Purchase Frequency Analysis

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

A direct-to-consumer brand doubled their customer lifetime value in eight weeks. Same product. Same price. Same advertising spend. What changed? Order frequency strategy. I understand how complex customer retention feels when acquisition costs keep rising. Your investment in building loyal customers matters deeply, and sustainable growth requires focusing on purchase patterns rather than one-time transactions. The breakthrough came from analyzing customer behavior after the first purchase. Four retention strategies that transform single buyers into repeat customers: First, create purchase timing triggers based on consumption patterns. Track when customers typically run out of products. Send reorder reminders three days before depletion. Timing-based outreach converts necessity into convenience. Second, bundle complementary products that extend usage occasions. Customers buying skincare need application tools. Coffee buyers need storage solutions. Complementary bundling increases order value while solving related problems. Third, develop educational sequences that maximize product benefits. Most customers underuse products they purchase. Tutorial content increases satisfaction and consumption rates. Educated customers buy more frequently because they experience better results. Fourth, offer subscription flexibility that reduces commitment anxiety. Rigid schedules create cancellation pressure. Adjustable delivery dates accommodate changing needs. Flexible subscriptions retain customers through life transitions. The brand implemented consumption-based reordering with educational follow-up sequences. Average orders per customer increased from 1.2 to 3.8 annually. Revenue per customer nearly tripled without increasing acquisition spending. Your retention strategy should anticipate customer needs before they recognize them. From my perspective, successful direct-to-consumer scaling requires treating every first purchase as the beginning of a relationship. What retention approach has most increased your customer lifetime value?

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

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.

Struggling to Scale? It’s All About CAC to LTV Let me ask you this: Do you actually know your LTV (Lifetime Value)? Most Shopify founders I talk to don’t. They’re scaling blindly, focusing on ROAS or revenue, while CAC (Customer Acquisition Cost) creeps up. But here’s the thing���without knowing your LTV, you’ll never know how much you can actually afford to acquire a customer and scale profitably. Here’s a step-by-step breakdown of how to calculate your LTV using Shopify and cohort analysis: 1️⃣ Start With Shopify’s Sales Reports Go to Analytics > Reports > Customers Over Time in your Shopify admin dashboard. Use this report to find: Average Order Value (AOV): Calculate this by dividing total revenue by the total number of orders. Purchase Frequency (F): Find how often your customers return to buy within a specific time period. Formula: AOV = Total Revenue / Number of Orders F = Total Orders / Total Unique Customers 2️⃣ Track Repeat Purchase Rates Over Time Run a cohort analysis to see how many first-time customers return and how much they spend. Look at: Month 1, Month 3, Month 6 customer retention rates. Spend patterns of customers acquired in specific months. 3️⃣ Calculate Customer Lifetime Value (LTV) Using your AOV and frequency data, apply the formula: LTV = AOV x Frequency x Customer Lifespan If you don’t know the customer lifespan, start with 12–24 months as an estimate for most eCom businesses. Why This Matters Without knowing your CLTV, you’re throwing darts blindfolded when setting budgets and scaling ads. This number directly informs how much you can spend to acquire a customer (CAC) while staying profitable. Want a walkthrough? Comment "LTV" and I’ll send you a step-by-step video guide to crush your numbers.

DTC Brands, to Increase LTV by 2X You Need to Know These Metrics When it comes to LTV there are 8 metrics I obsess over. LTV isn't one number Your customer's lifetime value is built from two separate equations with 6 distinct levers. All of which you can effect Here's what actually moves the number: LTV = Initial Value + Returning Value Initial Value: Units bought × Revenue per unit × Margin Returning Value: (Revenue per unit × Units per order × Margin) × Return Rate % × Purchase Frequency Eight metrics. Two of them matter 5x more than the others. The Priority Order (What to Optimize First) 1. Return Rate % - The Most Important Metric - This is the percentage of customers who make purchase #2. - Better yet: return rate correlates with frequency. - Fix one, you often fix both. - Grüns did this super well through rapid product iteration until they found something people loved. More people came back, and those that did bought more and stuck around longer. - Go back to the fundamentals - Make something people love 2. Purchase Frequency - Second Most Important - How often do your returning customers buy per year? - The gap between 2x and 4x frequency? That's a 2x LTV multiplier right there. - This compounds with return rate. - If 40% returns 4x per year vs 25% returns 2x per year, you've just increased returning customer transactions by 3.2x. 3. Margin, Units, Revenue Per Unit - Also important - You need good unit economics to start. - But the difference between 40% and 48% margin is 20% more profit. - Just dont let product quality slip Hardware + Subscription Model: -> Coffee machine: $400, 25% margin = $100 Initial Value -> Pods: $30/month, 70% margin, 12x per year = $252 per year Returning Value Consumer Products (Skincare, Supplements): -> First order: $60, 45% margin = $27 Initial Value -> Repeat order: $60, 45% margin, 3x per year = $81 per year Returning Value Pull last 90 days of data: 1. Calculate return rate % by cohort 2. Calculate purchase frequency for returning customers 3. Map which lever moves your LTV by 20%+ (it's probably return rate) 4. Build one retention mechanism this quarter, focused on that lever 5. Repeat When you know each metric on its own you know where to focus and how to make improvements.