Segment-Specific Promotions

Ever wondered how different customers get different discounts and offers from the same business? It’s not random. It is based on: data-driven customer segmentation. Recently, I worked on an "RFM Analysis" project to understand customer behavior and segment them based on their value to the business. RFM stands for: •Recency – How recently a customer made a purchase. Customers who bought recently are more likely to buy again. •Frequency – How often a customer makes purchases. Frequent buyers are usually more engaged with the brand. •Monetary – How much money the customer spends. Higher spending customers often drive a large portion of revenue. By combining these three metrics, customers can be grouped into meaningful segments. Some common segments include: ~Champions These customers bought recently. They buy often. They spend the most. They are the most valuable customers. ~Loyal Customers They purchase regularly. They engage consistently with the business. ~Potential Loyalists They purchased recently. But their frequency is still growing. With the right engagement, they can become loyal customers. ~At Risk Customers They used to buy frequently. But they have not purchased recently. These customers need re-engagement campaigns. ~Lost Customers They have not purchased in a long time. Reactivation strategies are required to bring them back. This segmentation helps businesses move away from one-size-fits-all marketing. Instead of sending the same promotion to everyone, companies can create targeted strategies: -Champions → Exclusive rewards and early access. -Loyal customers → Loyalty programs and personalized offers. -Potential loyalists → Engagement campaigns to increase frequency. -At-risk customers → Win-back discounts. -Lost customers → Re-activation campaigns. The impact is significant: ✓Better customer retention. ✓Smarter marketing campaigns. ✓Higher ROI from promotions. ✓Stronger customer relationships. Data doesn’t just describe customers. It helps businesses understand who to reward, who to nurture, and who to win back. If you're working in analytics, RFM is one of the most practical segmentation techniques to start with. Would love to hear how you are using customer segmentation to grow the business.

By 2027, the "sitewide sale" is dead. Not because discounting dies, but because blanket discounts are a margin tax on founders. The problem: You run 20% off sitewide. Your VIP customers who would've paid full price get subsidized. Your fence-sitters get more than they need. Your price-sensitive shoppers finally convert. You moved inventory. You also destroyed 18-22 points of margin from customers who didn't need the incentive. The shift happening now → Yield Management Airlines figured this out in 1985. Hotels in the 90s. DTC brands are finally catching up. Instead of blanket discounts: 𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗔 (𝗣𝗿𝗶𝗰𝗲-𝗦𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲) The only-buys-on-sale crowd ➤ 20%+ discounts to move inventory 𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗕 (𝗙𝗲𝗻𝗰𝗲-𝗦𝗶𝘁𝘁𝗲𝗿𝘀) Appreciates the brand, needs a nudge ➤ 10-12% or free shipping to increase frequency 𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗖 (𝗩𝗜𝗣𝘀) Buys for status, newness, necessity ➤ 0% discount, early access only Same promotion calendar. Three different incentive levels. Delivered via unique emails, landing pages and dynamic codes tied to the recipient. The bottleneck: You need to score every customer on discount sensitivity using purchase frequency, average discount at conversion, and days-to-purchase after promotion send. Most brands either (a) don't have the data infrastructure, or (b) have a data team that takes 6 weeks to build the segments. At LTV.ai we automate this by analyzing historical behavior patterns across your entire list. Every recipient gets scored, segmented, and sent the minimum effective incentive automatically. We're seeing $100M+ brands shift to private yield management. They're protecting 15-20 points of margin while maintaining the same promotion frequency. The brands figuring this out in 2025 get a 2-year margin advantage before this becomes table stakes.

𝗣𝗮𝗿𝘁 𝟮: 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 This could totally flip your marketing mindset!👇 After looking into market research, I prepared a short segmentation use-case. Trying to show one thing - There are different ways to segment. Some are more useful and actionable. 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗔: 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Company A sells athletic shoes and has a broad segmentation. They rely on general features and promotions, assuming customers within a category have similar needs. Segments: Athletes: 5% growth, $400M Fitness Enthusiasts: 7%, $300M Casual Wearers: 3%, $300M 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗕: 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Company B segments its market granularly. They identify unmet consumer needs within each segment and develop products and marketing strategies to address them. They consider 4P preferences, the holistic consumer job to be done, points of market entry, drivers, triggers, barriers, alternatives, customer lifetime value (CLTV), and willingness to pay. 𝗔. 𝗔𝘁𝗵𝗹𝗲𝘁𝗲𝘀 𝘾𝙤𝙡𝙡𝙚𝙜𝙚 𝘼𝙩𝙝𝙡𝙚𝙩𝙚𝙨 Segment Size: $80M Unmet Need: Affordable high-performance shoes 4P Preferences: High-performance product, competitive pricing Holistic Job: Enhancing athletic performance on a budget Drivers/Triggers: Performance enhancement, affordability Barriers: Budget CLTV: $600 per customer Revenue Uplift Percentage: 10% (by addressing unmet needs) Size of the Prize: $80M * 0.10 = $8M 𝙍𝙚𝙘𝙧𝙚𝙖𝙩𝙞𝙤𝙣𝙖𝙡 𝘼𝙩𝙝𝙡𝙚𝙩𝙚𝙨 Segment Size: $120M Unmet Need: Versatile shoes for multiple sports 4P Preferences: Versatile product Holistic Job: Participation in various sports Drivers/Triggers: Versatility, value for money Barriers: Lack of specialized shoes for specific sports Alternatives: Multiple pairs of sport-specific shoes CLTV: $500 per customer Revenue Uplift Percentage: 9% (by addressing unmet needs) Size of the Prize: $120M * 0.09 = $10.8M 𝗕. 𝗙𝗶𝘁𝗻𝗲𝘀𝘀 𝗘𝗻𝘁𝗵𝘂𝘀𝗶𝗮𝘀𝘁𝘀 𝙂𝙮𝙢-𝙂𝙤𝙚𝙧𝙨 Segment Size: $100M Unmet Need: Superior support and comfort shoes 4P Preferences: High-quality product, premium pricing Holistic Job: Achieving fitness goals comfortably Drivers/Triggers: Support, comfort Barriers: Higher price point CLTV: $700 per customer Revenue Uplift Percentage: 12% (by addressing unmet needs) Size of the Prize: $100M * 0.12 = $12M 𝙍𝙪𝙣𝙣𝙚𝙧𝙨 Segment Size: $120M Unmet Need: Lightweight, durable running shoes 4P Preferences: Lightweight product, specialty running stores Holistic Job: Enhancing running performance Drivers/Triggers: Light weight, durability Barriers: Higher price CLTV: $800 per customer Revenue Uplift Percentage: 14% (by addressing unmet needs) Size of the Prize: $120M * 0.14 = $16.8M 𝗧𝗟𝗗𝗥 If you segment, don't just build a high level map of the market. Understand unmet consumer needs and the value behind. 𝗬𝗼𝘂 𝗟𝗶𝗸𝗲 𝗧𝗵𝗶𝘀 Repost, save, comment below. I'm Julia Kinner. My consulting firm JK & Associates SA focusses on growth strategy & and execution for D2C products & services.

Brands LOVE telling me they have 50,000 subscribers. Then I ask how many opened an email in the last 90 days... Because THAT number is your real list, and everyone else is dead weight that's actively costing you thousands of dollars on Klaviyo. Gmail and Apple Mail both watch what happens when you send. • Keep hitting 9,000 people who never open • And the algorithm decides you're a sender nobody wants.  • Your emails get pushed to promotions and then to spam.  • Now even your buyers stop seeing you. Segmenting by behaviour fixes this. Instead of blasting one email at everybody, you split the list by how people actually act. We pulled a real account and broke it into four groups: → Engaged: opened or clicked in the last 30 to 90 days. These people hear from you the most. → One-time buyers: bought once, never came back. They get a sequence built to pull them into a second purchase. → VIPs: early access and exclusive offers that make them feel like an insider. → Lapsed: nothing in 90 days or more. Re-engagement campaign, or suppress them to protect your reputation. One of our engaged segment campaigns to a 90-day engaged group of 22,000 people, bounced emails excluded, pulled a 60% open rate, a 1.94% click rate, and $6,000 in revenue. The same brand ran a campaign that included unengaged, all-time non-buyers, and it managed a 29% open rate, a 0.99% click rate, and $2,900. There's STILL money in the unengaged... you just need to save them for the moments that matter (like a final-hours sale or a product drop) The engaged audience gets the regular sends, and the unengaged gets the big moments only. That protects deliverability for the other 28 days of the month, and you still squeeze the full list when it actually counts.

I made one brand $55,570 in 1 month with segmented email campaigns. A consumer CPG brand was struggling with low email conversion rates, sending generic promotions to their entire list. Engagement was dropping and they weren’t seeing the sales they expected. The Problem: No segmentation. One-size-fits-all emails. Minimal personalization. Here’s What I Did: 1. Segmented the Email List: Divided their audience into groups based on behavior—new subscribers, frequent buyers, and dormant customers. 2. Tailored Messaging for Each Group: New subscribers got educational content. Frequent buyers received loyalty-driving emails. Dormant customers were re-engaged with value-focused offers instead of just discounts. 3. Personalized Content: Added dynamic content to make emails feel personal and relevant. The Results: 35% increase in conversion rates. 25% boost in open rates. Higher engagement across all customer segments. An extra $55,570 in just 4 weeks. By sending the right message to the right audience, we turned their emails into a reliable sales driver. Stop sending the same emails to everyone. #EmailMarketing #CaseStudy #MarketingStrategy #CustomerSegmentation #ConsumerBrands #DigitalMarketing #RetentionMarketing Read this post and more on my Typeshare Social Blog: https://lnkd.in/ectNZQic

This Google Ads mistake can cost you thousands. Yet 95% of the brands we audited keep making it. Over the past 5 years, we've managed over 2,000 Google ad campaigns, working with major brands like Crafted and Wine by Lamborghini. Time and again, we see this same costly oversight holding back their results. Most brands treat all their customers the same in Google Ads. They run the same generic campaigns to everyone - recent buyers, old customers, and brand new prospects alike. This "spray and pray" approach burns through budget AND delivers mediocre results. Think about it: You're showing the same ads, with the same offers, to someone who just bought from you yesterday and someone who's never heard of your brand. You're wasting money retargeting recent customers with first-time buyer discounts they can't even use. Your high-value VIP customers are seeing the same basic promotions as one-time bargain hunters. And your churned customers? They're being completely ignored instead of strategically re-engaged. The key is proper customer segmentation. Here's how the top-performing brands do it: 1. Create distinct customer lists based on behavior and value Recent buyers (last 30-90 days) High-value VIPs One-time purchasers Churned customers Non-purchasers 2. Update these lists weekly to keep data fresh 3. Use unique campaigns and offers for each segment: Exclude recent buyers from acquisition campaigns. Target VIPs with exclusive early access. Re-engage churned customers with win-back offers. Convert one-time buyers into repeat customers. This targeted approach is simple to implement. Plus you get improved relevance and ROI across your entire account. Remember, it’s the little things you need to keep doing regualrly.

Growing customer base is expensive. Brands invest big in getting people to download their app and try the product. Inevitably some users become inactive, not ordering for a month, three months, six months. Can we engage new customers better, activate inactive customers and retain active ones while maximising profitability? Can we also improve service metrics and efficiency by redistributing some orders so that they occur during quiet hours of the shift? An initiative at Dodo Pizza addressing these issues is personalised offers in the app. These are highly relevant unique promotions with various mechanics that incentivise customers to place orders based on their preferences  🎯 The program involves the following: 1️⃣ grouping users based on their frequency and behaviour 2️⃣ identifying objectives for each group (retention, up-sell, cross-sell) and communication channels (push, email, sms, checkout etc) 3️⃣ implementing mechanics that create and deliver to customers personalised offers 4️⃣ analysing results and identifying opportunities for improvement We now work with several promo mechanics. First, there are personalised reduced prices for individual products. These are short-term promotions (1-3 hours, excluding peaks) that redistribute orders throughout the day for better service and efficiency. The algorithms choose promo products and their discount so as to optimise for likelihood of purchase and the product’s gross margin. We also work with promo mechanics for the entire ticket via personal promocodes: % discount, lump-sum discount, gift and increased cashback. For each customer there is a generated minimum order amount and the discount (or cashback) itself. Additionally, regional price levels (depending on the store’s location) are taken into account by the system when setting the minimum orders amounts for activation. Within each customer segment algorithms use historic data (tickets, location, app usage, promo usage etc) to calculate probabilities of purchasing and determine unique parameters of each offer. Offers also depend on how much discounting is optimal for each store. Some stores just don’t need additional orders, while others need aggressive promotions. And here we look at kitchen and delivery wordload, capacity and local market penetration, performance of comparable stores etc. About 35% of our monthly active users use at least one of these mechanics. This translates into 6% additional sales for Dodo Pizza. Not bad at all. 😎 Photo is from the recent Dodo AI Day

93% of the Indie games make NO MONEY The #1 big mistake I’ve seen supporting 100+ gaming studios is that The problem isn't their talent or technical capability It's their monetisation strategy. The smartest studios use... "Targeted Retargeting" The Impact? Without this conversion is 4% With this conversion, hit 6.5% That's a whopping 62.5% increase This strategy turns players into payers using: - Strategic segmentation &  - Customised messaging Here is how player segmentation works Let’s call them whales, dolphins, and minnows 🐋 Whales (1–2% of players) Premium pricing tiers ($49.99+), Focus: High-touch retention with minimal ad interruption 🐬 Dolphins (5–15% of players) Value bundles ($5.99–$19.99), Focus: Gradual conversion to higher-value purchases 🐟 Minnows (80–90% of players) Starter packs under $4.99 Focus: First purchase conversion and ad engagement How we implement this for high conversion (Targeted Retargeting): 1. Identifying the segments using spending patterns from the first 7 days We use this data to segment the audience's interest and paying capacity 2. Customising the offer bundles Based on the audience's preference We show them the bundles that would work best for them 3. Adjusting the ad frequency It is usually done inversely to spending potential (fewer ads for whales, more for minnows). 4. Creating segment-specific events We create events that appeal to the audience's interests 5. Implementing dynamic pricing We usually create the pricing according to player history We have used this method, seen results like: ✅ Up to 40% higher LTV ✅ 20 %+ improved retention ✅ Significantly more sustainable revenue growth It's always about strategic efforts >>> scattered ones. Let’s chat about how you can focus and monetise your game better !!