HOUSE OF APPS

When your online store should or shouldn't go headless - here's my honest point of view. Basically headless commerce means separating your storefront from the backend systems. Hence the name. The reason why it's trending among your peers: People want Amazon-level experience with smooth performance - even from smaller businesses. So, it's a tech response to customer expectations. Also headless commerce let's you: ??Move fast on the frontend without risking backend stability ??Deliver pages that load faster - milliseconds vs. seconds ??Use the same product data across channels ??Handle high-spikes like black fridays and such ??Great optimisation on development side. Frontend and backend teams can truly work independently. ??Resource management - easier to update a product description once and it's instantly correct everywhere - website, mobile app, in-store displays without errors or duplications. And these are the critical challenges that turn businesses off: ? It's more expensive - budget for 30-40% higher initial development costs. ??Highly skilled developers are needed. ??When something breaks, it's more complex to diagnose across multiple systems. Now here's the hard truth that I wish more dev teams were upfront about: Most ecommerce businesses under $5-10M in revenue have absolutely no business going headless. It's worth your time and investment, ONLY when the limitations have become real constraints on growth. ??You're losing significant (!) revenue from performance issues ??Your business is already omnichannel - not just thinking about it ??You have access to highly skilled developers - or resources to hire them ??Your current platform is limiting your growth Ideally, go headless if you answered yes to all of these. If your business isn't ready, but it already feels tech limitations, here's a conservative way you can move forward: ?? Maximize your current platform first. Most Shopify Plus, BigCommerce and Magento stores tend to use less than half of available customisation. ??Get headless frontend tools that work with platforms - like Shogun Frontend or Builder. io before full decoupling. ?? Run a cost-benefit analysis. I'd start with something concrete and measurable: ? Pick your highest-value user journey, like mobile checkout or product discovery and build a proof-of-concept. ? And check real performance metrics against your current solution. Founders that go all-in on massive replatforming without proving the concept first end up tied up in costly development cycles- shifting resources away from their core online retail business. That said, headless is a great architecture choice WHEN your business hits inflection points. Hope this was helpful. Let me know why you opted for or against headless for your online store. I'd be keen to read your specific reasons.

If all new products received this much love & care there'd be more successful startups. So what metrics to focus on once you launch? Taking your product off the ground is a big step in a very long race. It's how well you adapt in the first weeks what matters even more. Use the momentum of your product launch and focus on 4-5 metrics at a time. The key metrics vary across categories, so this should be a good starting point: 1. User engagement & retention Do people stick around? How often they interact? And is the product meeting real needs or just initial curiosity? ? This is how you measure it: - Daily/ Monthly Active Users (DAU/MAU) - Session length and frequency - Churn rates 2. Feature adoption What’s truly valuable for a user? Helps you focus and avoid developing features that add no value to users. ? Metrics to track: - Which features customers use most - Drop-off points where users abandon a feature - Feedback loops like in-app surveys, user interviews 3. User satisfaction & NPS ? Track user sentiments with these: - Net Promoter Score (NPS) - high NPS typically predicts positive word-of-mouth, while lower scores highlight areas needing attention. - Ratings and reviews on app stores or forums - Direct user feedback via polls and support tickets 4. Product stability and performance People don't give you credit for consistent bug-free performance. Yet any issues can derail adoption faster than anything else. ? Keep eye on these: - System uptime and reliability - Load times and response rates - Error rates and crash analytics 5. Customer support & issue resolution Absolute key to consumer apps. ? Track and react: - Ticket volume and resolution time - support requests often act as a real-time alert system - Common user pain points - User satisfaction post-resolution 6. Time-to-Market for improvements Agility keeps your product relevant. The faster and smoother you can roll out improvements, the more competitive you remain. ? Metrics to keep in mind: - Speed of releasing new features or updates - Frequency of iteration cycles - Implementation of user-driven suggestions We obsess implementing early improvements on MVP's against the first-in metrics and user feedback. There's a huge momentum to gain from between the launch and the next few months. Share your experiences below. What post-launch metrics have been most critical for your app?

Product-Market Fit is not about chasing unicorn stories ?? This is what it looks like in real life: There's been this misleading narrative in tech about what PMF is.? And the legendary lightning strike stories that often go with it: - Airbnb's explosive growth during the 2008 recession - Zoom's perfect timing for the remote work revolution or? - Notion's viral take-off in the productivity space Here's the uncomfortable yet liberating truth for most founders: These are outliers, not the norm. Reality: ??PMF is a spectrum, not a binary state Your product can be profitable and growing without experiencing that strong market pull you read about in LinkedIn posts ??Different levels of PMF require different strategies ??Market pull varies by sector Unlike Uber, many products reach PMF incrementally. Customers start noticing, but they’re not banging at your door. B2B enterprise software rarely see the viral adoption of consumer apps. That's perfectly fine—steady, profitable growth beats viral but unsustainable traction. ??The real work happens before PMF - Run systematic customer interviews - Test specific hypotheses, not vague ideas - Prioritize metrics that indicate real value delivery ??Stop comparing your PMF journey to unicorn stories. Instead aim for small wins that show you're moving in the right direction Most successful products—including ones generating $100M+ ARR—found their PMF through continuous iteration, not a single magical moment. So, how do you know you’re on the right track: ??Consistent retention Are early users sticking around? Loyalty is often a better predictor of PMF than explosive growth. ??Repeat customers Do customers see enough value to come back for more? ??Low acquisition costs Are you acquiring new customers at lower cost? ??Product usage evolution? Are users finding new ways to use your product over time? This ongoing value discovery can be as vital as initial demand. ??Gradual referrals Do you see organic growth, even if it’s slow? Referrals can still trickle in without virality. ??On a product side - customer feedback loops Do feedback cycles help you build a product users truly want? PMF is as much about listening and adjusting as it is about demand. The most valuable companies I've worked with and observed in my personal network didn't have explosive PMF moments. What they do have is expert teams who keep learning, iterating, and improving until the market responds. ?? For those who've found solid PMF,?was it a sudden breakthrough or a series of smaller wins? What was that journey like?

Why free is expensive for AI products? These 5 strategies help turn free users into paying customers. Users generally expect a free trial before they fully commit - even just attention. And exactly for this reason free is a lifeblood of user acquisition. What you need to know is that AI products the fastest path to growth is also the one that's the fastest to burning through your runway. The hidden costs you'll want to factor in your free models: - API calls - in-house or third-party - Compute power for training and inference - Storage for large datasets and model versions - Ongoing maintenance and model updates AI products have a non-linear cost scaling - what works at 1,000 users might bankrupt you at 100,000 Strategies that help take that 'free' beast under control: ??Value-based pricing - Align costs with perceived value - Price based on outcomes, not features ??Usage-based models - Create natural limits - Charge for API calls, compute time or data processed ??Freemium with clear boundaries - Setting hard usage limits on free tier - Well designed upgrade path is key ??Time-limited full-feature trials - Create urgency for conversion - Show full value for a brief time ??Bundled AI and human services - this is a growing trend - Pair AI capabilities with human expertise - This justifies premium pricing and differentiates from pure AI offerings The free in AI products puts ‘growth at all costs' into a perspective. A great instrument for user acquisition it only works if it’s a part of a larger monetization strategy. What's your experience with AI product pricing? Have you found innovative ways to balance growth and sustainability? image credit (kobo.com)

How can smaller retailers close the growing tech innovation gap? 4 tech strategies to help you keep up: I intended this post to be about ecom, yet these insights also directly address subscription-based models. In all fairness resources of large retailers will always be unmatched. Their scale of innovation and robotization are next level. I’m talking a decade ahead. Getting on top of what works already helps you understand what's possible and guide your efforts in building software that helps your business grow. While mobile and web platforms with great designs, smooth paths and fast checkouts is the baseline, The real innovation battleground is the customer experience. So this is where you should be aiming for: ? Micro-segmentation with machine learning Traditional segmentation is too broad. It's time for real-time, behavior-based targeting. Use AI to analyze these micro-interactions. Predict next-best actions at a granular level. ? Anticipate with predictive personalization Moving beyond offering recommendations only after specific actions. Predictive personalization uses AI to forecast customer needs before they sinal them. The way it works is by analyzing past purchases/ browsing habits/ broader trends, predictive algorithms anticipate a customer's next likely move. The true power of predictive personalization is in repeat purchases. ? Real-time personalization - at scale Legacy systems rely on batch processing, limiting dynamic experiences. Platforms like Kafka and Redis for instant data processing help create dynamic personalized experiences on demand. Key challenges with this are data privacy, resources and integration issues. ? AI product discovery Leaders and mid-size ecom make the most of the product discovery. In short, it evolved beyond search and filters. Visual search with images or voice search powered by natural language processing create hyper-personalized product explorations. This isn't just to drive sales, it's to excite and create emotional connections as part of a well-crafted shopping experience. Let me know in comments which innovations you are most intrigued by and if you have had any challenges with implementing them. #Ecommerce #CustomerExperience #AIRetail

How does the software engineer talent market impact startups? No, top engineers are not getting cheaper. This will be a high-context pov as most people in my network have a front-row seat to what's happening in the tech talent market right now. The opinions I've read so far swing between doom and gloom over mass layoffs and AI replacing all, and extreme optimism about companies eventually entering a new growth cycle and starting to hire again. Working with international startups, here's what I'm seeing: ? Talented people from big tech starting their own gigs Expect innovative startups founded by ex-FAANG engineers and product managers. In all fairness, this also raises the bar on competition in multiple categories. ? Levelling the playing field Startups are getting access to top talent from big tech. This is huge, as they bring in experts who can scale systems and processes. ? A growing pool of top-tier freelancers Greater access to highly skilled talent on a flexible basis allows to scale quickly without long-term hiring commitments. ? Focus on quality The processes and best practices these folks bring, help startup teams hit their milestones more efficiently and with smaller teams. ? Talent costs are not going down though What's interesting is that even with more engineers on the market, top talent remains expensive. That said, to compete for senior talent startups will need to get creative with compensation. ? AI as a force multiplier Engineers who integrate AI into the full stack are already will become key players on startup tech teams - at premium compensation. From my perspective, the current market for software engineers is a unique opportunity for startups to push forward as the competition gets only tougher. Let me know, how these talent market changes impacting your startup's growth and innovation.

The question isn’t really about whether AI recommendations drive sales, It’s where ecom players are taking it... Let's see the key differences between AI-powered algorithms and traditional product recommendation systems: 1. Data usage Traditional systems use less complex data points past purchases, click-through rates, other customers who bought this, promo calendars, etc). AI powered recommedations work off vast, diverse data sets, including contextual and real-time data on individual customer level, down to location, weather, if they’ve shopped for kids, etc. 2. Adaptability Where traditional systems rely on static, rule-based logic with infrequent updates, AI algorithms are dynamic, continuously learning and adapting. 3. Personalization ?AI-powered recommendations are hyper-personalized, considering individual and changing preferences, behaviors and contexts. 4. Prediction Instead of reactive and based on historical data, AI-powered recommendations anticipate trends, patterns and interests. 5. Complexity AI algorithms are complex models capable of processing intricate relationships and patterns, pushing customers to nuanced recommendations and subtle discoveries. Two reasons why this is important? First, the bottom line effects are measurable: → Higher conversion rates → Lower cart abandonment → Increased revenue and AOV → Improved customer loyalty → LTV → Enhanced user engagement → Richer customer insights Second, it's where all ecom players are headed - Predict and Anticipate. My vision for ecom is that we've moved on from reacting to customer behaviors and entered a new competitive wave where we help customers discover what they want before even they know it. Let me know if you're already using AI recommendations for your business. What have been the experience like - have you seen significant results yet? #ecommerce #aiecommerce image credits: hermes rivera unsplash

What needs to happen for AI to reach full autonomy? Not just tech... The promise of AI is autonomy. Decisions made and executed without human teams. The visions that excite me most are self-managing supply chains and automated customer service. Let’s take a moment to recognize the gap between how smaller businesses perceive AI autonomy as a far-off concept and the level of automation that the industry giants have achieved over the last 5 years. I won't even use the Amazon example. Rotterdam and Shanghai ports - the global hubs enable much of ecom today. The only humans you’ll see there are the ones managing the machines. At scale it can’t be done any other way. Still humans in the loop. Even self-driving cars need humans. So, what's holding back the AI autonomy? These conditions need to happen first to unlock that potential: 1?? Data infrastructure - Faster data collection pipelines - Secure and reliable data processing 2?? Model generalization - AI that works across different domains - Improved training methods and algorithms 3?? AI regulation - Clear regulations for AI deployment? - Ethical concerns need to be addressed head-on - Safety protocols for autonomous AI are needed While this is how things already unfold, there will be multiple shortcuts. To keep up with all of that, these are the actions that smaller businesses and startup teams can take: ??Upgrade data systems - invest in scalable, secure infrastructure ??Have a roadmap for upskilling staff on AI ??Pilot projects - start small to test AI capabilities in controlled environments To really compete we need to pursue change - test new ideas and open up to new ways of working. I’d be keen to hear your take on what stands between where businesses are at and the full autonomy - Share your thoughts in the comments. #AI #TechLeadership #AIautonomy #AIautonomy image credit: Bernd Ditrich, unsplash