Loyalty Program Analytics

I’m delighted to launch our latest thought leadership research with Transportation, Shipping, & Logistics at Amazon, looking at how delivery can drive loyalty. 🔍 Our pan-European analysis across UK, Spain, France and Italy uncovered some super interesting insights. For one (see graph), the affluence-age relationship isn't just a demographic split – it's aligned to a lifetime value predictor that’s heavily influenced by delivery. Knowing which consumer cohort to target and how, is a critical component of profitability. The data highlights a growing divide in consumer behaviour, emphasising the need for a tailored approach: agile, customer-centric delivery for the younger, affluent segments, and value-driven strategies to attract and convert older, more cautious shoppers. Another way of identifying target cohorts is to look at repeat purchases. Our research reveals a clear trend: affluent GenZ and Millennial shoppers not only buy more frequently, but also exhibit higher loyalty. From these cohorts, fast and convenient delivery options are crucial to capture their repeat business. Conversely, older and less affluent consumers are more price-sensitive and cautious, indicating a different value proposition is needed to engage and retain them. 🎯 The Strategic Imperative: This isn't just about who's buying more – it's about the fundamental reshaping of retail economics: 💥 The Loyalty Multiplier Effect: When high-affluence millennials increase their purchase frequency, they don't just buy more – they create a compound growth effect. Each additional delivery satisfaction point translates to a higher likelihood of repeat purchase. 💥 The Hidden Cost Dynamic: Less affluent customers show more price sensitivity, suggesting a different value proposition is needed to engage and retain them. When retailers align delivery pricing with segment-specific price thresholds, they can potentially reduce the cost to serve by consolidating consignments or extending delivery windows. Smart delivery segmentation can be a profit opportunity when mapped correctly to purchasing power. 💥 The Generation Bridge: The 35-44 affluent segment isn't just buying more – they offer foresight into the behavioural patterns that are likely to cascade down to other segments. Their behaviours today provide a glimpse into tomorrow's consumers in terms of life-stage, omnichannel behaviour and loyalty drivers. Ultimately, delivery options require a tailored strategy depending on the customer. There is no one-size fits all. Our report with Amazon Shipping is packed full of more insights so download for free and take a look! Download our FREE report now 🔗 https://lnkd.in/eJnCu3wW

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

Unleashing Insights: A Power BI Deep Dive into a Sample Retailer’s Customer Data Together with my talented collaborator, Hamed Soleimani we embarked on a data-driven journey to uncover hidden patterns and trends within a sample retailer’s customer base. Leveraging a snapshot of customer data encompassing demographics, purchasing behaviour, and membership details, we meticulously crafted a robust data model in Power BI. This foundation enabled us to construct a dynamic and interactive dashboard that empowers decision-makers to: • Visualize customer segments: Explore customer profiles based on geography, spending habits, and membership status. • Identify high-value customers: Uncover customers with the highest lifetime value and profit margins. • Optimize marketing efforts: Analyse customer behaviour to refine targeted campaigns and promotions. • Benchmark performance against KPIs: Track progress towards average spending goals for different customer segments. Our analysis revealed compelling insights into customer behaviour and performance. Key findings include: • Distinct customer segments: Customer spending and loyalty vary significantly by region and membership tier. • Profitability hotspots: Certain geographic areas and product categories drive disproportionate profit margins. • Opportunities for growth: Identifying untapped customer segments and underperforming product categories can inform strategic initiatives. Recommendations: 1. Targeted marketing and retention strategies: Implement tailored marketing campaigns based on customer segments, focusing on high-value customers and regions with untapped potential. Prioritize customer retention efforts in departments with low club membership rates. 2. Profitability optimization: Conduct a deep dive into departments with low profit margins to identify cost-saving opportunities or revenue enhancement strategies. Consider adjusting product pricing or assortment in these areas. While our analysis yielded valuable insights, it's essential to acknowledge the limitations imposed by the single-day dataset. Time series analysis, a powerful tool for identifying trends and forecasting, was unfortunately beyond our scope. Despite these constraints, we successfully transformed raw data into actionable intelligence, demonstrating the potential of data visualization to drive business growth. #PowerBI #dataanalytics #datavisualization #customerinsights #collaboration #businessintelligence

Back with some eye opening stats on how restaurants think about their ability to access and use data. These are again from our fabulous report with Nation's Restaurant News, in conjunction with co-sponsors Square and SMG, on loyalty, data, and tech trends in the space. • 42% of brands say that their ability to collect and use data is either not developed or early stage • Only 35% track order history from online orders (less than half of brands who have a loyalty program actively track this as well(!!)) • 19% of brands don't know or are not sure where their guest data is stored (this is the second largest category, behind POS, and higher than CRM at 15%). We are still so early when it comes to data maturity for the restaurant industry - especially customer data. Many restaurants rightly struggle with access, understanding, and action - especially when it comes to data around non-loyalty guests. Often times, the loyalty program is the only source of data for a brand. But here is how we've seen brands on Bikky leverage both sets - the anonymous, in-store guest data, alongside first-party loyalty data: ➡️ Understanding the lifetime value of loyalty vs. non-loyalty guests (anecdotally a few brands have shared that it's 6-8x higher). This is a great proxy when your CEO / CFO asks "what's the ROI of our program?", or when a franchisee says "I don't push loyalty because it's just about discounts." ➡️ Understanding how frequency or average check change when a guest switches from a non-loyalty to loyalty. A good proxy for measuring the incrementality of the loyalty program on an existing guest. ➡️ Measuring the churn of a pure loyalty guest vs. a purely in-store guest. A good proxy for understanding the "stickiness" of someone who first visits your brand through the loyalty program vs. a traditional ordering channel. This helps emphasize the loyalty program's role in driving long-term guest retention. ➡️ Understanding which menu items are popular with loyalty guests vs. first-time non-loyalty guests. A perfect opportunity for marketing to bring actionable ideas to ops and franchisees on what to promote to the guest in-store (instead of just falling back on your best sellers). Successfully using guest data will be a critical restaurant capability in the future. The business (and consumer and competitive landscape) has evolved too much in the past 3-4 years for it *not* to matter anymore. 🫡