The playbook I used to grow a Shopify app to 13,000+ merchants with zero budget, zero team, and zero connections. I was 17, living in France, and had just built a Shopify app. It was decent — an A/B testing tool that actually worked. The problem was nobody knew it existed. Page 3 of the App Store. Buried under apps with thousands of reviews, big marketing budgets, and actual teams. I had none of that. What I did have was time, curiosity, and a suspicion that the App Store worked like any other search engine. Two weeks later, I was Top 10 in two categories. Four months later, 13,000+ merchants had installed the app. This is the exact playbook I used. I've never seen anyone break this down publicly, probably because the people who figure it out don't want competition. But I'm not in the Shopify app game anymore, so here it is. The Realization That Changed Everything Most app developers treat the Shopify App Store like a lottery. Build something good, submit it, hope people find it. Maybe run some ads. Maybe beg for reviews. That's not how it works. The Shopify App Store is a search engine. And like any search engine, it has an algorithm. An algorithm can be understood. And once you understand it, you can optimize for it. I started thinking about it like SEO — because that's exactly what it is. Shopify needs to show merchants the most relevant apps for their search queries. They use signals to determine relevance. My job was to figure out what those signals were and max them out. Step 1: Scraping the Competition Before optimizing anything, I needed data. I wasn't going to guess what worked — I was going to reverse-engineer it from apps that were already winning. I built a simple scraper that pulled data from the top 100 apps in my category. For each app, I collected: App name and title structureFull description textBullet points and feature listsCategory and subcategory placementReview count and average ratingKeyword frequency in titles and descriptionsPricing model App name and title structure Full description text Bullet points and feature lists Category and subcategory placement Review count and average rating Keyword frequency in titles and descriptions Pricing model Then I did the same for apps ranking on page 2, page 3, and page 5. I wanted to see the difference between winners and losers. The patterns were obvious once you had the data in a spreadsheet. Step 2: The Keyword Discovery Process Here's what I found: top-ranking apps weren't just better products. They were better at telling Shopify's algorithm what they did. The apps on page 1 had specific keywords appearing in specific places: In the title: The exact phrase merchants search for. Not clever wordplay. Not brand-first naming. The literal search term. In the title: In the first 50 words of the description: The same keywords, plus related terms. Shopify's algorithm weights the beginning of your description heavily. In the first 50 words of the description: In the bullet points: Action-oriented phrases that match how merchants think about their problems. Not features — outcomes. In the bullet points: I made a list of every keyword that appeared in top-10 app titles and descriptions. Then I checked search volume using basic keyword tools and Shopify's own autocomplete suggestions. The autocomplete hack was simple: go to the App Store search bar, type the beginning of a keyword, and see what Shopify suggests. Those suggestions are based on actual merchant searches. Free keyword research, straight from the source. Step 3: Rewriting Everything My original app listing was trash. I had named it something "creative" that nobody would ever search for. The description talked about features in technical language. The bullet points were boring. I rewrote everything based on the data. New title formula: [Primary Keyword] + [Secondary Keyword] + [Brand Name] New title formula: Not sexy. Very effective. My app was an A/B testing tool, so the title became focused on the exact phrase merchants typed when looking for that functionality. New description structure: New description structure: First sentence: Primary keyword + clear value propositionSecond sentence: Secondary keyword + specific outcomeParagraph 1: The problem (using merchant language, not developer language)Paragraph 2: The solution (how the app fixes it)Paragraph 3: Why this app vs. others (differentiation)Bullet points: Benefits, not features — each starting with an action verb First sentence: Primary keyword + clear value proposition Second sentence: Secondary keyword + specific outcome Paragraph 1: The problem (using merchant language, not developer language) Paragraph 2: The solution (how the app fixes it) Paragraph 3: Why this app vs. others (differentiation) Bullet points: Benefits, not features — each starting with an action verb New bullet points: New bullet points: Old: "Visual editor for creating variants" New: "Create A/B tests in 60 seconds without touching code" Old: "Statistical significance calculator" New: "Know exactly when you have a winner — no guessing" The difference is subtle but massive. Merchants don't care about statistical significance calculators. They care about knowing when they have a winner. Step 4: The Review Velocity Hack Reviews matter for two reasons: social proof and algorithm signal. But here's what most people miss — it's not just total reviews that matter. It's review velocity. How fast are you getting new reviews? An app with 50 reviews that got 10 this week will often outrank an app with 500 reviews that got 2 this week. The algorithm interprets velocity as a signal of current relevance. I couldn't fake reviews (and you shouldn't either — Shopify will ban you). But I could increase the rate of legitimate reviews. The in-app prompt: After a user completed a successful action (like finishing their first A/B test), a subtle prompt appeared asking if they'd share their experience. Not immediately after install — that's annoying. After they'd gotten value. The in-app prompt: The email sequence: For users who'd been active for 7+ days, a personal email (actually from me, not automated-looking) asking for feedback. If they replied positively, I'd ask if they'd mind leaving a review. Most said yes. The email sequence: The support conversion: Every support ticket that ended positively got a follow-up: "Glad I could help! If you have 30 seconds, a review would really help other merchants find us." People who just had a good support experience are the most likely to leave positive reviews. The support conversion: Review velocity tripled in two weeks. Step 5: Category Arbitrage This one is underrated. Most apps compete in the most obvious category. That's a mistake. Shopify lets you list in multiple categories. The smart move is to find categories where: Your app is legitimately relevantCompetition is weakerSearch volume is still decent Your app is legitimately relevant Competition is weaker Search volume is still decent My A/B testing app could fit in "Store design," "Marketing," and "Conversion." The main "Conversion" category was brutal — massive players with thousands of reviews. But "Store design" had weaker competition and merchants searching there still wanted to improve conversions. I optimized my listing to rank in the less competitive category while still being discoverable in the main one. Within a week, I was top 5 in the secondary category. That drove installs, which drove reviews, which helped me climb in the primary category. Step 6: The Compound Effect Here's what happens when you get the algorithm working for you: Higher ranking → More impressions → More installs → More reviews → Higher ranking It compounds. The hard part is getting the flywheel started. Once it's spinning, it maintains itself. I went from page 3 to page 1 in about two weeks. The growth curve was flat, flat, flat, then suddenly vertical. That's how algorithm-driven growth works. You're either below the threshold where the flywheel kicks in, or you're above it and compounding. What I'd Do Differently Now Start with keyword research, not product development. I built what I thought merchants needed, then tried to find merchants who wanted it. Backwards. Now I'd research what merchants are actively searching for, validate demand exists, then build. Start with keyword research, not product development. Invest more in screenshots and video. The listing content got me ranking, but better visuals would have improved conversion rate once people landed on the page. I was lazy here. Invest more in screenshots and video. Build referral mechanics earlier. Word of mouth is the most sustainable growth channel. I added referral features late. Should have been day one. Build referral mechanics earlier. Don't ignore paid ads entirely. I was proud of growing with zero budget. But strategic ad spend early on could have accelerated the flywheel. Sometimes spending money is the smart move. Don't ignore paid ads entirely. The Bigger Lesson The Shopify App Store isn't special. Every platform has an algorithm. Every algorithm can be understood. Every algorithm can be optimized for. I've applied the same thinking to everything since — LinkedIn content, Product Hunt launches, even cold email deliverability. Figure out what the system rewards, give it more of that, measure the results, iterate. Most people treat algorithms like black boxes or lotteries. They're neither. They're systems built by engineers to solve specific problems. Understand the problem they're solving, and you'll understand how to win. I'm 20 now, building something new — an AI tool that automates CRO research for agencies. Different product, different market, same mindset. Understand the system. Optimize for it. Let it compound. The app that started all this doesn't exist anymore. I shut it down after realizing merchants didn't want testing tools — they wanted someone to tell them what to test. That's a story for another post. But the playbook still works. The Shopify App Store algorithm hasn't changed much. If you're building an app and stuck on page 3, this is your way out.

How I Reverse-Engineered the Shopify App Store Algorithm and Hit Top 10 at 17

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