Exploring Social Media Analytics

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Exploring Social Media Analytics

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As I talk with customers and colleagues the topic of social media analytics is often featured.  Some customers have already got a strategy defined and are executing to a plan while others are at a more nascent stage but believe in the potential to have a direct and almost immediate connection to their customers.

I'll admit that I am somewhat of a social media novice so it will be a learning experience for me too. I am intrigued by the depth of analytics that may be possible. Only this month did I setup my profile on Facebook and I'm 47! I have been using Twitter more regularly of late, since our Teradata Universe conference and I probably look at it 3 or 4 times a day, depending on what's on my schedule for the day. I am finding some interesting, funny and informative updates each day, as I slowly expand the number of people I follow. It is really a mixture of friends and work related contacts at the moment.  I have been a member of LinkedIn for a number of years and find it a useful resource from a professional perspective.  I am within the first 1% of members who subscribed to this site (I received an email to that effect, that I was within the first 1 million sign ups when the site hit 100 million). Finally I am keen on blogging more frequently when I have something interesting to share (i.e. this! :-) ) I had stopped blogging for about 5 years at one point.  I have also started with Flickr and YouTube as well. I'll be my own guinea pig in some ways as I explore and experiment on possible useful analytics in these social media channels.

However when most people think of Social Media and associated analytics, Facebook and Twitter are often mentioned first.   These social media systems do provide an API that can be used to readily access data, and these split into two broad categories that reflect the social media’s attitude to customer data.

The first approach is the open approach adopted by Twitter.  Users on Twitter are warned that their posts are visible to anyone (who can find them). The second approach is that adopted by Facebook. There is an extensive privacy model, and data needs to be accessed using authorizations (from likes and games).

  • Data is free but there is a lot of it
  • Identifying relevant stuff isn’t easy
  • History isn’t usually available
  • Public data is free through search
  • Basic data available through application
  • More sophisticated data available with permissions (using Oauth 2.0)
Focusing on Facebook and Twitter you see two very different levels of information.  Twitter provides only basic, row level data. Facebook provides much more complex, relational data. We'll explore these in more detail in future posts.

Data from social media must be linked in three ways:
·       Within the social media itself
·       Across multiple social media
·       Between social media and the bank’s core data

The most secure forms of linking are to use unique references: email addresses, IP addresses and telephone numbers.  This can be supported by direct access methods (i.e. asking the user for their Twitter name, or persuading them to Like the bank on Facebook from within a known environment).

However, even then the confidence in the link must be evaluated and recorded: this information is user provided and may be wrong in some cases. The notion of a “soft match” should be adopted – we think that this is the same person, but we cannot be sure.

I would like to end this post with a recommendation to read the following white paper by John Lovett from Web Analytics Demystified  Beyond Surface-Level Social Media. Lovett, who has written a book on Social Analytics , lays out a compelling vision for Deeper Social Analytics for companies.  He clearly presents the value for companies to go beyond surface level analytics of likes, followers and friends and challenges you to ask deeper and more important questions. This white paper has been sponsored by Teradata Aster and is available for free from here.

In reading this white paper you will gain an understanding of the term 'Surface-Level Social Media' coined by John and how it is possible to gain competitive advantage even operating at this level. He will outline how Generation-Next Marketing is being powered by Social Analytics backed up with a number of interesting customer examples. He goes on to outline a 7 point strategy to build your deeper social media strategy. Finally John concludes with how unstructured data can yield valuable customer intelligence.

I found it to be very informative and well written and gave me a number of new insights and points to ponder. I would be interested in your thoughts on it too. 

Published at DZone with permission of Donal Daly , DZone MVB. See the original article here.

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