17 Mar. 2014 | Comments (0)
What percent of your work day do you spend on email?
If you guessed 10% or 20%, sorry: Studies of office workers peg the average at 28%.
Maybe that’s average but that’s not me, you say? No need to guess. These days there are a number of auto-analytics tools that not only help you quantify how much time you spend on email, but also analyze other email behaviors to help you work more productively. Which of those tools offer the most potential? To find out we went to a scientist, founder and CEO of WolframAlpha, Stephen Wolfram. He’s undertaken a thorough “personal analytics” of his work routines. He told us about four analytical techniques that are relatively easy to use but provide new views into your mail habits that can help him tame the inbox.
1. Project Timeflow Analysis. In this scenario, Wolfram isolates emails related to a single project by searching for a project name, and then he visualizes the volume of mail over time using the email timestamps. The visual helps him determine how long ago a project started and whether it is progressing smoothly or instead is “running kind of slowly then has a bunch of activity.” For instance, on the day we talked, Wolfram pointed out that he had just contributed to a project that had been running for three years, “although it wasn’t super obvious it had been running that long” until he analyzed his email archives.
You probably have a decent sense of the various groups and projects you’re managing and tracking. But do you have a clear idea of how all these projects are progressing over time? Have you been paying enough attention to the right ones? Have some fallen off your radar? This technique could help you. Don’t know how to visualize this kind of data? Try this hack: simply search for emails that mention some keyword and put them all in a folder. Then use the time stamps to look at how long that keyword has been in play and whether the volume is smooth or tends to come in bursts. You may notice some project seems to always pop up at the end of the month, for example, and adjust accordingly.
2. Backlog Analysis. Periodically, Wolfram evaluates the total volume of his unread emails and his needs-a-reply emails. Wolfram points out this is “quite a useful thing” as it gives him insight into how the past few weeks have evolved in terms of his busyness and work responsibilities. “Right now, for example, I have a whole bunch of projects that are coming to a head and I can see in my email that I am really behind and I’ve been behind,” he says. Armed with real data, he can proactively manage his attention and direct those that help him plan his day. For instance, he makes his backlog visible to assistants who help schedule his calls and meetings. This allows his staff to be more analytical in forecasting the effects of scheduling decisions on his productivity. “If they schedule back-to-back meetings throughout some period of time, then [they] can immediately predict how much larger of an email backlog I will have at the end of it.”
You may not have assistants, but it’s worth analyzing your backlog as academic research shows that it’s a significant predictor of stress in the workplace. Consider quantifying your overall or weekly volume of unread or “still-needs-a-reply” email, and setting a target ceiling of such messages.
3. Action Analysis. Wolfram moved from intuition to analytics to understand what he really was doing with incoming email. He charted how he responded to specific senders and discovered a correlation: if there is almost no time lag between opening an email and either forwarding or deleting it, he knows the sender’s emails are of lower importance. “After one of these analyses, the main result was taking myself off a bunch of internal mailing lists,” he says. The savings in simply not having to acknowledge, read, or delete these bulk messages significantly decreased Wolfram’s email load, and because he analyzed the data, he knew he was getting rid of the ones that he would be least likely to miss.
Action analysis for you starts with an acknowledgement that there are only about six things you can do with an email: open, delete, file, forward, reply, or ignore. Research at Carnegie-Mellon and elsewhere shows that the perceived importance of a message (and the messenger) plays a significant role in determining which of the six tacks one takes. So, prioritize senders and subjects and remove yourself from lists that you don’t need to be part of. If you can’t bring yourself to decline incoming mail, consider automated filing so you never have to acknowledge certain emails in the daily stream and can instead set aside time later to rip through them.
4. Response Time Analysis. Wolfram used to answer email continuously throughout the day, but surmised the approach was “kind of silly because email needs responses on very different time scales—very little of it needs a response in 5 minutes.” As an experiment, he began a very different approach: waiting until a specific time late in the day before reading and responding to non-urgent messages.
Paradoxically, he discovered that this Bartleby-like approach of waiting outperformed active involvement in most email conversations. His analysis showed that “it’s much more efficient to let certain kinds of [problems and questions] mature…and resolve themselves,” he says. So, by not responding right away, he often created a situation in which he wouldn’t have to respond at all. This is classic email management advice, but Wolfram’s analytics have shown just how smart it is to get out of the constantly surging stream of correspondence and instead make email a daily task, at a certain time.
The future potential of email auto-analytics on productivity is huge. Find yourself writing paragraphs when a few sentences would do? Track word count. Co-workers complain you are too verbose and jargon-heavy? Track reading level. Procrastinate or dither too much on replies to important people? Track your response time to specific individuals. Too much back-and-forth via email when a phone call would be quicker? Track thread length. The possibilities are endless.
For most of us, our email archives represent the largest repository of personal data we have about how we manage others and get our work done. Google makes billions by analyzing the email and inbox behaviors of their Gmail users, and using those insights to sell more, better-targeted advertising. Why not adopt a similar approach to improve our own productivity and leadership effectiveness?
Maybe the real, future potential of “Big Data” isn’t only out there with customers and distributors, but inside our own laptops.
This blog first appeared on Harvard Business Review on 1/23/2014.
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