Tips for Validating Product Ideas

We built the perfect product. Nobody wanted it. Ouch. That stings, right? But it's a pain many tech leaders know all too well. Our grand vision? Shattered. Our carefully crafted features? Crickets Yet in that moment of failure, we stumbled upon an unconventional (yet effective) approach to true product-market fit. Here's the unconventional playbook we wish we'd had from day one: 𝗕𝗲 𝗮 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝘃𝗲, 𝗻𝗼𝘁 𝗮 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗽𝘂𝘀𝗵𝗲𝗿 Dive into customer struggles. Get your hands dirty. Pro tip: Shadow users like a ninja. 𝗦𝗲𝗹𝗹 𝘁𝗵𝗲 𝗱𝗿𝗲𝗮𝗺, 𝘁𝗵𝗲𝗻 𝗯𝘂𝗶𝗹𝗱 𝗶𝘁 Pitch your idea before writing a line of code. Create a landing page. Run ads. See who bites. 𝗖𝗼𝘂𝗻𝘁 𝘄𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀, 𝗻𝗼𝘁 𝘃𝗮𝗻𝗶𝘁𝘆 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 Forget page views. Focus on real user value. Track how people actually use your stuff. 𝗠𝗮𝗸𝗲 𝘂𝘀𝗲𝗿𝘀 𝘆𝗼𝘂𝗿 𝗰𝗼-𝗰𝗿𝗲𝗮𝘁𝗼𝗿𝘀 Build WITH your audience, not just FOR them. Host workshops. Create user forums. Get messy together. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗷𝗼𝗯𝘀, 𝗻𝗼𝘁 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 What are people really trying to do? Map customer journeys. Align your product to their goals. 𝙒𝙝𝙮 𝙙𝙤𝙚𝙨 𝙩𝙝𝙞𝙨 𝙢𝙖𝙩𝙩𝙚𝙧? - 42% of startups fail due to misalignment with market needs (CB Insights). - Pivoting strategically leads to 2.5x more funding and 3.6x faster user growth (Startup Genome) - Premature scaling without product-market fit increases failure rates by 70% (Startup Genome) So, pause. Reflect. Are you solving a real problem, or just adding to the noise? Slow down to speed up. Validate ruthlessly. Let the market guide you. What's one assumption about your market you haven't truly tested? How can you validate (or invalidate) it this week? Share your insights. 👇

When you're launching something new, you want to be sure it's going to work. Running in-market experiments prior to launch confirms hypotheses before you commit resources. Just as important,  experiments can often prevent big missteps. Here are four rules of thumb that make for powerful experimentation: 1. Test more than one concept or proposition with more than one target market segment. Sure, you can test just one concept with just one target, but you'll only learn if it succeeded or failed. If you test several concepts in parallel with more than one target, you can compare performance by audience and start to understand the drivers of success across concepts. 2. Make sure that tested concepts are distinct and differentiated. Each concept should be unique because the goal is to learn as much as possible. If you only test three shades of blue, you'll never learn that people actually want red. 3. Test more than once. As you see 'hot spots' form between concept and audience, test variations of your winning concept. Let’s say, for example, that you test three distinct versions of your new product concept—let’s call them Red, Yellow, and Blue. In the first experiment, Red tests well with all three of your target audience segments. In the next experiment, test three versions of Red with all three segments. This next experiment might explore value propositions or particular features or positioning. It’s a way to generate additional learning about strategy: →What problem does Red solve for customers? →Which features drive interest in Red? →Which positioning helps to interest people in Red? 4. Be aware of your testing environment and how it creates bias (or not) for your experiment. I prefer real-life in-market experiments, with just enough exposure to generate statistically valid results; others prefer ‘lab-based’ testing. Either way, think about how representative your environment is of your eventual launch. The next time you’re making a big move, remember: experiments are a powerful way to reduce risk, whether you are launching a new product, repositioning a brand, or prioritizing a product pipeline. Happy experimenting! #LIPostingDayJune

Building a product isn’t just about solving a problem - it’s about ensuring you solve the right problem, in a way that resonates with your users. Yet, so many products miss the mark, often because the feedback from the people who matter most - users - isn’t prioritized. The key to a great product lies in its alignment with real user needs. Ignoring feedback can lead to building features that no one uses or overlooking pain points that drive users away. In fact, 42% of startups fail because their products don’t address a genuine market need ( source: CB Insights). Starting with a Minimal Desirable Product (MDP) can help. This isn’t about launching the simplest version of your idea, but about delivering something functional that still brings delight - encouraging users to engage and share their insights. How to Integrate Feedback Effectively - Observe User Behavior: Watch how users interact with your product. Are there steps where they hesitate or struggle? Their actions often tell you more than their words. - Ask the Right Questions: Use surveys and interviews to go beyond surface-level feedback. Open-ended questions can reveal frustrations or desires you hadn’t anticipated. - Iterate, Don’t Hesitate: Apply feedback to refine your product. Prioritize changes that align with user needs and eliminate features that don’t serve a purpose. - Keep Listening: The market evolves, and so do user preferences. Regularly revisiting feedback ensures your product stays relevant. The Hidden Cost of Ignoring Feedback A study from Harvard Business Review shows that 35% of product features are never used, and 19% are rarely used. That’s not just a waste of resources - it’s a missed opportunity to deliver real value. Let’s be honest: integrating feedback is hard work. It’s not a one-time task but an ongoing commitment. Negative feedback can be tough to hear, but it’s often where the biggest opportunities for improvement lie. Great products are never built in isolation. How do you incorporate user feedback into your product journey? #innovation #technology #future #management #startups

Just because you have a waitlist of excited customers Doesn't mean they'll ever buy your product. I learned this while watching startups burn millions of investment... A lot of smart founders, I know started chasing compliments instead of contracts. Let me explain: Everyone says yes in a customer interview. 'Great idea!' 'We need this!' 'Keep us updated!' 'Add us to the waitlist!' But here's the truth: Interest is free. Budgets aren't. The moment you ask for money, everything changes: Those urgent needs become 'maybe next quarter.' So here's what actually works: Don't ask for feedback. Ask for a check. Don't request a meeting. Request a commitment. Don't build a waitlist. Build a customer list. The best validation question is 'If I build this exactly as described, will you pay for it today?' 95% of customer interest will disappear 4% will hesitate 1% will say yes That 1% is your real market. That's who you build for. That's how you start. Because one customer with a contract is worth more than a thousand with interest. Products are validated by credit cards Not compliments. Stop counting feedback. Start counting revenue. Your bank account doesn't care how many people love your product. It only matters how many people are paying for your product. Like and share with a founder who needs to hear this. #StartupAdvice #ProductMarketFit #Growth"

Most founders still follow the old playbook: 1. Have idea 2. Fall in love with idea 3. Build for months 4. Launch 5. Discover no one wants it 6. Repeat The AI validation approach flips this completely: 1. Have idea 2. Validate mercilessly 3. Kill it if data says no 4. Only build what people already want The hard truth? 90% of businesses fail because founders build products nobody wants. But that was before AI. I’ve wasted millions on bad business ideas. Now I can kill bad ideas in 72 hours, not 12 months. Here's my 5-step AI validation framework that saves you time, money, and heartbreak: 1. Problem Verification AI tools like Perplexity can search for people actively complaining about the problem you want to solve. Feed Reddit threads, forum posts, and review sites into ChatGPT. Let it extract patterns of pain. No real pain = dead idea. 2. Market Size Analysis If the pain is real, check if enough people have it. Let AI analyze Google Trends, search volume, and TAM (total addressable market) data. Create detailed spreadsheets of potential users. Too small = dead idea. 3. Competitor Assessment Feed AI your top 5 competitors' websites, pricing pages, and customer reviews. Ask it to identify gaps and oversaturation. Create a map of what's missing in the market. No clear advantage = dead idea. 4. Zero-Cost MVP Design Most founders build full products before validation. With AI, create "fake door" tests instead. Build a landing page that looks real. Create AI-generated mockups of your product. Run $50 of ads to see if people try to buy. No buyers = dead idea. 5. Early Adopter Interviews For ideas that survive steps 1-4, use AI to: - Draft perfect outreach messages to potential customers - Generate interview questions that reveal true buying intent - Analyze interview transcripts for patterns No enthusiasm = dead idea. This isn't just faster. It's an entirely different game. Come to my free AI masterclass and I'll show you my system for validating ideas and building profitable businesses in weeks, not years: https://buff.ly/THAXwgV

Smoke testing saves time and money. It's a quick way to validate ideas before investing resources. We once worked with a client who wanted to launch a new subscription service. Instead of building out the full platform, we created a simple landing page with a "Sign Up" button. When users clicked, they were directed to a page where they could join a waitlist. This smoke test revealed: 👉 Interest level in the concept 👉 Potential pricing sweet spots 👉 Key features customers wanted most By gathering this data early, we avoided wasting months of development on an unproven idea. We refined the offering based on user feedback before fully committing. Smoke testing lets you fail fast and pivot quickly. It provides real-world validation that surveys and focus groups can't match. The next time you have a new product or feature idea, resist the urge to dive straight into building. Take a step back and design a simple experiment to test your core assumptions first. Your smoke test doesn't need to be perfect. The goal is quick, actionable insights to guide your next steps. Start small, learn fast, and let data drive your decisions. Learn 5 steps to quickly validate your ideas with smoke testing: https://lnkd.in/gmujzk7P

Seven steps to validate your startup idea before you spend a dime 1. Talk to 30 potential customers. Skip market research firms; hit the street, Slack channels, WhatsApp groups, anywhere your audience hangs out. 2. Summarize their top three pain points in one sentence each. distill noise into clarity. 3. Sketch a landing page with your proposed solution. No code yet, just text and a “sign up” button. 4. Run a 5-day ad test on Facebook or LinkedIn with that page. spend $100 total, see if anyone clicks and signs up. 5. Call every single sign-up. ask, “What made you click?” then listen. 6. Refine your copy and offer based on feedback; relaunch another 5-day test. 7. If click-through stays above 2% and demo requests climb, congratulations, you’ve got early validation. If not, pivot or scrap. Validating cheaply saves months of wasted work and thousands in development costs. Do it first, build second.

How to quickly validate if your gen AI ideas will work? We must first validate if the problems are meaningful enough to merit an AI-powered solution. And we can do so without writing a single line of code. Inspired by Teresa Torres and her 'Continuous Discovery Habits', let’s look at how we can quickly test assumptions when developing gen AI products: Assumption testing is the art of systematically validating (or invalidating) product hypotheses to gain clarity and minimize risks before feature development starts. What should we validate: [1] #Desirability: Is there a market demand for the product? For gen AI products, consider asking how familiar is your target audience with AI (and how that may affect demand) and if the problem you’re solving is a 'must-have' or a 'nice-to-have' for your users? [2] #Viability: Will the product be economically sustainable? For gen AI products, consider the cost of acquiring the necessary data, and the scalability of whether AI can handle increasing amounts of requests without a proportional increase in costs? [3] #Feasibility: Is the technology available and can be developed to realize the product? For gen AI products, feasibility includes understanding the state of the art in AI, the data requirements for training robust models, the availability and accessibility of such data, and any technical constraints that might be inherent in the domain. [4] #Usability: Will customers be able to use the product effectively? For gen AI products, consider how transparent is the AI in the experience to build user trust and how steep is the learning curve for users unfamiliar with AI. [5] #Ethical: What could be the potential harm caused by the product? In generative AI, there are significant ethical considerations. I've dedicated a post to talk about the various challenges, limitations, and considerations. See link in the comment to read the post. By rigorously testing these assumptions, you not only validate the problem space but also construct a sturdy foundation upon which to build your generative AI product. It's not just about having a powerful AI engine; it's about making sure that engine drives real value for your users. - - - - - 📌 I hope you enjoy this post. If you are interested to dive deeper, grab my book, an Amazon best seller, “Reimagined: Building Products with Generative AI”, featuring over 150 real-world examples, 30 case studies, and 20+ frameworks: https://a.co/d/btmnJfu #ProductManagement #AI #GenerativeAI

I’ve seen countless founders waste $75k-150k on an MVP, by making the same mistake. I’ve built and scaled products for the last 2 decades, I’ve noticed a trend: Most product startup owners get excited and rush into launching MVP before laying the groundwork. This leads to unnecessary cash burn and failed products. But if founders hold off until they get the basics right, they can save money and build sustainable products. Here’s how: ▶ 1. Get inside your customers' heads - Dive deep into understanding your target audience. - Conduct thorough market research and user interviews. - Validate your problem statement to avoid building on guesswork. ▶ 2. Craft a crystal clear value proposition - Define the core value your product brings to the table. - Identify how it solves specific pain points better than anyone else. - Figure out why and how your MVP will resonate with users from the get-go. ▶ 3. Measure what matters - Pinpoint key success metrics for your MVP. - Know what to track - user engagement, feature usage, or conversion rates. - Gather meaningful data from the start to set the stage for future improvements. ▶ 4. Decide fast, act faster - Don't procrastinate if you are unsure about what to build. - Use the validation phase to make smart, informed decisions. - Be clear - if your solution doesn’t look promising, pivot without hesitation. By following these steps, you can ensure that you build a satisfactory MVP that sets the right path for your product. Have you ever built an MVP that failed? Do share your insights. #mvp #productbuilding #entrepreneurship

Amazon data is great to have. But are you using it properly? I sat down with Brandon Bastin from Jungle Scout recently and we chatted about how pairing Jungle Scout with PickFu can help brands: ✅ Validate Product Ideas → Use Jungle Scout to find high-demand, low-competition product ideas. → Create variations of potential product designs, features, or packaging. → Run PickFu polls to get feedback on which variations resonate with your ICP. ✅ Optimize Product Listings → Use their keyword research tool to find the best keywords for your product. → Write different versions of your copy incorporating these keywords. → Use PickFu to test which version of your listing copy is most compelling. ✅ Improve Product Images → Analyze competitors’ images to identify common visual themes & gaps. → Design variations of product images - lifestyle photos and infographics. → Use PickFu to test these images and find out which ones customers want. ✅ Test Price Points → Use Jungle Scout to understand the pricing landscape for your category. → Develop different pricing strategies based on your research. → PickFu to gauge customer reactions & understand their perceived value. ✅ Enhance Product Features → Identify popular features & customer pain points from Jungle Scout’s product reviews and ratings. → Develop several product feature sets or bundles addressing these insights. → Use PickFu to determine which feature sets your target audience prefers. So what do you think? Are YOU using both tools properly? ↳ Or are you hitting a "data wall"? "It all starts with the click." - Jay Lovelace