This is part 3 of my blow by blow account of Link Love London 2012, featuring a full run down of each presentation.
Social Media and Links : The Love Story (with numbers)
Branko Rihtman : SEO Scientist @neyne
After the corporate polish of Rand and the laid back charisma of Mike King, Branko’s presentation brought a change of pace. It seemed that playtime was over and the maths lesson had begun. Branko applies scientific principals to SEO. You only need to look at the mass of charts and tables he includes in every blog post to know that he isn’t just working on “hunch”. Mid-morning brain food – good stuff.
Branko’s presentation definitely got more technical that the earlier ones, so I found myself thinking more and taking notes less. For that reason I’d definitely suggest following along with his slidedeck, which you can find here.
Branko’s tl;dr version of Scientific Principals
- Don’t BS yourself (or others)
- Truth above profit
- Stay Curious
- There are lots of opinions about social signals and how much we should be concerned about them
- Still searching for evidence of anything ranking based on social signals alone
- Branko looked at what Pinterest pins rank in Google organic search for evidence of content ranking without links. Everything
Approach to Social Media in Link Building
- Usual thinking is “getting some people to see it results in a link – therefore, getting lots of people to see it will lead to lots of links”
- This brute force approach probably isn’t the smartest way to work
- We need to employ “The Minesweeper Effect” – where one result snowballs in to many
Focus on those who already share your content
- What do they links to?
- Who is linking to more than 1 piece of relevant content? Those are important to your niche
- Which content is being shared by > 1 person? There is your content inspiration
How to get the information
- Use APIs – doesn’t mean that you need to code
- Combine these with Excel & SEO Tools for Excel
Calculating “Effectiveness” of shares
- By scraping content shared from a domain, then looking at what links and shares that content got you can calculate the rate at which links increase as a function of tweets/shares/+1s
- In Branko’s examples (this page and this page) the +1s out performed tweets and shares +1s
Identify Power Users
- The example started with 2 URLs, which gave 214 Twitter Accounts that between them shared 12,000 URLs on 3,700 domains.
- [MB: at this point I was slightly distracted by seeing my name on screen. So there – I’m a power user. You should follow me @matbennett now!]
- Prune out selfish users – ie those sho only share a small number of domains
- ID Power users – ie those tweeting more/most niche content
- Measure “efficiency” of user – ie who tweets stuff that gets links
- ID niche relevant users (also see Seer Interactive on Using twitter & Backlinks to Build links)
How to use this
- Content generation ideas
- Trend spotting
- Finding social media targets
Branko had me struggling to remember maths lessons from my school days in a couple of spots, but the principals he was using were really solid. The idea of being able to quickly ID the most influential social media users in a niche is appealing. Even more so when you are measuring “most influential” as being those that seem to drive link building. I don’t know whether it is Branko’s intential to turn the methodology in to a public facing tool or not, but I wouldn’t be surprised to see it happen.
Next up was Jane Copland on Getting Golden Links.