By Phil Koolen, Director, Anchora
Did you hear about the brand that was accidentally sending blank emails to a whole segment of its customers for months? Thousands of dollars a week wasted on these blank sends – and the irony is they were getting decent open rates.
This wasn’t a fly-by-night startup on a shoestring budget; it was a business I’d consider one of the best-in-class at good lifecycle marketing practice – in fact, they only caught it because they did a proper audit of their setup.
Imagine how many less advanced brands are wasting precious resources doing the same thing. White space landing in inboxes, gaslighting customers who are repeatedly opening the email thinking there’s something wrong with their technology. That’s not a great look.
It’s easy to do when you’re not actually inside your own customer journey, and it’s a perfect illustration of why lifecycle marketing is becoming one of the most important conversations in the industry right now.
How often does the following scenario play out?
A marketing team spends months briefing an agency, wrangling the data team, aligning every stakeholder known to mankind. The result? One campaign, consisting of maybe one email with a social push layered on top.
How much of that business’ audience will actually open that email? Perhaps a better question is how many of us open the emails we receive from businesses?
Then the team waits three months for the reporting period to close, gets results that surprise, and please nobody, and the whole cycle repeats over and over and over.
It’s a toxic cycle borne of resource and budget constraints. But before you get too down in the dumps, these pitfalls aren’t unique to any one market or organisation; the majority of us are falling foul of them.
What lifecycle marketing actually means
The principle of lifecycle marketing is simple: your marketing responds to where the customer actually is, not where your content calendar says they should be.
Netflix doesn’t ask ‘What should we recommend this month?’. It reviews what someone watched last night, what they’ve previewed, what they’ve started and abandoned to understand what they’re most likely to want to watch right now.
Starbucks doesn’t build its year around product launches. It responds to seasons, behaviours and what people actually do. American Express doesn’t treat every card member the same way; it recognises life moments and responds to them in real time.
None of this is radical. But most businesses are still a long way behind, and the Agentic era dawning is going to expose a massive chasm that will need to be bridged fast.
Getting lifecycle-led is about changing how your organisation thinks about the customer and, in my experience, it comes down to three things.
The first is your operating model.
Most marketing teams are structured around internal divisions – brand, digital, comms, data. Each with their own KPIs, campaign calendars and definition of success. It might look like everyone is working together from the outside, but most teams are moving in parallel rather than collaborating.
Amidst those conflicting priorities, the voice of the customer gets lost. Lifecycle marketing requires a shared goal, where every team is oriented around the same question: what does this customer need from us right now?
The second is using data as a trigger, not just as a report.
Most businesses use analytics to track metrics like page visits, open rates, and email clicks. What they’re not doing is using data to spark action.
The customer who just consolidated their super should be immediately enrolled in a tailored onboarding journey. The person who hasn’t logged in for 90 days should be intercepted before they disengage. These triggers already exist in the data. Act on them in real time, automatically, without someone on the team having to manually set up a campaign.
That’s the shift.
The third is letting go of the calendar. I know it’s hard, but the whole point of marketing is “right message, right person, right time”. You cannot deliver that if you’re running static batch and blast campaigns on a schedule whose primary justification is that it best suits your team.
The customer who’s ready to make a decision in October doesn’t care that you ran your campaign in June. They need to hear from you in October. Lifecycle marketing flips the logic – instead of asking when you’re ready to send, you ask when your customer is ready to receive.
Why this just became urgent
Agentic AI makes getting to grips with this a serious priority. What we are talking about here are tools which can take a business goal, build a campaign plan, get your sign-off, execute it and report back – completely autonomously.
These systems promise unparalleled coordination and scale. But they run on clean, connected, governed data, housed within marketing programs that are already operating in a customer-led, signals-based way.
They don’t care how ambitious you are, or that you have good intentions.
Adopt an agentic AI tool without a lifecycle foundation in place, and it won’t transform your business; it will simply scale whatever you’re already doing, inefficiencies included. Another tool for the team to struggle with and eventually place in the too hard basket.
Remember that blank email? Imagine an AI agent finding it and deciding to scale it across every channel because those open rates were looking good. Blaming the intern won’t be an option in the future.
The personalisation that most people already expect is about to become mandatory, and the gap between businesses whose lifecycle foundations are in place and those that aren’t will become impossible to ignore.
So before your business starts evaluating agentic AI tools, here are some questions worth sitting with:
Can you trigger a communication to a customer within 24 hours of a meaningful behaviour, without anyone on your team having to do anything manually?
Are your data sources connected enough that you have a single picture of what a person has done across every channel?
Do your marketing, digital and data teams share a definition of what customer success actually looks like?
If the answer to any of those is no, the AI conversation should probably wait, because the lifecycle conversation cannot.
By starting with the lifecycle, you build a reliable foundation for knowing that what you’re doing is actually best practice. Then you can let the agents do what they’re designed to do and scale something that already works.