I’m glad to be back from vacation after a week completely off the grid. Talk about a drastic change in lifestyle, going to a place where devices don’t even work (thus removing the temptation to “just check in”). I recommend it heartily.

Before I left for vacation, I thought I’d run an experiment using reruns on social media to power my social media postings for the week. Instead of my normal routine of a new blog post each day plus a welcome message (2 links back to my website per day), I went to five reruns plus a welcome message (6 links back to my website per day). Each rerun was a link back to a past popular post of mine from the past two years.

Now, going into this, the logical hypothesis would be a 300% increase in website traffic, right? I literally tripled the number of direct links back to my website. In fact, it should be even more, because my audience has changed and grown in a year. Last year on Twitter alone, I had 7,000 fewer followers:

Followers_-_Twitter_Ads

So with an audience that’s bigger and triple the number of links, let’s see what the results were:

All_Traffic_-_Google_Analytics

Cue the womp womp trumpet, please. Yes, folks, you read that correctly. I had 43% LESS traffic this year compared to the same calendar week the previous year. The traffic source that drove the loss? Organic search traffic, where I had half the visitors from last year.

It’s been shouted far and wide that Google loves relevance, freshness, and diversity of content. Re-runs with no new content paint a bulls-eye on your butt for freshness and diversity, and in the world of the content shock, someone will always be creating more relevant content today than content you made a year or two ago.

The bottom line? Re-runs didn’t work for me in this particular test case. My site took a beating on organic search traffic by my taking my foot off the gas for a week. Does this mean re-runs won’t work for you? Of course not – as always, you need to test for yourself. However, go into that test with a modified hypothesis, now that you’ve seen at least one test case where the result fell far short of the hypothesis.


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