The AppLyft Way: Where Funnels, Data, and AI Are Really Headed

In this episode of the Retention Podcast, we’re joined by a product-growth expert with hands-on experience across multiple app categories. Kateryna Spazheva, Product Marketing Lead and Growth Manager at AppLyft, walks us through her journey into product marketing, the real differences between Health & Fitness and Utility apps, and what PMMs actually do when a company is launching something new.

AppLyft is one of Ukraine’s fast-rising product companies, building mobile apps across family safety, entertainment, and mental wellness — with more than 5 million monthly active users and revenue that continues to climb every quarter.

Why Utilities Sell Solutions While Health & Fitness Sells Emotion

Health & Fitness apps usually win people over through emotion. They lean on aspiration, hope, and the feeling that change is possible. Users don’t always arrive with a clear problem, so the funnel guides them toward a realization — “this is what I want to fix” — and then shows the promise of a better version of themselves.

Utilities are the opposite. Users already know exactly what’s wrong, and they come looking for the fastest way to fix it. The job of the funnel is not to inspire them but to prove that the app solves the problem quickly, clearly, and with as little friction as possible. A short demo, a quick micro-win, or even a simple preview goes further than any emotional hook. Where Health & Fitness sells a vision, utilities sell certainty.

Why Product Marketing Feels So Rewarding

Product marketing sits at the sweet spot between creativity and analytics. It lets you think like a product person and a marketer at the same time, which means you need a deep understanding of users and how they make decisions. The fun part is how many functions you get to touch: generating ideas, building hypotheses, running tests, and seeing the results almost immediately.

The work has a fast, clear feedback loop. You launch something, watch the numbers come in, 

learn from them, and move on to the next iteration. Unlike performance channels that sometimes feel like wrestling with opaque algorithms, product marketing gives you more control over the data and the outcomes. It’s hands-on, measurable, and creative all at once, which makes the job especially energizing for people who enjoy both sides of the brain.

What Makes an Experiment “Good” — And Why the Bad Ones Still Matter

In product marketing, the real work is not just launching ideas but understanding what they teach you. There’s no such thing as a “failed” experiment, because even the losing ones give you clarity about your users, your product, or your assumptions. The ideal experiment is simple: a clear hypothesis, clean execution, enough data, and a result that shows a real profit lift.

Early-stage products can’t always run classic A/B tests, so they rely more on research, competitive analysis, and borrowing validated ideas from other niches until the user base grows.

Some outcomes still surprise you. Lowering prices doesn’t always increase conversions, and sometimes the “obvious win” turns out to be a non-starter. On the other hand, adding extra steps to a funnel — something that feels counterintuitive — can outperform shorter flows when those extra questions make the experience feel more personal.

For new products, the role of product marketing becomes even more important. It’s about choosing the right audience, shaping the first funnel, and making sure the value is clear enough that people are willing to pay.

The Real Role of Product Marketing in a New App Launch

Launching a new product is always a team effort, but product marketing sits right in the middle of everything. The first job is to turn early interest into real intent — user acquisition can bring the traffic, but it’s product marketing that shapes the funnel, clarifies the value, and converts curious users into paying ones.

At the very start, PMM works closely with UA and product teams, watching how people react, fixing weak points in the flow, and adjusting the story until everything clicks.

When performance looks fine, but conversions are flat, it’s often product marketing that unlocks the breakthrough. A small change in positioning, a clearer promise, or a better-structured funnel can suddenly make the entire system work. 

Once users begin converting, the product takes over, delivering the experience that keeps them satisfied and paying. 

From there, scaling becomes possible. In this early stage, PMM’s role is simple: guide the user from “I’m interested” to “I’m ready to pay,” and help the team prioritize the ideas that actually move the needle.

How Product Marketers Choose Ideas Worth Shipping

Product marketers are flooded with ideas — their own, the team’s, leadership’s, and everything inspired by competitors. The real skill is deciding what deserves attention and what should be politely declined. Prioritization starts with validation. Some ideas come with strong signals, like competitors already testing and scaling them. If someone else is making money with a concept, that’s usually a sign it’s worth exploring. Others come from pure brainstorming and feel much riskier, which means the confidence level is simply lower.

There isn’t one universal framework that solves prioritization for every team. In practice, it’s a blend of what matters most for that specific product: data, competitive proof, effort, expected impact, and how the idea aligns with current goals. Most decisions rely on analytics tools like Amplitude, Tableau, or Looker, and on how quickly a PMM can see real behavior in the funnel. 

The more autonomy product marketers have to tweak flows, duplicate funnels, and adjust screens without engineering help, the easier it becomes to test responsibly without slowing the team down.

The Metrics Product Marketers Shouldn’t Ignore

Early in product marketing, it’s easy to focus only on conversion rates because they’re simple, fast, and immediately visible. But once you work with a subscription product, the real picture comes from money metrics. Predictive LTV becomes the north star. It tells you not just who converts, but who stays, who pays, and where long-term value actually comes from. 

Understanding how LTV is formed — and how different parts of the funnel influence it — is a much higher level of mastery than improving a single conversion step.

There’s also an area many PMMs underestimate: marketing compliance. It isn’t glamorous, and early in your career it doesn’t feel urgent, but ignoring it can break an entire launch. Local regulations, platform policies, and legal requirements matter more than people assume, especially in subscription apps. 

Conversions can tell you what’s happening today, but LTV and compliance determine whether the business survives tomorrow.

Why Predictive LTV Matters in Subscription Apps

Predictive LTV is a way to estimate how much revenue a user will generate over their entire time with the app. In subscription products, money doesn’t come all at once — it arrives monthly, quarterly, or yearly. But teams still need to understand performance right now. Predictive LTV fills that gap. Using historical data, patterns from similar niches, or early behavioral signals gives a realistic forecast of what a user is likely to pay over the next several months.

In mature products, these models become quite reliable and help teams judge whether a campaign is profitable long before all the renewals actually happen. For new apps, the picture is messier. There’s little data, many moving pieces, and most hypotheses fail quickly. But even a rough predictive LTV helps teams iterate faster, make smarter budget decisions, and avoid growing channels that look good in the short term but never pay back.

How to Spot Early When Something Isn’t Working

The fastest way to see trouble is to watch the conversion metrics that sit closest to the surface. Even if you don’t know the future LTV yet, early signals reveal whether the funnel is healthy. High unsubscribe rates, sudden spikes in refunds, or a big drop in engagement within the first hour usually point to a deeper problem. When numbers jump from something like five percent to fifty, you don’t need a model to know performance will collapse later.

Top-of-funnel behavior matters too. CTR, funnel conversion, and the first few in-app actions all stack together, and when one stage falls far outside normal ranges, the whole system suffers. Sometimes a funnel can outperform weak CTR, but that rarely lasts. Healthy performance needs everything to work together. If any step is five or ten times worse than expected, that’s the moment to stop, rethink, and test a different direction.

Why AI Utilities Are So Hard to Convert

AI is everywhere right now, and that hype makes the market tougher, not easier. People click instantly because the topic is hot, traffic is cheap, and curiosity is enormous. But most of those users don’t actually want to buy anything. They’re exploring, sampling, or simply following the trend. That creates a huge gap between interest and real intent — and closing that gap is the core challenge for teams building AI utilities.

The real difficulty is finding the bridge between a user’s problem and the specific way AI can help solve it. It’s not enough to say “we use AI.” The product has to deliver real value, and the funnel has to show that value quickly. When that bridge works, users try the product, subscribe to a trial, and start adopting it. When it doesn’t, you get massive click volumes with almost no conversions.

Compared to classic utilities, AI utilities attract a more curious, less committed audience. Other utility niches already have predictable patterns: teams know what users need, how to explain value, and which flows convert. AI attracts everyone, including people who aren’t looking for a real solution. That’s why product marketing becomes especially important — someone has to shape the message, guide expectations, and turn raw interest into actual usage.

Do Teams Need to Be Forced Into Using AI?

No — and it wouldn’t work anyway. People adopt AI when it genuinely helps them, and the difference shows up naturally across roles. Developers use it as a coding partner, creative teams use it to speed up production, and product marketers use it for copy, funnels, and quick research. Others use it rarely, and that’s fine.

Most companies focus not on forcing AI, but on teaching it. They show teams how to brief models properly, validate outputs, and use AI as a shortcut for the work that normally takes hours. The goal is simple: remove the manual parts so people can think more and execute faster.

At the same time, AI doesn’t fit every niche. In products built around security, family safety, or functional utilities, users don’t care whether it’s “AI-powered” – they just want a reliable solution. 

So far, AI can improve the engine behind the scenes, but it rarely becomes the selling point.

In a Nutshell

Health & Fitness marketing now runs on creativity, speed, and sharp thinking. Meta’s transparency and AI tools made every idea easy to copy, so teams win by producing fresh concepts fast and matching them to the right funnel. Web traffic added even more variation, which pushed lifecycle, product, and email teams to work smarter, not just harder.

Regional strengths shaped the field, too. Eastern European teams excel at measurable growth and rapid testing, while US teams lead in brand and long-cycle strategy. The strongest results come from blending both mindsets.

AI changed how teams work — speeding up research, content, and analysis — but it didn’t replace the fundamentals. Human judgment still decides what users value, how funnels behave, and which experiments deserve to scale.

In the end, growth belongs to the teams that adapt quickly, understand intent, and keep testing long after competitors stop. Think of it as survival of the fastest… with fewer privacy updates.