Social Media

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

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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: http://blog.custora.com/2013/06/e-commerce-customer-acquisition-snapshot/

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!

Three Data Collection Tips for Social Media Analytics

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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

social_media

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:

AMERICAN OR Airlines:

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

global-social-network

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.

Conclusion:

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?

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Introduction:

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

Conclusion:

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.

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How to start Analyzing Twitter Data w/ R?

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Over the past few weeks, I have posted notes about Analyzing Twitter Data w/ R, listing them here:

1. Install R & RStudio

2. R code to download twitter data

3. Perform Sentiment Analysis on Twitter Data (in R)

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

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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

I played with Twitter Firehose for couple of hours and how you can do so too:

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First up: what’s a Twitter Fire-hose?

It’s a real-time stream of tweets! I had pointed out in an earlier post that Twitter gets 340 million tweets per day!

twiiter fire hose 340 millions tweets per dayImage courtesy

Why did I want access to Fire-hose?

Curiosity.

I had heard – It’s expensive, Is it?

For an Individual: Absolutely! For companies: Not if they know how to create business value out of it.

Note the words “couple of hours” in the title. I’ll Explain that part later.

How did you get access?

via DataSift. They had a free trial w/ 10$ credit and I tried that. Check them out if want to play with Twitter Firehose. It’s fun!

What did I do with it?

I collected 15,000 tweets over a period of 2 hours containing words “Google” OR “Microsoft“.

Total cost for me: 3-4$

Note: I added the cost just so that you get a general Idea. Look at the pricing page of DataSift for more details.

Are their other Twitter Data  Resellers?

Yes. As of now, it’s DataSift, GNIP and Topsy. search for “Twitter Certified Data Reseller Products” to find the list. I was able to find a Free Trial by DataSift and that’s why I tried DataSift.

If I just want to play with Twitter Data, what are the alternatives?

you can work with their streaming API which gives 1% of tweets. you can find an example here: Grab Twitter search data using R and export to a tab delimited file

Conclusion:

In this post, I discussed about how you can try Twitter Firehose. Also pointed you to an alternative of using streaming API which gives 1% of tweets. I hope that helps.

Grab Twitter search data using R and export to a tab delimited file

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In this blog-post, we would see how you can grab Twitter search data using R and then export it to tab delimited file. Here are the steps:

1) First up, if we do not have R – you can install it by following the tutorial: Let’s install R Studio and R on windows machine

2) Instal Package: TwitteR if you haven’t

3) Look at the following code, modify the path in line #4 for write.table:

> require(twitteR)

> tweets <- searchTwitter(“#excel”,n=1500)

> tweetdataframe <- do.call(“rbind”,lapply(tweets,as.data.frame))

> write.table(tweetdataframe,”c:/users/paras/desktop/tweetsaboutexcel.txt”,sep=”t”)

4) so now you have tab delimited file having about 1500 tweets!

1500 tweets R excel tab delimited RStudio code

You can also export the tweets to Excel spreadsheet, SPSS and SAS. Check this out: quick R Exporting Data

Conclusion:

In this blog-post, we saw how you can grab 1500 tweets using R and then export it to a tab delimited file.

Mapping “Facebook Page Likes vs Country” using PowerView in Office 2013

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Just a quick note that you can quickly create maps in PowerView in office 2013. I just created one in 2 minutes:

Facebook Page likes VS Country

This seems like a great way to visualize where your fans are from. In my case, most of them are from India and so one actionable insight would be to schedule posts based on Time Zone in India. And I can imagine that such reports could be very helpful to brands who have sizable fan following on Facebook.

Here’s the screenshot:

Maps Power View Excel 2013 social media analytics
Thanks to the following blog-posts for inspiration:

1. Google Fusion Table & Data Visualization (He used Google Fusion Tables, I used PowerView!)

2. Creating Maps in Excel 2013 using Power View

A Social Media Analytics Sample Dashboard in Excel Powered by PowerPivot.

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I found a great sample Dashboard on Social Media Analytics in Excel that is powered by PowerPivot. Here’s the screenshot of the Dashboard.

excel powerpivot twitter social media analytics dashboard 1

Here are the steps if you want to download and play with the Dashboard:

  1. Install Power Pivot add-in
  2. Download the “Analytics for Twitter” excel sample (powered by PowerPivot). Link: http://www.microsoft.com/en-us/download/details.aspx?id=26213
  3. It creates an “Analytics for Twitter” excel file on Desktop > Open it.
  4. The dashboard is powered by data it pulls in the Power Pivot:excel powerpivot twitter social media analytics dashboard
  5. You can change the search queries:
    a. Edit the default search terms:excel powerpivot twitter social media analytics dashboard
    b. Refresh Data:excel powerpivot refresh data
    c. Updated Dashboard!excel powerpivot twitter social media analytics dashboard

That’s about it. And here’s a Youtube Video showing some features in this sample:

Conclusion:
In this blog-post, I shared a great sample dashboard built on top of PowerPivot model.

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Excel data Mining in Action: Forecasting Twitter Followers for next week

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OK, so you know I recently installed Data Mining Excel add-in: How to enable Data Mining in EXCEL powered by SQL Server Analysis Services? – and I couldn’t wait to go beyond the samples provided with the Excel add-in. So I decided to start with Forecasting. In this blog-post, I downloaded my Twitter stats into Excel. And of course, I had to clean and add computations which was equally exciting and I ended up with a data-set that had the follower count and also number of tweets I had.

The Date-Range in the Data-set is from 23 July. 2012 – 5 Sep. 2012. Of course, to get “better” forecast – you need to feed more historical data. In my case, the Twitter API didn’t allow me to pull ALL historical data at one go – let’s not get into details because that’s not the focus of the blog-post. But rule of thumb is that more historical data gives better forecast. And, Here are the steps I followed:

1. Loaded Data into Excel 2010. (I am using Twitter as an example here. Other real world scenario’s would be Sales Forecast). Note that I have kept it simple for the purpose of the demo.

2. Now, let’s create a forecast model.

Go to Data Mining Tab > Data Modeling > Forecast:

data mining excel forecast twitter followers

3) Forecast Wizard:

a. Getting Started with Forecast Wizard: NEXT

b. Select Source Data. Then Press NEXT

c. Select input columns. In this case, I selected Date as Time Stamp and Total Follower Count & Total Tweet Count as Input columns.

– Notice the Parameters Button? That is used to set the configuration of how the (Time Series) algorithm runs. For the purpose of this demo – I am going to explore that.

d. Finish.

4) It forecast-ed (Using the Time Series Data Mining Algorithm) the follower count for next week and if you can see – it says that on 12th Sep 2012, I would have 438 followers which is +3 when compared to today’s (5th Sep) follower count.

forecast twitter followers using excel data mining

5) Few Notes

a. I had selected Total Tweet count just to show that It can forecast more than one variable at same time. Here the model used the Date Column as the time-stamp while forecasting.

b. Of course, this may not happen for REAL because your follower count can go up or down based on

  • Tweet (Quality Tweets!) Frequency
  • Number-of-bots-that-decide-to-follow-you (kidding!)
  • Re-Tweeting interesting content and replying your followers. Basically being social!
  • If tweet gets picked by someone famous, your count increases
  • Other real life “surprises”..

Here’s the point though: This was just a Toy Example to show “forecasting” with Excel Data Mining – If I explore it further, I would document my experiences!

And oh, BTW here’s a nice video by @MarkTabNet and @SolidQ (SolidQ: I work at this amazing company!) on “Microsoft Data Mining Demo — Forecasting (SQL Server 2008 and Excel 2007”. And MarkTabNet is a great resource for Data Miners, Check it out!

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