8 valuable tips to improve tracking in analytics

Posted on Monday, 27. November 2017 in category Google Analytics. 6 min read • Written by

Karmen Krumpestar

Let me tell you a little secret. Our Boštjan always says: “When we have activities with a high ROI and good results, everything is just fine. When things are not looking good, we joke that it must be analytics’ fault.” #agencylife

And yes, sometimes analytics is broken. This is a situation that is usually the simplest to fix. Sometimes it is simply difficult to explain data or a situation, because there are so many different factors that influence a certain activity. Sometimes there are problems with analytics and we don’t even know it. To avoid such a situation, take a look at this list of the most frequent deficiencies that are too often overlooked or we just don’t know how to organise.

1) The bounce rate is (too) low.

When the bounce rate is really low, it is often because something is wrong. Of course, there is always a possibility that only relevant users visit your website who view several pages, so the bounce rate is correct. But there are very few such websites and your website is probably not one of them.

So how do we check if the bounce rate is measured correctly? First check if (directly on the website or through Google Tag Manager) the Google Analytics code was installed only once and is activated only once when the website is viewed. Then check if any other pop-up or chat window opens up when the landing page is loading that sends every viewing of a new website to analytics. All such add-ons must be marked as non-interaction events.

2) Measuring offline campaigns… is impossible?

Not true. Maybe it isn’t possible to measure everything in full, but at least some data can be obtained. How? Prepare a special landing page for an offline campaign which is accessible only if the user types its address directly in the URL address bar. The address should not be complicated and long, otherwise nobody will do this. That is how you can be sure that those users who visited this website did so only because of the offline campaign. We could go into further detail and prepare a separate landing page for each offline channel and there would probably not be enough visits to make an analysis or identify patterns, yet we would get very accurate information about how many people from individual offline channels can be prompted to visit the website. 🙂

3) Precise UTM tagging

Companies often brag about setting up UTM tags for their activities on social channels. Often only one UTM tag is used for the entire campaign and so the information on all versions of an ad are gathered in one place. Advertising interfaces usually contain some data about the success of individual ad versions, which can be great, but it is also impractical as we don’t know what happened to that user when they landed on our website. If all ads have the same UTM tag, you can only see, for example, that 1,000 visitors have made 20 purchases. If every ad has its own UMT tag, you can see that, for example, one ad within the sales campaign has received 100 clicks and 10 visitors made a purchase. A second ad version has received 900 clicks and 10 visitors purchased an item. So which ad version is better?

Yes, this can take a bit more of your time, as you have to mark all ads correctly, but think about how much money could be saved and/or how much better ROI you could achieve, if you could already figure out during the campaign which ad was not doing well and pause it.

 4) Clicks in Google AdWords do not match the information on sessions in analytics.

It’s true. Discrepancy, lower than 20%, is still acceptable according to Google. When it is higher, the AdWords system notifies you with a message “Clicks and Sessions Discrepancy”. You can always check by yourself if you have really done everything to ensure data congruence. Check if all landing pages, to which AdWord ads lead, have a correct and functioning Google Analytics code. You’d be surprised how many times this is the reason for the discrepancy! Check the settings in the AdWords system so that automatic tagging of AdWords activities is enabled. Check the loading time of your website too. Websites that load slowly often have click and session discrepancies, as many people, after clicking the ad, leave the website before it is completely loaded.

5) Completed AdWords campaigns still record traffic in analytics.

This situation occurs if AdWords campaigns enable the setting “auto-tagging” and the user saves or sends to another user the URL which still has all these parameters stored. A click on such a link can record a session and add it to the campaign even after the advertising has ended. Similar situations occur whena user clicks the link in the e-news that you were sending around a long time ago and visits the website. But in such case, noone asks how this is possible. No worries, in both cases the click on the link won’t cost you any money.

6) Facebook statistics shows higher numbers than the analytics.

It’s true. Discrepancies can be very high and they occur for many reasons. Facebook categorises more actions as a “click” than only a click from an ad that leads to the website. People click the link two or even more times. Facebook records all clicks, even if only one session eventually occurred on the website. It often happens (see item 3) that the website’s load time is long and there is no session at all. Even if everything is correctly installed and set, in most cases the discrepancy in data ranges from 30% to 50%, whereby the Facebook interface figures are always “better”. Rather than correcting the analytics, one should accept the fact that Facebook and Google have different measuring methods and that the number of clicks never equals the number of sessions.

 7) The same landing page is shown several times in analytics.

Have you ever noticed different data for the same landing page in analytics, only that sometimes they are written in lowercase and sometimes in uppercase? Analytics considers them as two completely separate landing pages, even if it is really only one page. Even if you have all sessions in the analytics, they are always divided incorrectly between the fictitious landing pages. This error can be resolved with an appropriate filter where the setting is that all addresses are written with small letters. This ensures that all data for one landing page are gathered in one place.

8) Analytics shows a lot of direct traffic. 

Direct users are considered to be those who access the website by directly entering the address in the URL address bar. Those who had the address saved among favourites and only clicked it are also included. Yet, the reality is somewhat different. This channel also includes visits from applications and social networks, the cases when the browser is unable to identify the referrer, traffic from Outlook, traffic from unmarked/incorrectly marked campaigns, traffic from websites without Javascript as well as traffic from offline PDF, Word and other documents. Last but not least, a large quantity of direct traffic can also be generated by the company itself, if the filters are not set properly to exclude visits by employees based on IP address. While in other channels traffic is very accurately segmented, direct traffic actually includes a multitude of sources that the browser is unable to identify.

What can you do? Make sure that all digital activities are correctly marked. Wherever possible, use the UTM tags or reroute part of the traffic that could be difficult to identify, to specific subpages which are accessible only via the forwarded URL address. Set the filter to exclude traffic from internal IP addresses, possibly also the IP addresses of business partners if this traffic negatively affects the display of data in analytics (e.g. traffic of the software developer or the media agency).

How certain are you that there are no such problems in your analytics? If your answer is not at least 110% sure, you should check everything mentioned above and improve it. We point the finger too easily at those who take decisions based on incorrect data. But we forget that we do exactly the same if our analytics is not well set up. Don’t let bad data lead you to wrong business decisions.

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