Building A Mobile App For Ecommerce

I analyzed 100+ loyalty programs in the last 30 days. Most brands still run loyalty like it’s 2009: Earn points, get a discount, repeat. The top 10%? They’re using loyalty to change behavior- not just reward it. If I were Head of Loyalty at a $10B+ brand today, here’s exactly what I’d do to build a program that drives LTV, repeat purchases, and real retention: 1. Stop Giving Away Loyalty - Make Them Pay for It Costco, RH, Barnes & Noble. When customers pay upfront, they buy in - literally and psychologically. Forget free points. Paid memberships = commitment, retention, higher LTV and emotional sunk cost. 2. Make Loyalty Required, Not Optional - Integrate Directly into Payments Starbucks preloads!!! When rewards are embedded in how people pay, behavior shifts faster, and for longer. This is probably the biggest opportunity in loyalty right now. 3. Forget Delayed Points - Instant Gratification is More Important Immediate dopamine beats theoretical future savings. Slow accumulation = slow engagement. Instant offers = repeat behavior. The 2nd purchase matters more than the 10th. 4. Make Loyalty Emotional, Not Transactional REI, North Face, Sephora. Customers want to belong, not just save. Identity, community, and shared values are outperforming cashbacks and discounts in driving long-term loyalty. Loyalty isn’t just a discount strategy, it’s a brand strategy. 5. Invest in Status + Experiences, not Generic Perks This isn't just theory – with companies like Rapha and Lululemon offering loyalty members exclusive product drops, community events and behind-the-scenes experiences. Lean into waitlists and exclusive product drops. Less financial. More status + psychological “being in the club.” 6. Reward Engagement, Not Just Transactions MoxieLash, Pacifica, Lucy & Yak. UGC. Reviews. Referrals. Loyalty now means participation. The modern flywheel starts before checkout - and lasts far beyond it. ~~ Bottom line? If your loyalty program is still playing a game from 15 years ago, your customers are going to find better options. Today, the best brands in 2025 aren’t just rewarding loyalty- they're engineering it. PS: We analyzed 100+ programs across QSR, retail, travel, and fintech. Next week I’ll share the Top 30 loyalty programs leading the way. Stay tuned🙏

Getting the right feedback will transform your job as a PM. More scalability, better user engagement, and growth. But most PMs don’t know how to do it right. Here’s the Feedback Engine I’ve used to ship highly engaging products at unicorns & large organizations: — Right feedback can literally transform your product and company. At Apollo, we launched a contact enrichment feature. Feedback showed users loved its accuracy, but... They needed bulk processing. We shipped it and had a 40% increase in user engagement. Here’s how to get it right: — 𝗦𝘁𝗮𝗴𝗲 𝟭: 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 Most PMs get this wrong. They collect feedback randomly with no system or strategy. But remember: your output is only as good as your input. And if your input is messy, it will only lead you astray. Here’s how to collect feedback strategically: → Diversify your sources: customer interviews, support tickets, sales calls, social media & community forums, etc. → Be systematic: track feedback across channels consistently. → Close the loop: confirm your understanding with users to avoid misinterpretation. — 𝗦𝘁𝗮𝗴𝗲 𝟮: 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Analyzing feedback is like building the foundation of a skyscraper. If it’s shaky, your decisions will crumble. So don’t rush through it. Dive deep to identify patterns that will guide your actions in the right direction. Here’s how: Aggregate feedback → pull data from all sources into one place. Spot themes → look for recurring pain points, feature requests, or frustrations. Quantify impact → how often does an issue occur? Map risks → classify issues by severity and potential business impact. — 𝗦𝘁𝗮𝗴𝗲 𝟯: 𝗔𝗰𝘁 𝗼𝗻 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 Now comes the exciting part: turning insights into action. Execution here can make or break everything. Do it right, and you’ll ship features users love. Mess it up, and you’ll waste time, effort, and resources. Here’s how to execute effectively: Prioritize ruthlessly → focus on high-impact, low-effort changes first. Assign ownership → make sure every action has a responsible owner. Set validation loops → build mechanisms to test and validate changes. Stay agile → be ready to pivot if feedback reveals new priorities. — 𝗦𝘁𝗮𝗴𝗲 𝟰: 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 What can’t be measured, can’t be improved. If your metrics don’t move, something went wrong. Either the feedback was flawed, or your solution didn’t land. Here’s how to measure: → Set KPIs for success, like user engagement, adoption rates, or risk reduction. → Track metrics post-launch to catch issues early. → Iterate quickly and keep on improving on feedback. — In a nutshell... It creates a cycle that drives growth and reduces risk: → Collect feedback strategically. → Analyze it deeply for actionable insights. → Act on it with precision. → Measure its impact and iterate. — P.S. How do you collect and implement feedback?

When I was head of growth, our team reached 40% activation rates, and onboarded hundreds of thousands of new users. Without knowing it, we discovered a framework. Here are the 6 steps we followed. 1. Define value: Successful onboarding is typically judged by new user activation rates. But what is activation? The moment users receive value. Reaching it should lead to higher retention & conversion to paid plans. First define it. Then get new users there. 2. Deliver value, quickly Revisit your flow and make sure it gets users to the activation moment fast. Remove unnecessary steps, complexity, and distractions along the way. Not sure how to start? Try reducing time (or steps) to activate by 50%. 3. Motivate users to action: Don't settle for simple. Look for sticking points in the user experience you can solve with microcopy, empty states, tours, email flows, etc. Then remind users what to do next with on-demand checklists, progress bars, & milestone celebrations. 4. Customize the experience: Ditch the one-size fits all approach. Learn about your different use cases. Then, create different product "recipes" to help users achieve their specific goals. 5. Start in the middle: Solve for the biggest user pain points stopping users from starting. Lean on customizable templates and pre-made playbooks to help people go 0-1 faster. 6. Build momentum pre-signup: Create ways for website visitors to start interacting with the product - and building momentum, before they fill out any forms. This means that you'll deliver value sooner, and to more people. Keep it simple. Learn what's valuable to users. Then deliver value on their terms.

🌶️ SPICY TAKE: Websites are becoming an outdated eCommerce tool That's because #AI is shifting the web from human-centered browsing to agent-led buying (More details here: https://lnkd.in/gCxTJKSv). As we move to machine-to-machine (M2M) commerce, #CRO and #A/B testing are likely to look very different In the near future, I envision a landscape where websites are no longer the primary touch points for shoppers Instead, I expect "websites" will be rich structured data snippets, hyper-personalized to the individual user, based on past AI agent-assisted searches and buying behavior The interface will likely be through an AI app, bot or GPT tool -- not a website or landing page From this vantage point, it's tempting to think CRO is imploding from the inside out. . . But, for forward thinkers, there are definitely opportunities. #Experimenters will be needed more than ever. Just in new places Here are the top-3 ways I anticipate experimenters will be able to reposition themselves in an M2M, AI-driven world: 1️⃣ OPTIMIZE FOR AGENT-FRIENDLY DATA AND FEEDS ⚡ AI agents, GPTs, and AI shopping bots will likely become the main eCommerce tools They'll likely rely on structured, accessible, and interpretable data As a result, they'll be increasing need for experimenters to specialize in optimizing meta data, schema, and structured data for AI product catalogs EXPERIMENTATION OPPORTUNITIES: ✅ Test tagging to determine which data format is the most visible and preferred by AI agents 🙉 The KPI will be higher-rankings in AI result/recommendations 2️⃣ DESIGN & TEST AI PROMPTS ⚡ Shopping will become more prompt-based, like “find me the best wireless headphones under $200” EXPERIMENTATION OPPORTUNITIES: ✅ Test prompt responses to favorably influence AI outputs ✅ Optimize LLM interaction design to show the most compelling product attributes 🙉 The conversion goal will become seeding AI tools to recognize and prioritize the client's brands offerings, first 3. OPTIMIZE EMBEDDED INTERFACES ⚡ With shopping likely to happen inside smart AI assistants, they'll be need to focus on multi-modal optimization (image + voice + text) and new forms of engagement EXPERIMENTATION OPPORTUNITIES ✅ Think like an AI-UX designer. Determine the journey when the AI is the customer, not people ✅ Test micro-interactions inside AI chat UIs, AR overlays, or voice interfaces ✅ Create copy that persuades bots, not people with the right algorithm inputs to see how how changes in backend data or structure impact AI result visibility 🙉 Conversion metrics will likely be "SERP" rankings in engines powered by LLMs, not just traditional SEO YOUR TURN: 📣 What do you think the future of experimentation looks like in this new AI, M2M paradigm? Share your thoughts and speculations below. Will the web as we know it cease to exist? ⬇️

Way too many e-commerce brands run bare-minimum loyalty programs that don't move the needle. Points. Discounts. It gets old quick. Your top 10% of customers likely drive 40-65% of your profit. But are you treating them like the VIPs they are? Or just sending them the same generic emails as everyone else? Brands that are crushing it right now are building tiered VIP ecosystems that transform transactional shoppers into high-LTV brand advocates. Speaking from 4+ years of experience, I’ve learned a few things that actually work: --> Early access drops that make top customers feel like insiders --> Exclusive product variants unavailable to regular customers --> Private Slack/Discord communities connecting your best customers --> Physical gifts that arrive unexpectedly (not just on birthdays) --> VIP-only virtual events with your founder/designers Data doesn't lie. Well-designed VIP programs consistently deliver 3-5x ROI compared to acquisition campaigns. These programs also cost dramatically less than constantly chasing new customers. Stop treating loyalty like a cost center using discounts, and start treating it like the profit driver it should be, like leveraging experiences, exclusivity, or building relationships. Your competitors are leaving millions on the table with lackluster VIP strategies. The opportunity is massive for brands willing to invest in their best customers the right way. Who's doing VIP programming exceptionally well in your category? Curious to hear some examples.

User experience surveys are often underestimated. Too many teams reduce them to a checkbox exercise - a few questions thrown in post-launch, a quick look at average scores, and then back to development. But that approach leaves immense value on the table. A UX survey is not just a feedback form; it’s a structured method for learning what users think, feel, and need at scale- a design artifact in its own right. Designing an effective UX survey starts with a deeper commitment to methodology. Every question must serve a specific purpose aligned with research and product objectives. This means writing questions with cognitive clarity and neutrality, minimizing effort while maximizing insight. Whether you’re measuring satisfaction, engagement, feature prioritization, or behavioral intent, the wording, order, and format of your questions matter. Even small design choices, like using semantic differential scales instead of Likert items, can significantly reduce bias and enhance the authenticity of user responses. When we ask users, "How satisfied are you with this feature?" we might assume we're getting a clear answer. But subtle framing, mode of delivery, and even time of day can skew responses. Research shows that midweek deployment, especially on Wednesdays and Thursdays, significantly boosts both response rate and data quality. In-app micro-surveys work best for contextual feedback after specific actions, while email campaigns are better for longer, reflective questions-if properly timed and personalized. Sampling and segmentation are not just statistical details-they’re strategy. Voluntary surveys often over-represent highly engaged users, so proactively reaching less vocal segments is crucial. Carefully designed incentive structures (that don't distort motivation) and multi-modal distribution (like combining in-product, email, and social channels) offer more balanced and complete data. Survey analysis should also go beyond averages. Tracking distributions over time, comparing segments, and integrating open-ended insights lets you uncover both patterns and outliers that drive deeper understanding. One-off surveys are helpful, but longitudinal tracking and transactional pulse surveys provide trend data that allows teams to act on real user sentiment changes over time. The richest insights emerge when we synthesize qualitative and quantitative data. An open comment field that surfaces friction points, layered with behavioral analytics and sentiment analysis, can highlight not just what users feel, but why. Done well, UX surveys are not a support function - they are core to user-centered design. They can help prioritize features, flag usability breakdowns, and measure engagement in a way that's scalable and repeatable. But this only works when we elevate surveys from a technical task to a strategic discipline.

If the only reward is $5 off after 500 points, you taught customers one lesson: “stay for discounts” Build a Loyalty Loop instead of a points ladder: 1. Moment 1 – Wow: unbox + surprise (hand written note, bonus sample). 2. Moment 2 – Teach: 72h later send a “pro tips” reel tailored to the item. 3. Moment 3 – Spotlight: after their first selfie tag, feature them in Stories → dopamine > dollars. 4. Moment 4 – Unlock: let repeat buyers vote on the next colorway (access, not coupons). 5. Moment 5 – Multiply: reward a referral with an upgrade, not a discount (free engraving, extended warranty). Points change price. Loops change identity.

Too many product teams believe meaningful user research has to involve long interviews, Zoom calls, and endless scheduling and note-taking. But honestly? You can get most of what you need without all that hassle. 🙅♂️ I’ve conducted hundreds of live user research conversations in early-stage startups to inform product decisions, and over the years my thinking has evolved on the role of synchronous time. While there’s a place for real-time convos, I’ve found async tools like Loom often uncover sharper insights—faster—when used intentionally. 🚀 Let’s break down the ROI of shifting to async. If you want to interview 5 people for 30 minutes each, that’s 150 minutes of calls—but because two people are on the call (you and the participant), you’re really spending 300 minutes of combined time. Now, let’s say you record a 3-minute Loom with a few focused questions, send it to those same 5 people, and they each take 5 minutes to write their feedback. That’s 8 minutes per person and just 5 minutes once for you. 45 total minutes versus 300. That’s an order-of-magnitude reduction in time to get hyper-focused feedback. 🕒🔍 Just record a quick Loom, pair it with 1-3 specific questions designed to mitigate key risks, and send it to the right people. This async, scrappy approach gathers real feedback throughout the entire product lifecycle (problem validation, solution exploration, or post-launch feedback) without wasting your users' time or yours. Quick example: Imagine your team is torn between an opinionated implementation of a feature vs. a flexible/customizable one. If you walk through both in a quick Loom and ask five target users which they prefer and why, you’ll get a solid read on your overall user base’s mental model. No need for endless scheduling or drawn-out Zoom calls—just actionable feedback in minutes. 🎯 As an added benefit: this approach also allows you to go back to users for more frequent feedback because you're asking for less of their team with each interaction. 🍪 Note that if you haven’t yet established rapport with the users you’re sending the Looms to, it’s a good idea to introduce yourself at the start in a friendly, personal way. Plus, always make sure to express genuine appreciation and gratitude in the video—it goes a long way in building a connection and getting thoughtful responses. 🙏 Now, don’t get me wrong—there’s still a place for synchronous research, especially in early discovery calls when it’s unclear exactly which problem or solution to focus on. Those calls are critical for diving deeper. But once you have a clear hypothesis and need targeted feedback, async tools can drastically reduce the time burden while keeping the signal strong. 💡 Whether it’s problem validation, solution validation, or post-launch feedback, async research tools can get you actionable insights at every stage for a fraction of the time investment.

The big disruption in commerce over the last 30 years happened in where we were shopping: (1) First we were shopping in malls, (2) Then we were shopping on websites, (3) Then we were shopping in apps. But each progression still hinged on a core principle: that *you* were the one doing the shopping. The next overhaul in commerce, in my mind, isn’t a change in where we shop, but in *who is doing the shopping*. With agentic commerce, we'll offload many of our shopping duties to savvy, personalized agents. If the big chart in commerce from 1995 to 2025 was e-comm penetration (we're now up to ~20% online penetration of retail), the big chart in the 2025 to 2050 might be agent penetration: what % of purchases will be agentic? (Example in the image below) There are a few big startup opportunities here: Agents that do the shopping. I think we'll use agents more for utility shopping (more tedious, high cognitive load stuff) than for emotional shopping (in this category, LLM-powered conversational commerce will dominate). Examples of agents: – "Price comparison agents" constantly scanning in the background for price drops on items you care about – "Negotiation agents" that haggle for you on marketplaces (so that you don’t have to send 26 messages back-and-forth on Poshmark or Facebook Marketplace) – "Taste agents" trained on your closet data, recommending roducts tailored to your specific style, wishlist, and browsing behavior We'll also see infrastructure for agentic commerce. Agents require accessible APIs, structured product data, agent-friendly checkout. Websites and mobile apps need to be readable by AI. You’d better make sure your checkout flow can talk to an AI agent, or you’re missing out on big dollars. You might ask your agent, “Find a blouse under $100, wrinkle-resistant, with good sustainability ratings.” Today the site that wins is the one that’s most consumer-friendly; sites are optimized for visual consumption. Tomorrow the site that wins might be the site with the best metadata that AI can parse. We'll need startups to build the connective tissue between agents and brands/retailers. This week's Digital Native digs into the rise of agentic commerce and what it might look like. Full piece here: https://lnkd.in/evVZ-XTx