Did you know that we process visual content 30 times quicker than we process a word? Appealing visual content can generate better engagement, likes, comments and shares than text-only content. Hubspot has noted a 37% increase in engagement from targeted customers when the article is optimised by adding more compelling visual elements.
Our way of consuming content is increasingly influenced by new technology. Naturally at the epicentre of these new practices, the Internet is now dominated by interactive content. We’re looking for content that’s more targeted and complex that we can access not only quickly but also effortlessly.
Our need to understand data has reached an unprecedented level. To achieve this level of understanding, we’re now making use of the data that’s available to us in huge volumes. Visual content such as data visualisation is, then, an efficient way of modelling a complex data set through visual and interactive content.
Data visualisation – added value and interactivity
In this context of increased focus on visual content and the need for meaning, the modern reader is changing. The new reader prefers long, detailed and quasi-scientific content with high added value, which will help them to take decisions. Here, we’re referring to long technical articles, whitepapers, infographics and, of course, data visualisation. This new interactive content is an intelligent, simplified and even artful representation of a set of often-complex data.
Data visualisation enables us to manipulate data and make modifications in real time. Such a tool can make reading a long set of figures visual and even fun. Modelling data has no limits and data visualisation can be broken down into different types, such as the following examples: what’s warming the world?, the skill of Kobe Bryant’s shots depending on his position, real time cyberattack stats, or the history of content marketing since 1732.
[iframe src=”https://cdn.knightlab.com/libs/timeline3/latest/embed/index.html?source=1-omnnVl5rQ2YPKBjCWMTHoi2AoZvcvIYZfu8W4ReC6k&font=Default&lang=en&initial_zoom=2&height=650″ width=”100%” height=”650″ frameborder=”0″]
A content piece to boost reader engagement
It’s no coincidence that journalists were among the first to adopt data visualisation to illustrate their articles. Newspapers such as the New York Times and The Guardian have also been using this tool to add value, by improving the understanding, interaction and engagement of their online articles. In doing so, they have been able to go upmarket by offering better quality content whilst continuing to fulfil their demanding readership’s new expectations.
Data visualisation is content with plenty of real advantages that companies have also caught onto, in both BtoB as well as BtoC. Thanks to interaction as standard, one aspect of data visualisation invites and also encourages the reader to make the content their own, making it more than just a simple article. According to the study by Demand Metric, 70% of marketers agree that interactive content is effective at engaging decision makers. Info.Gram also notes that articles containing charts and infographics generate a 16% to 34% increase in comments and shares, a 65% to 100% increase in time spent in session duration, and a 317% lift in depth of scroll.
Data visualisation can be integrated perfectly into a global content strategy. It plays a particularly important part in lead nurturing; an interactive and well-received piece of content that will impress your prospects.
There is also an educational and fun side to data visualisation. Readers can actively manipulate the data themselves, as well as do their own research and analyse figures. You can instruct your prospects by making them active players in your content.
How should you go about data visualisation?
The fascinating thing about data is that we can model it to generate new information and create meaning.
We can create a piece of data visualisation using different data sets:
- Internal data (software, behavioural data, statistics, etc.)
- Public data sets such as SNCF’s Open Data.
- Grouping together dispersed data (for example, how many times has Mr X been mentioned in the media in the last Y amount of years?)
Data visualisation is complex content, for which we need to assess the feasibility and truth of such a creation from the start:
- What data has been analysed? Do you have it to hand? If not, is it easily accessible? Will it need to be reprocessed?
- How will you combine and model this data set? Who will be in charge of developing/integrating this model? Free resources do exist (D3.js) but they’re generally limited and still in need of some development.
- It’s important to put into context what you are presenting so that your reader can quickly grasp the relevance of the data they’re consulting.
- Make the data visualisation interactive (clicks, scroll bars), as readers have to explore and develop the content themselves. Give priority to the content’s design and user experience. Remember, the goal is to present a relatively complex set of data in a clear and precise way, making it easy to understand.
- How do you plan to publish it and, on more generally, work it into your content strategy? A piece of data visualisation should be part of a wider content strategy and be broken down into several pieces and other formats (articles, social media posts, white books, etc.).
Interactive, pleasing to the eye and of high added value, data visualisation is clearly beneficial to any content marketing strategy.
In a time where data has a strong influence on decision-making, it’s important to transpose it into a visual and interactive format, which makes understanding, analysing and interpreting it easier. Data visualisation invites readers to be active, to interact with this content by highlighting the information they’re interested in, and encouraging them to share what they’ve just learned with their community.
Of course, this content requires a good quality data set and the time to develop it. We need to determine well in advance how to model and combine often complex data sets, as well as what will need to be put into place to make it happen. Finally, it’s important to capitalise on this type of premium content in your content strategy. Data visualisation has to be broken down into smaller, digestible nuggets, in different formats and be widely sharable.