Tag Archives: metrics

Teaching Students to Analyze Twitter data with Excel pivot tables: Social Media Analytics Assignment (Post 2 of 4)

In my last post, I discussed a new assignment that I’m using this semester in my Communication research class (all posts on that class).

That social media analytics project assignment contains 3 parts. Each part is related but unique, allowing students to pick up a new skill set. In this post, post 2 of 4 in the series I’m writing about this assignment, we’ll discuss part 1 of the assignment. If you haven’t read the assignment overview post, I encourage you to do so before proceeding. There you will see a copy of the assignment discussed in the below post.

Part 1 of the assignment asks student teams to analyze the Twitter data provided by their clients by creating pivot tables in Microsoft Excel.

social media analytics pviot tables excel Twitter data

If you aren’t familiar with pivot tables, they enable you to filter and visualize spreadsheets. This allows you to focus in on specific data points and quickly extract insights from large data sets.

I got the inspiration to create this part of the assignment from a very helpful conversation I had with Professor Stefanie Moore at Kent State University. A big thank you to Stefanie for taking the time to chat with me and for providing me with insights to how she teaches analytics. I am really impressed and inspired by what Professor Moore is doing at Kent State.

Preparation: Getting Twitter Data
In order to analyze Twitter data using pivot tables in Excel, you need to first download Tweets from Twitter’s analytics (ads) page. If you’ve never done this before, it is really quite easy.

The reason we use Twitter is because Twitter enables you to extract a ton of valuable account data from your account in the form of a CSV spreadsheet. But, as an aside, you could analyze just about any data with pivot tables.

My students were required to get the Twitter data from a client. Therefore, I created a step-by-step guide that they could provide to the client so that the client could extract the appropriate data and supply it to me.

To ensure we had enough data, I instructed the students to ensure that their client was posting at least a few times per week. I asked students to get 6 months of Twitter data if possible. In short, I wanted to ensure that there were at least 50 Tweets from the client in the time period we collected. This number is somewhat arbitrary. And ideally you’d like to have more. But, 50 Tweets is enough to sort and play with.

Here are the steps for extracting Twitter data from an account:

Step 1:  log into your organization’s Twitter account at http://twitter.com. Next, select your account profile picture (as shown below) and select “Analytics.”


Step 2: A new window will appear. Click “Tweets” from the menu at the top. Then, select the date range (see below). A menu will open. Please select a date range of at least 3 to 6 months back so that there are enough Tweets for the students to analyze.
Important: Click “Update” to change the selected date range.
In the below example, I selected Feb 1 through May 1 (3 months).

Step 3: Once the dates have been selected, click “export data.” A new window will appear. Click “save file” to save the file to your computer. Email that file (it should be a .CSV file named something starting with: “tweet_activity_metrics…”). You have your data. If someone else is downloading the data – such as a class client – , they will need to email the file to you or your student.

Using Pivot Tables to Analyze Twitter Data

A few days were set aside in class to work with the pivot tables and learn how to answer the questions students were asked to answer in the project. On day 1, I provided a brief lecture  (about 10 minutes). And then I instructed students to begin working with the lab guide I had created. If you’re a longtime reader of this blog, you know I am big on creating lab guides to assist students in learning software.

See the lab guide students used to learn to analyze their Twitter data using pivot tables: http://bit.ly/435_pivottableslab

While working with the lab guide, students were to have a copy of the assignment that contained the research questions they needed to answer using the pivot tables. Those research questions were:

  1. Which Twitter posts received the most (Fill in the blank – you need to decide what variables are important engagement data for your client. You’ll need more than 1 variable. And, you’ll want to show more than just the top Tweet for that variable, but the top few)?
  2. What is the client’s Twitter engagement by month? (again, you choose the appropriate engagement metrics)
  3. Come up with 1 other RQs for important data points you extract from your pivot table analysis that you believe will be of value to your client.

For the above questions, students needed to pick what engagement metrics they wanted to analyze. There are several engagement metrics in the CSV file when you download it from Twitter. Examples include retweets and favorites.

For research question #3, most groups analyzed engagement by Tweet category. As you’ll see in the lab guide, students learned how to comb through their Tweets and identify common themes by which to categorize their Tweets. Examples may include promotional Tweets, humorous Tweets, Tweets that ask a question, etc.

The above 3 research questions are just a sampling of what you could do with the pivot tables.

In Summary

In the next post, we will discuss part 2 of this assignment which gets students using Microsoft Social Engagement to answer some research questions about their client.  I will be publishing that post in 2 weeks.

In the meantime, if you want to get your feet wet, I encourage you to download your own Twitter data and walk through the lab guide above. Or, check out some of the sources listed below to learn how to analyze Twitter data with pivot tables.

As you will see when you take a look at the lab guide, you must first clean the data so that Excel can analyze it. I then walk you through a number of different ways you can analyze your Twitter data.

The fact is that I was a bit of a newbie to pivot tables when I created this assignment. To build the above-discussed lab guide I provided students to help them through learning how to use pivot tables, I relied heavily on several key resources. Much of what is in the lab guide is built directly on what I learned from these sources. To learn directly from the sources I learned from, check out the sources below. A big thank you to all of them for sharing their knowledge publicly. I hope I was able to honor them in adapting their work for a classroom assignment.

Update: You can now read the follow up posts to this blog series.

Sources:

 

The New Social Media Analytics Assignment for my Comm Research Class (Post 1 of 4)

A few months ago I wrote about how students in my social media class were using Microsoft Social Engagement to track metrics and do some social listening. At the time, I said I’d follow up with a post about how we were using the software in my communication research class. Well, the time has come! But, this post will do more than dive into how we are using Microsoft Engagement in my class. It will share with you a whole new project my research students are doing.

This is post #1 in a 4 part series on a new assignment my students are working on in my communication research class. The assignment spreads over several weeks with a good amount of time in class working in the computer lab. The project is the result of continued and ongoing efforts I’ve been making in a few classes to enhance student education in social media analytics. The project replaces the sentiment analysis assignment I wrote about a few years ago.

This post will cover an overview of the assignment (A copy of the assignment is below). Post #2 will discuss using pivot tables to analyze Twitter data. Post #3 will discuss Microsoft Social Engagement. Post #4 will discuss Netlyitic.

Update: Post #2 on pivot tables is now available, as is Post #3 on MS Engagement and Post #4 on Netlytic.

First, let me provide some context. In my communication research class (see all posts related to the class), students work in teams to complete 3 projects. Each project gets progressively more difficult. The project we are going to discuss today is project #2.

Overview of Social Media Analytics Project for A Client

The purpose of the assignment is for students to get experience performing a social media analytics audit of a client using a variety of social media analytics and social network analysis tools. The goal is for the students to try and understand their client’s current use of social media and provide insights and recommendations for enhancing that client’s social media presence.

Each team was tasked with going out and finding a client that would agree to participate. While I had hoped that most groups would approach local businesses, they tended to focus more on on-campus groups like athletic teams. This may have been a result of convenience because each team had to acquire several months worth of Twitter data from their client. I will explain that in further detail when we discuss pivot tables in post #2. So students tended to go to on campus organizations where they already knew who ran the Twitter account.

The three main components of the project are:

  1. Client Social Media Profile & Engagement Analysis
    1. Students use Pivot Tables to explore your client’s posts on social media and analyze their overall engagement. For example, students determine the top posts by their client which made that have gotten the most likes.
  2. Analyzing Trends
    1. Students use Microsoft Social Engagement to monitor and analyze the conversation surrounding the client’s brand.
  3. Social Network Analysis
    1. Students use Netlytic.com to build visual representations of their client’s social network on Twitter or Instagram and do some basic analysis.

For each component, I have created a set of research questions that students answer using the appropriate software. The students adapt the research questions a bit to their context when necessary. You can see the research questions in the assignment below.

The Plan in the Classroom

On day 1, I provide a 10 minute lecture on pivot tables. The rest of the class is a lab for students to work on learning how to create pivot tables to analyze Twitter data and answer the RQs.

On day 2, I give a 20 minute lecture about the social engagement software and talk a little about sentiment analysis so students understand what it is when they look at it in the Microsoft software.

Day 3 is a lab day to work on whatever they weren’t able to get done in the pivot tables or the social engagement software.

On day 4, I lecture about social network analysis and some basic concepts. (We cover some other material this day about writing research papers).

On day 5, we finish talking about social network analysis – about 15 minutes – and the students analyze their client’s data.

Research Write Up

After students complete all 3 parts of the project, they then have to write up their study. The research paper format I use in this class is inspired by Don Stacks book, Primer in Public Relations Research.

In the past, by the second project students are writing brief literature reviews. However, because this is the first time I’ve run this project and it has been a lot of work, I called an audible and removed the requirement for the lit review in this project. So, you will see in the assignment below that those requirements have been withheld.

Thus, by the second project students have been taught about writing research problem overviews (problem statement, campaign goals & objectives, research objective & RQs/hypotheses), methods, results and discussion sections.

The students write up their reports. And they are encouraged to share them with their client.

Limitations & Final Thoughts

There are a few drawbacks I’ve experienced thus far with this project.

First, there is a lot of info coming at the students with this project. The assignment sheet itself is several pages long. As such, it is important to explain things several times and work with the students as they are doing this project.

Students need to be responsible for getting the data for this project from their client, creating their own Netlytic account and setting it up to collect data. And, they need to provide me with who their client is and some competitors of the client far enough in advance that I can program it into Microsoft Social Engagement (I’ll go into more depth on this in the individual posts about each section). We had a few groups that made mistakes along the way and were short on data or had to do some last minute scrambling.

The data collection periods across the Twitter CSV file, the Microsoft Social Engagement and the Netlytic are not consistent. This is simply a result of the classroom setting and a lack of full control over when data collection happens. For example, a team’s client may have sent their Twitter data which covers the last 6 months one day, a teammate set up Netlytic to collect data another day, and the day I set up the Microsoft Social Engagement to collect data on their client on a third day.

With these another limitations in mind, the project has been fun thus far this semester. A major benefit of this assignment is that most of the tools used in this assignment are free or inexpensive and not too difficult to learn (and thus teach your students).

Over the next few posts, I will offer some depth on each section of the project.  So check back soon! For now, you can get a copy of the assignment below.

Update: You can now read the follow up posts to this blog series.

How to use Microsoft Social Engagement software to teach social media listening (Post 2 of 2)

This post is part 2 in a two-part series on how I currently teach social media metrics and social listening. You can see the previous post, which provided a spreadsheet that I use to empower students to track metrics for the social media accounts they manage in my social media class (2016 syllabus; and all articles about this class).

We’ll be using that same spreadsheet, though a different section of it, in this blog post. You can access it here.

In this post, we’ll discuss Microsoft Social Engagement and how I integrate it into the the social media class so students can engage in social listening.

About Microsoft Social Engagement

Microsoft Social Engagement, sometimes also called Microsoft Social Listening, is part of the Microsoft Dynamics Academic Alliance program via the Customer Relationship Management (CRM) software package. In short, Microsoft Social Engagement is one of the pieces of software bundled into the full CRM. It appears the Microsoft Academic Alliance program has recently gone through some changes since when I signed up last spring.  The website itself is quite different. However, I’m not personally familiar with the nature of any changes to the program.  The language on the website aimed at educators reads: “Demonstrate thought leadership and differentiate your institution by integrating Microsoft Dynamics CRM and ERP solutions into your curriculum. DynAA helps you innovate and remain relevant when working with prospective students, current students, and potential employers interested in hiring new graduates. Your free DynAA membership provides access to software, support, resources, and community-building opportunities that will prepare your students for exciting careers. ”

Through the Microsoft Academic Alliance program, I have been very fortunate to get my students access to the Microsoft Social Engagement software.

So what is Microsoft Social Engagement? In short, it is a social listening tool that enables users to track metrics for public social media accounts or posts (e.g., keywords or hashtags) such as posts on Facebook, Twitter and Instagram.  You can also track mentions forums and blog.

You do this by programming different ‘analysis focuses.’ That is, I can have 1 that searches one or a set of topics, keywords, social media accounts – say, my brand – and I can have another analysis focus that focuses on my competitors accounts, keywords, etc. From what I can tell, you can have as many as you want so long as you don’t go over your monthly quota of social data units.

For example, in the social media class we follow our social media accounts and mentions of them, and specific keywords surrounding our department’s brand, such as our hashtag.

The software enables you to quickly visualize several things such as key phrases, sentiment, social platforms or accounts that posts are coming from and their sentiment, posts across time, sentiment across time, geo-location, and geo-location across time. Below is a quick look at the main hub you see when logging into Social Engagement. In it, you can see sentiment in the top left. You can see the sentiment for each platform below that. In the center, you see the volume of posts across time for the keywords we are tracking. In the top right you can see the phrases being used related to those keywords. And in the bottom right, you can see the proportion of the posts that are being analyzed in this instance from each platform.

Click to enlarge.

There  are 4 main sections of the software: Overview (the page shown above), conversations, sentiment, location and sources. They are pretty self explanatory.

When you click on a pie chart or graph or keyword, it is interactive. What I mean by that is, it creates a filter in the app.

So, if I click a specific keyword in the phrases word cloud, I am filtering for only those posts that used that keyword.

For example, in the below GIF I am in the Conversations section of the software. I see all of the phrases surrounding our department’s social media accounts and blogs in the last month. That is, every post that mentioned 1 of our social accounts, our hashtag or our blog (Note: This is what I’ve selected for this analysis focus). I then click on the #shepcomm hashtag which filters for only those posts that contain that hashtag. So, I can see the other phrases that are in posts containing #shepcomm. You’ll see that the blog source gets filtered out because the 1 blog post does not contain the hashtag. Next, I click Twitter. Thus, only posts containing the hashtag and Twitter are being shown.  Lastly, I click on the neutral (gray) sentiment and we filter down to the 1 Twitter post that has neutral sentiment containing the #shepcomm hashtag. While not shown in the below GIF, in order to see what the 1 post was, I could click on the “posts” tab in the right-hand side of my screen to see the original Twitter post.

Click to enlarge.

For the sake of keeping this post length manageable, I will stop there. Suffice it to say, I am just touching the tip of the iceberg on how you can use this software. I will go into 5 key ways that we use the software in my class below which will further demonstrate its utility. And, you will get instructions on how to use the software for those 4 ways in the lab guide I provide my students which I will link to below.

Before doing that, a few notes: The reality is, there is a lot more than can be done with Social Engagement by linking it to other software within the Dynamics CRM. For example, as I understand it, it can be linked with other software for social media customer relations management. But I have not gone down that path yet.

One limitation of the software is that you have to program in what you want it to track ahead of time. Then, it begins tracking. For example, it isn’t like a Twitter search where you can go in and look into past 2,500 posts on a topic after the fact. If I know I’m going to want to track a hashtag or social media account, I have to program it and then I’ll get the data going forward from the time I programmed it. A second limitation is that it is not real time meaning that while you are looking at the software you don’t see the data changing if new Tweets are coming in.

How I integrated Microsoft Social Engagement into the social media class

Click to enlarge
Click to enlarge

As noted previously, students in my social media class are divided into teams and each team is in charge of running a social media platform for our department’s social media.

As I mentioned above and went over in depth in the prior post in this series, the students use a spreadsheet to track metrics for the social media content they create and post. Here is a copy of that spreadsheet as it was distributed in my Fall 2016 class.

Social Engagement is used to work on the “social listening” tab of that spreadsheet. In short, the other tabs in the spreadsheet are about tracking our own performance. The social listening tab is about, well, social listening – seeing what is being said about our brand. The spreadsheet asks the students to answer 5 questions each week. You can see them below. A hint is provided to students on where to look to find this answer by mousing over each question.

Click to enlarge

To help students learn how to answer each of these questions, I developed a lab guide (about lab guides). The lab guide teaches students how to use the software.

That lab guide can be accessed here: http://bit.ly/FSM_microsoftsociallistening

You will find that reading through it can teach you a bit more on how the software works and how it can be used to answer the above 5 questions.

In summary, these posts have provided an overview of how I taught social media metrics and listening my fall 2016 social media class. In my research class this spring we will be diving deeper into Microsoft Social Engagement and a few other tools for learning about social data. I am always looking to improve. I’m also looking to find new, cost-effective software solutions to expand social media measurement learning opportunities. If you have any suggestions, leads, or want to chat or collaborate, please Tweet me.

I hope you found this post helpful. If you did, please share it. It helps a lot.

– Cheers!
Matt

An Assignment and Spreadsheet for Teaching Students to Track Social Media Metrics in my Social Media Class (Post 1 of 2)

In the social media education community, there has been a lot of discussion about teaching social media metrics and analytics to students. This has been a challenge and frustration for myself and many others. Access to industry tools is cost prohibitive for many universities, making it difficult for us as educators to prepare our students for this aspect of their careers.

I’ve worked hard over the last few years to try and enhance how I’m teaching these concepts. And I’m not where I want to be. But I know there are many fellow educators also on this journey with me. So, I’d like to share how I teach students to track social media metrics as part of a semester long assignment and a few modifications I have recently made to enhance that aspect of my teaching.

I’ve split this topic into two blog posts for length purposes. In both of these posts, we’ll focus on my social media class (2016 syllabus; and all articles about this class). In this post, we’ll talk about the spreadsheet for tracking metrics. In the follow up post, we’ll discuss Microsoft Social Engagement and how I integrate it into the metrics assignment portion of the class.

Update: The follow up post on Microsoft Social Engagement is now available.

My aim in my social media class is to introduce metrics to students both in lecture & discuss (which I’ve been doing for some years) as well as by use of software. Then, when students get into the Communication Research class (2015 syllabus; articles about this class), they will get more in-depth learning about analytics. I’ve increased/improved my focus on this area in that class for next spring. And my long term hope is to really build that part of the class out. During the upcoming spring semester, I will write a blog post about what we will be doing with analytics. And, at that time, I will share all of my assignments and handouts.

Okay, back to my social media class. In past years we’ve used Twitter Analytics – which has been the best, free tool. Unfortunately, other platforms have been limited in their analytics. We’ve used a slew of free tools that have been here today, gone tomorrow.

This year, we still faced the challenges of relying on Twitter Analytics and whatever free tools we could find. But I also added a brief introduction to Microsoft Social Engagement (which will be discussed in the next post in this series).

But first, let’s discuss how I teach students to track performance metrics in my social media class.

In my social media class, students are divided into teams. Each team is in charge of running a social media platform for our department’s social media. In the past, I had my students use a spreadsheet developed by Jeremy Floyd to track metrics. At the time, I modified the spreadsheet for our purposes. At the start of this semester I modified the spreadsheet further simplify it and to add a section on Microsoft Social Engagementƒ (again, which I will discuss in the next blog post).

Here is a copy of the spreadsheet as it was distributed in my Fall 2016 class which you can use in following along with the below post. You can also download a copy for yourself to modify and use as you prefer. Again, credit goes to Jeremy Floyd for the original incarnation of this spreadsheet.

In lecture, I teach students about the activity, engagement and performance metrics discussed in Kim’s book, Social Media Campaigns: Strategies for Public Relations and Marketing. I also emphasize the importance of choosing metrics that are tied to goals. (You’ll see a tab in the spreadsheet discussed below, where students are to determine their objectives and what metrics would be important to those objectives).

Student teams begin with the planning tab, then they establish their metrics goals to use the spreadsheet to establish benchmarks and KPIs for their platform and track metrics over the semester. They then move over to reporting tab to track weekly metrics.

Tip. You can see tips by mousing over the small triangles in the upper right corner of some cells, as shown below. I’ve created these to help students when working on their spreadsheets in groups.

In the image below, you can see the ‘reporting’ tab of this spreadsheet. We start tracking in week 9 of the semester, but you can modify this as you like. After each week, you’ll see the percentage change. Of course, you can also modify what you are tracking. I throw in a number of potential metrics to track for different platforms. But, students can delete all the rows they don’t need and modify the individual metrics for that platform as needed. The metrics identified in the spreadsheet are just a guide.

I’ve also divided the spreadsheet up into different platforms so each team can pick their platform (as shown in image below) for tracking the success of their posts. The idea here, is that by tracking these posts across time, students can begin to analyze these metrics for trends (though, I don’t have any ways to quickly analyze and visualize this data at this time). This could help them learn when the best time to post is. However, you could also add variables about the post that can help them identify which is the type of content that is most successful. Other spreadsheets I’ve seen track variables such as whether an image was used, what hashtags are used, if links are used, etc. So, again, you can modify the optimization section as you see fit. I discuss other variables to track, but focus on the ones in this spreadsheet so as to not overwhelm students. I’ve found if I ask students to track too many things, nothing gets tracked as they get overwhelmed. So choose what you want them to track, and stick with it.

I’ve relied on Kim’s metrics categories for metrics students can track. Also, please know the metrics I have identified isn’t perfect and modification of what I’ve identified may be needed – some of my initial metrics may not work, or changes have occurred.

Integrating The Metrics Into the Semester-Long Assignment

As noted above,  across the entire semester of my social media class, students are strategizing, building and executing social media for my class. As a part of that, they present their content to the class for approval at intervals throughout the semester.  In the latter half of the semester, the students present their current metrics to the class alongside the content they are proposing for the next content time period. At the end of the semester, we discuss their metrics, whether they met their KPIs and during what week they did, and what they learned from them.

While the above enables us to track interaction with our social content and extract some insights, it doesn’t account for listening to competitors, following trends, etc. It also doesn’t take deeper analytics and the extraction of insights into consideration. We don’t do anything to plot or discern specific insights – I am saving that for the Communication Research class this spring. Said another way, the assignment and use of this spreadsheet in my social media class, as I executed it in Fall 2016, was really more about tracking metrics, following change and teaching students  to see the impact (outcome) of their efforts on social media, while connecting those back to objectives and KPIs.

In the next blog post, I go into the “social listening” tab of the spreadsheet and discuss how students got a little hands on use with Microsoft Social Engagement in my social media class during fall 2016.

In the meantime, if you have any thoughts or suggestions or resources you’d like to share about teaching metrics to students, please share them with me and the readers via a comment in the post or Tweet me. This is an important journey for all of us as we work to enhance hands-on metrics learning for our students.

I hope you found this post helpful. If you did, please share it. It helps a lot.

-Cheers!
Matt