25 Aug 2011

Part 2: Twitter analytics – Finding Top Influencers

3 Comments BI, Dashboards, QlikView, Social Media, Twitter Analytics

In this second blogpost in this series called “Twitter Analytics in QlikView” I will show how to use QlikView to find the top influencers in your industry and measure two important social media KPI’s: the number of brand “Promoters” and “Detractors”. For people new to Twitter I recommend reading the first blog post in this series called Introduction to Twitter for QlikView, where I introduced some of the Twitter basics and helped setting up a Twitter account. In this second part we will start analysing Twitter data using the “QlikView Twitter Analytics dashboard” developed by QVSource. If you would like to run the examples in this post with your own company or client’s data, simply download the Twitter Analytics Dashboard and follow the instructions on the QVSource Wiki. Make sure to download and run the latest version of QVSource before reloading the Twitter Analytics Dashboard.

The main focus areas of this post will be:

• Finding top influencers
• Measuring a person’s Influence with “Klout” scores
• Measuring brand “Promoters” and “Detractors” trends
• A list of influential people to add on Twitter

To analyse influence we will use the influence tab on the QlikView Twitter Analytics dashboard as shown in the screenshot below. If you click on the dashboard you can see the top brand “Promoters” and “Detractors”, plus influencer’s trends for the BI industry based on two weeks of data collected from Twitter. We will discuss the details of the graphs in the following sections.

Finding top QlikView influencers

One of the key elements in social media analytics is understanding who the top influencers in an industry are and start building meaningful relations with them. Marshall Sponder, author of the book “Social Media Analytics: Effective Tools for Building, Intrepreting, and Using Metrics” describes successful influence by quoting Mike Arauz:

“The most successful influencers are those who have a found way to channel their popularity and reputation into collective actions…”.

Top influencers are people that can help you build or damage a brand and spread your brand message. Top influencers do not necessarily have to be in your industry, but can very well be closely related to it. Stephen Few for example, authority in the data visualisation industry, can be considered a top influencer closely related to QlikView. Stephen Few recommending or mentioning a product in a blog post, tweet or book, as he did with one of QlikView’s competitors in his book “Now You See It: Simple Visualization Techniques for Quantitative Analysis“, can have a large effect on your website traffic or business.

A popular measure of Influence: Klout

According to Marshall Sponder it can be tempting to define “influence” as a combination of signals that can easily be measured such as number of tweets, retweets, followers, lists, etc. However, when looking closely to these metrics, none of them accurately define the ability to channel collective action. An extra challenge with measuring influence is topicality. While a person might be influential in a specific area, this does not necessarily have to translate to influence in other areas.

To get an indication of a person’s influence we will use “Klout“, provided by www.klout.com. Klout is a popular metric that can be used to measure a person’s influence on different social channels as Facebook, Twitter, LinkedIn, Youtube and more. In the screenshot below you can see the graph showing the most influential people for the keyword “QlikView” based on two weeks of Twitter data, sorted on Klout. At the time of writing Klout can be used as a good indicator of influence on social channels even though it is not recommended to use it as industry standard yet. The reason is there are still to many ways you can influence Klout scores. As Klout is continuously working on improving their algorithm and actively develops it’s platform, it can however give a good indication.


If you are interested in my personal Klout score you can access this through the following url on Klout.com: http://klout.com/QlikMetrics.

From influence to Brand Advocacy

A person’s influence or Klout does not necessarily explain whether or not the influence is positive or negative. To differentiate between positive or negative influence in the Dashboard we can use two different sentiment API’s provided by QVSource. In the screenshot below you can see two graphs. The graph on top shows the “Most Influential Brand Promoters” and the graph below shows the “Most Influential Brand Detractors” for BI vendors sorted on Klout influence.

In the screenshot you can see that each person has an “Sentiment” index, showing the avarage sentiment of Tweets. “Sentiment analysis” is an advanced technique that can be used to determine how positive or negative each message is, ranging from minus seven to plus seven for very negative or positive messages. The tweets in the dashboard have been run through an external sentiment API directly in the QlikView load script using the QVSource sentiment connectors (examples can be found in the dashboard). The average sentiment of a person can be calculated by taking the average sentiment of all Tweets.

To measure brand “Promoters” and “Detractors” as shown in the graph above we have used the following criteria:

• A Person that has Tweeted more the 5 times for a brand (like “QlikView”)
• Has a Klout score of 25 or higher
• Has an average positive sentiment for “Promoters” or negative sentiment for “Detractors”

To get an impression of total differences between “Brand Promoters vs Detractors” for BI vendors we can use the following chart from the dashboard (see screenshot below). Do remember this chart only contains two weeks of data so it would be too early to draw any statistical conclusions, nevertheless we already see some interesting trends. If you would like to create similar graphs for your company, or client’s data, change the company name and search terms in the configuration file of the Dashboard and reload the data. Hopefully you will be able to see interesting insights for your industry.

Actively engaging with people on Twitter

Getting an understanding of Sentiment or Brand advocacy trends can be a good first step in building a measurement framework for social media analysis. The second step is finding ways to positively influence these trends. Now even though designing social media campaigns is out of the scope for this blog post there are already things you can start doing. By actively responding to negative Tweets, answer support questions or stimulate brand advocacy through ReTweeting and/or responding to positive Tweets, you should already be able to reinforce a positive brand experience. In the dashboard you can select a person to get an overview of all his Tweets. In the screenshot below for example you can view all my personal Tweets containing the keyword QlikView for my account QlikMetrics. Notice that the Tweets contain a direct link to Twitter which will allow you to respond.

A List of interesting people to follow

To round up this post I would like to share a list of people who I consider interesting to follow on Twitter, or are influential on the topics QlikView or social media analytics. Do not forget to add your own Twitter account in the comment section of this blog when leaving a message!

  1. QlikView: Official Twitter account of QlikView
  2. donalddotfarmer: QlikView product advocate
  3. JohnLovett: Author of ”Social Media Analytics Secrets (2011)”. Influential in Web Analytics
  4. QVSource: QlikTech development partner and creator of QVSource – the QlikView API Connector
  5. EricaDriver: Product Marketing Manager at QlikTech and active Twitterer
  6. QlikMetrics: That’s me obviously!
  7. TiqView: QlikView partner in Germany that developed “SentiVal”: http://bit.ly/l6IDaJ
  8. Ungvall: Business Discovery advocate and active Twitterer/Blogger
  9. WebMetricsGuru: Author of “Social Media Analytics” (2011) – social media analytics authority.

If you like to get started with social media analytics for your company, or provide social media analytics services to clients, feel free to drop me a mail or contact me through my LinkedIn account. Also consider joining the early adapter program for QVSource.

In the following post I will start focusing on “Facebook for Pages Analytics in QlikView”.

Tags: , , , , ,
written by
The author didn‘t add any Information to his profile yet.
Related Posts

3 Responses to “Part 2: Twitter analytics – Finding Top Influencers”

  1. Reply Renco Smeding says:

    Great post! My Twitter account is @QlikMetrics :)

  2. Reply Theo says:

    Interesting post.

    I think that social media will just grow in brand importance. Measuring this is a good first step, doing something about it is the second.
    Twitter is today used by something like 1% of the worlds population but it is already used as a data mining and prediction tool.

  3. Reply Renco Smeding says:

    Hi Theo,

    Thank you for the reply! Don’t forget to add your Twitter account. :)

    I agree that using social media for data mining and predictive analytics is really interesting.

    In a post I recently read there was a discussion regarding the problems with customer surveys to measure the health of a business. Instead they suggest a simple measure called “Net Promoter Score”:

    http://blog.kissmetrics.com/nps/

    The formula is pretty straight forward. I quote from the text:

    “To calculate your company’s NPS, take the percentage of customers who are promoters and subtract the percentage who are detractors. To give you an idea of some (very high) NPS scores, here are a few scores from some familiar brands:

    • USAA – Banking 87%
    • Costco – Dept Store and Wholesale 77%
    • Apple – Computer Hardware 72%”

    It would be really interesting to see to what extend it is possible to use social media data to measure “Net Promoter Scores” for brands and see if it is possible to predict future health or performance of a company.

    I guess we need to do some additional research here … :)

    Cheers!

    Renco

Leave a Reply