Customization and Personalization in 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

People value what they create 63% more. Yet most digital experiences treat customers as passive recipients instead of co-creators. This psychological principle, known as the "Ikea Effect", is shockingly underutilized in digital journeys. When someone builds a piece of Ikea furniture, they develop an emotional attachment that transcends its objective value. The same phenomenon happens in digital experiences. After optimizing digital journeys for companies like Adobe and Nike for over a decade, I've discovered this pattern consistently: 👉 Those who customize or personalize a product before purchase are dramatically more likely to convert and remain loyal. One enterprise client implemented a product configurator that increased conversions by 31% and reduced returns by 24%. Users weren't getting a different product... they were getting the same product they helped create. The psychology is simple but powerful: ↳ Customization creates psychological ownership before financial ownership ↳ The effort invested creates value attribution ↳ Co-creation builds emotional connection Three ways to implement this today: 1️⃣ Replace dropdown options with visual configurators 2️⃣ Create personalization quizzes that guide product selection 3️⃣ Allow users to save and revisit their customized selections Most importantly: shift your mindset from selling products to facilitating creation. When customers feel like co-creators rather than consumers, they don't just buy more... they become advocates. How are you letting your customers build rather than just buy?

Build something yourself and you'll value it far more than anyone else does... Harvard students folded origami cranes following simple instructions. Researchers asked how much they'd pay for their own creations. Answer: $0.23 per crane. Then researchers showed those same amateur cranes to different people who hadn't built them. How much would they pay? About $0.05. When those non-builders looked at expert-made cranes, they valued them at $0.23. The students saw their amateur work as equal to expert craftsmanship. Everyone else saw reality clearly. In 2011, Michael Norton, Daniel Mochon, and Dan Ariely at Harvard documented what they called the IKEA Effect. They ran experiments where people assembled IKEA storage boxes, folded origami, and built LEGO sets. Then measured how much builders would pay for their own creations versus non-builders. The pattern held across all experiments. People who assembled IKEA boxes themselves were willing to pay 63% more than people evaluating identical pre-assembled boxes. Builders consistently overvalued their own work and expected others to share their inflated opinions. The mechanism is effort. When you invest labor into creating something, even just following instructions, you develop emotional attachment that dramatically increases perceived value. This has direct implications for product strategy. Nike By You lets customers customize shoe colors and materials. Neuromarketing research shows customers viewing their own customized sneakers exhibit significantly stronger positive emotional responses than standard options. That emotion correlates directly with purchase behavior and premium pricing. Build-a-Bear Workshop charges premium prices for children to assemble their own stuffed animals. M&M's personalization commands multiples of standard pricing. Bain & Company research found customers who customized products online showed higher brand engagement and increased repeat purchases. Dell pioneered build-your-own PC configuration. LEGO Ideas invites customers to submit product concepts. Even modest customization drives the effect. Betty Crocker discovered this in the 1950s when instant cake mixes failed because homemakers found them too easy. The solution? Require adding an egg. That small effort greatly increased baker perceived value. The strategic principle is straightforward. Give customers meaningful input into product creation, even if modest. Monogramming, feature selection, assembly, customization tools. Ensure they can successfully complete their contribution. The emotional attachment and willingness to pay premium prices or repeat purchase follow automatically. Often companies consider some of these ancillary configuration options as extraneous (e.g. minor customization does not change actual product performance or core attributes) but underestimate the emotional attachment and value it can create. When customers build it, they value it highly, even when nobody else does. Cheers!

Memory & personalization might be the real moat for AI we’ve been looking for. But where that moat forms is still up for grabs: •App level •Model level •OS level •Enterprise level Each has very different dynamics. 🧵 ⸻ 1. App-level personalization Apps build their own memory & context for users. Examples: •Harvey remembering firm-specific legal knowledge for law firms •Abridge capturing patient conversations & generating notes for doctors •Perplexity building long-term search profiles for individual users ➡️ Most likely in vertical applications with focused use cases and domain-specific data. This is where Eniac Ventures is currently doing most of our investing ⸻ 2. Model-level personalization The model itself becomes personalized and portable across apps. Examples: •ChatGPT memory & custom instructions •Meta’s LLaMa fine-tuned on personal embeddings ➡️ Most likely in general-purpose assistants and broad horizontal use cases where user context needs to travel across apps. ⸻ 3. OS-level personalization Personalization happens at the OS level, shared across apps & devices. Examples: •Google Gemini native to Android •Apple (maybe) embedding Claude via Anthropic ➡️ Most likely in consumer devices and mobile ecosystems where platforms control distribution. ⸻ 4. Enterprise-level personalization Each enterprise owns and controls its own personalization layer for employees & customers. Examples: •Microsoft Copilot trained on company data •OSS models (LLaMa, Mistral) deployed on private infra with platforms like TrueFoundry •OpenAI GPTs fine-tuned & hosted in secure enterprise environments ➡️ Most likely in highly regulated industries (healthcare, financial services) where data privacy and compliance are critical. ⸻ Why it matters: Where memory & personalization “land” may define who captures AI value. Different layers may win in different sectors. Where AI memory lives may reshape who captures the next decade of value.

We all want to build personalized products. But here’s the hard truth… Most product teams operate blindly – without really knowing who their customers are. This product team we worked with had a revelation that forever changed their approach to product development. 👇 A few years ago, they set out to build a new banking app. But like most product teams, they had no access to real customer data (due to privacy constraints). All they had were dummy datasets – and their own assumptions about what their customers needed from an app. Then they started using synthetic data. And they could finally see: → The full spectrum of their customers’ financial behaviors → Hidden patterns across demographics and microsegments → Surprising outliers they didn’t even know existed ...all while protecting their customers' privacy In fact, some income and spending patterns looked so odd that they initially refused to believe they were real. Thinking it must be due to a bug, they checked it against production data. And were stunned to find that some of their customers earned and spent their money in ways they never thought possible. This was a wake-up call. Relying heavily on their assumptions had blinded the team to entire customer segments and held them back from achieving their goal: building truly personalized and inclusive products. Using synthetic data, they created a banking app and adjacent services that catered to the needs of the diverse spectrum of their customer base. The takeaways? ➡️ Real personalization starts with real insights – not just assumptions ➡️You can’t cater to your blind spots – inclusive product development requires data diversity ➡️Synthetic data unlocks critical data access without compromising privacy

"We've identified all these user needs and created a roadmap to address them, but it's just a long list of disconnected features. We keep shipping, but we're not moving metrics or able to pivot quickly when we learn new things about our users." While starting with user problems is a great first step, translating them directly into specific feature solutions often leads to rigid roadmaps and narrow implementation paths that limit your team's ability to adapt as you learn more. There's a more strategic approach: Capability-Driven Roadmaps What's the difference? ↳ A feature is a specific solution to a specific user problem ↳ A capability is the power to address a class of user problems in multiple ways For example: 🔦 Users need control over their experience → Instead of "add dark mode," think "build preference management capabilities" 📚 Users need relevant content discovery → Instead of "add homepage recommendations," think "develop personalization capabilities"  💸 Users need seamless payment options → Instead of "implement Apple Pay," think "expand payment processing capabilities" Capabilities create more business value: 1️⃣ Adds optionality to your roadmap Each capability you build opens multiple paths to value, allowing you to respond quickly to market changes. When your experiment fails (and some will), you can pivot to a new implementation without rebuilding foundations. 2️⃣ Ties directly to your business outcomes Great capabilities are outcome multipliers. A robust recommendation capability doesn't just power one feature — it enables personalization across your entire product, directly impacting engagement, conversion, and retention metrics. 3️⃣ Compounds in unexpected ways Capabilities combine in unexpected ways. The intersection of your identity, personalization, and communication capabilities might enable an entirely new product experience you hadn't initially envisioned. 4️⃣ Improves time to market A capability-focused approach doesn't mean slow platform projects with distant payoffs. These capabilities can be built incrementally, with each providing immediate business value while expanding future possibilities. Ensure each capability: ↳ Addresses persistent user needs, not just one-off requests ↳ Connects directly to current business priorities ↳ Delivers at least one immediate, measurable win ↳ Starts with user problems, not technology solutions The most successful product teams start with user problems, but they don't just build one-off features — they systematically develop capabilities that address fundamental user needs while creating sustainable competitive advantage and immediate business impact. #productmanagement #productstrategy #leadership -------- 👋 Hi, I'm Nathan Broslawsky. Follow me here and subscribe to my newsletter above for more insights on leadership, product, and technology. ♻️ If you found this useful and think others might as well, please repost for reach!

🧑🏽 Designing Better Personalization UX. With guidelines on how to better tailor content and features to user’s needs and interests. ✅ Customization allows users to choose exactly what they want. ✅ Personalization anticipates what they want behind the scenes. ✅ We personalize to match specific needs without user’s effort. ✅ We allow users to customize preferences, filters, layout, data. 🤔 But often only very few people customize their experience. 🚫 Past behavior doesn’t always predict future actions. 🤔 Users often have different needs at different times. ✅ Design a wide range of presets, templates and defaults. ✅ Track frequent actions and errors, and suggest shortcuts. ✅ Always add content, or reshuffle it, rather than removing it. ✅ Expose users to non-matching topics to avoid filter bubbles. 🤔 Often users don’t know what they need, or what they’d like. ✅ Good personalization is deeply embedded in a user journey. ✅ Search for moments when you want to win user’s attention. ✅ Ask users explicitly about their intent to learn their context. ✅ Let users override personalization if it goes against their needs. ✅ When journey breaks, don’t stitch it, but tie a beautiful bow. We can’t personalize without research. Collect reliable data about users first. Then segment users into groups with shared needs. Decide what messages you have for each group. And define a user model, content model and metadata that go along with it. Then decide on individual or role-based personalization. Choose touchpoints where personalized UX will be served. Apply the logic across your channels, but give users full control of their data. In that process, define how the team will test and measure the impact of personalization over time. Such a project might often feel like a huge leap of faith without immediate benefits. But if done well, it can increase customer lifetime value significantly — but you will need short-term victories to get a long-term commitment. So start slowly. Run experiments. Personalize where you can make the highest impact. More often than not, the outcome will be worth the effort — even although most users will never even notice it, they might stay for many years to come. ✤ Useful resources Personalization Pyramid, by Colin A. Eagan M.S., Jeffrey MacIntyre https://lnkd.in/eaztWU8e Definitive Guide To Personalization (free eBook, PDF) https://lnkd.in/eggR4hzB Five Levels Of Recommendations, by Guillaume Galante https://lnkd.in/eKqsZtJ5 Personalization Planning, by Jennifer Leigh Brown https://lnkd.in/e9N48x6F Successful Personalization, by Amy Schade https://lnkd.in/eNSUgQ9B Personalization UX Stats (Medium), by Mallory Kim https://lnkd.in/eRy9pvqt ✤ Books – Hello {first name}, by Rasmus Houlind – The Person in Personalisation, by David Mannheim – The Personalization Paradox, by Val Swisher, Regina Lynn Preciado – Personalization Mechanics, by John Berndt #ux #design

MIT Technology Review Insights just dropped a powerful reality check: “Customizing Generative AI for Unique Value” (in partnership with Microsoft Azure) explores how enterprises are moving beyond out-of-the-box models to unlock competitive advantage through tailored AI. The mission? To understand how global tech leaders are customizing generative AI—and what it takes to do it right. The data is clear: → 67% of enterprises are using or exploring RAG → 54% are fine-tuning models → 46% are investing in prompt engineering Why? Because foundational models fall short for enterprise needs. They’re powerful—but generic. Customization is the new frontier of value. → 50% of tech leaders prioritize efficiency → 49% seek market differentiation → 47% aim for better user satisfaction → 42% cite innovation and creativity But it’s not without challenges: → 52% cite data privacy/security as their top concern → 49% struggle with data quality and prep → 45% can’t yet measure customization impact effectively What’s emerging instead? A smarter approach to AI development: → AT&T uses agentic systems to automate full software lifecycles → Dentsu achieves 95% accuracy in campaign planning with a customized RAG framework → Harvey AI builds legal-specific models that support real-world legal workflows And enterprises are moving fast: → 76% still need help identifying business use cases → 53% are enabling devs with telemetry and debugging tools → Multi-agent systems are being developed to simulate scenarios and generate synthetic data Bottom line: Generative AI is only as powerful as the context it’s given. Customization unlocks that context—transforming productivity, accuracy, and innovation. This isn’t just AI adoption. It’s the rise of AI transformation. Are you customizing yet?

"Can your product do exactly what we need?" If you’ve spent any time in enterprise SaaS sales, you’ve heard this question. And if you’re smart, you pause before answering. Because this is the most expensive question in the sales cycle. Not just in terms of cost, but time, energy, and sometimes—your product roadmap. Answer it wrong and: Say Yes, and you’ve just signed up your product team for 6 months of custom work. Say No, and you’re out of the deal. Say Maybe, and watch the sales cycle stretch endlessly. This is the customization paradox in enterprise deals: Too rigid → Lost deals. Too flexible → Lost scalability. The best companies (and sales teams) I’ve seen thread the needle by: ✔️ Offering configuration instead of chasing custom code ✔️ Prioritizing open APIs over deep product rewrites ✔️ Building modular architecture that scales ✔️ Providing templates that solve 80% of use cases out-of-the-box Smart customization isn’t about saying yes to everything. It’s about building the kind of “no” that still feels like a “yes” to the customer. And there’s data to support this mindset—36% of consumers actively seek customization in products or services (Tailoor quoting Deloitte). So the need is real. But the solution isn’t code. It’s design. TL;DR: Make your product flexible enough to bend. But strong enough not to break. #EnterpriseSales #ProductStrategy #SaaS #SalesLeadership #B2BSelling