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How Uplevel Measures the Impact of Chat Interruptions

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    Written By Anne Sallaska

    We’ve all experienced the “quick question” ping that breaks our concentration and completely derails our work. Not only is it annoying, but these innocent questions can be detrimental to productivity, work quality, and well-being. 

    The need for interruption-free work is especially true for developers, who need ample time to focus on solving tough problems. Uplevel's Chat Interruptions model helps organizations monitor their messaging trends holistically to make sure they aren’t getting in the way of work.

    How the Chat Interruptions Model Works

    To understand Chat Interruptions, we first need to talk about deep work, another Uplevel metric. Deep work essentially measures time available to focus. We derive this information from calendar data and look for open blocks or scheduled focus time of two hours or more. Our Chat Interruptions feature monitors the messaging activity during those blocks to see how often a developer is pulled in different directions. 

    The challenge is that in a given chat channel, any number of conversations may be going on simultaneously and will have messages that are interspersed temporally with other conversations.

    Dissecting which message belongs to each conversation is a big challenge from an algorithmic perspective. Think about an in-depth conversation going on in a channel with multiple participants and, right in the middle of it, a scheduled announcement message gets posted that’s unrelated to the current topic. Humans reading messages may be able to easily identify which messages belong to which conversation, but teaching a computer to do that on its own is not easy. 


    However, after conversation blocks are determined, the interruption calculation proceeds naturally. If someone else is chatting in a given conversation and you respond within a certain amount of time, that counts as an interruption for you (but not for the other person). Short messages and conversations count as less of an interruption when compared to long messages with a lot of back and forth. Based on this data, we’re able to estimate the amount of time developers are interrupted and how much it impacted their focus time. 

    No organization uses Slack perfectly, as many of us know. Though we only get actual message content from public channels, Uplevel redacts private channel and DM message text entirely, so we built two machine learning models and a rules engine to understand conversation patterns with just metadata, such as timestamps and message author. No model will ever be 100% accurate, but this version helps us get closer to that goal. 

    How to Use Chat Interruptions in Uplevel

    The Chat Interruptions model gives organizations a way to quantify distractions. They become aware of how much interruptions can impact developer productivity, helping them set goals and implement practices to mitigate the impact.

    The first step to examining the impact of interruptions is to understand your baseline. By taking a look at your data, they can see where they currently stand and what their trend over time is. Every organization is unique, so there isn’t one “right” number that works for everyone. It is important to get feedback from developers to see how they’re feeling to find the right sweet spot when it comes to messaging, before it causes diminishing returns.

    I would emphasize that having zero interruptions is bad, as some are really necessary to help unblock colleagues and improve the workflow of the organization as a whole. If you want to improve Slack interruptions, start by setting goals and implementing changes like reinforcing that asynchronous communication shouldn't require an immediate response during deep work time. Continue having an open dialogue about this with the goal of continuous improvement in mind. 

    Case Study: Uplevel

    At Uplevel, use Chat Interruptions ourselves! We check in to see how Slack is impacting our productivity. Especially since we’re growing quickly, we want to make sure that we don’t let practices that worked when we were half our size impact our productivity as we hire more and more folks.

    Some of us prefix some messages with certain emojis to give an indicator of message priority. It’s an org-wide initiative to make sure we have a culture where we’re able to block out the time we need for ourselves to get our work done. If something spikes, we look into it and talk about the root cause and fix it if we need to. Having this data helps us all stay on the same page. 

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