Key analytics issues for your website
Website analytics are your key for gauging how effective your website is at converting traffic into sales. By reading your analysis accurately you can strengthen your webpage, improve SEO, and maximize profits. But there are issues that may arise by misinterpreting the data being delivered by your analysis or responding to that data incorrectly. Here are some common analytics misconceptions and issues that may arise as well as how to deal with them.
Meaning of dwell time
One of the biggest ways analytics misinterpretations arise is not understanding the complete reason why statistics sway in favor of certain things. A good example of this is dwell time.
Dwell time is the amount of time a user spends on a webpage. Several general marketing studies have shown that websites with higher dwell times convert a larger amount of traffic into sales. By examining your own data you may find similar results. People who spend more time on a website usually prove more likely to purchase something. However keep in mind, the customer did not necessarily buy because they were on your site for a long time, instead it is more likely that they were on the site for a long time because they were already interested in buying. As a result, responding to this finding by trying to increase the time spent on the site up is not an appropriate response to this data, be aware of the issues that may arise by thinking this way about dwell time.
Treating dwell time in this way would be the same as advertising with only non-clickable banners to force people to physically type your web address in a browser to get to it just because you notice that direct traffic yields better quality traffic. It’s important to understand why the direct traffic is better quality. These visits are people who have already been to the website and know what they are looking for, and once again, already want to buy.
Cookie tracking methods have a set time limit. If a customer clicks a pay per click ad, browses your website for a bit, leaves, and comes back to buy after the cookie tracker time limit has expired, this will skew your conversion tracking results. The original visit will appear as a visit that did not result in a conversion, when technically, because the two separate visits were the same customer, they should count as one visit to one conversion. It may skew the data of how effective your pay per click ad (or other methods) are actually doing as well.
This means that you should not judge the effectiveness of your online ads or other pieces of your sales funnel solely on conversion tracking. Consider other possibilities and scenarios while evaluating the true ROI.
Bounce & Exit rate
There is a difference between bounce rate and exit rate. Bounce rate is when a user clicks on a link to a page on your website and immediately leaves. Exit rate is when a user clicks on a link to your website, visits another page, and then leaves. Exit rate is anytime a user leaves the website after visiting two or more of it’s pages. Bounce rate is when a user leaves after viewing the very first page they visited.
There are four scenarios where a bounce rate can take place: A user clicks the back button, closes the browser, types a new URL, does nothing but lets the page sit (each session times out after 30 minutes usually).
There are two issues that may skew your results.
The first one takes place when a user clicks on an external link on your page. This isn’t necessarily a bad thing if the external link is an ad which you have put on your webpage to generate side income.
But a bounce may also be counted when a user clicks on a page that redirects them to an account login that requires secure authentication, like logging into paypal to make a payment.
To be able to identify the difference try tracking outbound links. If you are using Google Analytics you’ll have a choice to have an outbound link that is tracked as event be toggled as an interaction or non-interaction event. This will also help you determine the difference.