{"id":4543,"date":"2017-08-03T07:36:31","date_gmt":"2017-08-03T14:36:31","guid":{"rendered":"https:\/\/3.14.248.234\/?p=4543"},"modified":"2020-06-26T15:43:27","modified_gmt":"2020-06-26T22:43:27","slug":"magic-linkedin-number-effects-2","status":"publish","type":"post","link":"https:\/\/bamf.com\/magic-linkedin-number-effects-2\/","title":{"rendered":"The Magic LinkedIn Number & It's Effects"},"content":{"rendered":"

Do You Know How to Reverse Engineer LinkedIn for Predictable Virality? Are You Aware of The Magic LinkedIn Number?<\/b> By Houston Golden:\u00a0<\/b>LinkedIn-<\/b> https:\/\/www.linkedin.com\/in\/houstongolden\/<\/span><\/a> Over the last several months, we\u2019ve seen the rise of viral LinkedIn posts.\u00a0<\/span>Millions of views, comments, and website visitors.<\/span><\/p>\n

Are you getting your slice?<\/span><\/i><\/h5>\n

To get ours, we took a scientific approach.\u00a0<\/span>We immersed ourselves in the data.\u00a0<\/span>Rather than relying on hunches, we wanted to know at what point you can consider a post viral.<\/span> How many Likes or Comments does it take?\u00a0<\/span>And in what period?\u00a0<\/span>With enough data, we discovered the magic number \u2013 here\u2019s how:<\/span><\/p>\n

Step 1: Discover a Correlation<\/b><\/h2>\n

We tested whether comments correlate to views. Guess what? We missed the mark.\u00a0<\/span>Then we tested whether Likes correlate to views. Guess what? We hit the bullseye.<\/span> At <\/span>BAMF Media<\/span><\/a>, we already A\/B test our own LinkedIn posts. We know when we\u2019ve hit virality to the point where if posts are not getting strong enough engagement, then we take them down.<\/span> If it\u2019s within the first hour, we still have time to adjust.\u00a0<\/span>Rewrite the first line. Draw in the emotion sooner. Emphasize the pain harder.<\/span> This process is raw. It\u2019s intuition combined with our best practices and relentless obsession for excellent copy.\u00a0<\/span>It\u2019s not a science, but it works.\u00a0<\/span>Here are our results so far.<\/span> The number 142.\u00a0<\/span>That\u2019s how many views a post will get for every Like \u2013 on average \u2013 with a 90.04% confidence.<\/span><\/p>\n

You can see how strong the correlation is in the line graph below:<\/span><\/em><\/h5>\n

\"BAMF<\/p>\n

LinkedIn posts have a limited window. Most only go viral for three days.<\/span><\/em><\/h5>\n

72 hours in three days. 704 likes \/ 72 hours = 9.78 Likes\/Hour.<\/span> Let\u2019s assume that people are asleep 8 hours a day. That leaves us with 16 hours.<\/span> 16 x 3 days = 48 hours.<\/span> 704 likes \/ 48 hours = 14.678<\/span> If you are aiming to get 100,000 post views, make sure that you get 15 Likes in the first hour -minimum.<\/span> Without taking any other velocity engagement metrics into account (which we don\u2019t have the data for yet), you need, at least, 15 likes in the first hour of a post if you want to get 100,000 views.<\/span><\/p>\n

How about 1 million views?<\/span><\/i><\/h5>\n

Assuming people don\u2019t sleep (LinkedIn is a global network):<\/span> 1,000,000 \/ 142 = 7,042 Likes \/ 72 Hours = 97.80 Likes within First Hour.<\/span> Taking into account the <\/span>views per like rate<\/span><\/i> increase as posts gain velocity and LinkedIn\u2019s algorithm picks it up, then this number is around 50 \u2013 60 Likes.<\/span> If you want to get 1 million views, keep A\/B testing your post until you get ~ 50 \u2013 60 likes within the first hour.<\/b> Our views per like ratio is 90% accurate when analyzing posts after they\u2019re live for three days. <\/span> Finding how to weigh likes and engagement more accurately in the early hours is our goal moving forward.<\/span><\/p>\n

2. Create a Predictable Model<\/b><\/h2>\n

If you\u2019re interested in running a linear regression model to find slope and correlation between engagements and post views, then there are many tutorials readily available via Google.<\/span> Here\u2019s an <\/span>inside look at our spreadsheet<\/span><\/a> where the magic happened. (Note: The data set has been reduced for simplicity.)<\/span> INITIAL GOAL:<\/b> Create an accurate estimate of \u201cPost Views\u201d even though it\u2019s not public.<\/span> NEXT GOAL:<\/b> Developing a chrome extension that provides accurate predictions of whether a post will go viral within the first hour based on past engagement stats. <\/span> To do this, we need to account for velocity \u2013 the rate at which LinkedIn increases your reach when your content is \u201cfire.\u201d<\/span><\/p>\n

How to BAMF Your Posts<\/b><\/h2>\n

Even if the magic number of Likes in the first hour is between 50 \u2013 60, it doesn\u2019t mean your post will go viral. You need an excellent copy to attract engagement.<\/span> Ask yourself:<\/span><\/p>\n

What\u2019s your hook?\u00a0<\/span><\/i>What\u2019s the learning lesson?<\/span><\/i><\/h5>\n

If you can transfer enough emotion to the reader, then you\u2019ll get engagement.<\/span> If all you have is a magic number \u2013 that won\u2019t move the needle.<\/span> So before you think you have it all figured out, buckle down, and write excellent copy with an aim to hit the viral benchmark.<\/span> If you\u2019re looking for more LinkedIn growth hacks, stay tuned and get notified of our Product Hunt launch by <\/span>subscribing to our weekly updates here<\/span><\/a>.<\/span> \"BAMF \"BAMF<\/p>\n","protected":false},"excerpt":{"rendered":"

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