 𝗳𝗶𝗿𝘀𝘁_𝘃𝗮𝗹𝘂𝗲(): Lock in that all‑important “first transaction” baseline 𝗙𝗶𝘃𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗧𝗶𝗺𝗲‐𝗧𝗿𝗮𝘃𝗲𝗹 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀: 𝟭.𝗦𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝗩𝗮𝗿𝗶𝗮𝗻𝗰𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲: Measure the difference between current and previous purchase amounts. 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: Partition by customer, apply lag(purchase_amt), then subtract—simple math, dramatic insights. 𝟮.𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲: Track the interval between consecutive orders. 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: Use lag(purchase_date) within each customer window, then compute datediff for gap analysis. 𝟯.𝗡𝗲𝘅𝘁‐𝗢𝗿𝗱𝗲𝗿 𝗣𝗿𝗲𝘃𝗶𝗲𝘄 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲: Display upcoming purchase amounts alongside present transactions. 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: Apply lead(purchase_amt) to tag each row with its future counterpart—foresight, delivered. 𝟰.𝗙𝘂𝘁𝘂𝗿𝗲 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 𝗗𝗮𝘁𝗲 𝗧𝗮𝗴𝗴𝗶𝗻𝗴 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲: Append next transaction dates to current records for cycle analysis. 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: Utilize lead(purchase_date) to integrate future timestamps into today’s data. 𝟱.𝗙𝗶𝗿𝘀𝘁‐𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 𝗕𝗮𝘀𝗲𝗹𝗶𝗻𝗲 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲: Mark every transaction with that customer’s first-ever purchase amount. 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: Leverage first_value(purchase_amt) over a customer-partitioned window—your historical reference point. dummy_data = [ (1, "user_001", 150.50, "2024-01-15"), (2, "user_001", 200.00, "2024-02-10"), (3, "user_001", 175.25, "2024-03-05"), (4, "user_001", 300.00, "2024-04-20"), (5, "user_001", 125.75, "2024-05-15"), (6, "user_002", 89.99, "2024-01-20"), (7, "user_002", 120.00, "2024-02-15"), (8, "user_002", 95.50, "2024-03-10"), (9, "user_002", 250.75, "2024-04-25"), (10, "user_003", 75.00, "2024-01-10"), (11, "user_003", 180.50, "2024-02-28"), (12, "user_003", 220.00, "2024-03-15"), (13, "user_003", 165.25, "2024-04-30"), (14, "user_003", 310.00, "2024-05-20") ] # Define schema for better performance and type safety schema = StructType([ StructField("transaction_id", IntegerType(), True), StructField("user_id", StringType(), True), StructField("amount", DoubleType(), True), StructField("date", StringType(), True) ]) #PySpark #DataEngineering #BigData #Analytics #ApacheSpark #Python #CustomerAnalytics #WindowFunctions #SparkSQL #LeadLag

✈️ 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.

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