Social media analysis: what data can teach us

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At SXSW 2012 Gilad Lotan from SocialFlow spoke about the “math that matters”.

Gilad’s R&D team spend a huge amount of time looking at data provided by the Twitter firehose and the bit.ly stream, using this information they are able to gain valuable insight into how Twitter users interact, and so predict the potential virality of certain content.

The team at SocialFlow applied this analysis to their own tweets. The results were interesting, as some tweets generated a large number of clicks but a low number of retweets, and vice versa. Using this information and by determining the characteristics of each “type” of tweet, SocialFlow were then in the position where they could target amplification (retweets) or engagement (clicks).

If you take this approach to your own tweets, you can work out when your users are paying attention and when they are likely to respond to your communications. You can also understand what topics are most interesting to your users. Once you have these two pieces of information you can start to ensure your brand is writing content, across your platforms, that will engage with your audience.

The focus of Gilad’s talk was on understanding audiences. One example below shows what topics the audiences of four major news networks are talking about:

News networks clicks from social flow

When looking at this information Gilad noticed that users clustered together into groups, further analysis showed that these clusters in some cases were geographic but in others they were groups around a single topic or even a single core influencer.

Geographic Socialflow social media data

They key take away from talk was that data can help you know your audience, understand what’s important to them, and when they are paying attention. Analysing this data into insight allows you to make every tweet count.

While this information is definitely useful, and a great starting point, the way that we would apply such insight is to go one step further and link it into existing business KPIs, such as measuring conversions from engagement into sales opportunities.

A guide to measuring Twitter (using the API)

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There are lots of tools emerging that appear to give us wonderful statistics and data about Twitter, but it’s hard to know which data we actually want and how we want to receive it.

As Twitter’s API has been undergoing a few changes recently, we thought it would be useful to give you an overview of the information that you can still get from the platform itself, as well as providing some guidance on the best way to measure the data.

The four main data types on Twitter are:

  • User data - relates to the user who posted the message.
  • Friend and follower data - relates to the relationship a user has to other users.
  • Tweet data - all the details and content relating to a particular tweet.
  • Places and Geographic data - the geographic and location based aspects relating to a person or tweet.

There are also four main measurements that we can use to measure this data in order to understand the impact of the activity on Twitter:

  • Impressions - aggregated users exposed to messages.
  • Reach - number of unique users exposed to a message.
  • Frequency - number of times each unique user reached is exposed to a message.
  • Relevancy - reach to specific demographics.

When it comes to the ROI of these messages, it’s important to think about how they compare to your other channels in terms of reach and impressions.

Take a look at the presentation below - we hope it helps to reveal some of the Twitter data you can access through the API and ways in which you might go about measuring it.

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