For a long time, brands could buy their way to growth through acquisition channels using the walled gardens. But in 2025, acquisition alone is no longer enough. CAC keeps rising, ROAS is less clear-cut, and even with endless resources — which most teams don’t have — it’s simply not a good use of budget to be fully dependent on paid channels.
Retention and LTV are the top priorities for many companies — which means that lifecycle teams now often own the most critical marketing channel in the organization. Companies are ramping up investment in owned channels like email, SMS, and push, but most teams don’t know where to start, what actually matters, or how to build a program that lasts.
Over the past 15 years, I’ve built and managed lifecycle programs and scaled them from ground zero to millions of users and millions of sends. In this blog post, I’ll share the exact principles I’ve used (and still use) to successfully build, scale, and optimize programs — designed to drive growth, connect with customers, and place lifecycle teams as strategic powerhouses within organizations.
Step 1: Start with the customer journey
The highest performing lifecycle programs start by understanding the behavior that drives long-term value for your business, understanding where customers perceive value within your product or service, and then identifying where the friction points are. One common fear marketers have is sending the wrong message to your customer — and if you’re having that feeling, it’s probably because the customer doesn’t need to receive it.
So how do you know when a message is worthy of a send? I use a five-question framework to ensure I don’t veer off course:
- What does the customer actually need and want at this moment?
- What behavior do you want to drive?
- What does success look like for both the customer and the business?
- What data do you have access to?
- What messaging channels do you want to prioritize, and which ones make sense for your customers?
This gut check helps you ensure that they’re keeping the customer journey front and center from the very beginning — and that gives you the strategic edge.
Step 2: Anchor your program to 3-5 core use cases
The number one mistake teams make repeatedly is overengineering their lifecycle programs, trying to build and solve for every edge case and every channel. This is always a bad idea. Customer loyalty is not about volume or channels; it’s about solving problems and shaping behavior.
The strongest lifecycle programs are rooted in simplicity and focus. Just think about your favorite brands, the ones you buy from again and again without even thinking. It’s because they’ve built trust. They understand your preferences, anticipate your needs, and show up at the right moments with real value to remove friction.
The key is figuring out what that looks like for your business. In my experience, I’ve found that most lifecycle programs want to solve one of the following use cases:
- Onboarding: Guide users to value quickly and set expectations early.
- Activation: Help users take that first meaningful action that signals future retention.
- Habit forming: Reinforce the right behaviors through consistency and value delivery.
- Re-engagement: Proactively win back attention with relevance, not just reminders.
- LTV expansion: Surface opportunities for upsell, cross-sell, or deeper product adoption.
Great lifecycle programs do all of the above, but the most important lever to prioritize is habit building. Habits give you consistent, predictable behaviors you can build every other lifecycle strategy on top of. The critical nuance though, is that not every habit is worth building. The right ones are those where customer value and business value are aligned — where the customer feels rewarded and the business drives profitable, repeatable outcomes. That alignment is what turns habit formation from a vanity metric into a true growth engine.
Step 3: Invest in your data infrastructure
Most lifecycle teams start trying to build journeys and optimize their existing flows. What many marketers fail to realize is that the best place to start is actually with the data — more specifically, your event data. This is ultimately the foundation of all lifecycle marketing. You can’t personalize, suppress, trigger, or test effectively if your events aren’t up to par. And that means data should be clean, consistent, and rich in metadata.
The most important thing you can do before sending a single message to your customers is work with your product managers and engineers to create and align on a data dictionary as a single source of truth with an agreed-upon naming structure to define event names and properties before you begin optimizing or building any new flows.
This is why it’s so important to start by breaking down every aspect of the customer journey. If you try to retrofit your data to your flows after the fact, you just end up hacking things together in downstream tools, and that’s not scalable or reliable. Everyone wants to move fast in this area, but trust me when I tell you not to do that. This is an area where you want to “measure twice and cut once.”
You can’t personalize effectively if you’re missing product IDs or categories, and you can’t build meaningful cohorts without the proper data fields tied to key behaviors like sign-ups, first purchase, most recent order, etc. You also can’t deliver your message on time if you’re missing this data, and suppressing the right users from your audiences is going to be nearly impossible if your events are missing context.
And when it comes to testing, the real blocker isn’t having too little data; what matters is that you have the right analytics data setup to measure your experiments correctly. For example, if you’re running an abandoned cart test with a second follow-up email, you need clean flags to separate test and holdout groups, reliable metrics to track incremental revenue, and behavior data in a time-boxed window. Without that structure, you can’t trust the results. Poor data choices compound over time, so plan not just for today’s campaigns but also for the experiments and journeys you’ll want to run six months down the road.
Bad decisions on the data side will ripple effects across your lifecycle program for years to come, so think about what you need today — and also where you’re headed six months from now.
Step 4: Use segments to power logic, not just personalization
One of the most overlooked parts of lifecycle programs is how segmentation actually fits into orchestration. Most lifecycle teams use segmentation to personalize what a customer sees, but the best ones use it to shape how the entire journey works. Events often trigger journeys, and segments usually control messages. The problem is that this approach is often based on the assumption that users are exactly the same, when the reality is very different. Audiences are made up of individual people.
For example, abandoned cart flows are one of the most important use cases in the lifecycle, but many programs fail to segment between first-time purchasers and repeat customers. A first-time visitor may need more context, trust building, or incentives to convert. Whereas a repeat buyer likely doesn’t; they’re closer to purchase and may be driven by different motivators like urgency, loyalty, or restocking.
These are the small details that separate great lifecycle programs from average ones because they show that you understand that while these two users look nearly identical on the surface, they’re actually completely different, and as a result, they need a completely different experience. Great personalization isn’t about knowing who your customer is; it’s about knowing what they need, which fundamentally comes down to a decision-making problem — choosing what message to send, what creative to use, what offer to include, and even what timing and marketing channel to deliver that experience through.
Step 5: Build a repeatable testing framework
One of the biggest missed opportunities in lifecycle is treating messages purely as outputs for conversions and sales instead of inputs for learning. And it’s not because they don’t want to: the reality is that most teams are technically running tests, but there is usually no framework for that testing or measuring success. A great example of this is A/B testing, which is almost always running, but the test itself is not tied to any sort of system. It’s just more of a checkbox item.
The highest performing teams treat every message as an experiment, even if it’s small (and honestly, smaller is better for most teams). Here’s one simple approach that I use:
- Start with a clear hypothesis: What are you actually trying to prove? (e.g., “Users who complete onboarding in the first 48 hours are more likely to activate if nudged with a personalized CTA.”)
- Define your primary and secondary KPIs: Know what success looks like before you send. Don’t let results retroactively define the goal.
- Validate that your data can support the test: Can you track the events, properties, or segments you need? Can you create a clean holdout group to measure incrementality? If not, you’ll never trust the outcome, and you shouldn’t run the test.
- Capture the learning and feed it back into the system: A test is only valuable if the insight is applied. Add a step in your process to document the result, update your flows, or inform your next hypothesis.
It’s a hard pill for many lifecycle teams to swallow, but every message you send where you don’t learn theoretically makes the next one worse. Don’t overcomplicate testing. The most important thing is that you’re learning and creating tests where you can isolate variations in behavior. That might mean simply testing messaging, timing, segmentation, etc. The key is to make sure your lifecycle program is iterative and adaptive.
Putting it all together
The strongest lifecycle programs aren’t overengineered. They solve real problems, they’re built to scale, and they adapt with your business, product, and customer as those things inevitably change. Great lifecycle programs help you make decisions about what to send, when to send it, who to send it to, and why.
Whether you’re building from scratch or optimizing what you already have, focus on the fundamentals because that’s how you build a durable and scalable program. That’s how you drive value for the customer and the business because ultimately, it should be a bilateral relationship.
