u/claspo_official

Disclosure, since the sub asks for it. I work on the onsite widget side, so the tests below ran across client stores, and I am not able to publish per-client percentages without their sign-off. No product names either, and no links.

For a long stretch we ranked opt-in widgets by opt-in rate, wheel beat form, form beat banner, ship it, move on. Then we started reading revenue per session on the same tests. Some of the rankings flipped.

The split runs at session level, revenue per session is arm revenue over arm sessions, and opt-in rate comes out of that same denominator, which is what lets a widget collecting fewer addresses win the comparison. Then a second read at 30 and 90 days, because first-order revenue and third-month revenue kept disagreeing.

Gamification wins the opt-in comparison almost every run we have logged, spin-to-win and scratch cards both. People like the animation, they spin, the address goes in the box, and the list grows on a schedule you can promise a client.

Product-finder quizzes collected fewer addresses per session and in several niches came out ahead on revenue per session anyway, which took a few tests to believe. Two stores in the same niche, comparable traffic, the same two widget types, opposite winners. So the quiz result is a thing that happened in our data, and it predicted nothing about the store one niche over.

Push opt-in rate up while AOV slides and you have run a winning test that pays you less money. Same story with repeat purchase rate. That one shows up a quarter later, when nobody is checking the widget report anymore.

The mechanism is still a hypothesis for me. The wheel plausibly pulls in discount hunters who redeem once and go quiet, while four questions about hair type or a dog's weight filter for someone already mid-purchase and hand you zero-party data for the flows afterwards. I can see the pattern in aggregate and I cannot prove why.

So, the questions I came with. What do you optimize email capture against, opt-in rate, first-order revenue, or LTV? Has anyone watched gamified signups underperform on LTV over a full year? And how do you attribute revenue to a popup cohort without double counting people who were going to buy anyway?

I work on email capture tooling, and the numbers below come from 10 stores that run it. Aggregate client data, in other words. I don't have a store of my own in here.

Last year, across the whole BFCM window, 93.5% of the emails those stores collected came in before Black Friday started. The sale weekend barely registered.

Which broke my assumption about how this works. Most of the stores I see build the popup and the offer for the weekend and treat that as go time, but by the time Friday arrives the list is already built out and there isn't much left to collect. The opt-ins pile up in early November, while people are still poking around and haven't committed to anything, and the discount has nothing to do with it.

So a capture form that goes live during BFCM week is fishing after the fish left. Move it up two or three weeks and you're collecting through the whole browsing run-up instead of the four days when everyone is already committed.

Anyone got numbers that contradict this? Cuz 10 stores isn't the whole market and I have no idea how representative our mix is. And does the same logic hold elsewhere in marketing? Feels like anything you ask for while somebody is still just looking around should be cheaper to get than the same ask once they're deciding, but I've only ever checked it on email.

Someone signs up through your popup, comes back two weeks later, and the same "10% off your first order" modal greets them like they've never been there. The tool doesn't know who they are, and every method it has for remembering them breaks in ordinary use.

Most popups handle this with a cookie written when you submit the form, and it holds until the cookie is gone, which happens constantly as people clear their browsers, sign up on a laptop and come back on a phone, or open your site from your newsletter inside Gmail's in-app browser, which keeps storage that shares nothing with the Safari they normally use. Safari caps a lot of script-set cookies at seven days on top of that, so a cookie nobody touched can expire before the person returns, and each of these resets them to new and fires the first-order offer again.

Tools that try harder reach for fingerprinting, building an ID from screen size, timezone, fonts and browser version so they can recognize the device without a cookie, which fails in both directions. Thousands of people carry the same print, because a stock iPhone on current iOS in one city looks like every other one, so you match strangers to each other, and it drifts the opposite way when a browser update or a new monitor changes the print and a returning subscriber reads as new. Safari's tracking prevention targets this kind of covert device recognition directly, and GDPR treats fingerprinting for identification as personal-data processing that needs consent your popup never asked for.

No browser-side signal is solid, because a tool running inside someone's browser can't know who they are with certainty, and the identification that holds up runs server-side against your actual subscriber list. A visitor arriving from an email link can carry a token that names them, and a logged-in account is known the same way, while everything past that is probability, so "is this a subscriber" works better as a confidence score than a yes or no.

Suppression never reaches 100%, so the cost of a miss matters more than the miss rate, and those costs aren't equal. A returning subscriber who closes a welcome popup is mildly irritated, while the same subscriber shown a first-order discount they can't redeem, or one cheaper than what they already paid, is where the trust goes, and you can design around that today even with the identity layer staying leaky underneath.

The standard email popup, the kind where you type your address for 10% off, converts around 3.5% opt-in for us. Wrap the same offer in a game (spin the wheel, scratch card, pick a gift) and it does about 9%. Our best variants have cleared 26%. Same discount, same traffic, roughly triple the signups.

These numbers come from our own company's tests (Claspo), so I at least know how they were measured. Biased sample, sure.

I spent a while trying to figure out why the wrapper matters this much, and I've landed on micro-commitment. A plain form opens by asking for your email, the single biggest thing it can request, before the visitor has done anything at all. A wheel asks for something tiny first. Pick a slot, scratch the panel. Once you've taken that small step, typing your email feels like claiming a prize you already half-earned, and the whole exchange reads friendlier because the ask lands last instead of first.

Welcome popups go to genuinely new visitors only. A browse-saver waits until someone has viewed 2-3 pages or hung around for 20-30 seconds, so it catches engaged people rather than bouncers. Cart-exit fires for someone leaving with items in the cart. Splitting the triggers this way did as much for us as the game itself, since the same widget lands as timely for one visitor and as noise for another.

Here's the caveat I'd raise if someone else posted this: these are opt-in rates. Someone who spun a wheel for a discount code can behave very differently downstream from someone who typed their email in cold, so the two lists may monetize at different rates per subscriber. The capture lift is real. If you test this, track what those subscribers buy after signup, along with how many you got.

Since someone will ask, there's no product behind this post. All in-house testing. Glad to share setup details in the comments.

We took a hypothetical US fashion DTC store on Shopify with 100,000 monthly visitors and modeled two email strategies through BFCM. Everything below is a model built on public benchmarks plus our own capture data, not client results, and the assumptions are listed so you can swap in your own numbers.

Strategy one runs a well-built gamified popup from mid-July, converts 9% of the visitors who see it, gives new subscribers a 10% code, warms them with flows, and sends the list a 25% offer during BFCM week. Strategy two runs a basic signup form over the same period, converts 3%, gives the same 10% code, and compensates at BFCM with a 50% offer.

Shared assumptions. 70,000 visitors see the popup monthly after subscriber hiding and frequency caps, AOV is $100, the welcome flow converts 5% of signups before BFCM, 15% of the list churns by late November, and the BFCM campaign converts 3% of the reachable list at 25% off against 5% at 50% off, since deeper offers do pull more redemptions. Repeat purchases and retargeting audiences are ignored, both favor the bigger list, so this is the conservative version of the gap.

Strategy one collects 25,200 emails by BFCM. The welcome flow produces 1,260 first orders at $90 net, which is $113,400 before the sale starts, and the 25% campaign converts 643 of the 21,420 still-reachable addresses at $75 net for another $48,200. Total around $161,600. Strategy two collects 8,400 emails, gets $37,800 from the welcome flow and $17,850 from the 50% offer, and lands around $55,700.

Same traffic, 2.9x revenue gap. Per reachable subscriber during BFCM week the deep discount actually wins, $2.50 against $2.25, because the higher redemption slightly outruns the margin loss. The entire gap comes from list size, which was decided back in July by the capture setup. The discount depth debate that dominates BFCM planning moves the result by cents per subscriber, while the capture rate moves it by a factor of three.

The model has obvious limits. The 5% welcome conversion and the 3% campaign conversion are the assumptions doing the heaviest lifting, and weaker flows narrow the gap. A 9% capture rate is not a given either, in our data it sits between the top 10% and top 3% of ecommerce campaigns, though gamified setups with a working frequency cap reach it regularly. Run the same arithmetic with your own numbers before quoting ours.

If anyone has real cohort data comparing summer-captured and November-captured subscribers through BFCM, that is the part no model replaces.

Disclosure up front, we're Claspo, this is a popup one of our clients built on our platform. We're keeping the client anonymous. Home & garden DTC brand, that's as specific as we'll get.

The reason we keep coming back to this campaign internally is that it ignores a lot of advice that gets repeated in this sub, and every deviation has a reason behind it.

https://preview.redd.it/d6wq1tl1pfeh1.png?width=330&format=png&auto=webp&s=297b0fc212b8f810ae7505e29f801e4d96aadf4b

The setup. Mother's Day weekend only, the popup lives for 3 days total. Headline promises 20% off storewide. A text roller underneath previews an EXTRA 15% / 10% / 5%, always opening on the 15. You enter your email, then pick one of three flowers, and get a bonus promo code on top of the storewide sale. So the funnel is discount promise, then email, then a game, then the code. Four steps where most brands use one.

Everything you read here says shorter forms convert better. And they do, for the form itself. But a flat "subscribe for 10% off" gives people nothing to do after typing the email. Here you type it and then pick a flower, and that pick is a small sunk cost. By the time you're choosing between three flowers the email is already gone. The roller anchoring on the biggest tier before signup works the same way "up to 70% off" banners do, except the smallest outcome (5% extra on top of 20% storewide) still feels fine, so nobody leaves feeling scammed.

A 20% storewide + extra code offer running year-round would train every visitor to never pay full price. Compressed into a Mother's Day weekend it reads as a one-off. The holiday explains why the discount is that big, so nobody expects it to still be there next month. That was exactly the client's goal, gamify a generous offer so the short seasonal window justifies its size without setting a permanent discount expectation.

The targeting is where it gets interesting. It only fires on the home page and collection pages. Product pages are excluded, and the reasoning is that someone on a PDP is already close to checkout, so a popup there mostly cannibalizes margin on sales that were happening anyway, while at the collection level it catches shoppers still browsing the catalog. Existing subscribers never see it, B2B pages are excluded, and it caps at once per session. None of this is platform-specific by the way, the targeting logic is the transferable part and you can rebuild it on whatever your stack is.

For results, the campaign landed in the top 10% of email capture campaigns on our platform for Q2. We're not sharing the client's exact numbers.

The honest caveat. Gamified signups skew toward deal hunters, and we don't have visibility into what the client's unsubscribe or repeat purchase rate looked like after the sale. A list that grows fast in May and is dead by July is not a win.

What we can't see from the platform side is what happens after the handoff to email. If you've segmented seasonal promo signups separately, did that cohort ever start behaving like your organic list, or did it stay a discount segment forever?

The usual explanation for popup fatigue is a weak incentive, that 10% off stopped being worth an email. Trigger data suggests the incentive is rarely the problem, because the same offer performs completely differently depending on when it appears 

Popups with no trigger, the ones that fire the moment the page loads, convert at 0.06% in our cohort. The same class of popups fired on a time-on-page trigger converts at 4.68%. A new-visitor trigger lands at 3.8% and exit intent at 2.17%. Nothing about the design changes between these groups. The store just picks a different moment to interrupt.

The instant popup fails for the same reason people resent it. A visitor at second zero has not seen a single product and has no reason to hand over an email, so the offer reads as a toll gate on the way to the content. Twenty seconds later the same offer lands on someone who has scrolled a collection and checked prices, and at that point a discount answers a question the visitor is already asking.

Exit intent is the trigger every guide recommends first, and in the cohort it converts at less than half of time-on-page. Our reading is that exit intent catches people at the moment of lowest interest by definition, since they are already leaving, and the discount has to reverse a decision instead of reinforcing one. It still beats no trigger by a wide margin and it stacks with other conditions, so this is an argument about priority rather than against the mechanic.

Some of the spread is correlation rather than the trigger alone. A store that sets up triggers usually gets the rest of the configuration right too, and aggregate data does not let us fully separate those effects.

The complaint and the conversion data point the same direction. The popup people describe when they call the format annoying, the one covering the screen before content loads, is also the one converting at 0.06%. Annoyance and performance are not in tension here, because the setups visitors tolerate are the same setups that convert.

What triggers are you running, and has anyone A/B tested time-on-page against scroll depth? Scroll depth is the one dimension our aggregate view does not break out, and single-store tests are the only place that answer exists.

Spin wheels are probably the most criticized popup format in ecom. We have aggregate conversion data across our ecommerce cohort, and it shows a different picture than the reputation suggests.

The median email capture popup in the cohort converts at 0.86%. Popups with a game mechanic average 6.50% against 0.50% for flat signup forms. Scratch cards convert at 6.95%, wheels at 6.54%, gift boxes at 5.29%. Inside the top 3% of all campaigns, where everything is already configured well, gamified setups hold 15.44% against 11.87% for non-gamified, so the gap is not explained by weak forms dragging down the baseline.

Part of the uplift is selection bias, and we want to name that before anyone else does. A store that invests in building a game usually also invests in targeting, so the mechanic and the discipline travel together. The wheels that generate complaints are almost always the ones missing the discipline half.

Popups with no display limit convert at 0.06% in our data. Adding a minimum interval between shows lifts conversion to 7.71%, which is the largest single effect in the entire dataset and takes one checkbox to configure.

Hiding the popup from people already on the list performs 62 times better than showing it to subscribers. Skipping this setting is the reason a visitor sees the same wheel for the eleventh time after already handing over their email.

The best converting discount band is 16 to 20 percent, at 17.47%. Offers of 21 to 30 percent convert at 1.38%, which is worse than a plain single digit code. We do not have a clean causal explanation for this part. Our working guess is that past 20 percent the offer starts reading as fake, and the shoppers who do believe it decide to wait for a sitewide sale instead of subscribing.

A wheel where most sectors are junk prizes teaches the visitor that the game works against them. The setups that perform have no losing outcome, and the smallest prize is still a usable code, so the game makes signup feel like an occasion instead of punishing the click.

None of this makes wheels right for every brand. A store selling $400 leather bags will be off tone with a wheel regardless of what it converts, and a quieter mechanic like a gift box does the same job. But the conversion problem attributed to gamification is in most cases a configuration problem, and the data separates those two cleanly.

BFCM prep usually gets treated as an October task. We see the capture side of it across our ecommerce cohort, and the numbers make a case for starting the list work in the summer.

The best converting discount band for email capture in our data is 16 to 20 percent, which converts at 17.47%. Offers between 21 and 30 percent convert at 1.38%, worse than a plain single digit code. BFCM offers live almost entirely in that second band. So building the list during the sale itself means asking visitors to subscribe for the offer type that historically converts worst, at the exact moment when every other store is asking the same thing.

A subscriber captured in July with a modest code works differently. The store pays 15 or 20 percent on the first order instead of 30, the welcome flow has months to run, and by late November that address has seen the brand more than once. We only see capture, the downstream ESP data belongs to the store, so we can't prove summer subscribers buy more during BFCM. Our working assumption is that someone who joined for a 30 percent code in November behaves like someone who joined for a 30 percent code, and stores that track cohort LTV in Klaviyo can check this against their own data in about ten minutes.

Disclaimer: November popups still have a job, just a different one. Scheduled short campaigns convert at 2.9% in our cohort against 0.05% for the same setups running without a schedule, and our strongest seasonal performers ran windows of three to nine days. A tight BFCM campaign converts the traffic spike well precisely because it reads as an event. What it can't do is compress four months of list building into one week.

Summer and early fall go to capture at normal discount depth, October goes to warming the list, and BFCM week goes to selling to the base through email, where you are not paying rising ad CPMs and not competing with every popup on the internet.

The honest counterargument is volume. November traffic is the highest of the year for most stores, so even a weak conversion rate produces a large absolute number of emails, and turning capture off during BFCM would be a mistake. The argument is about sequence, not about skipping November.

Not trying to change anyone's mind here, just sharing our numbers. If you have data or arguments that point the other way, happy to hear them.

We're on the list-building side of email, so we went and checked what happens to emails after signup now that AI reads them first.

Apple Mail writes its own summary of your email and shows it in the inbox list instead of your preheader. So for a chunk of your audience, the first impression isn't the preheader you wrote. It's whatever Apple Intelligence decided your email is about. Gmail works differently. Gemini generates a summary card, but only after the recipient opens the message, so it doesn't touch the open decision, just what happens right after.

Both pull from readable text near the top of the email body. Text baked into images doesn't count. We tested a few image-heavy promo emails and the summaries came out either vague or literally "this email contains images." A plain hero with one clear sentence of value got summarized almost word for word.

We assumed AI summaries would inflate opens the way Apple MPP did in 2021, but the data points the other way for Gmail. Validity reported open rate drops of 30% or more for some senders, apparently because Gmail cut back on image prefetching, which is what fires most tracking pixels. So opens got less reliable again, just in the opposite direction this time.

For now we treat the first 100-200 characters of real body text as the new preheader and ignore opens almost entirely. Has anyone here actually restructured templates for this, or seen the "contains images" summary on their own campaigns?

Every time I see a store with a huge catalog launch a product finder quiz, I have the same thought: this is either going to work really well or completely flop.

It usually starts with support getting flooded with "Which one should I buy?", so the team builds a quiz. Great idea. Then someone adds another question. And another. Six months later it's a 10-question survey.

The biggest mistake is that most of them only help the business. If a question doesn't get the shopper closer to the right product, it probably shouldn't be there. A quiz is supposed to feel like a game, not a customer survey. People don't mind answering questions when they're interesting or feel like they're leading somewhere. They do mind feeling like they're helping you segment your email list.

The other thing that bugs me is asking for an email before showing anything useful. I'd rather let people finish the quiz first and ask for the email to unlock the results. They've already invested the time.

And I've seen plenty of quizzes that never really had a chance. Someone builds it, hides it on a random page, never links to it anywhere, and a few months later decides that quizzes don't work.

So has anyone here tested a product finder quiz against a regular discount popup? Which one actually collected more emails? And roughly how many SKUs does your store have?

When I see another store with a big catalog and a finder quiz, I can see it flopping, and I know why. But maybe someone here can prove me wrong.

The pattern is almost always the same.  Support keeps answering "Which product is right for me?" → The team builds a product finder quiz. →  Six months later, they're wondering why nobody uses it.

Nine times out of ten, the quiz grows from five useful questions into ten. Questions like "What's your biggest frustration with this category?" might be interesting, but they don't narrow the product list. By question 3 or 4, people realize they're filling out a survey and aren't getting any help.

The timing is backward: the quiz asks for an email before it proves it's worth it. I'd rather gate the results than the experience. When people have already invested the effort, they are more likely to share their email to get the payoff.

And then there's the quiz that never had a chance. Built, published on some orphan page, forgotten. Not in the nav, not in the welcome email. Hard to measure a tool that nobody actually saw.

Does a decent finder quiz beat a plain discount popup for email capture? Or only when the catalog is huge enough to overwhelm shoppers? If you've tested both, which won? And roughly how many SKUs does your store have?

Quick disclosure, I work at Claspo and we build onsite signup forms, so I deal with this stuff a lot. For years we thought most junk signups were bots, so we threw CAPTCHA, rate limits, all the usual tech at it. Then someone on my team actually dug into what we’d flagged as bots, and, honestly, most of it wasn’t bots at all. It was just people mistyping their email: “gmial.com,” “gmai,” missing the @, or pasting in a space from somewhere else. Some were burner emails just to get a discount code, which is annoying but kind of fair, and a surprising amount were real, interested users just typing fast and sloppy to get through the form.

What really changed my perspective was seeing how easy some of these fixes are if you do them before the info hits your ESP. If you clean up whitespace, fix the case, or flag super-obvious domain typos before people can hit submit, you save yourself a lot of trouble. Once a bad email lands on your list and you have to clean it up later, you’ve already paid for that contact.

The catch, though, is conversion. Every little check adds friction, and every time we tested it, CAPTCHA just destroyed our popup conversion rates. So we try to avoid making users solve anything if possible.

I’m genuinely curious what others are doing. So are you using inline typo correction, blocking disposable domains, double opt-in, or just soft validation that nudges people without blocking them? And has anyone managed to find a CAPTCHA setup that doesn’t kill conversion, or have most people just ditched it like we have?

Hey, curious to hear your general thoughts on popups. Are you using them on your websites?

If not, why not? If yes, how do you have them set up? Would love to hear your experience.

Every time ecommerce people talk about popup performance, it's always the opt-in rate. Like that one number alone tells you the popup is doing its job.

I'm not convinced it does.

You can collect way more emails and still walk away with a worse list:

• people who only wanted the discount code
• people who never go on to buy
• people who unsubscribe right after the first email
• buyers who were going to purchase anyway and just got a discount out of it

So how do you actually decide whether a popup worked?

Is signup rate the only thing you look at? Or do you watch first purchase rate, revenue per subscriber, AOV, unsub rate, repeat orders, that kind of stuff?

Also curious if anyone here has tested gamified popups against plain discount forms and looked at what the revenue did after signup, not just the opt-ins.

⚠️ Heads-up before anyone asks: I will NOT recommend any tools or services in this thread. No pitching, no "use X instead." This is purely technical feedback on what to change in your popup.

We build popup software, so yeah, we have a horse in this race. But we also spend all day looking at how these things perform across a lot of stores, and the pattern is hard to unsee. Most popups aren't bad because the tool is bad. They're bad because of timing, targeting, or an offer that gives people zero reason to act.

So here's the deal. Drop your store link below and I'll give you an honest read on your popup. No pitch, no "you should switch to us." If it's already doing its job, I'll just tell you that.

It helps if you also mention:

• what the popup is supposed to do (email capture, discount, cart save, whatever)
• when it fires (instantly, on scroll, on exit, after X seconds)
• roughly how it performs if you know your numbers

Stuff I end up flagging constantly, so you can pre-check yourself:

• firing in the first 2 seconds before anyone's read a word
• asking for an email with nothing offered in return
• desktop and mobile running the exact same design (mobile almost always needs less)
• no follow-up after the signup, so the email just sits there doing nothing

I'll get through as many as I can.

These tools all look great on their landing pages, but the real picture only shows up once you've actually lived with one for a while.

The most useful feedback always comes from random threads where someone just vents about a thing that drives them nuts. So here's the direct version of that.

For anyone who's used popup or email capture apps, whatever they are, what's the thing you keep wishing existed and never quite finds? Could be anything. Too clunky to build, breaks on mobile, weak targeting, ugly templates, pricing that jumps the second the store grows, reporting that can't be trusted. Whatever actually annoys you.

No pitch here, just genuinely curious about the real frustrations. The stuff people complain about tends to be way more honest than any feature request form.

Every reply gets read. Thanks in advance.

These tools all look great on their landing pages, but the real picture only shows up once you've actually lived with one for a while.

The most useful feedback always comes from random threads where someone just vents about a thing that drives them nuts. So here's the direct version of that.

For anyone who's used popup or email capture apps, whatever they are, what's the thing you keep wishing existed and never quite finds? Could be anything. Too clunky to build, breaks on mobile, weak targeting, ugly templates, pricing that jumps the second the store grows, reporting that can't be trusted. Whatever actually annoys you.

No pitch here, just genuinely curious about the real frustrations. The stuff people complain about tends to be way more honest than any feature request form.

Every reply gets read. Thanks in advance.

Hi podcast creators,

Disclosure: I'm a CMO at Claspo - a popup builder.

We’re looking for ecommerce-focused podcasts where a practical conversation about popups, onsite conversion, email capture, and revenue growth would fit.

Not looking to do a product pitch. The useful angle would be more like:

• why many ecommerce popups underperform
• how Shopify merchants can use timing, targeting, gamification, exit intent, and multi-step flows without annoying visitors
• how popup strategy connects to email/SMS list growth, retention, and revenue
• how to avoid capturing only discount hunters

Best-fit audience: Shopify merchants from the US, UK, or EU, and agencies working with DTC brands.

If you host or know a podcast around ecommerce marketing, CRO, email marketing, or retention marketing, I’d appreciate recommendations.

Happy to share a few topic ideas or examples if useful.

I keep seeing ecommerce teams talk about popup opt-in rates like that number alone means something.

But I’m not sure it does.

You can get more emails and still end up with worse subscribers:

• people who only wanted the discount
• people who never buy
• people who unsubscribe after the first email
• people who would have bought anyway, but now bought with a discount

So I’m wondering how people here judge whether a popup actually worked.

Do you only look at signup rate?

Or do you track things like first purchase rate, revenue per subscriber, AOV, unsubscribe rate, and repeat purchases?

Also curious if anyone has tested gamified popups vs normal discount forms and checked revenue after signup, not just opt-ins.