Have you ever wanted to improve your email productivity? If you haven’t thought about it, now’s the time. There are more than 269 billion emails sent every day around the world, and despite advancements in mobile technology and chat-style messaging, email remains the dominant form of conversation in the professional world.
The trouble is, there’s no clear rubric or strategy for improving your efficiency at reading, writing, and organizing your emails (though we have published tips to help). You might have been taught some basic strategies at the beginning of your career, and you might get better naturally with experience, but there’s still no set of exercises, and no clear path that can make you a better emailer.
Thankfully, software engineers have been aware of this problem, and have been working diligently to solve it. One of the biggest problems in email productivity improvement—the lack of viable information to direct a strategy—is being solved, with the emergence of data visualization software.
So how is it that data visualization can help you master your email productivity?
Table of Contents
How Email Robs You of Time
First, you need to understand why email is such a vulnerable area; few professionals are truly efficient emailers, even if they’re brilliant communicators, and the amount of time wasted on email in just about any given organization is staggering.
You spend a lot of time on email—probably more than you’d like to admit. Email is a necessary communication medium, and in most cases is faster than traditional methods like phone calls, but every minute you spend on email needs to be spent wisely—or else it’s a minute wasted. Your productivity depends on your ability to handle email appropriately, giving complete information and attending to every inbound message, but it doesn’t take much to drift from this optimal ideal.
For example, spending too much time writing emails can result in wasted time, and long-winded emails that force your recipients to spend even more time reading them. If you agonize over the details of every impending message, you might spend twice as much time as necessary evaluating your new messages. Even glancing at your inbox to catch up on new material can result in lost time, especially considering it takes up to 23 minutes to fully recover from a single distraction.
Problems with email aren’t limited to your own habits and organizational skills. In fact, many problems can be traced to external sources. For example, you may have a member of your team who feels the need to email constantly, even when it isn’t warranted; just scanning these emails and deleting them costs you minutes of time. In an isolated incident, that may not matter much, but since the average worker sends about 121 emails a day, all it takes is a discrepancy of a few minutes per email to send your productivity screeching to a halt.
You may also find that the type of emails you receive from others are poorly organized, or needlessly long. On top of that, you may be sent emails on a CC line that you don’t have any part in. You aren’t emailing in a vacuum, so any external parties that lack productive email habits can wreak havoc on your own.
All forms of interference with your email productivity are complicated by the fact that email naturally takes up most of your day. By some estimates, the average worker spends 6.3 hours—or more—checking and managing their email. Imagine yourself able to improve your email productivity by just 10 percent—which isn’t such a tall order. That’s a time savings of nearly 40 minutes a day, which adds up to more than 3 hours per week. Now imagine an increase of 20 percent, or 30 percent—by that point, you’ll free up an entire workday of additional time.
Understanding the small, subtle ways that email robs you of your productivity is effective because even those small, subtle changes can add up to massive time gains. The trick, of course, is learning exactly where those points of productivity loss are occurring, and deciding how to deal with them.
Mastering the Data
You can’t rely entirely on your intuition to solve your email productivity problems, though you probably have a few suspected points of productivity loss in the back of your mind. I alluded in the introduction to the power of data visualization for addressing your email productivity, but for now, let’s focus on the “data” portion.
Why is objective data so important for understanding how to improve?
Removal of Bias
You might think you’re able to accurately assess the productivity and value of your own email habits, but chances are, your findings are being warped by unseen cognitive biases. Relying on objective, external data helps you filter out some of those biases, including:
- Pre-existing assumptions. Many people think of themselves as neutral investigators, but in most situations, we enter our investigations with pre-existing assumptions (whether we realize it or not). Let’s assume you’re going to evaluate how productively you’re emailing—without data—and you start your investigation assuming that you spend an average-to-low amount of time writing emails. Those pre-existing assumptions will lead you to the trap of confirmation bias; your mind will naturally favor emails that don’t take you long to write, and discount or make excuses for more time-consuming emails. External data prevents this from occurring.
- Cherry picking. Cherry-picking is another problem that can emerge without a reliable set of data to guide you. Here, you’ll use anecdotal evidence to back up your claims, rather than high-level objective overviews. For example, you might catch a glimpse of one extended email thread that wastes you and your colleagues’ time; but is this truly reflective of normal patterns, or is it just a fluke?
- Objective vs. subjective reporting. Data is also reported in an objective format, which makes it easier to precisely quantify. For example, you might feel like you spend too much time reading emails—but “too much time” isn’t nearly as accurate or as actionable as “an average of 3 minutes per email.” Your subjective perspectives can be distorted by hundreds of different factors, especially when related to the passage of time or your perception of your current workload; your objective metrics can’t be manipulated in this way.
External analytics systems also important because they don’t rely on you to measure things manually. If you had the initiative, you could time yourself with a stopwatch, before starting and after completing each email. You could dig around in every folder, and make written notes about how many emails you have coming in and going out. In theory, this series of tactics could provide you with similar data sets, but it would end up costing you more time than you could ever save by making productivity improvements.
The real benefit of modern data analytics systems is their ability to automate this measurement. You need something that lurks in the background, observing everything you do and recording all your interactions—without ever demanding your attention. Of course, you’ll still need to log in and view these data, to form your own conclusions, but the measurement portion is completely hands-free.
The automated component also ensures that no human biases interfere with the collection and storage of data related to your habits, and eliminates the possibility of gaps in measurement due to memory lapses (or apathy, in the case of other workers).
When employing such a system for a team, one of the greatest strengths of objective data (when properly organized) is its ability to allow for “apples to apples” comparisons.
Let’s say you and a friend are comparing running speeds. Your friend is able to complete the 100 meter dash in 15 seconds. You’re able to run a 10 km race in just under an hour. If you compare these two figures, it would appear your friend is faster—but they’re running a much shorter distance than you are. This is therefore an illogical and problematic comparison.
You’ll find the same problem if you try to measure the email productivity of multiple workers without an effective way to organize and display your data using similar formatting. Thankfully, most modern analytics platforms categorize every piece of data cleanly, and allow you to conjure side-by-side reports that look at exactly the same metrics, in exactly the same circumstances. This allows you to more accurately assess your employees’ strengths and weaknesses, and gives you the power to isolate more variables when comparing two team members side-by-side.
Tracking Changes Over Time
Finally, data collection gives you the power to conveniently track how your performance changes over time. Again, this approach eliminates the possibility of bias in your measurements. Any effort you make could result in a kind of placebo effect, making you “feel” like you’re more productive when you’re really not. The data, when properly compared to previous iterations, will show whether you’ve truly made an improvement or not.
This is especially important because the end goal of “mastering” your email productivity means it’s not simply enough to understand the numbers—you have to make those numbers change by changing your habits. Not all of your changes are going to be immediately impactful, and not all of them will be as effective as you think they will, so your only reliable method of determining whether you’ve met your goals is a “before and after” style comparison, using identical reports with identical variable controls.
The Visualization Advantages
So we know how vital data is to understanding and improving your productivity—but you probably opened this article already knowing that. The next step in understanding may be even more important: visualizing your data, rather than simply reading and interpreting the numbers on your own.
Data visualization has a significant history; if you’re ever seen a pie chart or a bar graph, you’ve already had some experience with visualization techniques. But only recently is visualization pairing with analytics in a way that allows users more freedom, more accuracy, and more intuitive control over how data is displayed and interpreted.
Loosely defined, data visualization is any technique or action that presents objective data sets with corresponding visual guides, such as graphs, charts, or maps. This may not seem like a big jump from tables of numbers, but the advantages are numerous and powerful.
The Big Picture
First and foremost, data visualization gives you the “big picture,” demonstrating the facts with a broad-strokes image, rather than forcing you to dig into the individual numbers that comprise it. This is important for several reasons.
- The deception of details. If you get stuck looking at individual details within the data, you could lose sight of the major conclusions you should be taking away—this is sometimes referred to as “missing the forest for the trees.” Remember the cherry-picking problem in the previous section? It returns here with a vengeance; if you focus too much on the little details, and singular bits of information, you could form your conclusions based on a single anecdote, or an outlier, rather than getting to see the “zoomed out” view.
- Prioritization. Visualization also helps you quickly understand where your biggest priorities should lie. Our brains aren’t made for processing numerical information, which is part of the reason why it’s so hard for us to comprehend large numbers (for example, can you accurately conceptualize the difference between a billion and a trillion?). When we see two columns of numbers indicating both our writing time and reading time for emails are undesirable, we may view them as equal in severity; but once compared to each other in a visual format, it should be obvious which of them demands more attention.
- Averaging. Assuming you’ve set the parameters wide enough to provide a sufficient selection sample, visualizations also do us the favor of presenting us with an “averaged” view of the numbers. Most maps and graphs naturally filter out “outliers” in our data sets, which can skew the figures and lead us to wrong conclusions. Outliers are still important, of course, but they should be treated as an additional piece of information when understanding a problem—not a complete understanding of the problem itself.
- Big data. The “big picture” is also becoming more important now that we have the technology to deal with incomprehensively large data sets. Sure, you could wade through the numbers on each email you’ve sent over the past week—but it would be nearly impossible for you to retain those numbers in your mind long enough to properly understand them. Visualization enables us to process the meaning behind big data—rather than the individual points of data themselves. More on this in a section on conceptualization and time savings.
Memory and Learning
Data visualization may also be important for learning and for memory. Again, our brains aren’t wired to process and compartmentalize large swaths of data; we’ve evolved to observe and store sensory experiences, so we can outlast predators and understand the difference between real-life threats and safe places. Of all our senses, sight is the strongest, so it’s usually easiest for us to learn new insights (and remember them) when they’re presented to us in a visual format. For example, you may not remember the exact number of CO2 emissions produced by mankind each year, but you likely remember seeing at least one chart with an upward climb over time.
Your memory of visual information is astoundingly good. According to one study, people only remember about 10 percent of information they hear 3 days after they hear it. However, if that information is paired with a relevant image, people can retain up to 65 percent of that information after the same amount of time.
That means you’ll have an easier time learning from data that’s presented to you visually, and you’ll remember it for longer—which could be enormously helpful when keeping your bad email habits top-of-mind throughout the workday.
Conceptualization at a Glance
Aside from the most passionate analysts and statisticians, nobody wants to spend hours of time wading through thousands of data points to find the answers they’re looking for. If you wanted to have the clearest, most accurate picture of your email habits, you could spend this time and walk away more informed for it—but in reality, most of us just want the high-level takeaways. We want to log in, spend a few minutes getting the “gist” of what we need to improve, and log out to return to our day. After all, the whole point of this exercise is to spend less time on repetitive tasks, not more.
Visualization is a shortcut that enables us to skip this time—without necessarily sacrificing the quality of our interpretations (which is a central tenet of data governance). When presented with a visual, we can quickly conceptualize what’s happening with the data, usually in a matter of seconds, making visualization one of the best time-saving data strategies we have.
Communication and Displays
The benefits of visualization aren’t purely about your own analysis and understanding. In fact, they may be even more beneficial for communicating data to others.
- Helping coworkers understand the problem. Roughly 65 percent of your employees qualify as visual learners. That means they’re likely to learn faster and retain information longer when it’s presented to them in a visual format. If you explain to a coworker that statistically, they’re more likely to contribute an excessive number of emails in a thread than their other coworkers, they might hear what you’re saying, but fail to internalize the concept. If you show them a graph comparing their email thread contributions to those of other employees, they’ll be able to grasp the concept immediately. Visualization is one of the best communicative tools you have for explaining data.
- Presenting your progress over time. If you’re trying to show your supervisors, your coworkers, or other parties that your email habits are improving over time, visualization makes it easier. Rather than digging around for numbers that look good side-by-side, you can use color-coded charts and graphs to display how your productivity has improved. Seeing a taller bar on a graph means more to most people than seeing an uptick on a single number.
- Proving ROI or similar metrics. Productivity isn’t just about doing the most tasks in the shortest amount of time; it’s also about spending your time wisely. One of the most important benefits of email monitoring is your ability to demonstrate the time it takes to work on each project (i.e., how much time you spend emailing your clients, or emailing coworkers about a given project). With this metric, you can prove the return on investment (ROI) you get from each project. When presenting the figure in minutes, or dollars, your findings will likely bear an impact—but they’ll bear an even bigger impact if you can show those numbers with images and visual projections.
Finally, data visualization helps you interpret changes more quickly. Toggling back and forth between two similar, yet distinct visual projections should allow you to quickly pick out which sections have changed—and which have changed the most significantly.
This is important because when managing big sets of data, it may be confusing to isolate variables, or try to wade through multiple categories to find which variables are having the biggest impact. Visualization shrinks this problem by giving you a medium you’re naturally better at interpreting; changes are instantly recognizable, and they allow you to “zoom in” to the most influential numbers in your data sets.
What Makes Data Visualization “Good”?
Now you understand why data visualization is so important, but it stands to reason that not all data visualization software is created equal. Obviously, some data visuals will be “better” than others—but what makes it better? What should you be looking for when you choose an analytics tool to use?
These are the six factors that matter most:
You won’t struggle to find a data analytics platform that provides objective data. In the email analytics realm, specifically, any platform that integrates directly with your account and reports on the email activity it finds can be considered objective.
Subjectivity only becomes a problem when looking at visuals created by human beings, manually. Any time a person is in control of which numbers get reported, there’s a chance for bias to enter the equation. Subjective perspectives could factor into your email productivity; after all, every professional will have their own unique preferences. However, when trying to improve your bottom-line productivity and ROI, objectivity is always better.
You also need a data visualization system with a fair degree of reliability, which ultimately comes down to its technical specifications. You need to draw data consistently, from all your accounts, without worrying about unexpected interruptions in data flow, or a limited capacity for display. Here, too, you’ll find more reliability in automated platforms than you will in any manual efforts.
3. Aesthetic Appeal
Depending on how you’re using the software, you could be staring at these graphs for hours, cumulatively. You could be presenting them to your bosses in a bid to prove your worth, or using them to convince your employees that their habits need to change.
The effectiveness of your graphs will diminish significantly unless they’re pleasant to look at. A “pretty” chart isn’t necessarily better at displaying the data than one that isn’t, but it’s going to make a better impression on anyone who sees it. Aesthetics here are subjective, so look for visuals that look sleek, precise, and suited to your tastes.
4. User Experience
There’s no single chart or graph that can answer all your questions, or tell you everything there is to know about your email productivity. With the right data visualization platform, you’ll have all the data you need and all the tools to present it however you want—but those data and those tools will only be effective if you know how to use them (and are comfortable doing so). Don’t underestimate the importance of user experience; if it takes you too long to put your requisite visuals together, the platform may not be worth the time and money you spend on it.
The visuals should also be easy to read, even for a novice. A complex, three-dimensional map of all your contacts may look impressive, but if it takes you 20 minutes to explain how to read it to your employees, it may defeat the purpose. This factor is another subjective one; only through experience will you be able to determine whether a visual display is easy to interpret.
If you’ll be tasking your employees with using the platform individually, it should also be relatively easy to learn; you don’t want to spend hours of time teaching new people how to master the controls, or include a separate chapter of orientation dedicated to how to read graphs.
6. Customization and Variable Control
There’s much to learn about your email habits—but only if you know how to ask the right questions. The right platform will offer significant customizable options, including the ability to filter out variables and discover the true root causes of your success or failure. You should be able to adjust charts with multiple sets of data, easily expand or contract your time parameters, and quickly compare multiple visuals against each other. All platforms will come with some “standard” reports, but the real power lies in the custom reports you’re able to build for yourself.
How to Make the Most of Data Visualization
Data visualization won’t automatically make you a better emailer. Here’s how to get the most out of your investment:
- Choose the right tool. There aren’t many tools on the market currently equipped to display customizable visuals on your emailing habits—in fact, there’s only one that meets all the above criteria (EmailAnalytics). Still, as more platforms start to emerge in the market, it’s going to become more important to discern between your options. Though there is a human component in how you retrieve and interpret your data, the strength and flexibility of your platform will be a major determining factor in your success.
- Start immediately, and measure consistently. How you measure is just as important as what you measure. If you want to get the most out of your data, start as soon as possible; the sooner you start reclaiming multiple hours of work each day, the more total hours you’ll reap as your reward. You’ll also need to measure consistently, checking in at least weekly and always controlling the variables so you compare apples to apples.
- Beware the emergence of bias. The numbers aren’t going to lie to you, but the way you choose to draw, arrange, and visualize those numbers can distort their true meaning. For example, you may focus on specific variables or metrics that already align with your previously held assumptions, tapping into confirmation bias. Try to remain as neutral as possible, and question your previously held beliefs.
- Form actionable insights. The new insights and conclusions you form may be interesting, but they’re meaningless until you put them into action. Don’t focus on what the data means; instead, focus on what the data suggests you should do. All of your points and conclusions should be transformed or should lead to something actionable. Every new insight should be accompanied by a change in your habits (or the habits of others).
- Hold your team accountable. You aren’t the only person responsible for your email productivity; for every email you send, you’ll likely receive one as well. Talk to your coworkers, employees, and even your bosses about their emailing habits and how it may affect your productivity and the productivity of others. If you request a change in their behavior and they agree to follow it, hold them accountable by following up with them, to ensure the change is made.
- Return and repeat. You can’t become an email master overnight. Over the course of your improvements, you’ll need to check back in regularly, and discover new ways for you to improve your habits further. Treat your email productivity improvements as an ongoing cycle of steady progress; the more you learn, and the more you grow, the more you’ll stand to gain.
Data Visualization With EmailAnalytics
If you’re ready to start improving your own email productivity, and you’ve absorbed the tactics mentioned in this list, the next step is finding a tool that gives you all the information you need.
EmailAnalytics is an analytics platform that’s the first of its kind: a data visualization tool designed specifically to help workers like you master their email productivity. With its intuitive controls and customization features, you’ll be able to see things like:
- How many emails you send per day.
- How many emails you have in each “category.”
- How long you spend writing emails.
- How long you spend reading emails.
- Who sends you the most emails.
- Who you send the most emails to.
- How long your average conversations are, and who starts them.
Independently, these statistics may be trivially amusing, but once you look at them in an intuitive visual format, you can form important conclusions about your own working ability—and what you can do to improve it.
You won’t become a better emailer overnight, and you can’t force your employees to embrace the formatting and procedural changes you’d like them to, but every informed change you make counts, and it’s the objective graphs and charts of data visualization that will help you make them.
Jayson is a long-time columnist for Forbes, Entrepreneur, BusinessInsider, Inc.com, and various other major media publications, where he has authored over 1,000 articles since 2012, covering technology, marketing, and entrepreneurship. He keynoted the 2013 MarketingProfs University, and won the “Entrepreneur Blogger of the Year” award in 2015 from the Oxford Center for Entrepreneurs. In 2010, he founded a marketing agency that appeared on the Inc. 5000 before selling it in January of 2019, and he is now the CEO of EmailAnalytics, and co-host of the podcast The Entrepreneur Cast.