The Great Engagement Split: AI Is Creating a New Product Elite
I’ve spent a lot of time thinking about why some products vanish from our phones while others feel like they’ve become part of our daily rhythm. And I’ll admit, even as someone who studies this for a living, I’m not immune to the pull. There have been moments when a well timed notification or a cleverly worded promo code has made me stop, tap, and engage, even when I had no intention of doing so. And even though I know the psychology making me want to take a certain action.
I remember once, I was about to close a shopping app after casually browsing. Then a push notification came through: “Your cart is about to expire. Use code SAVE10 to complete your order.” I knew exactly what was happening. I knew it was an automated trigger designed to create urgency. And yet, I felt a small spark of excitement. It wasn’t that I needed the item. It was that the offer felt personal, like the product was reaching out to me at just the right moment. That tiny psychological nudge worked, and I clicked………….but did not BUY.
These experiences teach. The most effective engagement tactics aren’t just about discounts. They’re about timing and relevance. When I see a promo code that aligns with something I’ve actually been considering, it doesn’t feel like spam. It feels like a helpful nudge. And that’s the line every product team should understand.
The Compounding Advantage
AI doesn’t just make companies faster. It makes their advantages compound.
Take Netflix. It uses multi-armed bandit algorithms to test thumbnails continuously, often showing multiple artwork variations for the same title. The version that gets the click depends on your behavior, your history, and how that thumbnail performs against millions of other users. Over time, that kind of micro-optimization becomes a moat. Manual testing teams can’t keep up.
Shopifyis doing something similar for merchants. Its AI tools can generate multiple product descriptions at once, letting teams test tone, phrasing, and positioning in real time. I’ve seen the same dynamic in my own work. When I used AI to generate multiple onboarding flows, I A/B tested them the same afternoon. The winning version boosted activation by 11 percent.
That’s the new competitive curve: not headcount, but learning velocity.
Growth Is Not the Problem
Many companies still over invest in acquisition, pouring money into Meta ads, influencer campaigns, and expensive launch videos, only to leak users immediately afterward. I’ve seen this pattern up close. Think of casual mobile games like Merge Mansion or Raid Shadow Legends. They flood TikTok and YouTube with cinematic ads but lose most users within 48 hours. I’ve installed those games myself after seeing a compelling ad, only to lose interest once the initial rush faded. The push notifications started rolling in: “200 percent bonus gems,” “limited time raid event,” “your daily reward is waiting.” They inflate the balloon with ad spend, but the air seeps out as soon as excitement fades.
Contrast that with Duolingo, where retention is built into the loop itself: daily streaks, XP progress bars, and just-in-time email nudges when your streak is at risk. A Duolingo notification doesn’t just say “Come back.” It says, “Your 47-day streak is at risk.” That’s not a reminder. It’s a psychological trigger.
Retention as a System
Uber Eats is a good example. Even before a push notification arrives, many users check the delivery tracker because they’ve been trained by the in-progress map. I do this myself. At that point, I’m not thinking about Uber Eats as a brand. I’m thinking about my food. That frictionless transparency is what keeps users returning.
Notion works differently, but the principle is the same. Every note, template, and system a user builds becomes sunk investment. I feel that in my own workspace. The product doesn’t need to beg me back with promo offers, because my identity is already tied to what I’ve built there.
Good games do this too. Call of Duty: Warzone pulls people back through seasonal content, weapon challenges, and social clustering. When friends rejoin, retention rises. I’ve reinstalled games just because I saw a friend in a party. When engagement loops are tuned well, users don’t feel manipulated. They feel committed.
The Three Engines of Engagement
The stickiest products usually rely on three core engines:
Progression: Fitbit (now part of Google) shows weekly step trends and daily goals. Seeing a rising bar taps into the human drive for completion. I’ve been that person pacing around my living room at 11:45 p.m. just to close a ring. Duolingo streaks and Snapchat streaks work the same way.
Variable rewards: Spotify’s Discover Weekly works because it’s just unpredictable enough. It’s familiar, but not too familiar. I open it every Monday because I might find the next song I didn’t know I needed. When Spotify sends a notification saying “Your new Discover Weekly is ready,” it feels less like a reminder and more like an invitation.
Identity and ownership: Roblox , Fortnite, and Canva all create stickiness through customization. Avatars, skins, and saved designs make the product feel personal. Once users build inside a system, leaving feels like losing something of their own. I’ve felt that in Canva myself. When a promo email highlights new design templates, it works because it taps into ownership.
AI and the Cost of Personalization
Before AI, personalization required teams of analysts and developers. Now, AI models like those behind Spotify, Netflix, and TikTok automate millions of unique behavioral adjustments daily. I’ve been on the receiving end of this and it’s remarkable. Spotify’s personalization is jaw dropping. “Discover Weekly” and “Daylist” playlists adapt to your context and time of day, while “AI DJ” generates commentary that mimics a human host. I’ve gotten a Daylist with a name like “nostalgic road trip emo Tuesday afternoon.” That parasocial connection makes the product feel alive, and I’ve found myself saying that Spotify “gets” me.
In commerce, Amazon’s home screen reshuffles categories, banners, and coupon placements every session based on behavioral vectors. I’ve noticed that discount on the item I searched for three days ago isn’t luck. That’s AI. The promo banner that appears with “20 percent off items you viewed” or the push notification that says “price dropped on something in your cart” are all powered by the same engine. I’ve acted on those notifications myself. This same approach is now seen in Shopify’s email automations, where abandoned cart offers are timed and personalized, and in Headspace’s dynamic meditation recommendations that adapt to your stress patterns. The cost to personalize at scale is now near zero. Users no longer praise personalization. They expect it. And honestly, so do I.
From Static Tools to Living Products
I believe the future belongs to adaptive products, systems that evolve alongside users. Duolingo personalizes lesson difficulty in real time, while Grammarly adjusts tone recommendations depending on your past writing habits and context. Canva’s “Magic Studio” now suggests layout adjustments and content ideas based on the document’s purpose. I’ve used these features and they feel different from traditional tools. Contrast that with static suite tools like Excel or Evernote before their AI updates. Once I learned the basic mechanics, nothing changed based on how I behaved. They were tools, not companions.
In living products, you move with the system. The loop between action, feedback, and adaptation is continuous. That responsiveness is what separates sticky apps from background noise. When a product sends me a push notification that references exactly what I was working on, or a promo offer that aligns with my usage patterns, it doesn’t feel like marketing. It feels like the product knows me.
Rethinking Product Strategy
Every product team should now answer three design questions. I’ve used these myself when advising teams.
What’s the behavioral loop? TikTok has “scroll to watch to anticipate next to repeat.” Duolingo has “lesson to progress to streak protection to next lesson.” If your product can’t articulate its loop, retention will stall.
How do you capture behaviorally rich data? Netflix and YouTube don’t just log watches. They log hover time and abandon rates. That’s what powers nuanced recommendation shifts and enables them to send push notifications that say “Because you watched X” with precision. I’ve seen how this level of data makes offers feel relevant rather than random.
What’s your engagement model? Look at how Apple Fitness builds identity with monthly badges, progress with cumulative trends, and variable rewards with surprise challenges. Or how Starbucks uses its rewards program, sending personalized offers and promo notifications based on your ordering habits. I’ve been a Starbucks customer and I can tell you those personalized offers work on me. They aren’t extras. They’re structural retention drivers.
Even small products can start simple. A notes app can show writing streaks or AI summarized highlights from your last session. A budgeting tool can adapt categories dynamically as habits evolve, sending push notifications when you’re close to hitting a savings goal or offering promo content like “you saved 15 percent this month compared to last.” I’ve advised teams to look for these untapped engagement levers.
Build Balloons or Build Systems
I’ve seen two distinct approaches. Some teams keep inflating balloons: big ad campaigns, splashy launches, viral influencer pushes, aggressive promo codes, and re engagement offers like “come back for 50 percent off your first month.” They celebrate spikes in DAU, then wonder why it deflates days later. I’ve been part of those spikes. I’ve felt the excitement of a big launch. But I’ve also felt the letdown when the numbers faded.
Others quietly build self reinforcing systems where user engagement loops feed data, data feeds personalization, and personalization feeds retention. These teams, the TikToks, Spotifys, Duolingos, and Canvases of the world, are no longer competing on features. They’re competing on learning speed, personalization depth, and habit integration. Their offers and push notifications don’t feel like desperate grabs for attention. They feel like natural extensions of the experience. I’ve noticed that when a promo code or notification lands well, I don’t feel marketed to. I feel understood.
AI has drawn the line between products that grow and products that are forgotten. The question now isn’t who can acquire the most. It’s who can keep the most. And that starts with asking not what your product does, but what it trains users to do. I’ve felt the pull myself. I’ve seen how a well timed notification or a relevant promo code can spark real engagement. And I’ve learned that the products which succeed are the ones that turn those moments into habits.
Helping Teams Create Products That Actually Stick
Michael Sorrenti and his team at GAME PILL help companies turn ideas into products people can’t stop using. With 26+ years of experience creating games, AI experiences, and digital platforms for global brands like Disney, Marvel, and Nickelodeon, they guide teams to design and launch products that drive engagement, revenue, and growth. From AI strategy and product design to market-ready execution, the team is able and ready to turn complexity into actionable results.
Sources:
Stanford Institute for Human-Centered Artificial Intelligence. AI Index Report 2024. Stanford University, 2024.
Ernst & Young. EY Global AI Adoption Index 2023. EY, 2023.
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