If you work in a digital environment, you are aware that meaningful data is information that has a narrative. It’s important to gather the correct data and let it tell its own story rather than just collecting tons of it. This still represents an uncharted area for many analysts and marketers.
Data visualization can help with this. Place it at the forefront of your web analytics plan! Why? Because if you can’t communicate your ideas effectively, no matter how much excellent information you obtain will be of little benefit.
By pointing out the important components, this article will assist you in approaching data visualization. We’re off!
Data visualization: What is it?
A graphical representation of information is the traditional definition. But it goes beyond that. It’s a technique for presenting data in an understandable and straightforward way by employing visuals like graphs, maps, charts, lines, and specialized software.
But due to IT and software, there are much more modern tools such as heatmap tools.
Humans comprehend shapes and colors more quickly than numerical lists. This is why thinking about both form and function is necessary when converting data into visuals.
Your data is more comprehensible and accessible when it is represented visually. This is crucial in the digital ecosystem because data gathered by analytics tools frequently involves a great deal of complexity. If you want to get the most out of it, you must break it down into manageable portions.
Benefits
You can find hidden connections between events in your business with the aid of images. They provide you the ability to track connections between your content, product, and conversions that you would otherwise miss.
When data is presented visually, you may more quickly identify areas that need your attention and improvement. You’ll discover what significantly affects your consumers’ behavior. You will also be able to quickly identify trends, outliers, and complex patterns so that you can make strategic decisions.
For instance, ambiguity is frequently introduced into your analytics reports by outliers, which are data values that significantly deviate from the majority of your data sets. Eliminating them improves the caliber of the inferences you can make from your data.
In addition to being simpler to remember, complex information that has been transformed into a visual format is also more convenient to share or present to a larger audience. By doing so, you can prepare meetings faster and share your insights with your team in a way that is both effective and efficient.
What to take into account
Before you begin converting your analytics data into visual chunks, keep in mind the exercise’s main goal: taking action on the information. Here is a list of the considerations that you have to make without fail.
- Audience
- Content
- Context
Audience
First off, you shouldn’t be writing reports for yourself; instead, you should tailor them to a particular audience. Consider carefully whether or not your audience is knowledgeable about the topic.
Also, take into account the expertise and experience of your end users. For instance, you can concentrate on simplicity when creating graphs for a design team. However, you may make things more complex for folks like data scientists who can grasp complex graphics.
Content
Depending on the kind of data you want to provide, choose your strategies. You must choose the right measurements since some are constant and others fluctuate with time. Knowing what to concentrate on will help you choose the right approach.
To demonstrate your rapid revenue trends, for example, you could use line charts. However, use a scatter plot when you need to demonstrate some correlation between two variables.
For instance, when you compare the number of views to the amount of time spent on a website.
Context
Presenting facts in a context is the fundamental guideline for doing so. Simple numerical data won’t be very illuminating. Include any and all titles, notes, and units that apply.
Depending on the specific case you want to present and the points you want to emphasize or minimize, use a different chart or graph. Think about the tools you can use to make your message more understandable to your audience.
Top methods
When you are aware of the information you need to focus on, it is time to consider your options for a straightforward graphical representation of your data. There are numerous options, some of which are straightforward and others of which are complex.
Today, we’ll concentrate on the ones that are most crucial for advancing your business objectives.
- Line and pie charts
- Bars
- Funnel charts
- Heatmap tools
Lines and pies
Because they offer a handy way to present data that leaves little opportunity for misunderstanding, line charts are among the most popular chart styles. They may be used to display patterns in data over a range of time periods, such as the number of blog post views every quarter or changes in stock market prices over an extended period of time.
The mainstay of data visualization is pie charts. They have a negative reputation for being deceptive. The fact is that comparing the slices is challenging.
However, you’ll succeed if you use them to calculate the current relative percentages and proportions. They are a wonderful option for illustrating how your budget is allocated to several teams. To make them operate, adhere to these few rules:
- Limit the number of categories in your pie to six.
- Conversely, do not use them for ordinal variables.
- Use them for percentages and proportions but not for comparing data.
Bar charts
When comparing two numbers, bar charts, sometimes referred to as column charts, are helpful. They may be used to nominal variables like the sorts of companies your conference attendees work for or the social media platforms that your app users utilize the most.
Use them to depict your budget allocation, online traffic sources, or regional product sales. Overall, both charts make it simple to identify highs and lows.
Funnel charts
Funnel charts are a wonderful method to show different stages of processes, like sales cycles, and they may also be used to identify possible obstacles. A funnel often begins at 100% before gradually getting narrower towards the bottom, making it simple to pinpoint the steps that contributed to the value decline.
Apply funnel diagrams to demonstrate:
- Trends in website visitors
- Conversion of sales and conversion rate
- The success of a marketing effort
Heatmap tools
A website heat map shows the categories on your website in a variety of hues, from the “most popular” in red to the “least popular” in blue, to show how people engage with your website.
Types of heatmaps
- Mouse movement maps
- Click heatmap
- Scroll heatmap
Mouse tracking heatmap
The term heatmap is frequently used to refer to a hover map. You can view areas on hover maps where people have moved their mouse cursor over a page. People are supposed to look where they hover, which demonstrates how website visitors read a page.
Eye tracking is a well-known usability assessment method that mouse tracking heatmaps are based on. Mouse tracking frequently falls short due to various extended conclusions, however, eye tracking is beneficial to learn how a person navigates a website.
Mouse-cursor tracking’s precision is debatable. It’s possible that people are looking at things they don’t hover over.
Click heatmap
You can view a heat map made up of aggregated click data using click maps. Warmer reds indicate more clicks; blue indicates fewer clicks; bright white and yellow spots have the most clicks.
These maps have a lot to offer in terms of communication. They aid in illustrating the value of optimization, particularly to non-optimizers, and what works and what doesn’t.
Does a large image that isn’t a link receive a lot of clicks? These are your two choices:
- Make a link out of it.
- Make sure it doesn’t resemble a link.
Additionally, it is simple to swiftly comprehend aggregate click data and spot broad trends. Be cautious to stay away from the convenient narrative.
Google Analytics also allows you to see where people click, which is generally preferred. The Google Analytics overlay is excellent if enhanced link attribution has been configured.
Scroll heatmap
Scroll maps display how far down a page a user has scrolled. You can see where users tend to stop using them.
Scroll maps are useful for sites of any length, but they’re particularly important when developing lengthy landing pages or sales pages.
In general, fewer readers will scroll all the way to the bottom of a page the longer it is. This is typical and aids in content formatting and prioritization. What is essential? What is just desirable? Prioritize and rank higher the information you want people to focus on.
Scroll heatmap may also be used to make design adjustments. People could not recognize a relationship between two components of your website or logical endpoints if the scroll heatmap displays abrupt color changes. It’s challenging to observe these abrupt drop-off spots in Google Analytics.
You might need to include navigation cues where the scrolling ends on longer landing pages.
Conclusion
It is not simple to convert your data into visual material that conveys your observations, conclusions, and suggestions. It requires not just the appropriate instruments but also, and maybe most crucially, a strong long-term plan.
The information in today’s post should make it easier for you to use data visualization techniques to enhance your reporting and decision-making.