How Break.com broke the one million fan barrier

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On January 19, Break.com crossed a million fans. Two years ago, they only had 193,000 fans. What is behind this nearly five-fold increase? How have they adapted to the many Facebook algorithmic changes? How does paid media most effectively counter decreases in organic newsfeed reach?

Chris Strickland of Defy Media is the social guru behind Break.com. He was courteous enough to give us a peek at the numbers. You can read our interview here.

We know that engagement is more important than just raw fan count. Break.com has been averaging 97,000 for their daily PTAT (People Talking About This), also known as storytellers. They hit 1.8 million active users when they reached a million fans.

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This is a 612% increase if we look at daily storytellers (people who like, share, comment, or check-in). What was behind this increase?

IS THE CULPRIT ADS?

A lot of folks pay to play for engagement. Break.com ran only half a million paid impressions in January. There was no correlation between engagement rate and paid efforts since the impressions represented only 0.28% of total impressions.

If it wasn’t ads, then it must be organic engagement.

THE ENGAGEMENT FUNNEL BROKEN DOWN

We know that there is a direct linkage between fans and storytellers, the model works like this: Fans x News Feed Reach x Engagement Rate x Percent of Storytellers x stories per storyteller = stories.

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The spike in PTAT was on the 19th, evidence by the green bars in the diagram above for News Feed reach. Of course, an active user base is predicated by a higher newsfeed reach often because of additional reach driven by actions that fans take.

In other words, Facebook allocates every page an initial amount of newsfeed exposure when the page first posts, but then, as fans interact if the engagement rate is high enough, the additional reach can be an order of magnitude more than the initial reach.

For the data geeks out there we simply downloaded the page and post level .CSV insights files. Then we calculated each of these ratios above and put conditional formatting on the cells to visually draw out trends. Send me an email if you are interested in getting a copy of the Excel workbook.

Facebook has been reporting this additional reach (viral) as part of your organic reach in the web-based insights. However, they do separate viral reach in the .CSV download and the API.

Let’s take a look.

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We overlaid viral reach against daily storytellers and find they match nearly perfectly. In fact, the correlation is 0.98. Our data scientist friends know that 1.0 is a perfect data correlation.

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Here are the raw numbers. At their peak, Break.com had a whopping viral reach of over 14.6 million. On that day, they drove almost 697K stories via 598K people. So, the average storyteller told 1.17 stories on that day.

A SURPRISING DISCOVERY

We plotted stories per storyteller against viral reach and found a -0.66 correlation. In other words, the fewer stories per storyteller, the greater the viral reach.

This seems highly counter-intuitive, but what is really going on is that when content spreads beyond the core fan base, we are activating less passionate, more infrequent fans. And it’s the weighted average of these fair weather fans that pulls down stories per storyteller.

BUT HOW ABOUT CONTENT EFFECTIVENESS?

The median average of their News Feed reach is 1.39 times their fan base. But on their million fan celebration day, they hit 14.9, which is 11 times higher.

Was it an amazing piece of content or perhaps an increase in post frequency?

Below we plotted posts per day against viral reach. There is a clear relationship between these two factors evidenced by a correlation of 0.38.