If you are trying to ramp up your email marketing this year, like so many other companies are, you should be clear on what the most common marketing metrics refer to. Email marketing is different than, say, using social media to broadcast a message. It can be much more personalized and is often seen as a more direct line of communication with the consumer. However, there is also the added burden of knowing that people can just as easily delete the message without reading as they can click to open it. Even the action of opening is misconstrued when looking at the most common email marketing metrics. The open rate doesn’t imply as much as many people want it to. For instance, if you sent the same email to 10,000 people and wanted to determine how many read it, you might be tempted to believe that the 30% open rate means that 3,000 people read what you had to say. Unfortunately, that is very likely not the case.
Consider how you read your email. If you are like many others, you might open one message and then use the navigation arrows to scroll from one to the next to the next. By all definitions, you have opened each of those emails that you scrolled through, but chances are you didn’t really read them.
There has been a great deal of research done that supports this example. According to numerous reports, as many as fifty percent of all ‘opened’ emails are viewed for less than two seconds. How much reading can really be done in two seconds? (Hint: The average reading speed is just 300 words per minute, or approximately 5 words per second.)
You can’t rely on a single metric when determining the worth of your emails. You can’t look at open rate alone. You have to look at the click through rates, the conversion rates, and even your bounce rate, for instance, if you want a clear picture of how worthwhile your campaign is.
Then, you must figure out which variables – subject lines, links, content, etc – are working and which aren’t. This is why many smart marketers will try multi-vate testing, creating many different combinations of those variables to determine which performs best, across an array of metrics.