Just about every marketer understands the difference between delivery rate, open rate, click through rate, and conversion rate. We know on the surface that each of these have to do with a particular step in the overall email marketing process, but the details that go into how each of those are specifically calculated across platforms can be overlooked – or worse – misinterpreted.
Within the competitive landscape of email marketing, many platforms tout their clients’ open and click through rates as a way to attract business. The suggestion behind such claims is that superior platform architecture yields superior results. There are some infrastructure configuration differences that may lead to putting their clients in better positions of success. However, not all platforms calculate metrics the same way, and all too often flashy stats have less to do with platform deliverability and often more to do with a different approach to a particular metric of interest.
An example:
The simplest example of these differences is in the click through rate.
Some platforms, like Mailchimp, report these metrics in a percentage of the number of clicks per the number of total emails sent:
(3 contacts who clicked in an email / 100 total emails sent) x 100 = 3% Click Through Rate
Whereas others, Klaviyo and Net-Results for example, report click through rate as a percentage of emails opened:
(3 contacts who clicked in an email / 25 contacts who opened) x 100 = 12% Click Through Rate
On the surface, it’s clear there’s a massive difference in how you could perceive the success or failure of your email marketing efforts based on these different calculations. It’s also clear that to the untrained eye moving from one platform to another that these differences could end up placing blame on the perceived outcomes on the operators or platforms if they’re not accounted for and understood properly.
And that’s not to say that either of these approaches to calculating click through rate are right or wrong. They’re just different, and there are merits to both approaches depending on the level and perspective you look at the data from.
At MHDG, we prefer to calculate click through rate as a percentage of opens, as it helps make a clear distinction between the subject line and audience fit that is captured in open rate, versus that actual content and calls to action within the emails that determine more of the click through rate.
However, if you’re looking at the overall amount of traffic being driven to a site or product page as your KPI, the click through rate relative to the overall audience size may give you a bigger understanding of how those email efforts can scale as you increase the audience size.
The point is to know where your CTR comes from and make the right business decision.
Other commonly tracked metrics that can be misunderstood
While click through rate may be the most obvious example in highlighting the distinction between the ways different platforms calculate their reporting metrics, its not the only one.
Some platforms track opens and clicks as simply ‘the number of unique contacts who opened or clicked a link in an email’ whereas others report the raw numbers of opens and clicks. Obviously the latter can inflate statistics significantly. In addition, many antivirus tools used by B2B email clients will often trigger opens and clicks as part of the virus check, and if not paid attention to can make you interpret that you’re getting far more engagement than you actually are.
We’ve also seen significant differences from one platform to another when it comes to how open rates are reported. In some cases, its reported as a percentage of the number of unique opens per total emails sent, in others, its based on unique opens per emails delivered. For outbound email in particular, the delivery rate may be lower than the total sends due to varieties of bounces or other delivery issues. Open rates also don’t lend much insight into how many emails are delivered into inbox/promotions rather than spam (a topic we’ll dive into more deeply in a future blog post).
Why understanding these differences matters
To highlight why these differences matter, we’ll use a real example from one of our clients, who use Interseller for their outbound email efforts. When we got on board with them, their reported open rates from these emails were anywhere from 25-35%, which is huge for outbound email.
It drove their strategy to double down their outbound efforts, pushed their strategy within to jump right to a sales pitch, and also increased the amount of emails sent in those outbound sequences from 3-5 up to 10.
We dug into the statistics on these sequences, and ultimately found that they were reporting their opens as ‘percentage viewed’ and that the numbers weren’t making sense given our understanding of open rate. Further digging revealed what was happening.
Ultimately, Interseller was reporting the total number of unique opens across the entire campaign (of multiple steps of email sends) per the number of total contacts in the entire campaign – not the total number of unique emails sent across the entire campaign.
This meant that if 100 contacts were sent a series of 3 emails, and 30 total opens (10 per sequence), that Interseller was reporting the ‘percentage viewed’ as 30%, even though the open rate for each email in the sequences was 10%.
With this information, we were able to show the client that we needed to reconsider both our number of emails, our strategy, and audience segmentation knowing that the actual open rate per email was much lower than they initially understood, and we’ve subsequently worked with them to successfully
- Build a custom reporting dashboard to better interpret and take action on their outbound efforts
- Re-tool the outbound strategy and content within, knowing that a series of 10 hard sell emails wasn’t working as well as they had perceived given the platform reporting.
Its critical to know how to accurately calculate and interpret your metrics before taking action on them. In a subsequent blog post, we go deeper into the dangers of misinterpreting metrics and why chasing them down without putting them into context can lead to problems and wasted marketing efforts.
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