Analyzing Elf on the Shelf

Dan Grady's picture
 By | December 23, 2015
in Business Analytics, Business Intelligence, Data Visualization
December 23, 2015

Word Frequency analysis of the conversation on the Elf on a Shelf Facebook page

It’s been several years since we decided to fight back and turn the tables on those sneaky little elves that sit on our shelves. You know the tiny guys in red that I’m talking about; our festive pals from the North Pole who sit in our homes during the Christmas season, watching our every move and reporting back to Santa. In 2013, we did an analysis of the conversations taking place on the Elf on a Shelf’s Facebook page and did some reporting of our own.

Two years later things have changed. I now have three kids instead of two, which means our house has been invaded by two elves and a reindeer (our third kid has been downgraded to a reindeer, something all the youngest siblings out there completely understand).  

Things have changed in the world of social media analytics as well. This past year was full of social platform acquisitions, changes in social data availability, and advances in text analytics. Probably the most important change was that more companies began to recognize the value in analyzing social data, and those who have been in the social media game for a while now have historical data about their social activities to benchmark against, adding more context to, and increasing the value of the insights they gleam from the data.

Let’s do the same with the Elf on a Shelf’s page and look at the data over the three-year period since we did our first analysis. We are going to focus on prime Elf season: December 1st through the 22nd of 2013, 2014, and 2015.

Here are some of the stats:

  • We pulled a total of 27,023 posts and comments on the page from over the three years.
  • 2014 had the most activity with 49.2% of the total commentary, or 13,294 posts.
  • Overall, and as you would expect, the conversation on the pages is mostly positive. Only 8% of the total commentary scored negatively with the Information Builders sentiment engine. 2013 had the highest percentage of negativity at 2.1%
  • In terms of the most popular themes for each of the years, we used word frequency analysis and broke the years down individually.
  • In 2013 the most popular conversation centered around the “Grinch” and this was due to a campaign the page ran - Elves LOVE watching Christmas movies! Can YOU guess which one this quote is from? "What if Christmas, he thought, doesn't come from a store? What if Christmas, perhaps, is a little bit more?"  This generated a significant response.
  • In 2014 the most common term across all the commentary was “Friend”. This is in large part due to the page engaging more with their fans. The Elf on the Shelf page refers to anyone who posts on their page as a “friend,” and in the 2014 the page was the most active and engaged, contributing 3.53% of the total conversation, or 954 posts/comments. This may also be why 2014 was the winner in terms of total conversation.
  • In 2015 “gingerbread” was a popular theme and again this was due to a quiz the page ran asking the fans to guess what a certain item was.


I found this piece of analysis above the most interesting. Take a look at the trend lines across the days for each of the years – I’ve added a linear trend line for each to help highlight this point. Every year, the activity on the page declines as we get closer to Christmas. What causes this? As parents are we all initially excited and full of creative ideas on how to pose our elves and then as the thoughts get less creative we feel less of a need to brag? How do the Elf and those who manage his site take advantage this insight? 

All these insights have value, and the extent of that value is determined by how much context you can put around the insight. That context can come from historical data, as we’ve done here looking at trends over a three-year period, or that context can come from integrating social data with other enterprise data. Insights drawn from social media data become significantly more impactful if you can surround and enrich them with corresponding enterprise data points.  We detail Four Impactful Social Media Analytics Use Cases in this blog post.

I hope your Elves have served their purpose this year.

Happy Holidays to you and yours.