Retention ≠ Resurrection; Retention = Engagement

1. Companies love win-back flows but rarely think about engagement metrics

Companies so often set up win-back email flows and re-engagement campaigns. It's easy to see why: setting up a "We miss you – here's 20% off!" email feels proactive and that's what we usually see in our inboxes every day from various companies we have forgotten about. In both e-commerce and SaaS, marketing teams pour time and money into these automated win-back flows. But here's the sad truth - they often over-invest in chasing ghosts while under-investing in actual engagement. Companies fixate on winning back back users who've forgotten about your product instead of analyzing why they churned in the first place.

The result? An avalanche of emails (and sometimes push notifications and SMS - yes, let's use all the channels) trying to revive users who have mentally left. At the same time, key engagement metrics like how often users log in, use core features, or find value get ignored. Many marketing teams couldn't tell you their average sessions per user, but they know their email open rates by heart. This is backwards. If you're not measuring engagement in your product, you're flying blind on retention. You might win a few users back temporarily with a promotion, but without improving engagement, they'll leave again.

Companies love these win-back tricks because they're tangible and easy to implement. Set up a drip campaign, tick a box. But focusing on engagement is a tougher longer-term game: it means understanding user behavior, integrating product hooks and continuously improving the experience. It's less sexy in the short term and a lot more hard so often marketing and product teams opt for the easier solution (win-back flows). Don't fall into this trap – real retention comes from improving the user's experience and contiuously showing value, not from how clever your comeback email is.

Remember: if you have to beg your users to come back, you've probably already lost them.

2. Resurrection is often expensive and doesn't scale that well

Chasing churned users with "resurrection" campaigns might seem cost-effective at first - after all, it's usually said that resurrecting a dormant user is cheaper than acquiring a brand new one. But "cheaper than new" doesn't mean "cheap" in the long run. Win-back discounts, incentive coupons, paid remarketing ads etc. - these costs add up and destroy your margins. Offering a 50% discount to lure back a churned customer may win a sale, but you've slashed your profit on that order and trained the customer to expect sales or special treatment.

More importantly, resurrection doesn't scale as a growth strategy. Why? Because if your product isn't engaging to begin with, you’re stuck in a leaky bucket scenario: Users churn in large batches, you spend resources to resurrect some of them then many churn again and the cycle repeats. It's like filling a bucket with holes - you're constantly adding water. I've read that many direct-to-consumer brands see that 50-70% of new customers churn within a month. In my practice I've seen numbers up to 90%!! That sucks! The first reaction is to "pull customers back with a discount" before they disappear for good. But if these users weren't truly satisfied or engaged initially, a one-time discount only delays the inevitable. You might resurrect them for one more transaction, but you haven't fixed the underlying issue that made them leave.

From a scalability perspective, resurrection has diminishing returns. There are only so many times you can revive the same inactive user with gimmicks. The first win-back email might get a decent response rate; by the fourth or fifth "We haven't seen you in a while!" message, users are numb to it (or have already unsubscribed). And that's if you actually have it automated, if not, you're sending large campaigns to mostly dead users again and again and again... And here's a scary thought: Every euro and hour spent on resurrection is an euro and hour not spent on improving the product or engaging current users who still have a chance. It's a zero-sum game for your resources. Wouldn't you rather invest in making your product so good that users don't want to leave at all? Or maybe you already have made the product much better but users just don't know it or have forgottent about features that make this product so good. A few clients of mine had this problem - they shipped a feature and forgot to tell users about it in the first place or later forgot to introduce new users to these features (because they weren't customers when this feature update was introduced in the first place). 

3. Most users don't reach the AHA moment - change that or lose them

Here's a "fun" fact: the majority of new users never even get to the "Aha!" moment. What's the "Aha!" moement? That magical point where the value of your product really clicks and user says - "Aha! Now this is valuable". In many apps user retention plummets within the first days. Only about 10-12% of users are still active a week after signup and only 4-5% last 30 days. This means that up to 90% of users stop using your product early, and most probably before experiencing your product’s core value. They churn not because they hated your product, but because they never truly understood it or got value from it. That hurts.

Why does this happen? Often poor onboarding or bad product experience means users don't find the key feature or benefit (the "Aha!" moment) quickly enough. The "Aha!" moment is typically a specific action or milestone that strongly correlates with long-term retention. Classic examples are: "Facebook famously found that connecting with 7 friends in 10 days was the key predictor of a new user sticking around" and "Slack discovered that teams which sent 2000 messages were incredibly likely (93%) to keep using Slack long-term". These actions became North Star goals during onboarding. If most of your users fail to hit those kinds of milestones, they're not going to stick.

How do you help more users reach that "Aha!" moment? Start with data: identify which user actions strongly predict retention. It could be completing a profile, making a first transaction, connecting to 5 colleagues - it all depends on your product. Then engineer your onboarding and UX to drive users to that action. Basically do whatever it takes for users to reach this.Try (experiment) different tactics. Give guided tutorials, tooltips, incentives - whatever is necessary to push users toward the core value quickly. Also, remove friction on the way to "Aha!". If there are 10 steps to get value, cut it to 5 or even 3. Every extra step is an opportunity for users to drop off. There's an exception for this though - some products need some trust to be built and it would seem scammy or sketchy if the process was too easy. e.g. many people probably wouldn't start investing with a company if they didn't do some KYC or identification processes beforehand. This is called the "positive friction" but I'll cover that in a different post.

In short - activation is everything. If a user never activates (reaches "Aha!"), all your retention emails in the world won't save them. I remember someone said something like this (might have been Adam Fishman but I'm not sure): "By the time a customer has decided to leave, it is already too late. To truly improve retention, you have to start with activation." Focus on delivering value upfront. Once users really experience your product's benefit, they'll have a reason to come back on their own. (or with a little nudge in case of less-frequent products)

4. Companies don't know their product's natural usage frequency

How often should your product be used? Daily? Weekly? Monthly? Many companies have never explicitly answered this, and it leads to retention goals that someone just decided based on own experience or what finance department dictated. Every product has a "natural usage frequency" – the cadence at which a typical user would naturally find value in it. For example, a social media app might be used multiple times a day, while a personal budgeting app might realistically be used weekly, and an app for comparing and buying your home or can insurance might be yearly. The problem is, if you don't know your product's natural frequency, you might label normal users as "churned" simply because they're not using the app every day.

A lot of companies make this mistake: they blindly chase daily active users (DAU) because, well, everyone talks about DAUs. But if you have a product like a food delivery service or travel booking app, daily use might be an overkill. Very few people book flights or order couches every single day. In these cases, weekly or monthly usage might be more appropriate. (not with buying a couch. If your business is only selling couches then you have to make them really good and leave some branding on the product so people know that this is the company they want to buy their couch from next time and would always recommend it to their friends) Retention should be measured on that natural interval. In analytics tools this means switching from daily to weekly, monthly retention metrics if that makes more sense. If your retention curves only flatten out on a monthly basis, that's a clue that monthly is the natural cycle for your product.

On the other side, not knowing your natural frequency can mask problems. Example: if your to-do list app is supposed to help people daily, but most users only open it once a month, you've got an engagement issue - not a "monthly product" but a failure to deliver daily value. The goal is to define how often you believe users should get value. If in reality users use it less ofthen then that's another issue. Companies that understand their natural frequency set more realistic targets and build features to support that cadence. Others end up spamming users with unnecessary "come back daily!" messages or mis-labeling healthy users as inactive. Know your rhythm: it’s hard to improve retention if you're defining what retention is wrong.

Oh, and don't look at averages. I look at natural frequency by taking the time since last usage for each time a product is used, then stack it on a bar chart based on amount of occurances.  e.g. 1 day 0%, 2 days 1%, 3 days 4% ... 6 days 20%, 7 days 35%, 8 days 20%.. etc. Where you see the hill is your natural frequency. Below in the example the natural frequency is around 7 days. If you have really retained users then better look at their natural frequency because it would have less randomness. 

You might see something like this as well. This would indicate that your product has different use-cases and you should explore it more. If you have no distinct "frequency bumps" then you should dig deeper in the data. Might be that different user segments have different frequencies and it's worth comparing how users with 2 day frequency differ from 8 day frequency.

5. Teams treat retention as just a marketing problem, not a product issue

"We need to improve retention - let's send a new email sequence!" If that sounds familiar, you're witnessing the common mistake of treating retention as a marketing campaign issue rather than a product experience issue. Sure, marketing and CRM can help – messages, promos, and outreach have their place, but they are just temporary patches, not cures. The real drivers of retention live in the product. As many growth leaders point out, "Retention is a product problem, not a marketing one." If users don't love your product or find habitual value in it, no amount of witty email copy will keep them around.

Unfortunately, in some companies retention falls entirely to the marketing or customer success teams. Product teams keep churning out features and assume "retention" equals sending churned users to the sales or CRM team for reactivation. This idea mentality is deadly. Retention must be owned cross-functionally, with product at the steering wheel. Why? Because the most effective retention strategies involve improving the product itself - making it stickier, more useful, more engaging. That's something marketing alone can't fix.

Think of it this way: If users are churning because your app is confusing or slow or not delivering value, is the solution really a prettier newsletter? Of course not. It's fixing the UX, addressing the value prop, maybe adding social features or better content. All those are product changes. Many companies learned this the hard way. They tried to "save" churning customers with intervention calls or tricky cancellation flows (even trying to talk users out of leaving) only to realize it's too late by that point. (Think of cancelling netflix, you probably don't cancel your subscription when you're in the middle of the season of your favorite show. You cancel it when you haven't found anything to watch for a while and an email that now you get same selection of shows that you don't watch just 15% cheaper won't make you change your mind.) The saving needed to happen earlier - during onboarding, during the product usage, before the user decided to abandon ship.

To shift this mindset, treat retention metrics (like D30 retention, churn rate, LTV) as product health metrics, not just marketing KPIs. In your team meetings, talk about user behaviors, feature adoption and customer feedback, not just open rates and coupon redemptions. When you view retention as a product responsibility, you start asking the right questions: Which features keep users coming back? Where are users getting stuck or dropping off in the app? What core action should we encourage more? Those questions lead to durable solutions. Marketing can assist by communicating with users, but the product must pull its weight in keeping users engaged.

In short: If you think you can "optimize retention" purely with marketing tricks while the product itself is lacking, you're just delaying churn, not preventing it.

6. Identifying meaningful engagement metrics that drive real retention

Not all engagement is created equal. Some metrics give a false sense of success  (so called vanity metrics) while others actually correlate with retention. The key is to identify meaningful engagement metrics for your product: the user behaviors that genuinely indicate users are getting value. For example, simply counting "logins per day" might not tell you much. A user could log in and immediately log out (or do nothing). Instead, define what "active usage" really means for you. Maybe it's number of messages sent (for a chat app), tasks completed (for a productivity app), or lessons finished (for an e-learning app). These are often called "value signals" - actions that reflect the user receiving core value .

A good process is: brainstorm 3-5 possible engagement metrics that could matter, then look at the data and see which correlate best with long-term retention. For instance, a SaaS team might hypothesize that creating at least 3 projects within the first month is a strong engagement indicator. By analyzing cohorts, they might discover that users who do that have 2x the retention of those who don't - that's a meaningful metric. It's important to do this analysis because every product is different. There are no one-size-fits-all engagement KPIs . One app's crucial metric (e.g. number of playlists created in a music app) could be irrelevant in another app.

Once you've nailed down the metrics that matter, double down on them. Use them to define what "active" or "engaged" means in your dashboard. Track them constantly over time. Share them with the team so everyone is aligned on what success looks like (beyond just MAU/DAU vanity stats). And importantly, build features or campaigns that boost those metrics. If "number of user-to-user interactions per week" is a key engagement metric, think of ways to encourage more interactions (notifications, prompts, community features, etc.).

Also, be careful not to mislead yourself with surface-level engagement. E.g. think of a content platform: a user scrolling for 5 minutes might look "engaged", but if they never like or save any content, are they really finding value or just killing time? You might find that a metric like "items favorited per day" predicts retention better than minutes of scrolling. In short, find the signals that tie to retention, and ignore the noise. It's better to improve one metric that actually drives retention than ten that just make your graphs go up without real impact.

7. Practical ways to discover your product's natural usage frequency

We talked about natural usage frequency – now let's discuss how to determine it for your product. Here are a few practical approaches:

• User Research & Interviews: Talk to your best customers. Ask them how they fit your product into their life or workflow. Do they tend to use it every morning? Only on Fridays? When a specific need arises? These qualitative insights can reveal patterns. For example, you might learn that most users treat your app as a “weekly planning tool” every Sunday night. That's a clue about natural frequency.

• Analyze Usage Data: Dive into your analytics and look at the intervals between user sessions or key actions. A handy method is to plot a histogram of user activity frequency. Tools like Mixpanel or Amplitude let you generate a frequency report - essentially showing what percentage of users use the product X days per week or per month . This can highlight clusters (e.g. a large chunk of users log in roughly once a week, another chunk daily). If you see a clear peak at, say, 7 days, that suggests a weekly cycle. If it's a flat line until a spike at 30 days, maybe monthly.

• Cohort Retention Curves: Look at your retention curves on different time scales. Does retention flatten out after a certain period? For instance, if daily retention drops off but weekly retention stabilizes at a solid percentage, weekly might be the natural cadence. Someone suggested that if your product is supposed to be weekly but users only show up monthly, you've got work to do. Similarly, if you expect daily but see weekly usage, time to examine if daily value is truly there.

• Consider the Use Case: Think about the problem your product solves. Is the problem inherently daily (messaging with colleagues, checking news), or is it situational (booking travel, filing expense reports)? The inherent nature of the use case often dictates frequency. As a LinkedIn post on product habits noted, if it's an insurance comparison and purchasing app, the natural frequency might be yearly. A taxi app might be used sporadically, whenever one needs a ride - which could be multiple times a day or once a week depending on the user.

By combining these methods, you can triangulate on a realistic usage interval. Once you know your natural frequency, several good things happen. You can set your retention targets appropriately (e.g. measure Week 1 retention instead of Day 1 for a weekly-use app). You can tailor your engagement strategy – for example, sending a gentle reminder if a weekly user hasn't come back in over a week (as opposed to spamming daily). You can also educate your team and investors on what "good engagement" looks like for your product, instead of blindly chasing DAUs or other metrics that don't fit your use case. It's all about being in tune with your product's natural rhythm.

There's one thing to be aware about with these natural frequencies. If your use case is under a month then users interact with your product often enough to generate retention but if it's more than one month or even no natural frequency at all (e.g. buying a house) then your product is in the so called "forgettable zone" and you will have a harder time to retain customers. Still there are tactics to achieve customer retention. At reforge they teach the ICED theory that can be applied to infrequent product.

• Introduce higher frequency features and use cases. The most popular example is Zillow - an app to find your next house. The main use-case is used once per few years at most so they introcued a feature called Zestimate. Zestimate tells you the approximate value of your real estate and this is a feature that people use monthly to see whether their property is gaining value.

• Moving product to a new, more predictable and influencable use case. This is similar but here you move the whole product to a different use-case - one that has higher natural frequency. Linkedin at first was a job seeking company but now it's a social media platform with much higher natural frequency and with user generated triggers (like someone changed a job or got a certificate and you get notifications about these events).

• Latching to other products. If it's impossible to make new use-cases for your product then you can latch your product on some other product. e.g. Airlines and creditcards that earn airline miles. If you think of it - why would credit card give airline miles? This is a classic latching example.

In short if you have an infrequent product, think of ways how users can be reminded of your product more often and in a way that gives users value (not just sending emails). 

8. Early churn signals and how to act on them

Churn rarely happens out of the blue - users usually give off early warning signals that they're about to leave. The trick is to detect these churn signals early and act fast, before the user is fully out the door. What are some common early churn indicators?

• Declining Engagement: One big red flag is a user's engagement dropping steadily. Maybe they used to log in daily, now it's every few days, soon it’s barely once a month. If you see usage frequency or duration trending down for a user (or a cohort of users), don't wait until it hits zero. That’s the time to jump in with re-engagement efforts or to ask if something’s wrong.

• Sporadic or Changed Usage Patterns: Similar to above, if a typically active user suddenly becomes irregular or skips sessions, that's a sign. For example, a core user who used your app every weekday hasn't opened it in the last 10 days – likely a churn signal. Track patterns like last login date, or create an "at-risk cohort" of users who were active weekly and suddenly went silent for more than 2 weeks.

• Feature Abandonment: Sometimes users stop using a key feature they used to love. In a B2B context if a power user on a team stops using the product (even if others on the team continue) - that's a bad sign. In B2C, it could be a gamer who stops playing new levels, or a social app user who stops posting content. A change in individual behavior, especially your power users disappearing, signals that they've lost value in the product.

• Negative Feedback or Support Issues: Not all signals are usage metrics; some come via communication. An uptick in support tickets from a user, complaints about missing features, or low NPS feedback can predict churn. If a user is voicing frustration, they're warning you that if nothing changes, they’re churning. Even silence can be a signal - users who used to interact with your community or respond to surveys but don't anymore might be disengaging.

So, how to act on these signals? Proactively and personally. When you identify at-risk users early, reach out in a helpful, non-generic way. For a high-value account showing declining usage, a personal call or email from a success manager might re-engage them ("Hey, noticed your team hasn't been using X feature lately. Anything we can help with? Any feedback?"). For a self-serve consumer app, it could be an in-app message or a special prompt: ("We miss you! Here's what's new since you last visited…") or a tailored tip to show value they might be missing. The key is to address the root cause if you can. If they stopped using a feature because it was confusing, offer a quick guide. If they haven’t logged in because they didn’t find success, highlight a success story or offer assistance.

Another strategy is to create automated trigger-based campaigns. For example, set up a trigger: if user hasn't logged in for 10 days (and has done <5 total sessions), then send a nudge email sharing a popular feature or inviting them to a webinar. Modern analytics and email tools allow such segmentation easily. The earlier you intervene, the better your chance to re-inspire the user before they mentally churn. Remember, by the time most companies run a "we want you back" campaign at 90 days inactivity, the user's mindset is long gone. Catch them at day 7 of no activity instead, when the relationship is salvageable. 

I usually use customer.io for such automations because they allow me to track all the users when they log in on my client websites. I've also set up some triggers to when users are visiting the cancellation page and that automatically triggers a campaign after a few days with some new feature in-depth explanation or a feedback form to measure the temperature. Then I set up next steps based on results I get and again chain a different campaign to communicate with the customer. Best part is that I can trigger messages from any channel e.g. email, sms, whatsapp or even call center. Doesn't matter as long as the channel has an API to connenct to.  

In summary, don't be surprised by churn - predict it. Monitor the signs (decline in engagement, changes in behavior, feedback cues), and respond with targeted, value-centric outreach. Early churn prevention is far easier than resurrection later.

9. Building effective engagement loops that actually work

Ever notice how some products seem to naturally pull you back in, again and again, without feeling like spam? That's the beauty of a well-designed engagement loop. An engagement loop is a self-reinforcing cycle that keeps users coming back and delighting them each time. It's not just about sending reminders - it's about creating a cycle where the user wants to return because each return yields value or enjoyment. A good engagement loop "creates excitement and motivation that keep customers returning".

So how do you build an engagement loop that works (and isn't just a trick)? One classic formula is: Trigger >> Action >> Reward >> Investment >> (repeat). You might recognize this as a variant of the Hooked Model (Trigger, Action, Variable Reward, Commitment). The idea is to give users a reason to come back (trigger), make it easy/fun for them to take an action, reward them for doing so, and encourage a small investment that increases their likelihood of returning.

For example: Duolingo, the language learning app, has mastered engagement loops with its streak feature. The trigger can be a push notification at a regular time ("Practice your German and keep your 5-day streak alive!"). The user takes the action by doing a short lesson. The app then gives a reward - not just the intrinsic reward of learning, but also an immediate feedback like points, a cheerful animation, and the satisfaction of extending their streak. Then Duolingo asks for a small investment: it could be as simple as setting a daily goal or inviting friends (which makes the user more committed to returning). This loop effectively motivates users to return every day. It's enjoyable, it's rewarding, and it leverages a bit of FOMO (fear of losing the streak). In result Duolingo reaches record-high daily active users and industry-leading retention, largely thanks to this engagement design.

Another example: many productivity apps like Asana or Trello create engagement loops by celebrating milestones. Marking a task complete triggers a delightful animation or congratulatory message (reward). The more tasks you complete, the more you might unlock (investment), and the positive reinforcement becomes a reason to come back and be productive again. Social media platforms do this by generating social feedback loops - you post something (action), get likes/comments (reward), which encourages you to post again or reply (investment), which triggers others, and so on.

The key is that each cycle of use delivers value and primes the next cycle. It's not just "log in to get a badge for logging in" (which is empty gamification). Instead, it's meaningful. A fitness app might loop: workout logging (action) >> see your progress for the week (reward) >> the app prompts you to set a new goal or challenges you (investment) >> which triggers you to come back tomorrow to log again. Over time, these loops form habits.

When crafting engagement loops, ask: What will prompt the user to return? What will they do? What immediate benefit do they get? And what will we do to encourage them to come back again? Align this with your product's core value. The closer the loop is tied to your product's intrinsic value, the stronger it is. Pro tip: Don't solely rely on extrinsic rewards (like points or badges) that aren't connected to real value - those can feel hollow after a while. Instead, amplify the intrinsic rewards of your product with some extrinsic fun. Done right, engagement loops turn one-time users into loyal, habitual users.

10. Case studies: engagement-driven retention wins

Sometimes the best way to get information settled is through real-world examples of engagement causing retention. We've already mentioned a few, but let's recap and add more to showcase how focusing on engagement (not resurrection) leads to big retention wins:

• Slack's Magic Number (2000 messages): Slack identified that teams which exchanged at least 2,000 messages were almost guaranteed to stick around - in fact, 93% of those teams were still using Slack long-term. Knowing this, Slack's team doubled down on features and onboarding tactics to encourage more messaging (like easy drag-and-drop file sharing, fun emoji reactions to increase message activity, etc.). By driving engagement in those early messages, Slack massively improved team retention. They weren’t sending "come back to Slack" emails but they were making sure users hit the messaging threshold that naturally keeps them engaged.

• Facebook's 7 Friends in 10 Days: This is a legendary example from the early days of Facebook's growth. They discovered that if a new user connected with 7 friends within 10 days, that user was far more likely to become an active, retained user. Facebook then oriented their entire new user experience around this insight - suggesting friends, encouraging imports of contacts, etc., to get you to that 7-friend milestone. By focusing on this engagement metric (friends added), Facebook dramatically improved retention, helping pave their path to 1 billion users. It wasn't a win-back campaign; it was an engagement campaign from day one.

• Duolingo's Streak Fever: As mentioned, Duolingo's gamified streak feature turned daily engagement into a fun challenge. Users became hooked on not breaking their streaks, which led to daily use and practice. Duolingo hit 17 million DAUs and sustained growth when the overall category was down. The streak essentially created a daily habit loop. And Duolingo went further - they introduced a "Streak Freeze" for when you miss a day (so you don't lose it entirely) and special quests to re-engage if your streak is lost (small insurance policies to keep you coming back). These engagement tactics directly translated into industry-leading retention for a learning app.

• B2B SaaS Onboarding (Example: Dropbox): Dropbox noticed that users who put at least one file in their Dropbox folder across multiple devices were far more likely to remain active. Why? Because they experienced the magic of seamless file syncing. So Dropbox's onboarding started actively guiding users: "Install Dropbox on your computer and phone. Add a file. See it everywhere!" That engagement (adding files, installing multiple clients) became a predictor of retention. Dropbox famously even offered extra free storage if you completed steps like linking a phone or referring a friend - all actions that increased engagement and commitment to the product. The result was skyrocketing retention and growth via referrals (a positive side effect of engaged users inviting others).

• Mobile Games – The 3-Day/7-Day Magic: Many successful mobile games focus intensely on getting players to come back the day after install, and the week after. They know if a player is still engaged by Day 7, chances of long-term retention go way up. So they pack the first week with engagement hooks: daily login rewards, newbie quests, free gifts, and so on. These are not random marketing giveaways; they're calculated to get the player invested in the game (leveling up a character, unlocking features). For instance, a game might give a powerful item on Day 2 login - if you skip, you lose it. That motivates Day 2 return. By chaining these, games have retained users far better than those that don't. It's all about creating an engaging early experience that converts a curious downloader into a regular player.

In all these cases, the common thread is clear: the companies didn't sit back and say "Oh well, users quit, let's send a coupon." They identified what engaged users did, and then they built strategies to get more users to do those things. Retention followed engagement like night follows day. These case studies show that when you deeply understand your product's value and how users realize that value, you can design experiences that maximize those moments. And when users consistently hit value moments, they stick around.

Bottom line: Retention is not about resurrecting users with gimmicks or chasing after them once they're gone. Retention is earned by keeping users engaged and delighted continuously. If you make engagement your priority - find your Aha moments, know your natural frequency, watch the warning signs, build those engagement loops then you won’t have to constantly play zombie revival with your user base. You'll have a healthy cohort of living, breathing, happy users who wouldn’t dream of leaving your product, because it’s delivering value and enjoyment every day.

And if they do slip away occasionally? Sure, a polite nudge is fine. But the true measure of success is when the majority of your growth comes from current users sticking around and doing more, not from how many old users you managed to drag back. Retention ≠ resurrection. Retention = engagement. Focus on engagement, and the retention will take care of itself.

After all, keeping the users you have happy is not only easier but it's incredibly profitable too (a small 5% increase in retention can boost profits by 25-95%!). So invest in engagement now, rather than paying the price of resurrection later.