Category: Newsletters

ConvertKit’s Creator Network is a cross-promotion engine built directly into the platform. Turn it on, and ConvertKit will recommend your newsletter to readers of similar publications—and recommend others’ newsletters to yours. It’s framed as free, mutual growth. No ad spend, no landing pages, just algorithmic matchmaking between creators.

The pitch is compelling, especially if you’re early and desperate for subscribers. But the mechanics are more nuanced than the dashboard toggle suggests, and the costs—while not monetary—are real.

How the network actually works

When you enable the Creator Network, ConvertKit starts showing recommendation cards to your subscribers. These appear in two places: in a dedicated email ConvertKit sends on your behalf, or embedded at the bottom of your broadcasts if you use their default templates.

The recommendations are algorithmic. ConvertKit looks at your content tags, subscriber behavior, and engagement patterns, then surfaces newsletters it thinks your readers will like. You don’t choose who gets recommended to your list. ConvertKit does.

In exchange, your newsletter gets recommended to other creators’ audiences under the same logic. You’re both publisher and advertiser, simultaneously.

ConvertKit takes a 50% cut of the exposure. If 100 of your subscribers see a recommendation card, ConvertKit will show your newsletter to roughly 50 subscribers on someone else’s list. The ratio isn’t strict, but it’s the general exchange rate.

What it costs you (and it’s not money)

The Creator Network is free in dollars, but expensive in control and attention.

You’re giving ConvertKit permission to email your list. If you enable the standalone recommendation emails, ConvertKit will send a message to your subscribers that you didn’t write, on a cadence you don’t control, promoting newsletters you didn’t vet. Your readers don’t distinguish between “ConvertKit sent this” and “my newsletter sent this.” It’s your sender name. It’s your brand. You own the confusion and the unsubscribes.

You’re training readers to expect content you didn’t create. Even if you only use in-broadcast embeds, you’re conditioning your audience to scroll past house ads. That’s fine if your newsletter is purely a growth vehicle. It’s corrosive if your newsletter is a trust engine or a product funnel.

You’re sharing your best readers with competitors. ConvertKit recommends based on engagement. That means your most active subscribers—the ones who open, click, and convert—are exactly the ones being shown other newsletters. You’re not cross-promoting to lurkers. You’re offering your most valuable segment to someone else.

You have no veto power. You can’t blacklist competitors. You can’t filter by monetization model or editorial quality. If ConvertKit’s algorithm decides a newsletter is similar enough, it gets shown. You’re trusting the platform’s taste and incentives to align with yours.

When it makes sense to turn it on

The Creator Network works best in three scenarios:

If you’re pre-monetization and pure subscriber count is your only goal, the trade-off is reasonable. You’re swapping attention for attention, and you don’t yet have a business model to protect.

If your newsletter is in a tightly-defined niche where most other creators are collaborators, not competitors—think local news, hyperlocal events, or hobby communities—cross-promotion strengthens the ecosystem instead of fragmenting it.

If you’re already doing manual cross-promotions and finding it exhausting, the Creator Network automates the same dynamic. You lose control, but you gain scale and consistency.

The non-obvious tip: use it as a discovery tool, not a growth tool

Here’s what ConvertKit doesn’t advertise: you can enable the Creator Network, see which newsletters get recommended to your audience, then reach out to those creators directly for a manual swap or partnership.

Turn on the network for a month. Watch the analytics. Identify which recommendations your readers actually engage with. Then disable it, email those creators, and negotiate terms you control: co-branded emails, shared lead magnets, or affiliate partnerships.

You get the signal without the long-term cost. ConvertKit’s algorithm becomes market research, not your growth strategy.

Want more breakdowns like this? Subscribe to One Two Three Send for weekly deep-dives on the tools and tactics that actually move online businesses forward. No fluff, no sponsored takes—just honest operator-to-operator breakdowns.

ConvertKit gives you two ways to schedule a broadcast: pick a fixed time, or let the platform choose based on each subscriber’s past behavior. The second option—send-time optimisation—sounds smarter. It usually isn’t.

Most solo operators turn on send-time optimisation assuming it’s a free win. Sometimes it is. More often, it fragments your send window, dilutes your traffic spike, and makes your analytics harder to read. Here’s how to decide which method fits your business model.

Fixed scheduling: what you’re actually choosing

When you schedule a broadcast for 9:00 AM Eastern, every subscriber gets it at 9:00 AM Eastern. Simple. Predictable. Your entire list receives the email within a few minutes, your traffic spike is concentrated, and your open rate settles within an hour.

This matters if you’re driving traffic to a live event, a product launch with a deadline, or a piece of content where early engagement (comments, shares, replies) creates momentum. If fifty people hit your article in the first ten minutes, your server logs show a spike, your social proof builds fast, and your analytics are clean.

Fixed scheduling also makes troubleshooting easier. If your open rate tanks, you know exactly when the send happened and can cross-reference it with deliverability logs, spam complaints, or external factors (a news event, a holiday, a platform outage).

Send-time optimisation: what ConvertKit is actually doing

When you enable send-time optimisation, ConvertKit looks at each subscriber’s past open behavior and schedules delivery for the hour they’re statistically most likely to open. Someone who always opens at 6:00 AM gets it at 6:00 AM. Someone who opens at 11:00 PM gets it at 11:00 PM.

Your send window stretches across 24 hours. Your traffic arrives in a slow trickle instead of a sharp spike. Your open rate might climb a few percentage points—or it might stay flat, because the algorithm is working with limited data and guessing based on past behavior that may not predict future attention.

The feature works best if you have a large list (10,000+ subscribers), consistent send history (at least six months), and content that isn’t time-sensitive. A Sunday essay, a weekly roundup, or an evergreen tutorial can arrive anytime and still deliver value. A flash sale, a webinar reminder, or a breaking-news commentary cannot.

When send-time optimisation backfires

If you’re running a small list (under 2,000 subscribers), ConvertKit doesn’t have enough data to meaningfully optimise. The algorithm falls back to rough estimates, and you’re spreading your send across 24 hours for no measurable gain.

If you’re tracking referral traffic in Google Analytics, a 24-hour send window makes attribution messy. Your traffic graph looks flat instead of spiked, and it’s harder to isolate which traffic came from the email versus organic search, social, or other sources.

If you’re selling something with urgency—early-bird pricing, limited inventory, a countdown timer—send-time optimisation means some subscribers see the offer twelve hours after others. That’s not personalisation. That’s a coordination failure.

The non-obvious tip: test it by segment, not by broadcast

Don’t enable send-time optimisation across your entire account. Instead, create two segments: one for engaged subscribers (opened at least three of your last ten emails), one for cold subscribers (opened fewer than three). Send the engaged segment at a fixed time. Send the cold segment with send-time optimisation.

Your engaged readers already open reliably. They don’t need algorithmic coddling—they need consistency. Your cold subscribers might benefit from a delivery time that aligns with their past (limited) engagement. If send-time optimisation lifts their open rate by even two percentage points, you’ve re-engaged a slice of your list without sacrificing the predictability your core audience expects.

Run this for a month. Compare open rates, click rates, and unsubscribe rates across both segments. If the cold segment shows improvement, keep it. If the difference is negligible (under three percentage points), revert to fixed scheduling and simplify your workflow.

ConvertKit’s documentation sells send-time optimisation as a set-it-and-forget-it win. It’s not. It’s a trade-off: you sacrifice timing control and traffic concentration in exchange for a potential (but not guaranteed) uptick in open rate. For most solo operators, fixed scheduling is faster to manage, easier to analyse, and just as effective.

Want more tactical breakdowns like this? Subscribe to One Two Three Send and get one operator-focused deep dive every day.

Klaviyo’s conditional split is the feature that separates operators who automate thoughtfully from those who automate chaotically. It lets you fork an email flow into multiple paths based on subscriber behavior, profile data, or event properties. Someone clicked your product link? Send them a discount. They didn’t? Wait three days and try a different angle.

The promise is elegant: personalized sequences that adapt in real time. The risk is building flows so complex you can’t debug them when something breaks—or worse, when nothing breaks but revenue stays flat because you optimized for cleverness instead of outcomes.

Here’s how the feature actually works, when it makes you money, and when it just makes your flow chart look impressive.

How conditional splits work in Klaviyo

A conditional split evaluates one or more conditions at the moment a subscriber reaches that point in the flow. You define the logic: Has opened an email in this flow, Has clicked a specific link, Profile property equals X, Has placed an order since entering this flow, and dozens more.

Klaviyo checks the condition, then routes the subscriber down the Yes path or the No path. Each branch can contain more emails, delays, and additional splits. You can nest splits inside splits, though your future self will hate you for it.

The split happens once, at evaluation time. If someone clicks a link five minutes after they pass the split, they don’t jump branches retroactively. Timing matters.

Klaviyo evaluates splits server-side, so there’s no delay or tracking pixel required—it’s reading from your account data in real time. That’s why splits based on opens are less reliable than splits based on clicks or purchases. Opens depend on image loading and Apple’s Mail Privacy Protection has made that signal noisier every year.

When a split improves outcomes

Conditional splits pay off when the two branches lead to meaningfully different next actions—and when you have enough volume to make both paths worth maintaining.

Post-purchase flows. Split based on product category or price tier. Someone who bought a $20 ebook gets content-focused emails. Someone who bought a $2,000 course gets onboarding check-ins and a Slack invite. The optimal next email is genuinely different.

Abandoned cart recovery. Split after the first reminder based on whether they’ve returned to the site. If they came back but didn’t buy, you know they’re warm—send a discount or a FAQ. If they haven’t returned, try social proof or a different product angle.

Lead magnet sequences. Split based on link clicks to gauge interest. Someone who clicked your case study link three times is ready for a sales email. Someone who hasn’t clicked anything gets more educational content before you ask for the sale.

The pattern: you’re splitting when behavior signals a different level of intent, and each path has a distinct strategic goal.

When a split just adds complexity

Splits fail when you’re branching for the sake of branching—or when the two paths aren’t different enough to justify the overhead.

Splitting on opens. Open rates are unreliable signals now. Apple MPP pre-loads images for many users, so an “open” might mean they read every word or it might mean their phone cached the email while they slept. Clicks and purchases are real actions. Opens are proxy data. If you’re splitting on opens, you’re optimizing for noise.

Too many paths too early. A five-way split after the welcome email means you’re maintaining five parallel sequences. Unless each path has a clear conversion goal and you’re sending enough volume to measure performance in each branch, you’re fracturing your learning. Simpler flows ship faster and break less.

Splitting when a tag would work. If the goal is just to segment people for future campaigns, add a tag and keep the flow linear. Splits are for in-flow decisions. Tags are for long-term segmentation. Mixing the two leads to flows that are half automation, half CRM, and fully unreadable six months later.

One non-obvious tip: test the No path first

Most operators build the Yes path—the engaged, high-intent path—first, because it’s more exciting. That’s backward.

The No path is where most of your subscribers will go, especially early in a flow when intent is uncertain. If your No path is a dead end or a lazy fallback, you’re leaving money on the table at scale.

Build the No path as if it’s the default experience, because it is. Make it a real sequence with real value. Then build the Yes path as the exception for people who signal higher intent. Your flow will convert better and you’ll spend less time wondering why 80% of your subscribers are getting the “backup” experience.

If you’re running email flows that do more than broadcast, Klaviyo’s conditional split is eventually unavoidable. Just make sure every branch you add is solving a real problem—not just making your flowchart look like a decision tree from a textbook.

What’s the most complex flow you’ve built—and did it actually outperform the simple version? Reply and let us know. We’re collecting operator stories for an upcoming piece on when automation goes too far.

Every newsletter operator watches the same number: total subscribers. It ticks up after a good Twitter thread, jumps when someone shares your work, and becomes the first thing you check each morning. The number feels like progress. It looks like traction. And optimising for it will quietly ruin your business.

Subscriber count is a vanity metric dressed up as a business metric. It measures accumulation, not value. It rewards growth tactics that attract the wrong people, encourages content that pleases algorithms instead of readers, and creates a list full of people who will never pay you, never share your work, and never open another email.

The operators building sustainable businesses have stopped chasing total subscribers. They’ve switched to metrics that actually predict revenue, retention, and referrals. Here’s why subscriber count fails—and what to track instead.

Subscriber count rewards the wrong acquisition channels

When your goal is adding subscribers, every tactic that grows the list looks smart. Viral Twitter threads that attract drive-by signups. Lead magnets designed to game SEO traffic. Free course giveaways that pull in freebie-seekers. Pop-ups with no context about what you actually send.

These tactics work—if “work” means inflating a number. But the subscribers they attract have no relationship with you, no investment in your topic, and no intention of sticking around. Your 30-day open rate tells the real story: half of them are already gone.

Compare that to subscribers who find you through a piece of cornerstone content, a trusted referral, or consistent presence in a niche community. They arrive slower. They don’t create the dopamine hit of a 300-signup day. But they open, they click, they reply, and they buy.

Optimising for total subscribers makes you prioritise channels that scale fast over channels that convert well. You end up with a list that looks impressive in a Twitter bio and performs like a ghost town in your analytics.

It hides the metric that actually matters: engaged subscriber density

The number that predicts your revenue isn’t how many people are on your list. It’s how many people on your list actually care.

If you have 10,000 subscribers and 1,200 of them open regularly, your engaged base is 12%. If you have 1,500 subscribers and 900 of them open regularly, your engaged base is 60%. The second list will out-earn the first every time—through sponsorships, course sales, affiliate clicks, or paid subscriptions.

Sponsors don’t pay for total subscribers. They pay for opens, clicks, and conversions. A 5,000-person list with a 45% open rate is worth more than a 25,000-person list with an 18% open rate. The second list costs you more to send, tanks your deliverability, and dilutes every message you write.

Engaged subscriber density is the metric that matters. It’s the percentage of your list that still wants to hear from you. And the fastest way to destroy it is to optimise for total growth without caring who shows up.

It makes you write for strangers instead of your actual audience

When subscriber count is your goal, you start writing for the people who might subscribe, not the people who already did. Your content shifts toward broad, safe, SEO-friendly topics that attract clicks but don’t build trust. You avoid sharp opinions because they might alienate potential subscribers. You write introductions for people who will never make it past the first paragraph.

Your actual subscribers—the ones who opened your last six emails—notice. The work feels less specific, less useful, less like it was written for them. They start skimming. Then skipping. Then unsubscribing, but only after months of quietly disengaging.

The operators with the highest retention rates write like their list is a tight community, not a leaky funnel. They assume familiarity. They reference past emails. They use insider shorthand. New subscribers either catch up or leave—and that’s fine, because the people who stay are worth ten times more than the people who bounce.

What to track instead

If subscriber count is out, what’s in? Three metrics that actually correlate with a sustainable business:

30-day engaged subscribers. How many people opened or clicked in the last 30 days. This is your real audience. Everyone else is payload.

Subscriber lifetime value (LTV). Average revenue per subscriber over their entire time on your list. A small list with high LTV beats a large list with low LTV every time.

Referral rate. What percentage of new subscribers came from existing subscribers sharing your work or forwarding an email. High referral rate means you’re attracting people who actually fit. Low referral rate means you’re dependent on cold acquisition that never warms up.

These three metrics tell you if you’re building a business or just inflating a number. They don’t feel as satisfying to share in a launch tweet. They don’t give you the sugar rush of a big signup day. But they’re the difference between a list that makes $200/month and a list that makes $20,000/month at the same size.

Stop checking total subscribers. Start checking who’s still listening.

What’s one metric you’ve stopped tracking because it didn’t actually move your business forward? Hit reply and let me know—I’ll feature the best answers in a future issue.

Beehiiv‘s referral program is one of its flagship features—a built-in system that lets subscribers unlock rewards by forwarding your newsletter. It’s fast to set up, requires no integrations, and can drive genuine word-of-mouth growth.

It can also quietly ruin your list quality if you don’t understand how it actually works.

Here’s the mechanical breakdown, the non-obvious tradeoffs, and when to disable it entirely.

How the referral system actually works

When you enable the referral program in Beehiiv, every subscriber gets a unique referral link. That link appears automatically in your newsletter footer (or wherever you embed the merge tag). When someone signs up through that link, the original subscriber earns credit toward rewards you define—free downloads, paid tier access, physical products, whatever.

Beehiiv tracks this server-side. No third-party pixels, no JavaScript. The platform knows who referred whom, even if the new subscriber uses a different device or email client.

You configure reward milestones in the dashboard: 3 referrals unlocks a PDF, 10 gets a paid subscription comp, 25 gets a shirt. Beehiiv handles the tracking and sends automated emails when someone hits a tier. You fulfill the reward manually (or auto-comp paid subscriptions if you’re using Beehiiv’s native monetization).

The feature is available on the Scale plan and above—$99/month if you’re under 10,000 subscribers, more as you grow. The Grow plan ($49/month) doesn’t include it.

Where it works well

Referral programs succeed when your content has shareability baked in—either because it solves urgent, specific problems (“how to fix X”) or because sharing it signals identity (“I’m the kind of person who reads this”).

If your newsletter is tactical—SEO breakdowns, monetization teardowns, tool comparisons—referrals tend to flow naturally. Readers share because the content is immediately useful to peers in the same situation.

If your newsletter is aspirational or community-driven—indie hacker diaries, niche hobby deep-dives—referrals work because sharing is a form of curation. “You should read this” becomes “you should be in this club.”

The mechanic also works if your baseline engagement is already strong. If 30–40% of your list opens consistently and 5–8% click, a referral program amplifies what’s working. If your open rate is 18% and falling, referrals won’t save you—they’ll just add more cold contacts.

Where it backfires

The biggest risk: misaligned incentives. If your rewards are valuable enough to motivate sharing, they’re also valuable enough to motivate gaming. Subscribers will invite people who don’t care about your content—they care about the reward.

I’ve seen lists add 400 subscribers in a week via referrals, only to watch open rates drop from 42% to 28% because half the new signups never intended to read. They clicked a friend’s link to help them hit a milestone, then ignored every send.

Beehiiv doesn’t penalize you for this directly, but your email provider’s reputation system does. If new subscribers consistently don’t open, Gmail and Outlook start filtering you to spam—even for engaged readers.

The second issue: reward fulfillment overhead. If you’re offering anything other than automated digital perks, you’re adding manual work every time someone hits a tier. Sending books, comping Stripe subscriptions, or giving one-on-one access doesn’t scale quietly. At 50 referrals a week, it’s a part-time job.

Third: the program becomes the content. Once you launch referrals, a chunk of every send has to remind people the program exists, explain how it works, and show the leaderboard. That’s 80–150 words per issue that aren’t about your core topic. Some readers tune it out. Others resent the hustle.

The non-obvious tip: test with time limits first

Instead of launching a permanent referral program, run it as a 30-day sprint. Announce it, track it, fulfill rewards, then turn it off and watch what happens to your engagement metrics over the next month.

If open rates stay flat or improve, and new subscribers engage at the same rate as your baseline, the program worked. If engagement drops or you see a spike in unsubscribes from recent signups, you attracted the wrong people.

Beehiiv makes it easy to pause the program without losing historical referral data—just toggle it off in settings. The links stop working, the leaderboard disappears from your emails, and you can reactivate later if the economics make sense.

One more thing: if you do run referrals, set a cap on reward tiers. I’ve seen operators offer “lifetime free access” at 100 referrals, then realize someone hit it by spamming Reddit. Build an upper limit—maybe 50 referrals max—so your highest reward doesn’t become a liability.

Want to see how other operators are thinking about growth mechanics? Reply with what you’re testing—I read every response and the good ones become future pieces.

Heads up — some links in this article are affiliate links. If you sign up through them, we may earn a small commission at no extra cost to you. We only recommend tools we use ourselves.

ConvertKit gives you two ways to automate what happens after someone subscribes, clicks a link, or buys a product: the visual automation builder and the older rules-based system. They look different, they feel different, and they solve different problems.

If you’ve ever clicked into Automations and felt paralyzed by the blank canvas—or built a simple rule and wondered why everyone raves about visual workflows—you’re not alone. The platform doesn’t make it obvious when to use which, and picking wrong costs you time or flexibility down the road.

Here’s how to decide.

What each one actually does

The visual automation builder is the flowchart interface. You drag triggers, conditions, actions, and delays onto a canvas. It handles multi-step sequences, conditional logic, tagging, field updates, and webhook calls. You can see the whole journey at a glance.

The rules system is text-based and hidden under Settings → Rules. Each rule is a single if-this-then-that statement: “If someone subscribes to Form A, tag them with Lead Magnet B.” No branching, no delays, no visual feedback.

Both run in the background. Both fire instantly. But they’re built for different levels of complexity.

When to use the visual builder

Use the visual automation builder when your subscriber journey has more than one step, or when the next action depends on what someone does (or doesn’t do).

Examples where it shines:

• Onboarding sequences with conditional branches based on link clicks or form submissions
• Lead nurture flows that tag people differently depending on which resource they download
• Product launch sequences that change behavior based on purchase status
• Re-engagement campaigns triggered by inactivity, with different paths for openers vs. non-openers

The visual builder also makes debugging easier. You can see where subscribers are in the flow, spot bottlenecks, and identify where people drop off. If you’re running anything more complex than a single tag or email, the canvas pays for itself in clarity.

One non-obvious tip: use the Wait Until condition instead of fixed delays when timing matters. Instead of “wait 3 days,” set “wait until tag is added” or “wait until custom field changes.” It keeps automations responsive to real behavior, not arbitrary calendars.

When rules are faster (and safer)

Rules are better when you need a single, permanent action that should never change based on subscriber behavior.

Use rules for:

• Auto-tagging every new subscriber to a specific form
• Assigning a custom field (like “source” or “signup date”) the moment someone joins
• Moving subscribers between sequences when they complete a purchase
• Unsubscribing people from a broadcast tag when they click an opt-out link

Rules execute faster because there’s no flowchart to traverse. They’re also harder to accidentally break—there’s no risk of pausing the wrong branch or deleting a connector by mistake.

The biggest advantage: rules don’t require you to “start” subscribers. Visual automations only affect people who enter after you publish. Rules apply to everyone who meets the condition, past or future, unless you explicitly turn them off.

The hybrid approach most operators miss

You don’t have to choose. The most reliable setups use rules for foundational tagging and field assignment, then use visual automations for behavior-based sequences.

Example: a rule tags every new subscriber with their lead magnet name. A visual automation checks that tag, then sends a welcome sequence tailored to what they downloaded. The rule ensures no one slips through; the automation handles nuance.

This also future-proofs your setup. If you later want to add a branch for people who clicked a specific link, you’re editing an automation—not rewriting a dozen rules.

One warning: don’t duplicate logic across both systems. If a rule and an automation both try to add the same tag based on the same trigger, you’ll get double-fires and confusing analytics. Pick one place for each action.

Where most people get stuck

The most common mistake is building a visual automation when a rule would’ve been cleaner—usually because the visual builder feels more “powerful.”

If your automation is a straight line with no branches, no delays, and no conditions, it’s probably a rule. Save the canvas for when you actually need decision trees.

The second mistake: not testing both. ConvertKit lets you preview automations, but it doesn’t simulate real subscriber movement. Before you publish, manually trigger the flow with a test subscriber and confirm every tag, email, and field change fires as expected.

Want more breakdowns like this? One Two Three Send covers the tools and tactics solo operators actually use—no fluff, no affiliate spam. Subscribe for one article like this every week.

ConvertKit and Beehiiv both call themselves newsletter platforms, but they’re built for fundamentally different operators. One is a creator-focused email marketing tool that happens to support newsletters. The other is a media company in a box.

If you’re trying to choose between them, the feature lists won’t help much. What matters is how you plan to grow, what you’re selling, and how much control you need over your reader experience.

What each platform is actually optimized for

ConvertKit was built for course creators, coaches, and small product businesses who use email as part of a broader funnel. It excels at segmentation, tagging, and automation sequences. You can build complex subscriber journeys based on behavior, purchases, and custom fields. The landing page builder is serviceable. The forms are flexible. The visual automation builder is genuinely good.

Beehiiv is a publishing platform first. It’s optimized for ad-supported or subscription-based newsletters that feel like media properties. The editor is purpose-built for newsletter writing, with built-in polls, referral programmes, and recommendation networks. Monetisation tools—ad network, premium subscriptions, boosts—are baked into the core product, not bolted on.

If your business model is “grow an audience, sell them a thing,” ConvertKit makes more sense. If it’s “publish regularly, monetise attention,” Beehiiv is the better fit.

Pricing and what you actually get

ConvertKit starts at $25/month for up to 1,000 subscribers on the Creator plan, which includes landing pages, forms, and basic automation. The Creator Pro plan is $50/month and adds the visual automation builder, subscriber scoring, and advanced reporting. Pricing scales with list size—10,000 subscribers costs $119/month on Creator Pro.

Beehiiv’s free tier is surprisingly generous: up to 2,500 subscribers, unlimited sends, and access to the referral programme and basic analytics. The Scale plan is $42/month for up to 10,000 subscribers and unlocks the ad network, custom domains, and audience segmentation. The Max plan at $84/month adds premium subscriptions, priority support, and no Beehiiv branding.

Beehiiv is cheaper if you’re just publishing and growing. ConvertKit costs more, but you’re paying for automation depth and integration flexibility.

Monetisation: built-in vs. bring-your-own

Beehiiv’s monetisation stack is its clearest differentiator. The ad network connects you with sponsors once you hit 2,500 subscribers. Premium subscriptions (paywalled content, member-only issues) are native. The boost feature lets you pay to get recommended to other Beehiiv newsletters. It’s all designed to work without leaving the platform.

ConvertKit has a tipping feature (one-off payments via Stripe) and supports paid newsletters through Commerce, but it’s not the core experience. Most ConvertKit users monetise by selling courses, memberships, or services outside the platform and using email to drive conversions. Integrations with Gumroad, Teachable, Circle, and Stripe are solid.

If you’re building a subscription newsletter or want sponsorship revenue, Beehiiv’s infrastructure saves you months of setup. If you’re selling products or services, ConvertKit gives you more control over the funnel.

Migration, design control, and lock-in

ConvertKit exports are clean. You own your subscriber data, and you can leave with a CSV and your sending domain intact. The template system is flexible but not beautiful—you’ll need custom HTML if you care about design.

Beehiiv’s editor is opinionated. You get a polished, mobile-friendly design out of the box, but customization is limited. Exporting is straightforward (subscriber list, post archive), but if you’ve built revenue through Beehiiv’s ad network or boost ecosystem, that doesn’t port anywhere.

Neither platform holds your list hostage, but Beehiiv’s monetisation tools create stickiness that ConvertKit’s automation doesn’t.

Who should pick which

Choose ConvertKit if you’re selling digital products, running a service business, or need complex automation. It’s the better tool for operators who think in funnels, lifecycle stages, and multi-step sequences. You’ll outgrow the design limitations, but the automation won’t let you down.

Choose Beehiiv if you’re publishing content on a schedule, building a media brand, or monetising through ads and subscriptions. It’s faster to set up, cheaper at scale, and the built-in growth tools (referral programme, recommendations, boosts) are legitimately useful if you’re trying to grow without paid ads.

Both platforms are competent. The wrong choice isn’t about features—it’s about picking a tool that doesn’t match how you actually plan to make money.

Want more comparisons like this? One Two Three Send breaks down tools, tactics, and trade-offs for online operators every week. Subscribe to get the next one.

Some links in this post are affiliate links — we earn a small commission if you sign up through them, at no cost to you. We only recommend tools we actually use.