InsightaaS: Moz is an interesting combination of SEO initiatives, including tools, instruction materials and an active community, built around the "TAGFEE (transparent, authentic, generous, fun, empathetic and exceptional) code." The post featured today shows how this kind of approach (both business and cultural) can help companies navigate the complexities of the online business world. The piece is authored by Peter Bray, who founded Follwerwonk (purchased by Moz in 2012), and is currently Moz's VP of social strategy. In it, Bray looks at data for 800,000 Twitter "days" (collected across 4,000 Followerwonk users) to identify factors that impact follower gains and losses. Working with the data in Excel, he uses a variety of statistical techniques to quantify the impact of hashtags, URLs, favorites, and even weekends.
The post works on two levels. For the statistically-inclined, it provides an easy-to-follow description of how to use Excel to extract correlations and linear regressions from a data set. For those interested in how to optimize Twitter activity - likely, everyone reading the blog! - the post provides intriguing insight: images and hashtags result in a 2% increase in new followers, retweets (by you, rather than of your content) drive 4% increases, and engagement with other users results in a 6% uplift; URL links and weekends have a negative impact; great content and RTs/favorites of your material are a sound basis for long-term growth. These findings make intuitive sense - but as social success becomes more important to business success, there is a real need for means of moving beyond intuition to fact-based insight.
What factors go into determining how many Twitter followers you gain (and lose) each day?
I was driven in part by Rand Fishkin's recent "mad scientist" experimentation that he touched on at MozCon. There, he noted that his tweets with images resulted in significant follower losses.
Do they? And what other behaviors result in more (or fewer) followers?
I've found some interesting gems.
Of course, it's worth noting that aggregate, general trends don't necessarily speak to your specific situation. In fact, as you'll see, they're often exactly the opposite! To that end, I want you to play along at home...
I created a day-by-day summary of new and lost followers. My data set included roughly 800,000 "days" for over 4,000 users, and requiring analysis of millions of tweets.
The result was a large spreadsheet with a lot of content metrics.
For example, I determined the # of tweets with images, those with URLs, those that are "broadcasting" vs those that are @mentioning someone, and so on.
I did this because my hypothesis is that follower growth (and loss) is significantly impacted by the content that one tweets...