Is Slack Messenger Right for My Team? Analytics and Answers

Slack

From AOL Instant Messenger to WeChat stickers, digital communication has always fascinated me. From the beginning, there has always been so much we don’t understand about digital communication. It’s kind of like GMO; we just started using it without considering the implications.

We are continually learning how to use the the digital medium to achieve our communication goals. And meanwhile, our digital communication tools are ever evolving to better suit our needs. A prime example of this is the team messaging app, Slack.

Slack

Slack has adapted well and I would argue that it has dominated its ecosystem. There are a few reasons why I believe that it’s earned its position:

  1. It’s easy.
  2. It’s flexible.
  3. It’s not too flexible.

As a tool, Slack is malleable enough to form-fit your communication theories and practices and it does little to dictate them. This means that its utility and its effect are less a factor of the tool and more a factor of the our ability to shape its use.

So when the question was posed, “How well does Slack fit our needs as a team?” I have to admit I wasn’t sure. Days later, in my head, I answered the question with two more questions:

How well have we adapted the tool to us?

How well have we adapted to the tool?

The questions felt somewhat intangible but I had to start somewhere and me being me, I asked the data. I’ll admit I haven’t gotten to the heart of the questions… yet. But I did start to scratch the surface. So let’s step back from the philosophy for a minute, walk through the story, and start answering some questions.

So yeah, we tried Slack… Six months ago

A recently formed, fast moving and quickly growing team, we believed that we could determine own our ways of working. In the beginning, we set some ground rules about channel creation and, believe it or not, meme use (hence the #wtf channel). And that was about it. We promised ourselves that we would review the tool and its use. Then we went for it.

A while later, as I mentioned, a manager pointed out that we had never reviewed our team’s use of Slack. It seemed fine but the questions started to crop up in my head. Me being me, I had a to ask the data.

This all happened about the time that I started to play with Pandas. I didn’t answer the questions but I did get frustrated. Then I read Python for Data Analysis, pulled the data out of the Slack API (which only provides data about channels) and went a bit crazy with an iPython notebook.

To answer my theoretical questions, here are the first questions I had, a few that I didn’t and their answers.

How is Slack holding up over time?

Stacked Time Series

Don’t judge me. This was my first go with matplotlib.

This stacked time series shows the number of post per channel (shown in arbitrary and unfortunately non-unique colors) per week. The top outline of the figure shows the total number of messages for each week. The strata represent different channels and the height of each stratum represent the volume of messages during a given week.

It appears that there is a bit of a downward trend the overall number of messages per week. A linear regression supports that. The regression line indicates that there is a trend of about two fewer messages than the week before.

Linear Regression

If you ask why there appears to be a downward trend in total use over time, I think there a few ways to look at it. First, the stacked time series shows that high volume weeks are generally a result of one or two channels having big weeks rather than a slowing of use overall. This makes sense if you consider how we use channels.

We have channels for general topics and channels for projects. And projects being projects, they all have a given timeframe and endpoint. This would explain the “flare ups” in different channels from time to time. It would also explain why those same channels come to an end.

One way to capture the difference between short lived project channels and consistent topic channels is with a box plot. Box plots represent the distribution of total messages per week for each channel by showing the high and low week totals for a channel and describe the range (Interquartile Range) that weekly message totals commonly fall into.

Slack Analytics Channels Box Plot

Each box plot represents a Slack channel. The Y axis scales to the number of messages in that chanel

For a specific example, the channel on the far left (the first channel created, named #generalofficestuff) has had a relatively high maximum number of messages in a week, a minimum around 1 or 2 (maybe a vacation week) and 50% of all weeks in the last six months fall within about 7 and 28 messages with an average of 10 messages per week.

On the other hand, channels on the right side of the chart, more recently created and generally project-specific channels, describe the “flare ups” that can be seen in the stacked time series chart above. If you wanted to look deeper, you could make a histogram of the distribution of week totals per channel. But that is a different question and, for my purposes, well enough described with the box plot. 

So… how is Slack holding up over time?!

The simple answer is, use is declining. Simple linear regression shows this. The more detailed answer is, it depends. As the stacked time series and box plots suggest, in our case, use over time is better understood as a factor of the occurrence of projects that lend themselves especially well to Slack channels. I know what you’re saying, “I could have told you that without looking at any charts!” But at least this way nobody is arguing.

Projects… What about People?

Another way to look at this questions is not by the “what”, but by the “who.” Projects, and their project channels are basically composed of two components, a goal/topic and a group of people that are working toward that goal. So far we have only looked into the goal but this leaves the question, “are the people a bigger factor in the sustainability of a channel than the topic.

I looked at this question many ways but finally, I think I found one visual that explains as much as one can. This heat map shows the volume of messages in each channel per person. It offers a view into why some channels might see more action than others and it also suggests how project/channel members, and the synergy between them, might affect a channel’s use.

Slack Analyttics Hierarchical Clustering Heatmap

Volume of messages is represented by shade with Users (user_id) are on the Y axis and channels are on the X axis. Hierarchical clustering uses Euclidian distance to find similarities.

What I think is most interesting in this visualization is that is shows the associations between people based on the amount of involvement (posts) in a channel. The visual indicates that perhaps, use is as much a factor of people as the channel’s project or topic, or time.

There are, of course, other factors. We cannot factor out the possibility of communication moving into direct messages or private groups. But again, that is another question and beyond the bounds of this investigation.

So what?

So we got a glimpse at the big picture and gained a pretty good understanding of the root cause of what motivated the question. This is my favorite part. We get to sit back, relax, and generate a few new hypotheses until we run into a new question that we can’t avoid.

What I think is coolest about the findings is that it suggest a few more hypotheses about what communication media our team’s communication occasionally moves to and what media it competes with. Now these investigations start to broach the fundamental questions that we started with!

There are a few things at play here. And the following are just some guesses. It could be that email dominates some projects or project phases because we are interacting with outside partners (people) who, for whatever reason, cannot or will not use Slack. Sorry Slack. It could also be that, due to the real world that we live in, communication is either happening over chat apps like WeChat or WhatsApp.

In either case, we return to the idea of people adapting to tools that are adapting to people. The use of digital communication tools reflects the people who use them and each person’s use reflects the structure and offerings of the tool.

And what’s next?

Hopefully, if you read this you have more questions about this reality and I might (probably) go on to try to answer a few more. I think there are a few interesting ways to look at people are norming with Slack.

Maybe, you are interested in how all this Pandas/matplotlib stuff works because I am too. So I think it will be fun to post the iPython notebook and show how it all works.

Otherwise, it will be interesting to watch how this tool and this team continue to evolve.

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