So now,\u00a0the credibility of open rates is in question<\/b>\u00a0more than ever.<\/p>\n
But\u00a0open rates still have value<\/b>. Even if some false opens are unpreventable, you still want to stomp out the open rate inflation that\u00a0is<\/i>\u00a0preventable.<\/p>\n
Which is why\u00a0it\u2019s so important for cold email and email marketing services not to inflate their open rates<\/b>.<\/p>\n
In this article, we\u2019ll discuss\u00a0seven ways we\u2019ve seen email service providers inflate their users\u2019 open rates<\/b>\u00a0now and in the past few years.<\/p>\n
We\u2019ll also cover\u00a0remedies for those situations<\/b>\u00a0(beyond just \u201cswitch to an email service that doesn\u2019t do these things\u201d).<\/p>\n
And finally, we\u2019ll cover the\u00a0relevance of open rates<\/b>\u00a0in the modern email landscape. How can you still use open rates to evaluate your campaigns if the rates are less accurate than ever \u2014 and when should you work around them?<\/p>\n Here are seven ways we\u2019ve seen email platforms inflate users\u2019 open rates \u2014 along with steps you can take to make sure\u00a0your<\/i>\u00a0open rates are as accurate as possible.<\/p>\n Your emails most likely pass through some bots<\/b>\u00a0before they get to your contacts.<\/p>\n Apple Mail isn\u2019t the first email client to falsely open emails.<\/b>\u00a0False opens in Gmail<\/a>\u00a0have been around for years; Google bots trigger an open as soon as an email arrives. Other bots like spam checkers do the same.<\/p>\n If your email service provider isn\u2019t recognizing those false opens (and filtering them out by recognizing their User Agent), you\u2019ll see inflated open rates.<\/p>\n Take a look through your campaign reporting data.<\/p>\n If you see User Agents that look suspiciously like bots \u2014 they just might be bots. (You can check to see if they\u2019re real User Agents from popular email clients at\u00a0user-agents.net<\/a>. )<\/p>\n If you can download your analytics from your email provider you can calculate your own open rate by removing the bots from your spreadsheet. You probably\u00a0won\u2019t<\/i>\u00a0have the time to do that for every campaign you send, though.<\/p>\n But if you calculate the rate for a handful of campaigns, you can\u00a0estimate how far off your numbers usually are<\/b>. If your open rates are usually around 10% inflated by bots, you can keep that in mind as you look at your analytics in the future.<\/p>\n It\u2019s one thing when you can blame false opens on bots or Big Tech overstepping. It\u2019s another thing when you\u2019re responsible.<\/p>\n There are\u00a0multiple ways you can trigger opens on your open emails<\/b>.<\/p>\n You, of course, don\u2019t want stats on how often\u00a0you<\/i>\u00a0open your own emails. You only want stats on other people opening those emails.<\/p>\n If you\u2019re a GMass user, we\u2019ve created a Chrome extension called\u00a0Tracker Blocker<\/a>\u00a0to end self-opens. It blocks you from triggering your own open pixel so nothing you do will count toward your open rates.<\/p>\n If you\u2019re not a GMass user, there are other\u00a0pixel blocking<\/a>\u00a0extensions out there \u2014 though we\u2019ve found their performance is (to be generous) spotty.<\/p>\n Also, if you open a bounce notification, GMass won’t count that as an open (even if you don’t have Tracker Blocker installed). Some other email sending platforms do this, some do not.<\/p>\n If you send 100 email messages and only one person opens it \u2014 but they open it 85 times \u2014 what should your open rate be? 1%\u2026 or 85%?<\/p>\n 1% feels far more accurate than 85%. That\u2019s a hyperbolic example, for sure \u2014 but even if the numbers weren\u2019t exaggerated for effect, the principle remains true.\u00a0You don\u2019t want one overzealous person (or a few people) screwing up your open rates.<\/b><\/p>\n If your email provider calculates open rate by dividing total opens by recipients, you\u2019d get the 85% \u2014 because every open counts. You could even see an open rate climb above 100%.<\/p>\n Unique opens divided by recipients is a far more accurate reflection<\/b>\u00a0of how your recipient list responded to your email.<\/p>\n You can find out if your email provider is calculating total or unique opens by sending an email to yourself. Open it multiple times, then check your analytics. Your open rate should stay at zero.<\/p>\n GMass uses the (unique opens)\/(recipients) formula. As you can see in the screenshot below, GMass\u2019s open log is showing you how often someone opens and re-opens an email\u2026 but the open rate only moves based on unique opens.<\/p>\n What\u2019s the \u201ccorrect\u201d formula for open rate? Is it (unique opens)\/(recipients)\u2026 or is it (unique opens)\/(recipients minus bounces)? There are arguments for both ways.<\/p>\n The logic behind removing bounces is a person can\u2019t open your email if it never reached them (because their email address isn\u2019t working). If you eliminate bounces from the denominator, you\u2019re only calculating opens for people who received your email and had a\u00a0chance<\/i>\u00a0to open it.<\/p>\n But\u2026\u00a0removing bounces inflates your open rate<\/b>.<\/p>\n If you\u00a0don\u2019t remove bounces<\/b>\u00a0from the equation, you\u2019re getting a\u00a0purer and more honest calculation<\/b>\u00a0of your open rate. It\u2019s a higher-level look at your campaign: You sent to X people and Y of them opened it. No caveats.<\/p>\n Keeping bounces in the equation also speaks to the quality and health of your recipient list.<\/b>\u00a0Bounces lower deliverability overall \u2014 the more you bounce, the worse your deliverability will be.<\/p>\n Here\u2019s an example of why keeping bounces in the equation leads to more accurate open rates (big picture).<\/p>\n If you send 100 emails, and 15 bounce and 10 are opened, the 10% open rate is more honest than an 11.7% open rate. Your rate shouldn\u2019t be calculated out of 85 sends. Because if you only sent 85 messages (removing the 15 bad emails in advance), you\u2019ll probably get\u00a0more<\/i>\u00a0than 10 opens \u2014 since your deliverability won\u2019t be punished along the way for sending an email that\u2019s bouncing so much.<\/p>\n That\u2019s why we leave bounces in the equation at GMass.<\/b>\u00a0Other platforms, including Mailchimp and Campaign Monitor, remove them and will show higher open rates as a result.<\/p>\n You won\u2019t have to worry about whether or not bounces factor into your open rate if your bounce rate is zero (or negligible). Bounces play a large role in deliverability \u2014 both for an individual campaign and for all of your campaigns in the future.<\/p>\n Take steps to eliminate bounces from your emails where you can:<\/p>\n If someone opens your email just to unsubscribe, should that count as an open? (On a side note for another day, should that count as a click?)<\/p>\n Someone who unsubscribes technically does have to open your email to do so. But\u00a0is that really a valid open worth paying attention to<\/b>?<\/p>\n Not really\u2026 but if your email provider counts it, it inflates your open rate.<\/p>\n You should be able to calculate whether or not your email provider is\u00a0removing unsubscribes<\/a> from your open rate. (Calculate your open rate yourself with (unique opens)\/(recipients), then try (unique opens – unsubscribes)\/(recipients). See which one matches the quoted open rate in your stats.<\/p>\n Either way,\u00a0if you\u2019re running quality campaigns to targeted lists, unsubscribes should be almost negligible<\/b>. (And if you\u2019re running cold email campaigns, there\u2019s a good chance you\u2019re not including an\u00a0unsubscribe link<\/a>\u00a0in your emails.)<\/p>\n It\u2019s common \u2014 and frustrating \u2014 to receive a wave of \u201cout-of-office\u201d replies when you send a cold email campaign. You also may receive automated messages from companies using aggressive spam filters requiring you to jump through hoops before your message goes through.<\/p>\n In both cases,\u00a0it\u2019s possible the bots on the other end \u201copened\u201d your email to send a reply<\/b>. (Or, like we talked about with triggering your own opens, if the autoresponders include the original message in their reply, you might trigger the tracking pixel by opening the notification.)<\/p>\n But\u2026 some email providers will count those as opens.<\/p>\n An even bigger concern: If your email provider counts those as\u00a0replies<\/i>.\u00a0Your reply rate is far more important than your open rate.<\/b>\u00a0Plus, if those messages count as replies, that can cut off your auto follow-up sequence to those contacts. (At GMass, we\u2019re always working on our algorithm to\u00a0avoid counting autoresponders as replies<\/a>.)<\/p>\n You can run tests (as we\u2019ve covered earlier) to manually calculate your open rate to figure out if autoresponders count.<\/p>\n But the bigger issue, again, is whether autoresponders count in your reply rates.<\/p>\n If your email provider does count automatic replies as replies, run a search in your email to find all the autoresponders. (Something like\u00a0 And if they have a list builder feature, you can create a new list from those. (GMass has a\u00a0quick list builder from a Gmail search<\/a>\u00a0\u2014 but you won\u2019t need it in this scenario. Because GMass recognizes autoresponder replies and doesn\u2019t conflate them with real replies.)<\/p>\n Open tracking is reliant on a tracking pixel. But\u2026 what about\u00a0people who have images turned off<\/b>\u00a0in their email client? Or what about\u00a0plain-text emails<\/b>\u00a0you send? In both cases,\u00a0your email provider won\u2019t be able to calculate opens \u2014 because the tracking pixel doesn\u2019t fire<\/b>.<\/p>\n So\u2026 some email providers will use clicks to\u00a0estimate<\/i>\u00a0opens. (Here, for example, is Mailchimp\u2019s\u00a0explanation<\/a>\u00a0for doing so.)<\/p>\n Are estimated opens better and\/or more accurate than un-counted opens?<\/b>\u00a0That\u2019s for you to decide \u2014 and you\u2019ll decide based on whether you like bigger numbers or smaller numbers.<\/p>\n There\u2019s nothing you can do about someone who has turned off images in their email client.<\/p>\n But\u00a0there is something you can do about your tracking pixel and plain-text emails<\/b>.<\/p>\n Rather than plain-text emails, you can\u00a0send rich-text emails that resemble plain-text emails and still have your tracking pixel<\/b>. (\u201cFaux\u201d plain-text means: No text formatting, no images, use full links rather than linking a word.) Rich text is Gmail\u2019s default anyway\u2026 you have to take extra steps to send an email as plain text.<\/p>\n Beyond that fix, though, this entire scenario speaks to a decreased reliance on open tracking.\u00a0No matter what your email provider does in its calculation, perfect accuracy is never guaranteed with open tracking.<\/b><\/p>\n Which takes us to the next debate\u2026<\/p>\n With all of these potential variations in open rate from platform-to-platform,\u00a0is open rate still a worthwhile metric<\/b>? Plus, with Apple Mail Privacy Protection making the somewhat shaky open rate metric even shakier\u2026 does it still have\u00a0any<\/i>\u00a0value?<\/p>\n Hardcore cold emailers would largely say no. Open rate isn\u2019t worth it, especially since it means including a tracking pixel URL in your emails \u2014 which has the potential to cause deliverability issues and spam placement. (Although GMass\u2019s\u00a0unique and sophisticated tracking pixel<\/a>\u00a0can help you avoid that fate.)<\/p>\n That\u2019s why many\u00a0serious cold emailers turn off open and click tracking<\/a>.<\/p>\n We argue\u00a0open rate still has value \u2014 you just need to know how to use it<\/b>.<\/p>\n Can you compare your open rate to other people\u2019s open rates? That\u2019s not ideal. This article has demonstrated the variance in open rates across email platforms (all based on their formulas and open filtering).<\/p>\n Plus, open rates can vary wildly based on industry, deliverability, name recognition, subject line,\u00a0preview text<\/a>, time of day, and list quality.<\/p>\n You may even see a\u00a00% open rate<\/a>\u00a0for a campaign from an error somewhere in the sending process.<\/p>\n Where you\u00a0can<\/i>\u00a0still\u00a0compare open rates is against yourself<\/b>.<\/p>\n Maybe your open rates from the pre-Apple Mail Privacy Protection era are lower than today. But you can still watch trends over the past few months, or from campaign-to-campaign,\u00a0looking for\u00a0relative<\/i>\u00a0changes not\u00a0absolute<\/i>\u00a0changes<\/b>.<\/p>\n In other words, you want to come to conclusions like, \u201cOur open rates are trending up\u201d versus \u201cYear-over-year our open rates have gone from 43% to 57%.\u201d<\/p>\n And if you\u2019re struggling to migrate away from a deep reliance on open rates, here are some techniques and adjustments you can make.<\/p>\n It\u2019s common to use open rate as the metric in an\u00a0A\/B test<\/a>\u00a0on subject lines.<\/p>\n And you still can. But, as we discussed earlier,\u00a0focus less on absolute open rate numbers and more on relative open rates<\/b>.<\/p>\n If you\u2019re A\/B testing two subject lines where variation A gets a 56% open rate and variation B gets a 28% open rate, you know which to choose. And that would be true if the numbers were 8% to 4%, 90% to 45%, or 50% to 25%. Because it\u2019s about the relative proportional difference between the numbers.<\/p>\n But \u2014 you need to\u00a0make sure you\u2019re working with a larger sample size<\/b>. That way you can mitigate some of the open rate pitfalls (e.g., Apple opening emails, your email platform\u2019s potential tracking quirks).<\/p>\n If you\u2019d normally test variation A on 15% of your list, variation B on 15%, then send the winner to the remaining 70%\u2026 think about bumping up those numbers. Try a 25%-25%-50% split to try to get your open rate to regress to the true mean.<\/p>\n To A\/B test in GMass, set up your variations using GMass\u2019s\u00a0simple spintax notation<\/a>.<\/p>\n Then, in the GMass campaign settings, go to the Advanced section and check the box next to A\/B testing.<\/p>\n To do a 25%-25%-50% split, set the test percentage to 50%. Then decided on:<\/p>\n Here\u2019s\u00a0another option for A\/B testing<\/b>\u00a0your subject lines.<\/p>\n While subject lines and opens are closely intertwined, they might not be\u00a0as<\/i>\u00a0tight as you think. One\u00a0study<\/a>\u00a0found two-thirds of email users say who an email is\u00a0from<\/i>\u00a0is more important than the subject line as they decide whether or not to open.<\/p>\n Plus,\u00a0the ultimate action you want a recipient to take in most email campaigns isn\u2019t an open \u2014 it\u2019s a click or reply<\/b>.<\/p>\n So rather than A\/B testing your subject lines on opens, A\/B test your subject lines on clicks or replies<\/b>. After all, those metrics don\u2019t just tell you who\u2019s opening your email \u2014 they show you who\u2019s opening your email\u00a0and<\/i>\u00a0taking your desired action.<\/p>\n Once again, you\u2019ll need to use a larger sample size \u2014 since emails receive fewer clicks and replies than opens.<\/p>\n One way to break your reliance on subject lines (and the open rates they induce)?\u00a0Stop using subject lines on all your campaigns.<\/b><\/p>\n GMass has a unique feature where you can\u00a0send a new email campaign as replies to your last email with a contact<\/a>. (Or to a prior campaign.)<\/p>\n So\u00a0your new campaign won\u2019t have its own subject line \u2014 because it will be a reply in a prior thread<\/b>.<\/p>\n Not only does this minimize the power of the subject line (and its open rate potential), but it\u00a0feels<\/i>\u00a0more like an organic, one-on-one email. And since that genuine personal feeling is a cornerstone of successful cold emailing, sending-as-replies can pay off big.<\/p>\n To send as replies in GMass, head to the settings for a campaign. In the Advanced section, choose to \u201cSend as\u201d Replies. You can choose to send as a reply your last conversation with each recipient or as a reply to a specific previous campaign.<\/p>\n Segmented, behavior-based campaigns<\/a>\u00a0are a popular way to re-engage prospects from prior campaigns.<\/p>\n In GMass, you can\u00a0create new campaigns based on what groups of recipients did or didn\u2019t do in a previous campaign<\/b>\u00a0(e.g., send to everyone who didn\u2019t click, or everyone who opened but didn\u2019t reply).<\/p>\n To work around open rate variance, you can send re-engagement campaigns to\u00a0multiple lists using GMass\u2019s Multi Merge<\/a>.<\/p>\n For instance, if you want to send a behavior-based email to everyone who opened but didn\u2019t reply, you could build lists from the segments \u201copened but didn\u2019t reply\u201d\u00a0and<\/i>\u00a0\u201cdidn\u2019t reply.\u201d<\/p>\n Yes, the latter will bring in some people who didn\u2019t open. But\u00a0they were qualified leads<\/b>, which is why you put them on your list in the first place. So sending them a second campaign isn\u2019t the end of the world.<\/p>\n And GMass will automatically suppress emails to people who unsubscribed or bounced (and won\u2019t send duplicate emails if recipients are on multiple lists).<\/p>\n That way\u00a0you\u2019ll be sure to hit everyone who opened but didn\u2019t reply<\/b>\u00a0\u2014 even though you\u2019re bringing along some non-openers in the process.<\/p>\n Inflated open rates are common<\/b>\u00a0\u2014 if not standard \u2014 in the post-Apple Mail Privacy Protection world.<\/p>\n And\u00a0it\u2019s possible your email provider is inflating your open rates further<\/b>. Some ways they may do so:<\/p>\n At GMass, we aim to provide\u00a0the most accurate possible open rates<\/b>\u00a0for you.<\/p>\n But\u2026 even though we\u2019re not inflating your numbers,\u00a0we expect you\u2019ll still see similar or better open rates with GMass than another provider<\/b>\u00a0\u2014 thanks to a\u00a0boost in deliverability<\/b>.<\/p>\n 99% of GMass users report the highest deliverability they\u2019ve ever had.<\/b>\u00a0(Our massive suite of\u00a0deliverability tools<\/a> like\u00a0free email address verification<\/a> and Spam Solver<\/a>\u00a0are big parts of that success.)<\/p>\n Better deliverability means more inbox placement \u2014 and more inbox placement should mean more opens.<\/p>\n You can\u00a0try GMass for free<\/b> \u2014 use every single GMass feature and send up to 50 emails a day for your free trial. Then when you upgrade to send more emails, GMass has the most affordable pricing in the cold email industry<\/a>.<\/p>\n Get started with GMass today<\/b>\u00a0by downloading the\u00a0GMass Chrome extension<\/a>\u00a0from the Chrome Web Store. And check out our\u00a0GMass Quickstart Guide<\/a>\u00a0to send your first campaign in a matter of minutes.<\/p>\n","protected":false},"excerpt":{"rendered":" Open rates are going through some things right now. Someone should probably reach out, you know, just to say hi. Open rates have always been somewhat iffy metric\u2026<\/p>\n","protected":false},"author":29,"featured_media":21230,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[4267,30],"tags":[],"class_list":["post-21196","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","category-open-rate"],"acf":[],"yoast_head":"\r\nInflated Open Rates: Table of Contents<\/h2>\n
\n
7 Ways Email Platforms Inflate Your Open Rates (and Solutions)<\/h2>\n
1. Letting bots trigger opens (not filtering out based on User Agent)<\/h3>\n
Solution: Check your analytics to spot suspicious bots<\/h4>\n
![\"Check](\"https:\/\/www.gmass.co\/blog\/wp-content\/uploads\/2022\/07\/useragents-example-tinified.png\")
<\/a><\/p>\n2. Letting YOU trigger opens<\/h3>\n
\n
Solution: Tracker Blocker will block your pixel.<\/h4>\n
![\"Tracker](\"https:\/\/www.gmass.co\/blog\/wp-content\/uploads\/2022\/07\/trackerblocker-screenshot-tinified.png\"){kind=tracking}
<\/a><\/p>\n3. Calculating (total opens)\/(recipients) instead of (unique opens)\/(recipients)<\/h3>\n
Solution: Run a test on your open rate<\/h4>\n
![\"GMass](\"https:\/\/www.gmass.co\/blog\/wp-content\/uploads\/2022\/07\/uniqueopens-gmass-tinified.png\")
<\/a><\/p>\n4. Taking out bounces before dividing opens by recipients<\/h3>\n
Solution: Rather than worrying about bounces in your open rate, stop bounces from happening<\/h4>\n
![\"Email](\"https:\/\/www.gmass.co\/blog\/wp-content\/uploads\/2022\/07\/verifyemails-gmass-tinified.png\")
<\/a><\/p>\n\n
5. Counting unsubscribes as opens<\/h3>\n
Solution: Test to see if your email provider is removing unsubscribes from opens<\/h4>\n
6. Counting out-of-office messages and other automated replies as opens<\/h3>\n
Solution: Build and re-send to the list<\/h4>\n
subject:(\"re:\") \"quick question\" AND \"out\"<\/code>\u00a0should catch pretty much all of them.)<\/p>\n7. Estimating opens with clicks<\/h3>\n
Solution: Send faux plain-text emails\u2026 and lower your open rate reliance<\/h4>\n
![\"A](\"https:\/\/www.gmass.co\/blog\/wp-content\/uploads\/2022\/07\/fauxplaintext-example-tinified.png\")
<\/a><\/p>\nHow Relevant Are<\/em> Open Rates Today?<\/h2>\n
How open rate is still relevant \u2014 if you know how to use it right<\/h3>\n
4 Ways to Mitigate the Power of Open Rates<\/h2>\n
Still A\/B test subject lines on opens \u2014 using a larger sample size<\/h3>\n
![\"A\/B](\"https:\/\/www.gmass.co\/blog\/wp-content\/uploads\/2021\/08\/AB-notifier-time-to-choose-a-winner-72kb.png\")
<\/a><\/p>\n\n
![\"A\/B](\"https:\/\/www.gmass.co\/blog\/wp-content\/uploads\/2022\/07\/abtest-example-tinified.png\")
<\/a><\/p>\nA\/B test subject lines on clicks or replies<\/h3>\n
Send new campaigns as replies to prior campaigns<\/h3>\n
![\"Send](\"https:\/\/www.gmass.co\/blog\/wp-content\/uploads\/2022\/07\/sendasreplies-settings-tinified.png\")
<\/a><\/p>\nUse multiple lists for segmented behavior-based campaigns<\/h3>\n
![\"Behavior-based](\"https:\/\/www.gmass.co\/blog\/wp-content\/uploads\/2022\/07\/segmentedcampaign-options-tinified.png\")
<\/a><\/p>\nInflated Open Rates: Conclusions and Takeaways<\/h2>\n
\n