ClearSellerSignal

Category: Uncategorized

  • Are Some of Your Shopify Ad Clicks Fake? Here’s How to Check

    Somewhere out there is a person — or more likely a script pretending to be a person — whose entire contribution to the internet is clicking on other people’s ads. We don’t know their name. We do know they’re spending your money.

    If your Shopify ad spend keeps climbing while your sales don’t, the direct answer is: yes, some of those clicks may not be from real potential customers at all. Not all of it, not always a lot of it, but often enough to matter. Here’s how to actually check, instead of just suspecting it.

    What does “invalid” or “fake” ad traffic actually mean?

    It’s any click on your ad that didn’t come from a genuine shopper who might buy something. That covers a few different sources, and they’re not all the sinister kind:

    • Automated bots that crawl ad networks and click links, sometimes to drain a competitor’s budget, sometimes for no clearer reason than “it’s what the bot does.”
    • Click farms — low-paid human click-throughs, often from overseas, with no intent to buy anything.
    • Accidental clicks, especially on mobile, where someone’s thumb hits your ad while scrolling past it.
    • Repeat clicks from the same person checking a price, comparing a listing, or clicking back into your site multiple times in one session.

    The first two are what most people mean by “ad fraud.” The last two are just noise. Both inflate your click count and your spend without adding a single real buyer, which is why a campaign can look busy and still underperform.

    How do you tell if it’s happening to your store?

    You’re looking for a mismatch between how many people clicked your ad and how many of them did anything resembling shopping once they landed. A few checks, roughly in order of how quickly you can run them:

    • Compare click count to session count. Your ad platform (Meta, Google) reports clicks. Your Shopify analytics or GA4 reports sessions from that same campaign. If the ad platform says 500 clicks and your site only logged 300 sessions for that traffic source, roughly 40% of those “clicks” never actually produced a real visit worth counting — that gap is your first clue.
    • Look at time-on-site and pages-per-session for that specific campaign, not your whole store. A real shopper usually looks at more than one page or spends more than a few seconds deciding your site isn’t for them. A campaign where the large majority of sessions last under 5 seconds and view exactly one page is a pattern worth a closer look, not proof on its own — some real shoppers do bounce that fast too.
    • Check where the traffic is geographically concentrated. If you sell mainly to US customers and a meaningful share of a campaign’s clicks are suddenly coming from a country you don’t advertise in or ship to, that’s worth investigating.
    • Watch for click timing that doesn’t look human. A cluster of clicks landing in the same few seconds, repeatedly, at odd hours for your audience’s time zone, is a pattern bots produce and people rarely do.

    None of these alone proves fraud. Together, especially the click-to-session gap, they tell you whether it’s worth doing anything about.

    What can you actually do about it?

    A few practical moves, from easiest to most involved. Most ad platforms already filter out a lot of obvious bot activity before you’re ever charged for it, so don’t assume every platform is asleep at the wheel here — but their filtering is tuned for their own definition of “invalid,” not necessarily for your definition of “a real shopper for my store.”

    • Google Ads and Meta both let you report suspicious click activity for review — it won’t get you money back instantly, but repeated, documented reports matter over time and feed into their own filtering.
    • If you’re running Google Ads, IP exclusion lists let you block specific IP ranges that are clearly not real customers, once you’ve confirmed a pattern over a few weeks rather than reacting to a single bad day.
    • Narrowing your targeting — geography, device type, placement — usually does more to reduce junk clicks than any after-the-fact cleanup, because it keeps the traffic from arriving in the first place.
    • If one specific placement (say, a particular app or partner site in a display network) consistently produces sessions with the fast-bounce, one-page pattern above, excluding that placement is often the single highest-leverage fix available to you.

    Why this matters to you

    Here’s the concrete check you can run this week: pick your best-spending campaign from the last 30 days, pull its click count from the ad platform, then pull sessions from that same source in your Shopify or GA4 analytics for the same date range. If the gap between those two numbers is large — more than the typical 10-20% you’d expect from normal tracking noise — you’ve likely found real budget leaking out before a single potential customer ever saw your product. That’s not a reason to panic or pull your ads. It’s a reason to narrow your targeting, exclude the worst-performing placements, and stop paying to advertise to nobody.

  • We Finished Building the App. Then We Had to Prove It to a Stranger.

    We finished building the feature. We tested it. We were, frankly, a little proud of it. Then someone reminded us that “done” and “allowed to show this to actual merchants” are two very different states, and the gap between them is filled almost entirely with paperwork.

    Getting an app listed on the Shopify App Store isn’t a toggle you flip once the code works. It’s a real review process, and a meaningful chunk of it has nothing to do with whether your app actually functions — it’s about proving, to someone who has never used your product and has no reason to trust you yet, that it does what you say it does.

    What does it actually take to get an app listed on the Shopify App Store?

    For us, it meant two things that took longer than expected: a recorded walkthrough showing a reviewer exactly how the app is meant to be used, start to finish, and a written set of testing instructions explaining how to set up a test account and exercise every part of the thing we’d built. Neither of those is a line of code. Both of them took real time to get right, and both of them exist for the same reason — so a reviewer who has never seen your product before can verify it does what the listing claims, without having to take your word for it.

    That part surprised us a little. We’d budgeted time for building the feature. We hadn’t really budgeted time for explaining the feature to a stranger who was going to judge whether it was allowed to exist in front of real merchants.

    Why does a working app still need a video and a script?

    Because “it works” isn’t actually the question a reviewer is answering. The question is closer to: does this app do, reliably and exactly, what it tells a merchant it will do — and can someone outside the company that built it confirm that, on their own, without the builder standing over their shoulder explaining it away? A screen recording and a written path through the product are the two most reliable ways to make that answer checkable by someone else, instead of just asserted by us.

    It’s a slower process than we expected going in. It’s also, once you sit with it, a reasonable one. The alternative — an app store where listing is just “upload your code and go live” — is the version where a merchant has no real signal that anyone outside the app’s own team ever looked closely at it before it showed up asking for access to their store.

    Why this matters to you

    When you’re deciding whether to trust a new app with your store’s data, whether it’s listed on the Shopify App Store (rather than only available as a private link or a direct install) is a real, checkable signal — not a guarantee of quality, but evidence that someone other than the people who built it looked at a working walkthrough and a documented test path before it was allowed to go live. It’s worth noticing the difference between an app that’s genuinely listed and one that’s just linked to you directly, especially before you connect anything that touches customer data.

  • Why Your Email Open Rate Can’t Be Trusted Anymore (And What to Track Instead)

    Somewhere in your email platform right now, there’s a chart showing you a 45% open rate and a little green arrow pointing up. We’d love to tell you that means 45% of your customers opened your email with genuine excitement. We can’t. The number is lying to you, and it’s not even trying very hard to hide it.

    Why can’t you trust your email open rate anymore?

    Because a huge share of your “opens” aren’t a person opening your email at all — they’re Apple’s mail servers opening it for them, automatically, whether the person ever looks at it or not. If a meaningful slice of your list reads mail on an iPhone, iPad, or Mac (and for most stores, that’s most of the list), your open rate is now measuring something closer to “how many people have an Apple device” than “how many people are interested in what you sent.”

    What is Apple Mail Privacy Protection, in plain terms?

    Apple’s Mail app has a privacy setting called Mail Privacy Protection, on by default for most users since it rolled out. Here’s what it actually does to your numbers: before a person ever sees your email, Apple’s own servers pre-load it in the background — including the tiny invisible tracking pixel that email platforms use to count an “open.” That pre-load registers as an open in your dashboard. It happens automatically, in the background, often within seconds of the email landing, regardless of whether the person ever scrolls past the subject line.

    The result: an email can show a 60%, 70%, even 90%+ open rate from Apple Mail users specifically, almost none of which reflects an actual human looking at the email. It’s not a bug, and it’s not your email platform’s fault — it’s Apple protecting its users’ privacy, which is a good thing for them and a confusing thing for your reporting.

    How do you know if this is affecting your numbers?

    • Check your email platform’s device or client breakdown for a recent campaign. If Apple Mail, iOS Mail, or Apple devices make up a large share of your “opens,” that’s your signal — this isn’t a hunch, it’s visible in the same report that gave you the inflated number.
    • Look at the open rate on Apple Mail traffic specifically versus everything else (Gmail, Outlook, Yahoo). If Apple’s open rate is dramatically higher and suspiciously close to “everyone who received it,” that’s the pre-load effect, not genuine interest.
    • Watch for opens that land within a second or two of send time, in a tight cluster, well before a person would realistically have read a subject line and decided to click into the email. That’s a server doing the opening, not a customer.

    What should you track instead of open rate?

    Click rate and click-to-open rate. A click requires an actual person to actually interact with your email — there’s no equivalent “privacy protection” feature quietly faking clicks on your behalf. If you need one number to watch week over week, make it clicks, not opens. If you want to compare campaigns against each other fairly, click-to-open rate (clicks divided by opens) gives you a sense of how compelling the content was to the people who did engage, even with a noisy opens denominator.

    Past clicks, the number that actually pays your bills is conversion — did the email lead to a visit that led to a sale. That’s a harder number to get right (attribution windows and last-click rules complicate it, which is its own topic), but it’s the one that was never going to lie to you the way open rate now does.

    Is open rate completely useless now?

    Not completely — it’s still worth a glance for deliverability. A sudden, sharp drop in open rate across the board (not just Apple Mail) can still be an early warning that you’ve landed in spam folders or a sending domain problem has started. What it’s no longer good for is telling you whether a subject line worked, whether a segment is engaged, or whether a campaign was a success. Those questions now belong to clicks and conversions.

    Why this matters to you

    If you’ve been making real decisions off open rate — which subject lines to reuse, which segments to keep emailing, whether a campaign “worked” — go check the device breakdown on your last few sends before you decide anything else. If Apple Mail is a big chunk of your opens and the open rate there looks implausibly high, you’ve been grading your email program on a number that was never really measuring your customers. Switch your main scoreboard to click rate and actual sales, and you’ll get a truer read on what’s working — even if the chart doesn’t climb up and to the right quite as dramatically as it used to.

  • We Picked Our Price the Slow Way: By Actually Checking What Everyone Else Charges

    Picking a price for a brand-new product feels like it should take ten minutes and a gut feeling. We tried the gut-feeling method first. It lasted about ten minutes before we talked ourselves out of it.

    So we did the less exciting thing instead: before we wrote down our own number, we actually went and looked at what real competitors charge, one pricing page at a time, rather than trusting a secondhand roundup or a “competitors typically charge” line from a pricing consultant’s blog post.

    Why not just pick a round number?

    Because a round number picked from a gut feeling tends to mean one of two things later: you’re leaving money on the table, or you’re asking merchants to pay more than the market will bear for what you’re actually offering. Both are avoidable if you just go look. So we went and looked — at ten tools in our own category that a Shopify store owner would realistically be comparing us against.

    What we actually did

    We opened each tool’s live pricing page ourselves, not a comparison chart someone else put together, and wrote down what each one actually offered at each price point: what counted as the “free” tier (if there was one), what features were gated behind which plan, and where the real jump in price happened. It’s slower than reading a roundup. It’s also the only way to know you’re comparing what’s true today, not what was true when someone else last checked.

    What surprised us

    Going in, we expected most of these tools to offer some kind of real free tier, since that’s the industry’s default move. Only a minority actually did once we looked closely — several “free” options turned out to be time-limited trials dressed up as a free plan, or a free tier so limited it couldn’t realistically run a real store’s reporting.

    One honest caveat here: pricing pages change, sometimes without much notice. Anything specific we noticed during our own research is a snapshot of that day, not a permanent fact about any competitor. If you’re using this as a reason to check a specific tool’s current pricing before you rely on it for your own decision, that’s exactly the right instinct — go look at their live page the same way we did, not an older summary of it, ours included.

    Why this matters to you

    When you’re comparing analytics tools for your own store, don’t stop at the sticker price. Pull up each tool’s actual current pricing page yourself and check three things: whether the “free” tier is genuinely free and genuinely usable (not a disguised trial), which features are locked behind the plan above the one you’re looking at, and whether the price jump between tiers lines up with a real jump in what you get. That ten-minute check, done directly on the source instead of a comparison article, is the same homework we did before we set our own price — and it’s the fastest way to catch a tool that looks cheap until you actually need the feature that was never included.

  • What’s a Normal Shopify Cart Abandonment Rate? (And How to Tell If Yours Is a Real Problem)

    Somewhere around 7 out of every 10 shoppers who put something in their cart will leave without buying it. If that number just made your stomach drop, take a breath — it’s not a verdict on your store. It might just be Tuesday.

    The short answer: a cart abandonment rate somewhere in the 60–80% range is normal across e-commerce generally, including Shopify stores. If you’re sitting inside that band, the number itself isn’t telling you anything is wrong. What actually matters is whether your rate is unusually high for your specific situation, and whether the carts you’re losing were ever going to convert in the first place.

    What’s a normal Shopify cart abandonment rate?

    Industry benchmarks compiled across many retailers (not just Shopify) have put average cart abandonment in roughly the 60–80% range for years, depending on the study and the year. There isn’t one magic number that applies to every store — mobile-heavy stores tend to run higher than desktop-heavy ones, and stores with expensive or considered purchases (furniture, equipment, anything people “think about”) tend to run higher than impulse-buy categories.

    So the first useful move isn’t comparing yourself to a generic industry number. It’s comparing this month to your own last six months, and comparing your mobile abandonment rate to your desktop one. A rate that’s stable over time, even if it’s on the high end of that range, is a very different situation than a rate that just jumped 15 points in the last two weeks.

    Why the raw number doesn’t tell you anything by itself

    Cart abandonment rate is usually calculated as carts created minus completed purchases, divided by carts created. That math treats every cart the same — but not every cart represents a real, almost-bought item.

    • Price-checkers and comparison shoppers. Someone adding your product to cart to see the total with shipping, then closing the tab, was never a lost sale in the sense that matters. They were doing research.
    • Bots and automated traffic inflating the “carts created” side. If a chunk of your sessions aren’t human (a separate, very real problem for a lot of Shopify stores), some of those non-human sessions can still trigger cart-add events, which pushes your abandonment rate up without a single real customer changing their mind.
    • One person, multiple carts. Someone shopping on their phone at lunch and finishing the purchase on their laptop that night can register as an “abandoned” cart on mobile and a completed one on desktop — two events, one happy customer.

    None of that means the number is useless. It means the number needs context before it becomes a decision.

    How do you tell if your abandonment rate is actually a problem?

    Instead of asking “is my abandonment rate too high,” ask three narrower questions, each of which you can actually check in your own store’s data:

    1. Is it trending, or is it stable? A sudden jump is a signal worth chasing — a checkout bug, a shipping cost surprise added late in the flow, or a new traffic source that isn’t converting. A flat, consistent rate over months is just your store’s baseline.
    2. Where in checkout are people actually leaving? Shopify’s own checkout funnel reporting breaks this down by step. Losing people at “shipping information” versus losing them at “payment” point to completely different fixes — one’s a trust or friction problem, the other might be a payment method you’re missing.
    3. Does it match your traffic quality? If you know (or suspect) a meaningful share of your sessions are bots, scrapers, or low-intent comparison traffic, expect a higher abandonment number as a side effect of that — and fix the traffic-quality problem first, rather than redesigning your checkout to chase a number that was never fully real.

    Why this matters to you

    Here’s the concrete thing to actually go do: pull your store’s cart abandonment rate for the last 90 days, broken out by device (mobile vs. desktop) and by traffic source, if your analytics lets you slice it that way. Don’t look at the headline number in isolation — look at whether it’s stable or moving, and whether the worst segment lines up with a traffic source you already suspect is low-quality.

    If the number is stable and sits in that 60–80% range, you likely don’t have an abandonment problem — you have a normal store. If it’s climbing, or if one segment is wildly worse than the rest, that’s where to spend your next hour of attention, not on the topline percentage itself.

  • Why Your Shopify Conversion Rate Never Matches Google Analytics (Or Your Ad Platform)

    If you’ve ever opened three dashboards before your coffee finished brewing and gotten three different conversion rates for the exact same day, take comfort: you haven’t broken anything. You’ve just discovered the one truly reliable fact in ecommerce analytics, which is that nothing agrees with anything.

    Here’s the actual answer, before anything else: none of your reports are wrong. They’re just not measuring the same thing, even though every single one of them slaps the label “conversion rate” on it.

    Why do Shopify, Google Analytics, and your ad platform all show different numbers?

    Three things are different behind the scenes, and any one of them alone is enough to throw the numbers out of sync:

    • What counts as a “session.” Shopify starts and ends a session on its own clock. Google Analytics (GA4) does the same thing, but with different rules for when a session resets, what counts as a new visit, and how it handles someone who leaves a tab open overnight and comes back the next day. Same visitor, two different session counts.
    • What counts as a “conversion.” Shopify counts a completed checkout. GA4 counts a purchase event it was told about through your tracking setup, in its own time window, which can lag or miss entirely if tracking isn’t wired up perfectly. Your ad platform counts a sale it believes it influenced, using an attribution window it picked for you (commonly something like a 7-day click or a 1-day view), whether or not that was the last thing the customer touched before buying.
    • Who gets filtered out. Every platform does some amount of bot and automated-traffic filtering before it shows you a number, and every platform does it differently, with rules you don’t get to see. One tool’s “clean” session count and another tool’s “clean” session count can disagree before you’ve even gotten to the conversion question.

    Put those three together and you get the thing that confuses almost every store owner at some point: your ad platform says it drove 40 sales this week, but your actual Shopify order count only went up by 25. That’s not fraud and it’s not a tracking disaster. It’s three different scorekeepers, each playing by their own rules, all reporting from the same game.

    What is each platform actually counting?

    It helps to think of each number as answering a slightly different question, not the same question with a different answer:

    • Shopify Analytics is answering: “Out of the sessions Shopify itself recorded, how many ended in a completed order?” This is the closest thing you have to ground truth for revenue, because it’s tied directly to your actual order system.
    • Google Analytics is answering: “Out of the sessions GA4 recorded under its own definition, how many triggered a purchase event it was told about?” Useful for behavior and funnel detail, but one step removed from your real order ledger, and only as accurate as your tracking setup.
    • Your ad platform is answering a different question entirely: “Of the sales that happened anywhere, how many do we believe we deserve credit for, based on the attribution window we chose?” This is a credit-assignment number, not a counting number — which is exactly why it can add up to more “sales” across all your campaigns than you actually had orders.

    Which number should you actually trust?

    For “did we make money and how much,” trust Shopify’s order count over anything else. It’s the one number tied directly to a payment actually clearing. Treat Google Analytics as your tool for understanding behavior — where people drop off, what they look at, how a session actually unfolds — rather than as a second opinion on your revenue. Treat your ad platform’s reported conversions as a directional signal about which campaigns are pulling their weight relative to each other, not as a literal count of sales you can add up across platforms and expect to match your bank deposit.

    None of the three is lying. Each one is just built to answer a different question, and the mistake is assuming they’re all trying to answer yours.

    How do you read conversion rate without fooling yourself?

    1. Pick one system as your scorekeeper. For most stores, that’s Shopify’s own order and session data, because it’s the system closest to the money actually moving.
    2. Use the other platforms for trend and shape, not precision. If your ad platform’s reported conversions are climbing week over week, that’s worth noticing even if the raw number doesn’t match your Shopify total. The direction is more trustworthy than the absolute figure.
    3. Compare like with like. Before you put two numbers side by side, check that you’re comparing the same unit — sessions to sessions, not sessions to users; same date range; same time zone; same attribution window if one applies. A lot of “why don’t these match” moments disappear once you notice you were comparing a 7-day window to a 1-day window.
    4. Only chase a discrepancy that survives this check. If your ad platform claims 40 sales from a specific campaign, go look at your actual Shopify orders for that exact day, filtered to that campaign’s own UTM tag, and compare that to what the ad platform claims for the same day. If it’s still wildly off after that, you’ve found something real worth investigating. If it closes the gap, you’ve just confirmed the numbers were never going to agree in the first place.

    Why this matters to you

    The next time two reports disagree, you don’t have to guess which one is “broken,” and you don’t have to quietly panic that your tracking is falling apart. Pull up your Shopify orders for the specific day and campaign in question, filtered by UTM, and compare that single number against the single number the other platform is claiming for that same day. That one check — same day, same campaign, same window — will tell you in about two minutes whether you’re looking at a real problem or just three scorekeepers doing their jobs differently. Most of the time, it’s the second one, and that’s one less thing to lose an afternoon over.

  • We Built a Permanent Fake Customer. On Purpose. For a Very Boring Reason.

    Every piece of software ends up with at least one feature built entirely for an audience of one person who will never pay you anything. Ours doesn’t even have a name on the invite list. It’s just an account.

    Here’s the direct version: before yoursignalstack could go live on the Shopify App Store, a human reviewer at Shopify needed to actually see what our most expensive plan looks like — the real thing, every feature switched on, nothing held back for a demo. The catch is obvious once you say it out loud: that reviewer isn’t going to pull out a card and pay for our top tier just to check our work. So we built a permanent account, locked to the top tier, that exists for exactly one reason — letting someone see the real product without a single dollar changing hands.

    Why can’t a reviewer just use a free trial?

    A trial seems like the obvious answer, and it’s the first thing we considered. The problem is that a trial is built to behave like a trial — it runs on a clock, it’s meant to convert or expire, and depending on when a reviewer starts it, it might not even land on the exact plan that needs checking. App review isn’t a one-time glance either; a reviewer can come back to re-check something weeks later, after any reasonable trial window has closed. We needed something that would look identical no matter when someone showed up to look at it, which a time-limited trial can’t promise.

    So what did we actually build?

    Nothing clever, honestly — that’s half the point. It’s a standing internal account, permanently set to our top pricing tier, with no live billing attached to it at all. It doesn’t go through checkout, it doesn’t have a card on file, and it isn’t reachable by anyone outside the review process. It just sits there in the exact state our highest-paying real customer would be in, available whenever someone needs to confirm that what we say the top plan does is actually what the top plan does.

    It’s a small thing. It took more thought than you’d guess to get right, and it will never show up in a changelog or a feature announcement, because it isn’t a feature — it’s plumbing for a process our actual customers never see and don’t need to think about.

    Why this matters to you

    The honest takeaway isn’t really about us — it’s about how much of building software has nothing to do with the features you’ll ever click on. A real chunk of development time on any app goes to logistics like this: satisfying a platform’s internal process, proving something works to someone who will never be a customer, building one-off tooling that exists purely so a review can happen at all.

    Here’s the concrete version you can actually use: the next time you’re comparing apps on the Shopify App Store and a listing shows screenshots of a top-tier plan you haven’t paid for, that image usually isn’t a mockup. Somewhere behind it is very likely a setup like ours — a standing account built just so someone could show the real thing without routing it through live billing. It’s a small detail, but it means those top-tier screenshots are generally more trustworthy than you’d assume by default, precisely because somebody had to go out of their way to make that view possible in the first place.

  • The Bug That Looked Like Our Billing System Was Broken. It Was One Flag.

    Every product has one bug that ages the person who found it by about a year in the space of a single afternoon. This is ours. It never touched a single dollar of anyone’s money, but for a few hours it felt like watching the whole payment stack quietly catch fire.

    Here’s what actually happened: the Shopify Admin API started silently returning 403s for a subset of merchants. No error banner, no obvious pattern at first — just requests that should have worked, quietly failing. The eventual root cause was an access-token expiry edge case, and the fix that resolved it was passing a single parameter, expiring:1, on the relevant API calls.

    Why did a one-flag fix take real debugging to find?

    Because the symptom didn’t point at the cause. A 403 on an Admin API call looks like an authorization problem — a wrong scope, a revoked access, a permissions bug in our own code. It does not look like “this token is a specific kind of expiring token, and the request needs to say so explicitly.” We chased the obvious suspects first, because that’s what the error actually resembled. It took working backward from which merchants were affected, and what their tokens actually had in common, before the real shape of the problem showed up.

    What the fix actually was

    Once we found it, the change itself was small: pass expiring:1 so the API call correctly accounts for tokens that expire, instead of assuming the older, non-expiring token shape. It shipped as migration 0078. The real lesson wasn’t about the flag itself — it was how much debugging can hide behind a fix that, once found, looks embarrassingly simple.

    What we’d tell someone hitting the same thing

    If you’re integrating with an API that has more than one token type or auth flow, and you see 403s that don’t match the permissions you think you’ve granted, don’t assume the bug is where the error is pointing. Check whether the token itself is a different shape than the one your code was written against. That mismatch is invisible until you go looking for it specifically.

    We’re building yoursignalstack in public, bugs included, because we’d rather you see the real process than a highlight reel. If you want to follow along, leave your email and we’ll let you know when it’s live.

    Get notified when yoursignalstack launches →

  • We Checked Our Own Pricing Page Like a Customer Would. We Didn’t Love Everything We Found.

    There’s a particular flavor of discomfort in grading your own homework and realizing you wouldn’t pass it. We got a taste of that recently, except the homework was our own pricing page, and the person grading it was also us. Somebody had to do it honestly, so we did.

    Here’s the short version: we went through our product line by line and checked whether what we actually let a merchant do matches what our pricing page says they can do, depending on which plan they’re on. It mostly matched. Not entirely. We found a few places where the gap between the promise and the product had quietly opened up, and we closed it.

    What’s the difference between a pricing page and the product itself?

    Your pricing page is a promise written in marketing language: “Starter gets you X, Pro gets you X plus Y.” The product itself is a pile of code that has to actually check, every single time a feature loads, which plan the account in front of it is on, and decide whether to allow or block accordingly. Those are two separate things, written at two separate times, usually by different parts of a small team moving fast. They start out matched. Then the product changes — a feature gets reworked, a new one gets added, an old restriction gets refactored — and the pricing page doesn’t always get updated in lockstep. Nobody plans for that drift. It just happens, the same way a junk drawer fills up without anyone deciding to fill it.

    What did we actually find when we checked?

    We went through the pricing page claim by claim and tried to break it the way a skeptical customer would: log in on each tier, try the things that tier isn’t supposed to get, and see what actually happens. In a few spots, the code wasn’t checking as carefully as the pricing page implied it would. Nothing dramatic, nothing that cost anyone money or exposed anyone’s data — but real gaps between “here’s what you’re paying for” and “here’s what the software actually enforces.” We fixed what we found. It wasn’t a fun afternoon, but it was a necessary one.

    Why does this happen even when nobody’s trying to cut corners?

    Nobody sits down and decides to let a Starter-tier customer quietly use a Pro-tier feature. It happens in small steps: a feature gets rebuilt and the new version forgets to re-check the old restriction, or a feature gets added to one plan and someone assumes the gating will “obviously” carry over, or the pricing page gets reworded in a sales meeting and the engineering ticket to match it never gets filed. Every one of those is a small, forgivable, completely human miss. The only real defense is going back and actually checking, on a schedule, instead of assuming the page and the product are still telling the same story.

    Why this matters to you

    This isn’t just a story about us. It’s a habit worth borrowing for anything you pay for. The next time you’re on a free trial for an analytics tool, a marketing app, or anything else billed by plan tier, don’t just read the pricing page and take it on faith. Actually try the things your plan is supposed to include, and if you can see what a higher tier offers, try poking at those too. If something you shouldn’t have access to works anyway, or something you’re paying for doesn’t actually show up, that’s worth a support ticket, not a shrug. Pricing pages are promises. The only way to know a promise is being kept is to test it yourself, the same way we had to test ours.

  • Why Shopify Keeps Labeling Real Marketing Traffic as “Direct”

    Every store owner has, at some point, stared at a pie chart and watched “Direct” eat half of it, then quietly assumed their customers are extremely loyal people who type the full store URL from memory. Some of them are. Most of them are not.

    Here’s the direct answer: “Direct” in your analytics doesn’t mean “typed the URL in.” It means “we couldn’t tell where this visit came from.” Those are very different things, and the gap between them is usually filled with real marketing traffic — ads, social posts, emails — that got stripped of its tracking information before it ever reached your store.

    What does “Direct” actually mean in Shopify analytics?

    Every analytics platform, Shopify’s included, tries to figure out where a visitor came from by reading two things: the referring URL the browser hands over, and any UTM parameters tacked onto the link they clicked. When both of those are missing or unreadable, the platform doesn’t guess. It falls back to “Direct” as a catch-all.

    That label was accurate in 2010, when most lost-source traffic really was someone typing your domain into their address bar or clicking a bookmark. It’s a much weaker signal now, because there are more ways than ever for a real referral to lose its tracking information on the way to your store.

    Why does real marketing traffic end up labeled Direct?

    A few common culprits, roughly in order of how often we see them:

    • Missing or broken UTM parameters. If a link in an ad, email, or social post doesn’t have UTM tags on it — or they got dropped somewhere along the way, like when a link is shortened or reposted — there’s nothing left to tell your analytics where it came from.
    • In-app browsers on social platforms. When someone taps a link inside Instagram, TikTok, or Facebook, it often opens in that app’s own built-in browser rather than Safari or Chrome. Those in-app browsers are inconsistent about passing along referrer information, so a click that absolutely came from a social post can still land in your analytics as Direct.
    • Email clients stripping referrer data. Many email apps, and especially mobile ones, don’t send a readable referring URL when someone clicks a link inside an email. Without a UTM tag on that link, the visit shows up as Direct even though it came straight from your newsletter.
    • “Dark social” — links shared outside your view. Someone sees your product, copies the link, and pastes it into a text message, a group chat, or a DM. There’s no referrer to pass along in any of those cases, so that visit is Direct too, even though a real marketing moment (your product being good enough to share) caused it.
    • HTTPS-to-HTTPS security behavior. Browsers sometimes withhold referrer information when the security context doesn’t line up a certain way between the sending and receiving page. It’s a smaller factor than the others, but it adds a little more noise to the pile.

    How can you tell how much of your “Direct” traffic is actually something else?

    You can’t get an exact number without better tagging going forward, but a few checks will tell you whether “Direct” is worth investigating on your store:

    1. Look at the landing page, not just the source. Someone who genuinely typed your domain from memory almost always lands on your homepage. If a meaningful chunk of your Direct traffic is landing on a specific product page, a sale page, or a URL with a long slug they’d never remember, that’s a strong sign a real link was clicked and its tracking just didn’t survive the trip.
    2. Check the timing against your own campaigns. Pull up the days you sent an email blast, launched an ad set, or posted on social, and compare them against your Direct traffic for those same days. A Direct spike that lines up suspiciously well with a campaign you ran is traffic that campaign generated — analytics just lost the paper trail.
    3. Break it down by device. If your Direct traffic skews heavily mobile, that’s consistent with in-app browsers and email clicks, both of which happen disproportionately on phones. A more even device split leans more toward people genuinely navigating in on their own.
    4. Watch what happens when you add UTM tags to everything. This is the most reliable test of all. Go a few weeks tagging every link you control — every email, every social post, every ad — with UTM parameters, and watch your Direct percentage. If it drops noticeably, that confirms a real chunk of it was always marketing traffic that simply wasn’t labeled as such.

    What should you actually do about it?

    Tag every link you control. That’s the whole fix, and it’s less work than it sounds like. Any link you put in an email, a social bio, a paid ad, or a QR code on packaging should carry UTM source, medium, and campaign parameters before it goes out. Most email platforms and ad managers will do this automatically if you turn the setting on — check that it’s actually enabled rather than assuming it is.

    Don’t pull budget from a channel just because your dashboard says “Direct” is outperforming it. A channel that’s secretly feeding your Direct number is still working — it’s just not getting credit. Cutting a real, working channel because an undercounted “Direct” bucket looks stronger is one of the more common ways stores talk themselves out of something that was actually bringing in sales.

    Why this matters to you

    An inflated “Direct” number isn’t just a labeling quirk — it changes real decisions. It makes your actual marketing channels look weaker than they are, which can push you to cut spend on something that’s working or pour more budget into something that just happens to get credited correctly. The fix doesn’t require new software or a bigger analytics budget: pick one week, audit every outbound link you’re sending traffic through, and make sure each one carries a UTM tag. Then watch what happens to your Direct percentage over the following weeks. If it drops, you’ve just recovered visibility into channels you were underrating — and that’s information you can act on immediately, not someday.

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