Showing posts from 2013

Twitter Topic Explorer - Free tool

Jeff Clerk from Neoformix has come up with another beautiful tool called as Twitter Topic Explorer.
This tool does the text analytics on a person tweet. Based on word cluster algorithms, it classify the words used in tweets in different groups and display it beautifully.
You can explore the tool at
The tool retrieves the recent tweets of a particular twitter ID and extracts most common words from those tweets.
The size of the bubble is based on the word frequency and the words most often used together are grouped together with same color. This tool also works on the twitter lists.
We can use this tool in many ways:

As Jeff suggested, we can identify who to follow on Twitter. We can analyse who have similar interests and are tweeting about the topics which are meaningful for you.We can decipher the tweets of competitor and analyse what they are tweeting aboutWe can find out the topic trends based on our brand market influencersWe can correlate this dat…

How to build Taxonomy for Social Media Analytics

According to survey done, billions of mentions on social media are on product and services of various brands. Through text analytics techniques, we can decipher the sentiments and dimensions of customer conversations around these products and services.
In my below posts, I have emphasized on the benefits of dimensions analysis on social media data:
Online and InStore Analytics using Social Media DataSegmenting big unstructured data (Social Media) on different dimensions – Part 1Segmenting big unstructured data (Social Media) on different dimensions – Part 2

Below process shows the steps involved in building the taxonomy:

Sample taxonomy will be like this:

We need to correlate this ontology with the sentiments and structured data like social platform author demographic information, Geo location, number of followers etc… to do deep dive social media analytics and to get actionable insights.
Do you face any challenge in building taxonomy? Please share with us and we will try to help you…

Analyzing Big Data based on Segments

TV has become an integral part of our lives. Because of explosion of TV channels, it has become very competitive for Channel companies to attract attention of the viewers. Channel companies are overloaded with various data points like viewership data, syndicated data, call center data, social media data, set up box data, broadband data etc…

Till we don’t integrate all these data sources, we cannot make a sense out of this BIG DATA.
The best way of integration is by SEGMENTS.

Below are the tables having audience segments of UK population:
Social Grade Social Status Occupation A upper middle class higher managerial, administrative or professional B middle class intermediate managerial, administrative or professional C1 lower middle class supervisory or clerical, junior managerial, administrative or professional C2 skilled working class skilled manual workers D working class semi and unskilled manual workers E those at lowest level of subsistence state pensioners or widows (no other earne…

How to find influencers Klout Score

Using Klout, we can measure how the people who are active in social media influence each other online.

We can use Klout API to get the scores of people who are speaking about the brand on various social media platforms. These Klout scores can also be used as a benchmark to measure the engagement of brand’s owned media as compared to competitor’s social media handles.
Below is the process of generating the Klout scores using their API:
Generate API key from URL API key in twitter handle name instead of “BarackObama”It will a generate IDAdd that ID in in place of “2055” and write API key in endIt will give the Klout  score to you

Image Credit :

How you are leveraging Klout Scores in your social media strategy? Please share via comments....

Online and InStore Analytics using Social Media Data

This is in continuity of my previous posts “Classifying social media mentions on various dimensions - Part 1 and Part 2”.
Various retail brands have options of both online shopping and instore shopping.
For social media analytics monitoring, we have to make sure we do separate classification of dimensions for online and instore shopping experience of customers.

Below are some sample dimensions for Online shopping experience analytics: ·Price·Delivery Time·Quality of Products·Pre-Ordering·Customer Service·Convenience·Availability and Collection
Below are some sample dimensions for Instore shopping experience analytics: ·Price·Quality of Products·Staff·Convenience·Availability and Collection·Billing Time

Social Network Analysis - NodeXL - Intro

NodeXL is a free and open source network analysis and visualization software package for Microsoft Excel (2007/2010).
You can download the NodeXL from

Once you download the NodeXL, run it and you can see that as a separate tab in your Excel. Click on the tab and goto Import. You will see many options there. In this post, we will be focusing on “Twitter Search Network”.

Twitter Search Network will help in importing a Twitter network of people whose tweets contain a specified hashtag or word. For example, I have used here word “airtel”. When you are first time using the NodeXL, you have to get the app authorized with Twitter.

Once authorized, it will give you a PIN which needs to be added to NodeXL.

When the authorization is done, NodeXL will extract all the data in different sheets.

You can extract only one week old data of Twitter. You might need to connect to services like GNIP or Data Sift for achieve Twitter data.But still using NodeXL, you can keep appending the …

Linkedin Network Analysis

If you are passionate about Social Network analysis like me, then this post is for you.

Try out creating social networks for your linkedin connections using

Below is the social network for my profile

I will be doing the deep dive analysis on this in later posts. Till then enjoy this beautiful free Linkedin network analysis!

SPSS Text Analytics for Surveys – Tips and Tricks 6

Here is one more post in this series of best practices/tips on SPSS Text Analytics.
There is a possibility that the tool has missed any important sentiment which is important for you. But directly from the response, you can select any particular word and then add the sentiment associated with it.
Select the word from response  -> Right Click -> Add to Type -> Select relevant sentiment

Please share your best practices/tips of Text Analysis !!!

Free Webinar : Are You Harnessing The Power Of Big Data?

RACI Matrix for Web Analytics Project

If you are doing a web analytics project in collaboration with a tool vendor, then this post is for you. To make the project successful it is always better to remove all ambiguities and confusions before the project starts.
Here I have created a RACI matrix to cover all the phases of Web Analytics (Discovery, Design, Deploy, UAT and Reporting).
RACI stands for Responsible, Accountable, Consulted, and Informed
For more details, please refer
The first step is to create a project plan and this RACI matrix. Get it signed off from vendor and your client before you starts the project.
Responsible Accountable Consulted Informed Business Requirement Document Vendor Vendor Client Client Solution Design  Document Vendor Vendor Your Company

Social Media Segments for Retail Industry

In today’s time, Social Media is a best place for Retail industry to explore and act. With the growth of smartphones, customers are expressing more on social media. Customers are writing about the product, customer service, bargains/offers/coupons etc… There is rich information available on social media platforms which Retail industry can leverage and integrate that with their other data sources.
The challenge is to find the demographic information of the customer for creating personas as many mentions are by “Anonymous” visitors or because of privacy settings of social networks.
But we can still do the persona analysis on social media data by using Text Analytics techniques.
Some of the segments which can be created using social media data are:
Price Conscious ShoppersLoyal ShoppersGrab and Go Shoppers

Using the Text Analytics power, we need to first create a bag of terms which will help in identifying the customers of each segment.
Some keywords which can be used for each segment are: