Data analytics is a red-hot field in terms of growth and popularity, but there’s a relatively new segment of the field that’s starting to catch fire: Email analytics.
Typically, email analytics have referred to email marketing, including measures such as open rates, click-through rates, and unsubscribe rates. But what about everyday emails that you send to your colleagues, superiors, employees, clients, and vendors?
New tools are starting to emerge for this type of analysis, such as Gmail Metrics,which visualizes data about everyday, ordinary email usage. But even with the intuitive power of visuals, it’s easy to draw the wrong conclusions or misinterpret information that’s right in front of you.
Following these best practices can help you avoid such pitfalls:
1. Be wary of bias. The human mind is a complex machine, and it has a lot of advantages that has helped our species become dominant, but unfortunately, some of our interpretive abilities have become too sensitive, resulting in cognitive biases that affect the way we perceive the world. Even with data visualization facilitating a cleaner view into your hard statistics, it’s possible for those biases to creep in and affect the conclusions you ultimately take away. One of the strongest examples here is confirmation bias; if you have a preconceived notion about how something works, or a conclusion you’ve already formed about the way something works, you’ll be naturally drawn to data that verifies these conclusions, rather than more powerful data that contradicts it.
2. Don’t oversimplify. It’s important to remember that email, like most other functions in a workplace, is a complicated area that can’t be reduced to a single numerical inbox statistic. You’re dealing with complex human beings, engaging with each other in complex ways, and no one bar graph or pie chart will be able to tell you everything that’s going on. The concise demonstrative power of visual data will tempt you into boiling these multifaceted ideas down into bare-bones conclusions, but try not to allow this to happen. Look at data points beyond your basic visuals, and remember the key complicating factors and variables that are influencing this landscape.
3. Remember your objectives. When you open the door to email data, you’ll feel like you’re walking into a candy store. There are so many options, all of which are interesting in their own ways, and you could easily be drawn in one direction or another based on how appealing certain data points seem at the time. Because of this, it’s important to remember your main objectives—and these may vary depending on your specific organization’s goals. For example, your main priority may be improving the quality of communication between your employees; if this is the case, you’ll focus on different email metrics than if you’re more worried about how your workers are spending their time.
4. Ask the right questions. Data is objective, and the conclusions you form with it can be neutral, unbiased illustrations of how your employees actually work. However, data alone doesn’t tell you anything. It’s on you to group that data meaningfully, and draw your own conclusions. Because of this, it’s on you to ask the right questions of your data. If you’re looking for the wrong metrics or interpreting them the wrong way, it won’t matter how objective or thorough the data you’ve collected is.
5. Focus on actionable takeaways. It’s also important to remember that data visualization is not a toy. It’s fascinating to peruse different data points, project how your employees are working, and look at interactive graphs that help you form various conclusions about the way your business operates.
However, none of this will, by itself, help your organization improve. If anything is to change, you need to focus on forming actionable takeaways from the conclusions you’re drawing. Without action and change, your email productivity statistics exist in a vacuum, and can’t have any effect on your bottom line.
Email analytics is a relatively new field, but don’t let that result in novice missteps. Follow these best practices and you’ll be able to put these insights to good use.
Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed https://www.datasciencecentral.com/profiles/blogs/5-best-practices-for-email-data-analysis
