Why Kred is better than Klout in my opinion.

One simple word, transparency!

When I go to Kred I get a first class user interface that shows all of my “shares” and the interactions they received – this is hands down the best feature I love about the site. Klout attempted this with “Your Moments” but its flaky and never seems up to date.

The second best feature is the fact I can see how my Kred score is calculated on my points page. I can learn what gets more points and for what reasons. Once again, I share and share and share and see what value the different shares bring by what interactions have taken place on them.

Measuring Influence – My Algorithm

Over the weekend I had some great dialog with some Twitter folks (@peoplefw, @TedRubin)  about the value of Klout, Kred, etc. Spawning from my previous post about “Rewarding Influential Customers” the discussion quickly became one of the value of such applications and are they really accurate. I spent some time thinking about an algorithm that “could” suffice for establishing true influence.

In my simple mind the concept of sharing should not even be in the equation of an influence score. Anyone can “share” something. Real influence is how many people read it, “liked it”, commented on it, and re-shared it. And that’s where I think the focus should be.

Influence also changes over time, so having the algorithm time boxed is key. You may be influential today but not tomorrow. So from the aspect of “sharing” you must at least share something to get an influence score – so “sharing” does have an impact.  What won’t have an impact is the number of times you share.

The points would be calculated in a tree model based on likes, shares, and comments. Let’s keep it simple and have a similar scale as Klout, 0-100 points total. The points would only really be calculated on the “best” share you did. So if you had a share that was re-tweeted 200 and another re-tweeted 2 times you get credit for only the most shared content and you don’t “add” the two together.

The same scoring could be used for Facebook, LinkedIn, Google+, Pinterest, Instagram, etc. As you can see you would have to have thousands, if not millions of shares to achieve a score of 100. Most of us, people like me, would be happy with a score of 10.

My scoring system would be based on a point system for different periods, 30, 60, 90 day periods. In the graphic below you can see this persons influence has diminished in the past 90 days. Could be lack of sharing or lack of sustained influence.

The problem with this scoring system is the system would need access into a persons account and all of their children accounts and their children accounts down the line. This is what makes a true scoring system almost impossible due to the OAuth 2.0 model and access into the child accounts data.

Now, if all of these social networks subscribed to this service it could work. The systems could then show your global “influence score” on your profile page and the score would not come from another site. Don’t be surprised if the big boys come out with their own influence score in the future. Getting them all to buy into this is most likely impossible.

This scoring system is only a few hours worth of thinking and probably has many holes in it. I would be interested in hearing your opinions about influence scores in general or if you have your own idea of scoring. Maybe this would be a great open source project – OpenInfluence!


Rewarding the influential customer – Part 1

So you have heard of Klout Scores or Kred Influence and are not sure what it means in the end. One scenario I have played out is rewarding an influential social person when they comment or rate a product on a commerce site. This will be a two-part post where I set up the logic engine in the first step then show the result in the second post. In the scenario below I have extended the Management Center tooling with a new Klout Score checker, allowing the marketing person to target customers with a specific Klout Score or a Klout Score range.

In this scenario we want to reward a person who has posted at least 3 reviews on our site and has an influence score above a certain number. I will set up two rules in the WebSphere Commerce tooling Management Center. The first rule will put the qualified customer into a dynamic customer segment :

Click for larger image

Let’s look at what this rule does. The first icon checks to see if the customer has reviewed at least 3 products and has rated those products with a rating of 3 or higher. Here is a screen shot of that flow elements definition:

The second icon checks the customers Klout score. If the customers Klout score is 60 or higher, then continue to add the customer into the dynamic customer segment named “Influential Customers“. In order to even get the customers Klout score they will need to supply their Twitter Id or authenticate with their Twitter Id. Once you get that id you can call the Klout REST API’s to get the scores and then cache them into a table. According to the Klout API docs you can cache the Klout score for up to five days. I highly recommend caching so your calls are going against the database versus the live REST call for performance reasons. More information on the Klout API here.

Next we create a promotion for the 20% off and simply select the customer segment “Influential Customers”. That should be pretty straight forward so I wont show any screen shots here.

Lastly, in the second rule we want to advertise to the customer that they are eligible for this promotion and show an advertisement on the web site:

We will use the Promotion Checker target to see if the customer is eligible to see the advertisement. The great thing about this tool is once the customer uses the promotion the advertisement will no longer appear.

The next blog post will show an end to end video of the different rules and site in play.