In my previous post Does size matter? Debunking PivotStack’s Top 50 Ad Agencies Ranking, I discussed how I recommend ranking ad agencies based on the performance of their own social media profiles.

Today, I’ve taken my own advice and created the FAWC! Report. I’ll let you figure out the name.

The FAWC! Report ranks top advertising agencies by mashing up the number of followers they have on Twitter, Facebook, and LinkedIn and what I’m calling Social Goodness, which measures quality.

As I stated in my prior post, it’s not just size that matters, but how you use it. It’s for that reason that IDEO beat out Ogilvy for the #1 spot despite Ogilvy’s larger follower count.

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What you’ll find in the FAWC! Report

I have included 120 agencies on the list. For each agency, you’ll find the:

  • Rank
  • FAWC! Score
  • Twitter, LinkedIn and Facebook follower counts
  • Social Goodness score
  • Strength, Sentiment, Passion and Reach numbers
  • Web address

How the FAWC! score is calculated

First, I needed to create an index for each variable in my calculation. Why? Because the follower counts are really big whole numbers and the inputs for the Social Goodness variable are in percentages, so a really small number. Therefore, the only way to make Social Goodness have an impact was to create a level playing field – in other words, an index.

For each social network, I took the number of followers for a given agency and divided it by the average number of followers for all 120 agencies evaluated. This is what it looks like:

Twitter Index = # of Twitter followers / Average Twitter Follower Count for All Agencies

Rinse and repeat for Facebook and LinkedIn. The result was three indices.

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You could also think about this approach as calculating the expected number of followers and then comparing it to the actual number of followers.

For the Social Goodness score, I used Social Mention to capture the Strength, Sentiment, Passion and Reach of the agency’s Twitter account (more on why just Twitter below).

I used the same procedure as above to create an index for the Strength, Sentiment, Passion, and Reach and then averaged that all out to create one Social Goodness number. The resulting percentage was then multiplied by the number of Twitter followers.

Adjusted Twitter Follower Count = # of Twitter followers * Social Goodness

With this approach, I am able to take quality into account not just quantity. An agency can always buy followers, but do they know how to interact with those followers?

Just so we’re clear, I now have four new variables for each agency.

Bringing it all together, I used this formula to create the final FAWC! Score:

FAWC! Score = 27 * ln ( Average of each of the four variables ) + 100

Why use the natural log and multiply by 27 and add 100? Basically, to make it a meaningful and manageable number (more on that in this great post by Moz). It doesn’t affect the integrity of the number. It just makes it more digestible.

How to read the FAWC! Score

Read it as you would any other index. Above 100 are the agencies that perform above average within this peer group. Below 100 are the agencies that perform below average.

Here’s a snapshot of the distribution of FAWC! Scores, which has a pretty decent bell curve.

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Distribution of FAWC Scores

Limitations

First, I mentioned above that the Social Goodness metric only applies to Twitter. Why just the Twitter account? Because that’s what Social Mention basically captures.

In a perfect world, I would have the Engagement Rate and Net Sentiment for each social network. But I’m working with what is available for free (hey Social Bakers or other social analytics company, want to partner and provide some data?).

Second, updating this information is a little time intensive. I would love for it to be completely dynamic and always up-to-date. But alas, I have to manually update it. So, it will only be updated monthly.

(If any programmers want to help me out, I would love it!)

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And let me know what you think in the comments below.