Predicting Content Success With Twitter Analytics & “Favorited Rate”

UPDATE: Getting access to Twitter Analytics seems to be unclear in some cases. I posted this for anyone needing info on how to access it (please let me know if you still have trouble).

If you already have access to Organic Tweet Analytics, read on for one way to come up with content ideas…

Do you ever feel like some people “just get” content ideation? They’re creative. They know exactly what to write about and what their audience wants to hear?

Then some of us are like this kitten trying to play with a fake porcelain cat;

We try really hard, keep doing the same thing over and over, are clueless to what’s working and what’s not – but we still look pretty cute doing it 🙂

“Great” Content Creators Listen To Their Audience.

What separates most of those gifted creators? They listen to their audience.

For Twitter, it’s probably safe to assume that tweets that people have favorite would make for good article, blog posts, tools, ideas – because your  audience has told you they like the idea with their engagement. At the least, the contents of those tweets for some reason got their attention.

Fortunately for us, Twitter released a free and pretty robust analytics platform (that no one noticed).

The trouble is – if you simply just look at the raw numbers – # of favorites, clicks etc – impressions can skew the perception of popularity. For example, (obviously) when someone like Rand Fishkin retweets me – that Tweet is going to get more favorites because of more impressions. But this does not mean it had the highest percentage of impressions to favorites.

Abandoned Tweets

The other problem is – sometimes we tweet GREAT stuff. And then forget about it. I do this ALL of the time.

Don’t let your forgotten tweets rot away like this abandoned car.

And our old tweets end up like this car abandoned in the woods – lost and forgotten.

This post will show you a simple method for calculating “favorite rate” and then using it to find content ideas.

Let’s go!

Step One – Login & Export

First – login to Twitter Analytics at https://analytics.twitter.com. Then export your data by clicking the export button on the top right hand side;

Download and open the spreadsheet.

Step Two – Prep The Spreadsheet

Let’s say we want to get a simple favorites rate. Here’s the way I did it (and believe me, I’m no excel wizard – I’m sure there’s fancier ways!)

You will be presented with a big wall of Twitter data. Yeah not that helpful on it’s own;

Make a new sheet where we are going to put only the metrics we care about at the moment;

Copy and paste in only the data we need to calculate favorite rate – which in this case would be twitter URL, tweet text, impressions and favorites;

Now we have the data we need in one simple sheet, and we’re ready to get the favorited rate.

Step Three – Calculate Favorite Rate

As noted at the beginning – we could be doing this for clicks, or emails too. But to get the rate, it’s just simply adding a new column and getting the percentage;

It’s not rocket science;

• just divide favorites by impressions
• set cell type to percentage

Step Four – Filter & Sort

Next, sort your spreadsheet descending by favorite rate;

You may notice that because Twitter does not have all past impression data, there’s some missing information at the top. So we’re going to set some filters;

• Filter out any cells that say “#DIV/0!”
• Filter out favorites less than 1-5 (You’ll have to play with this. I found that sometimes a tweet would get 1 favorite but such a low amount skewed the percentage;

Step Five – The Fun Part – Look For Ideas!

I’m looking for topic ideas (that I’ve probably forgotten about) that show promise for future posts, articles, speaking topics etc. here’s what I found;

I found this tweet from almost two months ago (I’d honestly forgotten about the tweet);

Does your content provide utility? 1 Zoom out 6 months in GA 2 Apply 4+ sessions segment http://t.co/Am7PZneG2S 3 Look for low performers

— Dan Shure (@dan_shure) July 9, 2014

 

I found a fleeting opinion I shared one day, but apparently it resonates with others;

SEO done wrong is worse than no SEO at all. — Dan Shure (@dan_shure) March 27, 2014

This would not only make for an interest article – but it could even be the title.

Lastly – A Confession

I also have a confession: I had to show GREAT restraint writing this post. I have more ideas for how to use the new Twitter Analytics than I know what to do with. I spent more hours than I care to admit, drafting a “how to use Twitter Analytics” post. Three times.

I never hit publish.

But I’m so excited about the possibilities of what can be done with Twitter Analytics, I wanted to share at least one tip out of the many screenshots I have collecting dust on my hard drive.

Let me know if YOU have any ways you’ve been using Twitter Analytics. OR if you want more! I’d love to share some more of my ideas with you if you’re interested!