Experience-Driven Product Development

In the clutter of D2C brands, customization can make you win. Last weekend, I was trying to buy a gift for my friend's anniversary, but every option felt generic. Basic. Non-memorable. Then, I found a leather wallet and cardholder set online where I could add their initials, choose the leather texture, and even include a hidden photo inside. Suddenly, it became a gift they’d remember. This experience made me realize that as the landscape matures, we’re moving from an era of 'product-market fit' to 'product-person fit.' Here’s why I think mass customization is becoming the new competitive advantage in retail: 1/ The New Consumer Psychology Five years ago, customization was a luxury add-on. Today, it's becoming the baseline expectation. When I asked my teenage nephew why he refused a popular sneaker brand, his answer was telling: "If I'm wearing the exact same thing as everyone else, what's the point?" The data confirms it: > 60% of Millennials and Gen Z prefer customized products. > More surprisingly, they’re 4x more likely to recommend brands that offer customization. 2/ The Business Transformation The most fascinating insight I’ve discovered as an investor: Customization is creating an entirely new business model. Take Traya – they analyze your background, health, diet, and lifestyle through a 30-question diagnostic, then create regimens with 4x higher efficacy. The result? ₹7Cr → ₹300Cr in 2.5 years. Or Bombay Shirt Company – by letting customers design everything from the collar to the thread, they’ve achieved what seemed impossible: mass-produced customization at scale. 3/ The Economic Advantage When we analyze the unit economics, customized products are creating an unfair advantage: > Customer acquisition costs drop by 35% (word of mouth increases). > Return rates fall by 55% (customers keep what they helped design). My favorite examples: > Perfora’s name engraving on toothbrushes. > Mokobara’s luggage monograms (they started it). > Lenskart.com’s custom-fit frames. Yes, it adds cost and effort. But it makes you stop while you’re scrolling. And it makes the customer feel like the ONLY customer. That’s everything today. 😉 Which customized product experience has impressed you the most? #ConsumerTrends #Customization #Retail #D2C

🚀 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

Marketers tell you to "Personalize everything!" So you do. "Hi {{first_name}}," "We noticed you viewed {{product_name}}" "Based on your purchase of {{last_order}}" But it didn't help.. Truth is personalization can create privacy anxiety. Every time you prove you're tracking them, you remind them they're being watched. And people don't buy from people watching them. The insight: Personalization based on behavior = surveillance Personalization based on outcomes = service People want to feel understood. They don't want to feel tracked. For your brand: Start personalizing based on what they NEED, not what they DID. Not: "You viewed this product" But: "People redesigning their kitchen usually need this next" Not: "You haven't ordered in 30 days" But: "Most people reorder around now" The shift: From: "We're watching you" To: "We understand people like you" One feels invasive. The other feels helpful. Personalize for what they need, not what they did.

You can have the best product in the world. But if your marketing feels generic, customers won’t stick around. We’ve seen this across gaming, eCommerce, and digital platforms. Retention doesn’t just come from what you offer. It comes from how 𝘴𝘦𝘦𝘯 the customer feels over time. That’s where personalized marketing really earns its place. Not just “Hi First Name” emails. Real personalization like… - Recommending offers based on playstyle - Timing messages around user behavior, not a calendar - Nudging next steps based on what someone 𝘩𝘢𝘴𝘯’𝘵 done yet When a player gets the right message at the right moment, they come back. When they don’t, they leave and rarely explain why. Personalization isn’t just about conversion. It’s about building a relationship that lasts past the first purchase or download. And the longer you keep that relationship relevant, the less you have to keep chasing new customers.

Your users shouldn't perceive purchasing your product as the act of making it their own. It should just feel like a formality 🤔 𝐏𝐬𝐲𝐜𝐡𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐨𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩 is the sense that a product feels "ours" even without legal ownership. This connection fosters care, responsibility, and loyalty, leading to higher conversion rates. Here’s how to leverage it in your product; 1️⃣ 𝗘𝗻𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝗜𝗻𝗶𝘁𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝘀 (like Warby Parker): The more users interact with a product, the 𝒎𝒐𝒓𝒆 𝒕𝒉𝒆𝒚 𝒇𝒆𝒆𝒍 𝒊𝒕’𝒔 𝒕𝒉𝒆𝒊𝒓𝒔. Offer trials, customization, or simulations. This builds a strong connection and boosts conversions. 2️⃣ 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲 𝘄𝗶𝘁𝗵 𝗡𝗮𝗺𝗲𝘀 (like Nutella): Products with users’ names feel special. Adding their names taps into their identity and makes a purchase more likely. 3️⃣ 𝗘𝗺𝗽𝗵𝗮𝘀𝗶𝘇𝗲 𝗖𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 (like Pillsbury): When users contribute to the final product, their attachment grows. Co-creation or customizable options build engagement and loyalty. Know other methods that raise a user’s sense of ownership? Share them in the comments!

Personalization in #b2becommerce isn't broken. It's just (usually) built on the wrong model. Most e-commerce personalization platforms were designed for B2C, where personalization means *preferences*. Your demographics, your browsing history, your past purchases—these things are relatively stable. Netflix knows you like documentaries. Amazon knows you have a baby at home. B2B is fundamentally different. In B2B, personalization means *applications*. That contractor who bought stainless steel fasteners last week? Today they're working a different job, for a different customer, in a different environment. The machinist who ordered carbide end mills for aluminum is now cutting titanium. The maintenance tech who needed bearings for a food processing line is now sourcing for a replacement used in the paint shop. In B2C, your customer's needs are driven by who they are. In B2B, your customer's needs are driven by what they're working on right now. This creates a level of dynamicism that preference-based personalization simply can't handle. The variables that B2C holds constant—B2B can't. So when you're evaluating personalization solutions, push your vendors on this. Ask them to walk you through use cases that account for application-driven variability, not just industry-based segmentation. Look at their case studies. Are they showing you B2C wins dressed up in B2B language, or do they actually understand that your customer's context changes with every project? The conversation will tell you a lot.

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.

Using "Hey {first name}" in your marketing emails and calling it personalization is like picking up a rock and calling it a hammer. Technically, it works. But we have better tools now, and failing to take advantage of them is going to leave you choking on the dust of your competitors. Here's how to catch up with the times and use TRUE personalization to boost engagement, loyalty, and conversions: 1. Use dynamic content fields to customize emails based on customer attributes, behaviors, and preferences. Go beyond just {first name} – incorporate product views, past purchases, and customer lifecycle stage. Don't be creepy! Be conversational. You want the reader to feel like you understand their needs, not like you've been peeking through their blinds. 2. Set up behavior-triggered automations like browse abandonment and cart recovery flows. Make these highly relevant by including viewed products, social proof, and timely offers. Marketing is all about getting the right offer in front of the right person at the right time, and behavior-based emails are one of the best ways to do that on a consistent basis. 3. Implement Recency, Frequency, and Monetary Value (RFM) segmentation to deliver personalized messaging to different customer groups. Target VIPs, at-risk customers, and prospectives customers with specific messages to convert or retain them. 4. Create personalized journeys that adjust the user's experience based on customer data or actions. For example, if you're sending the exact same post purchase sequence to a repeat purchaser as you are for a first-time buyer, you're missing a huge opportunity. 5. Use replenishment flows for consumable products, reminding customers when it's time to reorder. Or, capture email addresses on PDPs for sold out products and notify them when the item in back in stock. Easy sales. Be careful to avoid these common personalization mistakes: 🙅🏼 Over-personalizing in a way that feels intrusive or creepy 🙅🏼 Sending irrelevant recommendations due to inaccurate or outdated data 🙅🏼 Over-segmenting to the point where segments are too small to be effective 🙅🏼 Using templated, robotic language that sounds unnatural The key is finding the right balance ––  personalized enough to be relevant and engaging, but not so specific that it becomes cringey or off-putting. When done well, personalization makes customers feel heard, understood and valued. This builds loyalty, increases engagement, and ultimately drives more conversions and revenue. Level up your personalization with one (or more!) of these strategies, and your KPIs are going to shoot up and to the right.

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

You are not the same person at 8am and 8pm. But every personalization system treats you like you are. This is the biggest mistake in AI-driven personalization and almost nobody talks about it. I've built personalization engines at Best Buy, Target, and Olo across 100M+ customers. The thing that made the biggest difference wasn't a better algorithm. It was a concept from psychology called the Fundamental Attribution Error. Most personalization programs assume your behavior comes from who you are. Your traits. Your profile. So they build one model of you and serve the same recommendations whether it's Tuesday morning or Saturday night. That's wrong. Your behavior is mostly driven by your situation, not your identity. Think about food. Hungry at noon on a workday, you want something fast and close. At 7pm on a Friday, you're browsing, aspirational, open to trying something new. Same person. Completely different buying behavior. At Olo, I built personalization strategy around this for 80,000 restaurant clients. Instead of one static profile per customer, we used day parting. Breakfast you, lunch you, and dinner you are three different customers. Research on this showed 30-40% sales increases versus traditional one-identity personalization. This applies way beyond restaurants. At Best Buy, conversion went from 1% to 17%. A big part of that was understanding someone browsing laptops at 10am Monday is researching for work. Same person browsing TVs at 9pm Saturday is in a completely different headspace. Same customer ID. Different person. At Target, we built cross-device personalization spanning 100M+ loyalty members. The biggest unlock wasn't the technology. It was mapping behavior to context, not just to a customer profile. The psychology of personalization matters more than the technology of personalization. Most teams jump straight to the algorithm. Collaborative filtering. Recommendation engines. ML models. Those are tools. If you're feeding them a single-identity model of your customer, you're optimizing a flawed assumption really efficiently. Start with one question: who is my customer right now, in this moment? Not who are they in general. Anyone else building personalization that accounts for time of day and context? Or is everyone still stuck on one profile? #Personalization #AIStrategy #DataScience