Are You Excited That You Are Boosting Your Own Web Analytics

There is one person who will repetitively visit your website, you.  When consultants, trainers, or professional coaches decide to make online marketing a priority, they often begin checking in on the site to see changes or to inspire ideas.  While that level of engagement is great, it can cause false results on the analytic reports.

The last post was about critically analyzing web analytics and a good example recently came up.  A client was running three separate campaigns to boost site traffic.  The push was set up because they were testing three separate offers to see which call to action created the best conversion. At the end of two weeks the client called and said, “We’ve seen a boost in traffic of 30% this month (roughly 300 more visitors).” While a jump was expected such a marked change so quickly seemed excessive.

So we pulled up the analytics and began reviewing where the traffic came from.  As it turned out, some of the increased traffic was legitimate but about half of it was self-created.  This client had five trainers that were all being asked to provide their input on the calls to action.  As suggestions came in, the trainers would go back to the calls to action pages to review revisions.  Doing this several times resulted in the group creating a false 150 hits.

So why is this important?  The significance of the hits has two primary effects.  The first is that we never want to create false data that guides our decision making.  The campaign did have a good start but it was about a 15% increase.  Making a decision on the calls to action or traffic generating campaigns would not have had true tested data. The second effect is it can skew trends.  At the end of the second two weeks, the increase was just under 25%.  Had we not reviewed the hits it would have appeared as if we had peaked quickly and were now regressing, when in reality we were continuing to see gradual improvement.

For this particular example, the resolution was to implement filters for the IP addresses of the firm’s computers.  But as an illustrative example, it’s a reaffirmation of keeping a close eye on analytics and questioning results that seem overly positive or negative.