Personalization Engines in Ecommerce

🚀 How do you ensure your customers see what they want to see — not just what you want to show? With AI and ML becoming core to ecommerce (both B2B and B2C), product discovery is getting a lot of attention. And rightly so. But here's the truth: most recommendation engines fail not because the models are bad, but because the first two steps were never right. Let me explain. Many product managers (especially in fast-paced orgs) jump into building rec engines with a "let's plug in collaborative filtering and see how it goes" mindset. But without clearly defining what type of recommendation makes sense for your use case — and how it ladders up to a business metric — you're setting yourself up for rework. Here's how I approach it when working with teams: Step 1: Business Understanding: Start with the why before touching the how. ◾ What are you recommending? Products? Content? Users? Services? ◾What does success look like? Higher CTR? More revenue? Better retention? ◾Where will it show up? Homepage, PDP, cart, email, app banner? ◾What constraints exist? Does it need to be real-time? Can it be batched overnight? Without alignment on this, even the most advanced ML model will fall flat. Step 2: Choose the Right Recommendation Type: Now comes the how — but it should be tailored to your product + user journey. ◾Content-based filtering: “You liked this, so you’ll like these similar items.” ◾Collaborative filtering: “Users like you also bought this.” ◾Hybrid models: The best of both worlds — widely used in ecommerce and streaming. ◾Knowledge-based systems: Rule-driven, useful when personalization is constrained (e.g., insurance, banking). Let me make this concrete with a simple example: Imagine you’re building a recommendation module for a first-time visitor on your site who hasn’t logged in. If you apply collaborative filtering, it’ll fail — there’s no past data to compare. But if you use content-based filtering on the item they’re browsing and pair it with trending items, you instantly make the experience better. It’s not about which model is smarter. It’s about which makes sense for the scenario. Let’s be honest — your recommendation engine’s success doesn’t start with machine learning. It starts with product thinking. #AI #ProductManagement #Ecommerce #Personalization #RecommendationEngine #ProductStrategy I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar

For years, true personalization in ecommerce felt out of reach, too complex, too reliant on massive data infrastructure But in 2025, it’s not just possible, it’s expected * Customer Data Platforms (CDPs) can now unify behavioral, transactional, and anonymous data to recognize visitors in real-time and dynamically segment audiences. * Generative AI builds on that foundation, automating hyper-personalized product recommendations, emails, and even entire storefronts tailored to browsing habits, purchase history, and preferences * Today’s ecommerce personalization means: individualized landing pages, AI chat that understands customer intent, and product suggestions that evolve with each click Brands are no longer optimizing for demographics, they’re creating a “segment of one” The results? Higher conversion rates, deeper customer retention, and a distinct competitive advantage But unlocking this requires more than tech; it demands a strategic approach to data, tools, and team readiness Are you leveraging personalization as a growth engine? 

Personalization at scale is the holy grail of ecommerce. Many brands try this, but their attempts end up feeling artificial or breaking under load. Then I saw what UnionBrands accomplished with FERMÀT. What makes their case particularly interesting is the inherent tension in their business model. With brands like Gladly Family (baby gear) and BravoMonster (luxury RC cars), they're essentially running multiple distinct businesses under one roof. Each brand serves completely different customer personas - imagine the complexity of speaking authentically to both RC car collectors and parents shopping for family-friendly gear. Here's how they approached this challenge using FERMÀT: 1. Persona-Driven Experience Architecture → Each audience segment gets its own tailored journey → The messaging adapts naturally across collector, racer, and gift-giver segments → Brand integrity remains strong while speaking to specific buyers 2. Seamless Ad-to-Cart Alignment → Seasonal offers feel authentic and contextual → Their beach-themed funnels mirror specific UGC content → The narrative flows naturally from first impression to purchase 3. PR-Driven Funnel Optimization → Press coverage leads to custom-built experiences → Publication audiences see perfectly aligned messaging → Direct attribution captures real PR impact Their results validate this approach in remarkable ways: • First week of launch: FERMÀT funnels drove 3X the revenue of their website • PR placement performance: Their collector-specific funnel hit a 14.29% conversion rate when UnCrate featured Bravomonster • Seasonal campaigns: Their beach-themed funnel achieved a 4.56 ROAS What I find most compelling is how they've reframed the personalization challenge. Instead of rebuilding their core site for every audience segment, they’re creating AI-powered FERMÀT funnels to create targeted experiences that preserve brand integrity while delivering true personalization. As Jen Johnson Latulippe, UnionBrands founder, puts it: "FERMÀT allows a smaller team to get bigger results, faster. We can create a whole shopping experience in a few hours without having to touch the website."

Stop getting it wrong with #AI: So, here’s the thing, if your idea of AI personalization is slapping a customers name on a promotional email or serving up “Customers who bought this also bought…” pop-ups, then congratulations—you’re stuck in 2012. I came across a solid research published in #HBR by #BCG on how leaders and laggers in various industries are applying AI. Yes consultants have a habit of putting things into frameworks and metrics, but this one was good BCG. Breaking some #myths on #Personalization and #AI : • 🧟Myth 1: AI is just for automation. No, it’s not. AI is for making people feel like you get them. #Netflix doesn’t just automate recommendations—it fine-tunes them to your weirdly specific taste for crime dramas, maybe some dark content with a hint of comedy. That’s connection. • 🧟Myth 2: Personalization = profits. In reality, loyalty and trust bring growth and add to profits. #Starbucks tailors offers through its Rewards app, focusing on loyalty first—and the profits follow. • 🧟Myth 3: Data hoarding equals success. Spoiler alert: it doesn’t. Collecting data without actionable insights is like hoarding junk. #Amazon, on the other hand, uses its data so well that 35% of its revenue comes from its AI-powered recommendation engine. It integrates browsing habits, past purchases, and customer reviews to suggest items that resonate. To quantify personalization maturity index multiply the below metrics: 1️⃣Empower Me(50%) Personalization starts with solving real problems not just offering flashy features. Example #Alibaba’s AI-driven tools empower small businesses by providing tailored logistics and financing solutions. 2️⃣Know Me(10%) Understanding your customer is essential. #Sephora’s AI-driven app uses purchase history and skin tone matching to suggest relevant products. 3️⃣Reach Me(10%) Timing and channels make or break personalization. #Uber’s predictive AI sends ride prompts exactly when users are most likely to need a car ride. Contrast this with brands that bombard customers with irrelevant offers, eroding trust. 4️⃣Show Me(10%) Visual and contextual relevance elevate personalization. #Sephora’s virtual try-ons demonstrate how personalized content enhances decision-making. Companies that rely on generic or mismatched ads lose credibility & engagement. 5️⃣Delight Me(10%) Creating unexpected moments of joy: #Spotify’s “discover weekly” doesn’t just predict your mood but it surprises and delights customer with a 56% engagement rate to prove it. 6️⃣Remaining 10% score weightage is attributed to CXOs championing AI projects Companies that treat AI-powered personalization as a strategic imperative, rather than a cost-cutting tool, stand to gain the most. Leaders like Netflix, Uber, Amazon, Starbucks, Spotify, Alibaba Group and SEPHORA dominate the Personalization Maturity Index because they’re masters of combining AI with human-centric strategies. Meanwhile, laggers just don’t know how to turn data into meaningful actions.

Amazon’s success isn’t just about having everything in one place. It’s about knowing what you want—often before you do. By analyzing user behavior, preferences, and past purchases, Amazon created a recommendation engine that feels personal to each shopper. But how did they do it? -Recommendation Engine: Every time you search, browse, or purchase, Amazon tracks your behavior. Their sophisticated algorithms analyze this data to create personalized product suggestions. This not only increases sales but also keeps customers engaged by showing them exactly what they’re looking for—even when they didn’t know it. -Customer Segmentation: Amazon divides its vast customer base into micro-segments based on preferences, buying history, and even browsing time. This allows them to target customers with highly relevant offers, email suggestions, and promotions. It’s not just mass marketing—it’s targeted, personalized marketing. -Anticipatory Shipping: Using predictive analytics, Amazon can forecast what products customers are likely to order soon and ship them to nearby fulfillment centers before an order is placed. This cuts down delivery time significantly and enhances customer satisfaction. Their data insights predict trends even on a micro scale—right down to individual customer needs. -Product Reviews & Feedback: Amazon uses customer reviews and ratings not just for quality control, but also to shape future recommendations. Negative feedback helps the algorithm refine suggestions, while positive feedback boosts product visibility. They’ve turned the review system into another data source to further enhance personalization. -Dynamic Pricing: Using data on demand, competitor prices, and buying trends, Amazon can adjust product prices in real-time. This ensures customers are seeing competitive prices and gives Amazon an edge in retaining price-conscious shoppers. What can your business learn from this? -Leverage customer data to understand behavior: By tracking user actions on your website or app, you can tailor their experience, just like Amazon. -Segment your audience: Identify patterns within your customer base and target each group with content or offers that speak directly to their needs. -Predict customer needs: Use data to anticipate what your customers want before they even ask for it, ensuring faster service and more relevant offerings. Key insight: Data isn’t just numbers—it’s the foundation for a personalized, optimized experience that keeps customers coming back for more.  

As a team, we talk about solving the hard problems first. The commerce industry has been talking about personalization for a decade plus. Yet we still land on websites and apps that feel generic. Why? Because personalization is a hard problem to solve - most solutions barely scratch the surface. To truly personalize the shopping experience, you need more than a few clicks, prompts or purchase history. You need context (why someone is shopping), historical intent (not just what they’ve bought, but what they’ve considered), a taste layer (what styles, brands, and price points resonate), and real-time product data that’s structured and attributed correctly. Not “you bought socks, here are some sneakers.” (The socks might be for loafers you are on the hunt for.) Not “you like Nike, here are random Nike sneakers.” Personalization that works feels like someone who knows you, surfacing products at the right time, in the right way.. Otherwise as shoppers, we bounce. The solutions that will win are powered by real-time, variant-level product data (not inaccurate affiliate feeds), combined with rich shopper insights - enabling experiences that respond to every signal. It’s not about optimizing for clicks.. it’s about understanding intent and solving real user problems (even if it's hard to solve).

"Personalization" doesn't mean what you think it means. What most distributors think personalization means: 👉 Adding the customer's name and company on their "my account" screen. What real personalization looks like in B2B ecommerce: ↳ Timing reorder reminders to align with actual buying cycles ↳ Product recommendations based on purchase history ↳ Detecting when customers are browsing categories they haven't purchased from yet ↳ Reactivating lapsed buyers with relevant, low-friction offers ↳ Segmenting messages by customer type, vertical, or seasonality Because in B2B, personalization isn’t about inserting {First Name}. It’s about understanding where the customer is in their buying journey — and why they might purchase again. Even the best ecommerce platforms are unable to guide B2B buyers through complex reorder cycles, long sales windows, and multi-SKU decision making. They can't understand the "where" and the "why" I just mentioned That’s where a thoughtful email strategy makes the difference: → It surfaces products when they’re actually needed. → It nudges reorders before a competitor swoops in. → It stays relevant even when the customer isn’t actively buying. → And it keeps distributors top of mind between orders. Personalization isn’t a field in your CRM. It’s a system that adapts to how real buyers behave.

I ran several DTC marketing teams before I joined Digioh. We obsessed over retention — flows, segmentation, loyalty, maximizing LTV through email and SMS. What we didn’t realize at the time was simple 👉 Our storefronts had no memory. Here’s an example that’s far more common than most teams think A customer buys three times. Comes back a month later. And sees a popup they've already opted into before purchase number one “10% off join our email list.” It doesn’t matter how they came back, whether its direct, organic, paid, email, QR from retail. The site resets. Every time. The CRM remembers them. The storefront doesn’t. That gap is where margin quietly leaks. It trains loyal customers to wait for discounts. It ignores purchase history. It resets product education. It flattens what should feel like a dynamic relationship the way an in-store experience would naturally evolve. Most brands are still running session-based personalization. Cookies expire. Sessions reset. Devices change. The experience forgets. Across the 3,000+ DTC brands we work with at Digioh, the teams pulling ahead are leaning into identity-first personalization. They don’t just recognize shoppers. They activate that recognition onsite. They suppress generic offers for known customers. They replace discounts with loyalty or subscription prompts. They personalize PDP modules based on purchase history. They reduce friction for repeat buyers. It’s about building a storefront with memory — one that treats every visit like the unique experience it should be. We’re building Digioh around this thesis because we believe identity-first personalization is the next major shift in ecommerce. The pixel changed ads. Identity changes the post-click experience. And if your VIP experience looks identical to first-time traffic, your growth ceiling is lower than you think.

Online shoppers now trigger more than two thousand micro decisions every minute. Retailers quietly swapped human merchandising for silent AI engines that personalise every click. The shift is bigger than it looks. Entire storefronts now rearrange themselves mid scroll. Prices bend. Recommendations reshape. Discovery becomes a moving target. It feels smooth to the shopper but it rewires the entire game for operators. The tension is clear. Platforms want higher margin and faster conversion. Brands want visibility and stable pricing. Both sides are now negotiating with an algorithm that never sleeps. The funniest part is how invisible the change is. One user sees a cosy winter feed. Another sees an urgent clearance push. Same site. Same moment. Completely different journey. Behind the scenes the power balance tilts toward whoever understands how these engines decide what to surface and when. 🧲 Product placement now depends on structured data quality 🔍 Search positions shift based on real time behaviour 🧪 Price tests run quietly on micro segments 📦 Slow movers get pushed to users most likely to convert ⚙️ Recommendation engines shape the majority of first touches For operators this is no longer optional. Success on any marketplace now depends on understanding how your data feeds the machine and how the machine rewards or buries your listings. Welcome to merchandising by algorithm. #ecommerce #marketplaces #dtc #onlineretail #aiincommerce

AI Driven Product Recommendations in Sexual Wellness E Commerce Most e commerce still operates on a simple model. Show more products. Hope something converts. But in sexual wellness, this approach falls short. The category is highly personal. Preferences vary significantly. And decision making often involves uncertainty. This is where AI driven recommendation systems are becoming essential. Instead of generic bestsellers or broad categories, AI can: Analyze user behavior and browsing patterns Identify preference signals over time Recommend products based on similar user profiles Adapt suggestions as behavior evolves This transforms the shopping experience. From overwhelming to curated. From transactional to guided. And that shift directly impacts conversion. Because when users feel understood, they are more confident in their decisions. It also reduces friction. Less time searching. Less second guessing. More alignment between product and expectation. We have already seen this model succeed in industries like streaming and retail. Sexual wellness is now catching up, but with higher stakes due to privacy and sensitivity. Which brings up a critical point. Personalization must be balanced with discretion. Users want relevance, but they also want control. They want smarter recommendations, but without feeling exposed. The brands that solve this balance will dominate the category. At V For Vibes, the focus is on building a more intelligent shopping experience. One that feels private, personalized, and seamless from discovery to purchase. Because the future of sexual wellness e commerce is not about more options. It is about the right option at the right time. #SexTech #Ecommerce #AI #Personalization #DigitalHealth #Innovation #StartupGrowth