Klout Tips from Artificial Intelligence and Machine Learning

 

Most of us who are interested in Klout Scores have seen multiple articles on the topic. And most of those articles offer very similar advice: be certain to connect all your social media accounts, engage more with your followers and fans, and look for others with scores higher than your own.

Unfortunately, that advice can leave you with an unsatisfied feeling.

First, have you ever wondered how the authors of those articles reached those conclusions? Second, do you feel you follow that advice consistently only to watch your score slowly sink?

These questions led me on an 18-month quest to try a different approach- applying theories of artificial intelligence, machine learning and experimental design. While that process continues, the early results indicate that some of that advice may be wrong.

Let’s cut right to the chase. Here are some Klout tips from our upcoming book:

1. Klout has already given you most of what you need to figure out what’s impacting your score- you just need to know where to look (see the table)

Resource Where to find it Implications
Your score breakdown https://klout.com/#/measure This is the obvious place to start, showing your score, your change over time, your Network Contributions, and the Score Impact of recent engagement on different networks.
The official Klout explanation of score calculation https://klout.com/corp/score Unless you hover your mouse over the orange boxes in the “Scored Network” section, you won’t see the list of exact factors Klout uses.I strongly believe that they are not only lists, but roughly in priority order: the earliest factors in the list have larger impact than the factors later in the list.Hence, what are the only two actions you can perform here?  Easy- you can follow new users or create content to share in hopes of driving engagement
Klout blog, specifically regarding Klout Scores http://blog.klout.com/category/understanding-the-klout-score/ This link will bring back blog stories from Klout solely focusing on the understanding the score.
Hints from the Klout “Create” tab https://klout.com/#/create While no longer apparent, Klout was pretty blunt in the past launch of “Create” that the two most important drivers of your score was your number of followers and the action you drive with your posts.

 

2. Klout’s original premise of Reach, Authority and Network may not be as dead as you think.

3. Engagement from those with very high Klout Scores has a ridiculously strong impact.

4. You need to manage your Network Contribution scores on Klout so that your strongest service gets the highest percentage.

5. Retweeting a trending tweet actually helps your score in addition to the original sender.

6. Send new tweets as responses to your most powerful existing tweet, not as a standalone new tweet.

7. Make yourself your own CEO on LinkedIn- Klout used to actually weigh your title in determining your Klout Score!

8. Stop checking in on Foursquare/Swarm- leave tips or leave the service.

9. Re-share your highest-performing Facebook content.

10. Like it or not, your number of Twitter followers has a HUGE impact on your score.

11. Build an action plan according to the following tool, built directly from Klout’s web page.

12. You can leverage Klout’s content sharing function to significantly boost your reach.

13. Drop your number of outgoing tweets significantly unless you consistently get 5 “green dots” on your tweets.

 

Here is how and why these tips will help you:

Sometimes Klout has been pretty explicit about what drives your score, and other times much more mercurial. Ironically, most of the best information has been staring you in the face for years, but unless you moved your mouse pointer over the right areas, it may have gone right past you.

And remember, Klout was even more direct in the past. While I’m certain that they’ve changed things with score updates, the original three pillars do NOT seem to have changed much.

10668305 10153150204379307 1633381549 n 300x300 Klout Tips from Artificial Intelligence and Machine Learning

 

Also co-authored by Miriam Slozberg

and Carly Alyssa Thorne

 

 

Stay tuned for the next article to find out more about what was discovered.

Read more at: Miriam Slozberg

Read more at: Linked Local Network » Miriam Slozberg

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