When we A/B tested a blog post on body language, we split it between two variations:

·        Version A had a giveaway at the end

·        Version B did not

The result? Version A doubled the amount of shares. In other words, instead of 1,100 shares, we got 2,200. Here is the simple CTA we used at the end for version A:

We kept running these tests and seeing the same results — insert a giveaway, 2x the shares on the article. The compounding effect on traffic for this experiment was monumental for us.

2. Twitter was our “most valuable” share source

Just because you’re getting more shares, doesn’t mean you’re getting more traffic.

For example, after someone (let’s call him John) Tweeted our article, the goal was for someone else to click the link that John Tweeted. If someone new did NOT click that link, someone new did NOT visit our blog. If that happens, John’s Tweet isn’t delivering any return, because we’re getting zero new visitors to our content.

Thus, the more people that click a link after it’s shared, more “valuable” the share becomes.

By using a tool called Filament, we could automatically calculate our most “valuable” social media source through this simple equation:

Total Pageviews Per Network
 ——————
 Total # of Shares Per Network

3. Influencer sharing articles results in significant shares spike

We will publish an article about a known food blogger. Get an influencer to share your article and you’ll get more shares.