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Collaboration At Work: Better Together

Reading time: 3 minutes

By James Cousins and Alisha Martin, Senior Statistical Analyst and Digital Marketing & Events Coordinator

Recently, there was an inspiring example of how collaboration at work can bring positive results. An engineering professor hoped to solve a challenge that his students faced year-over-year, and turned to the psychology and sociology departments for help. Through the combination of their perspectives, the DFW (grades “D”, “F”, or “withdrawal”) rate decreased from nearly 33% to only 11%. A reminder of how collaboration at work can bring about great improvements.

Enabling collaboration in the realm of data preparation and analysis is one of our greatest points of pride at Rapid Insight. In this blog, Alisha and James share their collaboration at work and discuss how each performs within the same tools, but in different ways entirely.

Alisha, Digital Marketer

As a digital marketer, I am constantly analyzing data to improve my marketing strategies. This often leads to intense Google searches to see what other digital marketers are doing, what tools they’re using, and which efforts work. A platform that we heavily rely on for exposure is Twitter.

After reading dozens of articles on other marketers’ “best practices for Twitter,” I came across a Google Sheets add-on: Twitter Archiver. This add-on was pretty self-explanatory: It archives tweets from specified keywords or accounts, trending hashtags, tweets that are geotagged, and so much more. The rules you set up are entirely customizable to your interests. I hit a data goldmine.

I installed the add-on, set up my rules, and let it run for a couple of days before diving into the data. Looking back, I probably could’ve only given it a few hours before beginning to analyze it because the amount of data it gathered in 48 hours was massive. I turned to Construct to prepare the data in a format that was easy to read.

James, Senior Data Analyst

As Alisha was exploring the data, she would occasionally ask me a few questions. I would take a look at the data she created, the steps she was taking in her Construct job, and I was up to speed pretty efficiently. I proactively offered to chip in some effort and dig into the data more analytically, per my background.

I am not involved in marketing efforts, and I don’t know all of the logistics that a marketing person would. As I prepared my analysis, I was able to include Alisha in the process, and she improved my analysis from her position of subject matter expertise.

For example, at one point I noticed that there are definitive hot-spots throughout the day. Tweets at certain hours see more engagement and interaction. It is plausible to set up reminders in a calendar to log on and tweet something out. You can also save drafts and schedule tweets easily. I kept those sorts of variables in my analysis, and Alisha was able to work in Construct to both learn and contribute along the way.

Figure 1: Alisha, the marketer, on the left, and James, the analyst, on the right

Collaboration Conclusions

As we collaborated throughout the data gathering and analysis process, more and more answers within the data were revealed. Alisha found more questions that could be answered within the data than was originally planned for. For example, James had set up a filter in Construct that would pull tweets that contained an image and moved them into another category, “Picture Tweets.” Alisha was able to further conclude that tweets with photos performed considerably better than tweets without pictures. This isn’t rocket science – but without James’s additional analyses, Alisha couldn’t have quantitatively identified it as a performance factor on Twitter.

What we discovered through our efforts continues to guide our work and our tweet strategies. What is most important is that each of us is perfectly capable of exploring the data, each in our own way, with our distinct backgrounds. However, when we came together, not only did the nature of the Rapid Insight platform help us to contribute, but the difference in our perspectives resulted in our final product being so much better than our individual efforts.

Neither of us could have done as well on our own, and Construct enabled both of us to contribute to each other’s effort. And that’s why examples like the story of the psychology and sociology departments working together are so exciting.

Collaboration is the only efficient and reliable way to account for all angles. We’d love for you to discover what kind of collaboration at work you can accomplish using the Rapid Insight platform. After all, not everyone needs to be performing every task, but results are far easier to achieve when everyone contributes their perspective.

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