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Data Visualization for Audience Analytics

Everything You Need to Know about Data Visualization in the Context of Audience Analytics

     

Data visualization plays an essential role in fields involving large datasets or data collection on a large scale. And in the age of big data, it is becoming more important than ever.
In the field of face analysis for audience analytics, too, data visualization is important for deriving actionable insights.

What is data visualization?

Data visualization refers to the presentation of data in a graphical form, for example using charts, diagrams, graphs, maps, or other similar elements. It falls into the broader category of data science.

In the image below, you will find examples of:

  • A pie chart (left)
  • A column chart (top right)
  • A line graph (bottom right)


 

Data visualization should facilitate storytelling through data. The purpose of data visualization is to make large quantities of data more accessible and easier to interpret. For this reason, it’s important to make sure that the design of the chart or graph or other element does not distract from the message that the graphic is meant to convey. 

Here are a few tips for creating visuals with your data:

  • Use 2D rather than 3D charts and graphs, as a 3D format could misrepresent your data
  • Use a monochromatic color scheme (as in the image above) to make the visuals clear and easy to interpret
  • Use the visual element that best fits the data and the message you want to convey

What are the benefits of data visualization?

Humans are visual creatures, and can process visual data much more quickly than lines of text. Data visualization, therefore, enables us to analyze large datasets more quickly and easily. This, in turn, enables quicker data-driven decision making.
And that’s not all. Another benefit of data visualization is that it helps us spot patterns and trends and detect anomalies that we might not have noticed if we’d been presented with the raw data.
From these benefits, we can see that it would be far more efficient for decision-makers to use charts, graphs, and other visual elements, rather than to comb through lines and lines of text.

How to visualize data in the context of face analysis

Face analysis software produces a large quantity of data. It collects data on age, gender, facial expression, attention time, and time of day for all faces analyzed. Without a way to visualize this data, it would be quite challenging to use it for data-driven decision making. 

For this reason, various data visualization tools exist to help you create graphical representations of your data when you implement face analysis software. If you’re using Sightcorp’s face analysis Toolkit, for example, you will also have access to our Toolkit Visualizer, which provides a graphical representation of the data being collected by the software. 

Another option would be to integrate your face analysis software with a reporting dashboard. If you don’t already have your own dashboard, and you’re using Sightcorp’s face analysis software, then we have ready integrations available for you to use.

Here is an example of our partner dashboard integration with

The line chart in the image above makes it easy to see, at a quick glance, the times of day at which most viewers are passing by a particular digital screen. 

Of course, you also have the option to export the data to a tool such as Excel, and then to create your own graphics to represent the data. Our Toolkit outputs data in an easy to use CSV file. You can either use the raw output which gives you full control and flexibility over thresholds and filtering, or you can use the aggregated output which is ready to be used!

If you’re looking for more information on how to visualize data when using face analysis software, click here.

Here are other articles that you might find interesting:

 

   

Technical Specifications

The table below shows how FaceMatch SDK performs on the Labelled Faces in the Wild (LFW) dataset:

FPRTPRThreshold (Inverse of distance)
0.10.99900 ±0.002130.55448
0.010.99667 ±0.005370.59791
0.0010.99367 ±0.006050.62989

FPR = False Positive Rate
TPR = True Positive Rate

These results are an indication only and are based on the specific dataset Labelled Faces in the Wild. Customers can expect similar performance, with possible variations due to hardware and the availability of annotated data.