New Digital Marketing Analytics Report shows social media is not the best source of acquiring customers:

It’s great to see Insights that data can uncover. I saw a nice insight in a report I read about Analyzing customer acquisition channels for e-commerce sites and in this blog post, I am sharing it with you. So what are the top customer acquisition channels for Commerce sites? The Top channels are Organic Search, Emails & Paid Search.Here’s the report: E-Commerce Customer Acquisition Snapshot

It was not surprising to me to see Organic Search and Emails being among the Top customer acquisition channels but what surprised me was  relatively poor performance of social media in acquiring customers. Here’s the chart showing performance of various online channels for acquiring customers:

ecommerce analytics percentage of customer acquired vs. channel

Data Source:

Note #1: The post is NOT about devaluing the benefits of social media and it comes to down to understanding the goals of having a social media presence in the first place. While computing the ROI of social media, there are other factors like increased brand awareness, customer loyalty to be considered. But I posted this data because it’s a great way to show how data can uncover insights and sometimes it may surprise you

Note #2: The percentage of customers acquired does not add up to 100% for a year because the data does not include things like direct traffic. The author of the report confirmed it over an email w/ me.

That’s about it for this post. Your comments are very welcome!

The role of Sentiment Analysis in Social Media Monitoring:

I’ve posted tutorial/resources about the Technical Side of Sentiment Analysis on this Blog. Here are the Links, if you need them:

LingPipe (Java Based) | Python | R language resource | Microsoft’s Tool “Social Analytics

Apart from this, I’ve used other Tools per project requirements and It’s been fun designing and developing projects on “Sentiment Analysis” primarily using Social Media Monitoring. Having worked with clients on projects that use “Sentiment Analysis” – I reflected about the role of Sentiment Analysis in Social Media Monitoring. And in this blog post, I am sharing these reflections:

What is Social Media Monitoring?

Social Media Monitoring is a process of “monitoring” conversations happening on social media channels about your brand/company.

Is it NEW? Not really. The idea of monitoring or gathering data about what is being talked about the brand/company is not new. Earlier, it was newspapers and magazine-articles and now, it’s the social media channels including online news, forums and blogs and thus the name given to this process is “Social Media Monitoring”

brand monitoring social media

What is Sentiment Analysis?

Analyzing data to categorize it under a “sentiment” (emotion).

Example. Is this review saying positive, negative or neutral thing about our product.

sentiment analysis positive negative neutral

side-note: Sentiment analysis is often categorized under “Big Data Analytics”.

What’s the Role of Sentiment Analysis in Social Media Monitoring?

We’ve seen that in social media monitoring, we gather all online conversations about a brand/product/company. Now wouldn’t it be great to take the data that we have and bucket it under “Positive”, “Negative” or “Neutral” categories for further analysis?

So few questions that can be answered after we have results from sentiment analysis:

1) Are people happy or sad about our product?

2) What do they like about our product?

3) What do they hate about our service?

4) Is there a trend or seasonality in sentiment data?

Among other business insights that may be not be easily answerable with just plain text data.

Thus sentiment analysis is one of the step in social media monitoring that assists in analyzing sentiment of all the conversations happening on the social web about a brand/product.

That’s about this for this post. Here’s a related post: Three Data Collection Tips for Social Media Analytics

your comments are very welcome!

Three Data Collection Tips for Social Media Analytics

Data integrity is important especially if critical business decisions are based off on data. To that extent, in this post, I’ll write about five data collection tips to help you have accurate data for “social media analytics”. So here are the tips that are applicable to social media analytics irrespective of the tool you are using:

1. Social Media Platform


Select the right social media platform for capturing data. You do not want to select few such that you miss data.And you do want to select irrelevant social media platforms because if you do, then you’ll introduce noise in the data. Let me take an example. If your project needs to be based on USA only then you do not need to add “sina weibo” (Chinese social network) in your social media sources.

Now, Based on your business need for “social media analytics” campaign, you should test all possible social media platforms – you never know who might be talking about things that you are interested in. After you have selected the right social media platforms for your project, let’s go the next step:

2. “Search Keyword” Selection

Some of the social media platforms let’s you collect data via “search keywords”. Like twitter allows you to collect data via “hashtags” and/or keywords. So if you want to collect data about all social media posts having “american airlines” then you should not collect data using:


If you select the above rule, then it will introduce a LOT of noise because we’ll collect data people talking about just “American” PLUS data about people talking about just “airlines”. That’s bad!  What you want is rules like these:

1. American AND airlines

2. “American Airlines” (as a phrase)

american airlines social mediaNow, I can’t stress the importance of selecting the right search keywords enough. Choosing wrong keywords will add noise that would be bad for analytics. So choose keywords such that you are not adding noise as well as not missing on conversations. There’s no secret formula here, continuous improvement is the way to go!

3. Language & country Filtering


Social networks are GLOBAL in nature and so it’s important to filter (or include) based on the project that you’re working on. Not doing so would add noise in your data. And also remember to include country and language because you do not want to miss out on conversations either.


Three Data Collection Tips for Social media analytics that I shared in this post are:

1. Select Right Social Media Platform

2. Select Right search keywords

3. Select Right Country and Language.

Guest Blog: How to measure ROI of Social Media Marketing?


This is Guest Blog by Jugal Shah. Jugal is pursuing MBA w/ focus on Marketing from a premier university in India. He shares his views on marketing, sales and strategy via his Blog & Facebook.In this post, He briefly comments on “How to measure Social Media Marketing ROI”.

Jugal Shah’s Short post on Measuring Social Media Marketing ROI:

In social media marketing, ROI is not in just monitory terms. So, for social media ROI, my focus would be on
1) to how many people I have reached
2) How many people I have engaged through online activities
3) Becoming a conversation enabler and perception driver

Then focus on

1) how much increased revenue is due to social media reach (you can do this by tracking referred link)
2) How many leads you generated through social media
3) How social media efforts helped to resolve customer query/problems and led to more customer satisfaction (remember customer acquisition cost 10 times more than customer retention cost).

In a nutshell, It’s of utmost important to use Social Media as:

  • conversation enabler
  • perception driver
  • customer retention


Paras: Jugal, Thanks for this post. I am sure, this short post would be a great food for thought for readers who are interested in Digital Marketing Analytics or analytics in general. Readers, Feel free to reach out to him on his blog and/or Facebook page.

Three Data Visualizations I liked this week:

I have been working on creating Dashboards for one of my projects. As a part of the research, I looked at few Dashboards out their on the inter-webs. Here are three of them that I liked:

1. Social Media & Sentiment Analysis:

What I like about this Dashboard is the creative use of Data via Sentiment Analysis:

sentiment analysis social media dashboard

2. Microsoft Research’s Viral Search Project:

What a creative way to visualize viral content!

visualize viral social network data microsoft viral search

3. Social Media analytic’s Dashboard:

Nice one page social dashbaord!

social media analytics dashboard

Do you see the bottom right part of the report that shows you engagement levels by post type, if you want to compute it – here’s my blog post on that: Social Media Analytics. Facebook Page Smackdown: Status updates vs Images?


Sentiment Analysis using LingPipe on windows 7:

In this post, I’ll point you to the resource using which you can perform sentiment analysis using LingPipe on a windows OS. Along with that I’ll share couple of issues that I ran into when I was trying to run this demo on a Windows 7:

So first up, here’s the resource:

Now here are a couple of issues that I had:

1. Error: could not find or load the main class PolarityBasic

lingpipe could not find or load main class polaritybasic

To solve this error, you’ll need to build the files given under the C:\lingpipe-4.1.0\demos\tutorial\sentiment – we use ANT for this. Let’s see how to do that:

2. Building sentiment.jar using ant jar

After successfully downloading ant on windows and setting the ANT_HOME variable to c:\apache-ant-1.8.4 – I was still getting the error that ant is not a recognized command.

So I ran following commands:

C:\>set ANT_HOME=C:\apache-ant-1.8.1
C:\>set JAVA_HOME=C:\jdk1.6.0_24
C:\>set PATH=%ANT_HOME%\bin;%JAVA_HOME%\bin
C:\>ant -version
// it worked!


Now I ran the following command:

build sentiment.jar ant lingpipe

3. In the tutorial they used POLARITY_DIR – I didn’t use that, Instead I just inputted c:\review_polarity because that’s where I unzipped the movie review dataset:

movie review sentiment analysis polarity

Here’s the screenshot about the command that does basic polarity analysis:

sentiment analysis lingpipe windows

And Thanks:

Sentiment Analysis in R w/ Twitter data feeds

I followed instructions on this site to perform sentiment analysis about Starbucks from Twitter data feeds.

Here are data visualizations:

1. Sentiment Analysis: Starbucks on Twitter

sentiment analysis starbucks on twitter

2. Comparison cloud:

comparison cloud data visualization

That’s about it for this post, Here are some related tutorials:

If you want to Install R on windows machine, here’s a Tutorial:

If you want to try out out Hadoop on windows, Hive and Hive excel add-in w/ Twitter Data, Here’s a Tutorial:

If you want to Grab Twitter search data using R and export to a tab delimited file. Here’s a tutorial:

Two ideas to make your social network activities “Searchable”:

Some time back, I wanted to search one of my own social network post. It was a resource I had shared and somehow I was not able to “google” it (again). I eventually found it – but it took me 15 odd minutes to scroll down to my twitter feed. It was NOT fun! And I thought to myself – there’s got to be a better way! And I thought – It’ll be great if I solve it for not just Twitter but all my social network activities that includes LinkedIn, Facebook Pages, Google+. So here’s couple of things thats working for me, I hope it helps someone out there too:

Now, before we begin when I say “Searchable” – I mean searchable by YOU (or a human being) and not necessarily search engines. But it turns out, both my ideas increase your chances of getting your social media activities Indexed! With that, Here are the ideas:

1) Syndicate your Social Network Activities (Posts/Images/Updates) to Tumblr/Blogger

I use IFTTT to syndicate my Twitter, Facebook and LinkedIn activities to Blogger

2) Create a post about your social network activities on your blog:

Here’s an Example: Things I shared on Social Media Networks during Oct 19 – Nov 11

Though Idea #2′s main goal is to keep my blog readers updated about my social network activities – But it also acts as a good way to make my social media posts “searchable”.

And remember I said earlier that the chances of your social network posts getting indexed by search engines increases? That’s because WordPress, Tumblr & Blogger’s posts are accessible by Google (unless you choose to block it). So that’s about it for this post. If you like the idea(s), please let me know! And if you have other ideas – also let me know, I am always looking for ways to make my social media activities easily searchable to me as well as for anyone else.

Let’s connect and converse on any of these people networks!

paras doshi blog on facebookparas doshi twitter paras doshi google plus paras doshi linkedin

Three V’s of Big Data with Example:

In this blog-post, we would see the Three V’s of Big Data with Example:

1. Volume:

TB’s and PB’s and ZB’s of data that gets created:

From the webinar “How to Walk The Path from BI to Data Science: An interview with Michael Driscoll, data scientist and CEO of Metamarkets” – A global surge in Data

2. Velocity:

The speed at which information flows.

Example: 50 Million tweets per day!

twitter 50 million tweets per day

(This is back in Nov. of 2010 – the number must have increased!)

UPDATE 23 Nov 2012: on, wikipedia it says – 340 million tweets per day!

twitter 2012 340 million tweets per day

3. Variety:

All types of data is now being captured which may be in structured format or not.

Example: Text from PDF’s, Emails, Social network updates, voice calls, web traffic logs, sensor data, click streams, etc

data variety big data

Image courtesy

And this may be followed by other V’s like V for Value.


In this blog-post, we saw Three V’s of Big Data with Example.

Related Posts:

Who on earth is creating “Big data”?

Examples to help clarify what’s unstructured data and what’s structured?

Things I shared on Social Media Networks during Oct 3 – Oct 10

I am fairly active on social network channels. I engage with people on Social networks and I thought It would be great if my blog readers also get a recap of conversations I am having on different social networks and so I’ve thought of compiling a “Things I shared on Social Media networks during the week” . Here is the recap for the week of OCT 3 – OCT 10:

1. Gaurang Patel commented about the Blog: “Five things I like about RescueTime” on FB page: i am using this one it is really fantastic . 

The blog is here: Five things I like about RescueTime:

Gaurang Patel on Five Things I like about RescueTime

2. Photo with a caption that I added:  That’s the CLOUD.

Originally shared by Windows Azure account

That's the cloud windows azure servers data centers

3. Quote my APJ Abdul Kalam (Ex-President of India) . Did you knew that in 2011, I got a chance to meet him! Read here: Met revered APJ Abdul Kalam (Ex President, India) at an event organized by Times Foundation

Quote by APJ Abdul Kalam

4. Nice Data Visualization: Originally shared by and link to the source of the data Visualization:

Nice Data visualization paras doshi

5. “I was working on Business Intelligence project requirement analysis. One requirement that I saw across all department: Need a data mart (single version of truth)”

This post was shared by SolidQ on Google+

Enterprises need data mart and data warehouses

6. On G+, SolidQ shared my post that I wrote a while Back. Here’s the post: Step by Step guide to Export a SQL Azure Database to Azure storage via Import and Export CTP

SolidQ shared paras doshi blog on Google plus

BTW, did you knew the same blog post was Re-Tweeted by Scott Gu on 31st Dec 2011! Yup!

Scott Gu RT'ed Paras Doshi's Tweet

7. RescueTime tweeted:

Check the end of this post to see how RescueTime user @Paras_Doshi was able to cut his social networking time in half!

8. SolidQ shared this post: “Earlier Today: delivered a two-hour session on PowerPivot and Power View to a client. they had some very Interesting questions for their scenario!”

paras doshi delivered a power pivot and power view session solidq

9. SolidQ shared this post on G+:

Data Visualization: Created HeatMap/TreeMap like the one shown below for a client. Looking forward to receiving their feedback. btw, I used the Black, Grey and white for the shades. image courtesy:

paras doshi created a heat map for a client at SolidQ

Let’s connect and converse on any of these people networks!

paras doshi blog on facebookparas doshi twitter paras doshi google plus paras doshi linkedin