Personalizing Product Recommendations For Better Sales

Most brands drown in the process of personalizing too much. I recently worked with a brand that went super deep into this, making users create detailed customer profiles through their pop-up with specific interests. Their welcome series was completely segmented, if you clicked on "couches," you'd only see couch-related content throughout the entire sequence. Most people would think this hyper-personalized approach is “cutting edge”, and leave it alone. This left a TON of revenue on the table since it limited their brand discovery. After looking at the data, we tested a different approach right away. We featured best sellers of the brand, highlighting each of them with individual product categories underneath the existing segmentation. By keeping the personalized elements but introducing best-selling products across categories, we significantly lifted engagement and revenue metrics. It’s simple: - Customers don't always know your full product range - Limiting visibility to one category restricts discovery - Your best-sellers have proven market engagement regardless of initial interest - Site exploration leads to higher average order values The welcome series absolutely crushed it with this strategy. We also found that their original strategy worked better in the post-purchase flow. Customers are more inclined to accept other offers of the same category after they purchased a product, rather than getting bombarded with 100 different couches at the beginning. The key takeaway here is to test the balance between personalization and data. Testing will always be King. Don't always assume that extreme personalization is always the answer, sometimes a hybrid approach delivers the best results.

Do you cater to multiple customer personas? Guiding them to the right products from the get-go can significantly enhance their shopping experience. One effective strategy is to implement a "Choose Your Own Adventure" approach on your ecommerce homepage. Why This Approach Works: → Personalization: By allowing customers to select their persona or interests, you can tailor the shopping experience to their specific needs and preferences. → Improved Navigation: This method helps visitors quickly find the products that are most relevant to them, reducing the time they spend searching and increasing the likelihood of a purchase. → Enhanced Engagement: A personalized experience keeps customers engaged and encourages them to explore more of your catalog and return in the future. How to Implement It: → Identify Key Personas: Start by identifying the main customer personas you serve. For example, if you're a skincare brand, your personas might include "Teens," "Adults," and "Mature." → Create Clear Pathways: Design your homepage to feature clear, clickable options for each persona. For instance, you could have buttons or images labeled "Teen Skin," "Adult Skin," and "Mature Skin." → Tailor Content: Once a visitor selects their persona, direct them to a customized landing page that features products, testimonials, and content relevant to their needs. Show product recommendations tagged for each persona. Bonus points: Setup a personalization campaign that adapts each page of your site with language and imagery to match each persona. e.g. A teen would see imagery of other teens and copy on the page follows suite. By implementing a "Choose Your Own Adventure" approach, you can create a more personalized and joyful shopping experience for your customers, ultimately driving higher conversions and revenue.

Let's build a Recommender for an E-Commerce clothing site from scratch. 🛍️📈 This notebook shows how to deliver personalized, scalable recommendations even in cold-start scenarios. 👉 Product details include: - Price, - Rating, - Category, - Description, - Number of reviews, - Product name with brand. We have two user types, defined by their initial product choice at registration or general preferences around price range and review requirements. We'll use the Superlinked Framework to combine product and user data to deliver personalized recommendations at scale. Let's dive in 🏗️: 1️⃣ Data Preparation ⇒ Load and preprocess product and user data. 2️⃣ Set up the Recommender System ⇒ Define schemas for products, users, and user-product interactions. ⇒ Create embedding spaces for different data types to enable similarity retrieval. ⇒ Create the index, combining embedding spaces with adjustable weights to prioritize desired characteristics. 3️⃣ Cold-Start Recommendations ⇒ For new users without behavior data, we'll base recommendations on their initial product choice or general preferences, ensuring they're never left in the cold. 4️⃣ Incorporate User Behavior Data ⇒ Introduce user behavior data such as clicked, purchased, and added to the cart with weights indicating interest level. ⇒ Update the index to capture the effects of user behavior on text similarity spaces. 5️⃣ Personalized Recommendations ⇒ Now it's time to tailor recommendations based on user preferences and behavior data. ⇒ Compare personalized recommendations to cold-start recommendations to highlight the impact of behavior data. Ant that's a wrap! 🔁 Adjusting weights allows you to control the importance assigned to each characteristic in the final index. This tailors recommendations to desired behavior while keeping them fresh and relevant... it's easier than chasing the latest fashion trends. ✨ Dig into the notebook to implement this approach 👉 https://lnkd.in/edeQW344 Why not show some support by starring our repo? ⭐️ We'd appreciate it more than a free fashion consultation! 😉

Personalization isn't just about adding a <dynamic name tag> in your follow-ups. That’s table stakes. Go deep, get relevant, and it will add rocket fuel to your paid efforts by lowering CAC and driving LTV. Here are 5 key personalization and segmentation tactics we’re running with Klaviyo this year to supercharge our growth: 📈 1. Triggered flows from high-intent actions Quiz completion, PDP views, cart hovers…we don't wait for them to just remember us. We create experiences that tie back to their interests and behavior. The setup: - Someone completes our quiz → immediate flow based on their results - Product page browsers → targeted follow-up for that specific SKU - Cart hoverers → urgency sequence before they forget Result: better conversion than universal welcome emails because they're contextual, not generic. 🔁 2. Dynamic segments that update in real-time Goal here is to build logic, not static lists. If someone browses 2+ collagen SKUs but doesn't purchase, they're moved into a "Collagen Consideration" segment automatically. If they buy, they're moved out. This keeps messaging relevant and timing tight, without needing manual intervention. 🧠 3. Predictive churn alerts + automated winbacks We use churn prediction scores to ID high-risk customers before they stop buying. Example: When someone views your 'Cancel Subscription' FAQ, they automatically get a churn prevention sequence within 24 hours. The flow: → Educational content + stronger value props → One-time discount to "pause" rather than cancel → Reminder of points or rewards they'd lose Win back a higher percentage of your churn-risk users this way (without hoping to retarget them on Meta). 🎯 4. On-site personalization from zero-party data When a customer shares goals or preferences in a quiz, we don't let that data sit. We use it to personalize everything from email subject lines and SMS follow-ups. "Looking for joint support?" → Product recommendation shows collagen SKUs, not fat burners. This creates a more relevant buying journey and lowers decision fatigue. 🔄 5. Cross-channel sequencing (email → SMS → onsite) We build orchestration into the flow logic, not just "blast and pray." Day 0: Email with their quiz results Day 1: SMS with a limited-time offer Day 3: If they return, they see a pop-up based on their quiz results This cross-channel sequence drives higher engagement while avoiding overexposure on any one channel. The tool that makes this possible is Klaviyo, and this is just a small example of what we’re building with it. Because it’s a full-on B2C CRM, Klaviyo lets us create highly personalized, high-performing flows at every stage of the funnel. If you’re still just batching and blasting, I recommend checking them out: https://lnkd.in/d7pKaQRB #Klaviyopartner

Ever wonder why most shoppers never finish checking out? Nearly 70% of carts get left behind. Feels personal, right?   The truth: shoppers bail because what they see doesn’t 𝑓𝑒𝑒𝑙 made for them. Static pages and generic product blocks just aren’t cutting it.   𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 fixes this.💡   When your store adapts in real time, → Shoppers spot products that fit their vibe → Banners shout out sales and bundles that matter to them → Every touchpoint feels personal, not random   We've seen it firsthand. Conversion rates double. Bounce rates drop. Customers actually come back.   𝗤𝘂𝗶𝗰𝗸 𝗪𝗶𝗻𝘀: ✅ Show recently viewed products upfront ✅ Personalize offers based on browsing history ✅ Use urgency—like “selling fast”—only for 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑡 items   People want to feel seen, not sold to. It's not about tricking them into buying. Make every visit feel like it was designed 𝑗𝑢𝑠𝑡 𝑓𝑜𝑟 𝑡ℎ𝑒𝑚, and your abandoned cart problem shrinks.   Are you personalizing content for your visitors yet, or still rolling with the same template for everyone?   https://lnkd.in/g-GPkvCW   #eCommerce #ConversionRate #Personalization #CRO