This helps consumers to make informed decisions, this is because the data tends to be accurate and detailed enough. It is important for people who would like to make personal decisions and not to be given a conclusion.
It’s a sad truth, but consumers don’t trust marketing anymore. Marketers have eroded the relationship with their customers and prospects over the years almost beyond the point of rescue. It started with brands “shouting” their message on every available channel including print, television, cinema and eventually the internet. As consumers became more crafty (“Wait… there can’t be TWO best Cola drinks of all time”) marketing evolved.
As consumers began craving data and statistics in order to make more informed decisions, marketers started using this information in their advertising efforts. However, the overuse and manipulation of statistics caused consumers to mistrust that form of marketing (“How can 4 out of 5 Dentists prefer EVERY type of toothpaste?”).
From this past the modern consumer is born – one who wants to be able to understand the information they’re being given and where it came from. A consumer who wants to make their own decisions and not have the conclusions given to them.
Infographics present a unique challenge in this landscape. The very nature of a data visualization is to take complex data and boil it down to an easily consumable story that your audience can relate to. However, the amount of data you can present is limited for these key reasons:
- The infographic may be exploratory or explanatory, but serving both purposes creates a complex and difficult-to-read (thus ineffective) visualization.
- The static visual format allows little room to provide a context to the data being presented, and without context, the impact is lost.
- The static nature of an infographic means that the data can become out of date and unintentionally misleading.
These limitations and the relative ease with which data can be skewed can cause consumers to mistrust the information presented in the infographic. Consumers may be reluctant to believe the story your infographic tells if they can’t make the same conclusions themselves.
An Important Note: Infographics are still very effective marketing tools and a significant number of marketers do not use them to mislead their audience. You shouldn’t stop using them – instead you should be aware that as consumers continue to crave more and more information, infographics may have to evolve to meet the need.
Interactive Data Visualizations: An Evolutionary Step Forward
Well-designed interactive data visualizations have a significant advantage over their static counterparts: They can include both the conclusions and the data used to draw them.
This is done by creating interactive layers that give users the ability to dig deeper into your data story. For example:
- An interactive graph showing that a majority of the sample preferred your product can be clicked to show the exact question that was asked and explain who answered it.
- Real time business data in an analytics dashboard can show trends which can be clicked to show changes over time and the original data used to make predictions and draw conclusions.
- Recommendations can be drilled-down to show the user the actual data that was used to create them, instead of leaving them to wonder if it came solely from a desire to steer their dollars towards one product or another
- An interactive data visualization using real time web traffic data to show the most shared content on social media networks can be clicked to show the actual number of clicks and visits per post.
The advantage to this type of marketing is that you can still present an uncluttered data story. At the same time, you allow investigative consumers to see the data that you used to reach your conclusions (and then draw their own, hopefully similar, conclusions).
The Landscape Isn’t the Only Thing That’s Changing: If your data story is based on information that can change frequently, creating a static infographic each time is a costly undertaking. In addition to user interactivity, interactive visualizations can dynamically re-draw themselves based on a fluid data set.
Understanding current consumer trends is critical to effective marketing. Currently, that means giving consumers the ability to inform themselves. Embrace this digital marketing trend and give them access – if you’re representing your information fairly and ethically, they’ll draw the same conclusions as you and that will only serve to improve your relationship with them.
Data visualization tools help represent data results in pictorial/graphical formats. And while data visualization tools are meant to help analysts see trends and understand data, there are significant limitations that can become problematic as data sets grow. Amongst these problems, below are the top 4 limitations of your conventional data visualization tools and solutions to those problems.
There is different interpretation by the users and it can’t be easily transferred. The main aim of online should be customer friendly and easy to understand. It provides security that you can’t depend on.
1. Data visualization tools show but they don’t explain:
While data visualizations can be generated in real-time, they do not provide any explanations. In fact, the process through which companies draw insight has not changed in the last 30 years. Analysts look at data and then write reports. This process is too slow for the market and too costly for the company. At the same time, data visualization tools expect the user to be an expert in all of the data and all of the corporate best practices.
2. Different users draw different insights:
Two different users confronted with the same data visualization may not necessarily draw the same conclusion, depending on their previous experiences and particular level of expertise. This presents several problems for companies. On the one hand, certain users could be erroneously drawing conclusions which cost the company money and on the other, in highly regulated industries, users’ incorrect conclusions could actually put the company at risk.
3. No guidance:
What if the user interpreting the data lacks expertise and/or training? He or she could make mistakes that may affect the entire company. At the same time, analysts could provide clients with incorrect or substandard advice. Even systems with Natural Language Query, expect the user to know what they are looking for. This works with simple data but the industry trend is towards big data, data lakes and complex analysis. It’s so complicated you might not even know what you don’t know, to paraphrase an American Defense secretary. The answer is so simple that its easy to miss. We don’t speak data, we speak English, so software that explains the data to us in plain English. This is the value of Natural Language Generation software, the last mile in the data analytics workflow.
4. Data visualization provides a false sense of security
Graphics are great for conveying simple ideas fast – but sometimes, they are just not enough. If words could be replaced by pictograms, they would have been a long time ago. To express a complex situation, sentences and phrases are required along with a system that is smart enough to articulate its reasoning process. Importantly, language also makes sure the end user really understands. Graphics can make users think they are making data driven decisions or think they fully understand the data when in reality they are only seeing a picture but they don’t know the full story.