What's up everyone, today we have the pleasure of sitting down with Anna Gelfand, Director and Head of Lifecycle & Retention Marketing at Miro.
- (00:00) - Intro
- (01:14) - In This episode
- (04:29) - Why Lifecycle Marketing As We Know It Has No Future
- (08:53) - How To Codify The Qualitative Side Of Lifecycle Marketing
- (14:52) - Testing Email Campaigns On Digital Twins Of Your Customers
- (25:05) - Shipping The Criteria Instead Of The Campaign
- (34:23) - Why Lifecycle Teams Should Stop Calling Themselves The Email Team
- (42:32) - Why Your Workflow Should Define Your Martech Tools
- (49:12) - What Lifecycle Marketers Will Do When Agents Run Campaigns
- (51:58) - The Skills Lifecycle Marketers Should Double Down On In The AI Era
- (01:00:33) - How To Decide What Deserves Your Energy At Work
- (01:02:38) - Fiction Book Recommendations From A Marketing Leader
Summary: Anna has a take she'll defend on stage: lifecycle marketing as we know it has no future. She breaks down what codification actually does to the job, why she built digital twins of 100 real customers to test campaigns without ever bothering a live user, and how the smartest lifecycle teams are quietly refusing to be called the email team. She gets into the 20/80 flip between building and thinking, why she'd rather own responsibility than a channel, and the one belief that survived 3 ESP migrations and 7 years at Miro. Stick around for the part where she admits she kinda likes suffering, intellectually speaking.
About Anna Gelfand
Anna Gelfand is the Director and Head of Lifecycle & Retention Marketing at Miro, based in Amsterdam, where she's spent close to 7 years building and running lifecycle and retention for a global product-led company at real scale. She leads a team responsible for email, in-app, and the wider customer journey, and she's known for treating lifecycle as the connective brain of the organization rather than a single channel.
Before Miro she worked in lifecycle and retention at Carrot Quest. She's also comfortable holding a contrarian view worth arguing over: lifecycle marketing as the industry knows it doesn't have a future, and its center of gravity is already moving toward strategy, content, and codified systems that agents run.
Why Lifecycle Marketing As We Know It Has No Future
Spend enough years in lifecycle marketing and the work starts to rhyme. You build the segments, ship the campaign, wait for the numbers, and a large share of the time the test comes back saying nothing at all. Next quarter you run a version of the same thing with a slightly different subject line. When the uplift arrives, it's a fraction of a percent for months of effort.
Anna hit that wall at Miro, and she's careful to point out it started before AI was disrupting everything left and right. She's been there a long time, long enough to watch the product and the market reinvent themselves more than once. By that point you've already picked the obvious wins, then the not-so-obvious ones, and what's left is weeks of work for a result that refuses to tell you anything.
That line sounds strange until you've lived it. A clearly negative result kills an idea and hands you a lesson. An inconclusive one burns the same 3 weeks and teaches you nothing, so you're right back where you started with less time on the clock. Anna's take on the plateau is that manual, human-level optimization simply can't reach the relevancy customers deserve anymore. That's especially true at Miro, where the product is an open canvas and users can build almost anything on it. You know your personas and your core use cases, but hand-tuning content one segment at a time won't make an email feel genuinely built for the person opening it.
Here's the part that keeps Anna in the craft anyway. Lifecycle is intimate, even when you're sending to millions and thinking in the billions. The human aspect of the communication is the whole point. So the real question she's chasing isn't whether to throw technology at the problem, because everyone will. It's what actually survives once you do. The plateau isn't a Miro problem or a tooling problem. Any mature lifecycle program eventually runs out of manual optimization road, and the teams still treating inconclusive tests as a normal cost of doing business are the ones that get lapped first.
Key takeaway: Pull your last 2 quarters of lifecycle experiments and count how many returned a clear win or a clear loss. If most came back inconclusive, stop feeding new tests into the same tired playbook. Redirect that time toward a faster way to kill weak ideas before they eat a full test cycle.
How To Codify The Qualitative Side Of Lifecycle Marketing
Codification is the word doing the rounds right now, borrowed from Luke Kline's writing on the topic. The idea is simple enough: take the marketing knowledge locked in your head and your docs, make it accessible to AI agents, and let them operate on it directly. It works beautifully for the quantitative half of lifecycle. Propensity models, churn scores, who bought what and when, all of that is already data, so agents eat it easily. The harder question is the other half. How do you codify a gut feel, the sense of what makes a subject line bad, a nudge too pushy at the wrong stage, or a campaign a little too clickbaity?
Anna's answer starts with a shrug and a strong opinion at the same time. Codification is inevitable, she says, because lifecycle was always the most technical, most data-informed corner of marketing. Expecting AI to leave that part alone would be strange. So the work becomes feeding agents the right tools and the right context.
That tension runs through everything she describes. Anna trusts data and segmentation when the warehouse and the models are in good shape. She does not yet trust a machine to write the message. Her example makes the stakes concrete. Miro is a B2B2C company with a wall of self-serve users on one side and large enterprises on the other. She's always wanted lifecycle communications that feel like a partnership rather than a vendor broadcast, and doing that by hand only scales to the biggest accounts. Codification changes the math. Now an agent can pull a public customer's stated goals from their own reports, cross them with Salesforce and Gong data, and paint the email in that customer's colors with their use cases baked in. The account-based treatment she could only afford for a handful of logos becomes something she can run wide.
The catch she keeps circling is judgment. She won't call it control, but she wants a clear moment where a human applies quality and qualitative feedback before anything ships. Part of that is buying back time to actually talk to users again, which lifecycle teams almost never do enough of. The other part is a bet on content. As AI floods every inbox with more messages than anyone can read, the scarce thing stops being the ability to produce and becomes the ability to stand out.
The uncomfortable implication for most teams is that codifying the qualitative layer isn't a copywriting problem, it's a context problem. If your brand voice, your stage-by-stage rules, and your customer intelligence live only in senior people's heads, an agent has nothing to operate on and will happily generate confident garbage at scale.
Key takeaway: Write down the judgment calls your team currently makes by instinct. Document what a bad subject line looks like, what's too pushy at each stage, and the brand rules you'd never let slip. Feed that to your agents as context, then set one named human checkpoint where quality gets approved before anything sends.
Testing Email Campaigns On Digital Twins Of Your Customers
Plenty of marketers have built a digital twin of them...
