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Reimagining a better digital consumer experience with reinvented heatmaps thanks to Neuro-AI

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User Experience Testing and Usability Testing have been limited to a reactive approach by traditional heatmaps and tools which are becoming outdated as companies enter the new era of highly accurate prediction optimization.

Now more than ever, UX Designers, Marketers and Product Managers seek to automate the UX testing processes in order to: 

  • Speed up time to market

  • Increase new customer acquisition

  • Build strong customer digital experience

     

In our highly competitive world, automation, speed and message accuracy are critical to improve consumer digital experience and gain a competitive advantage.  The standard process “Release, Measure, Adjust, Re-release”, is no longer a workable model. Traditional heatmaps and tools are quickly becoming outdated as companies enter the new era of highly accurate prediction optimization. “Design, Know, Adjust, Release, Capitalize” is the new winning model to foster creativity and speed up time to market.

 

Neuro-AI driven heatmaps give UX Designers, Marketers and Product Managers superpowers! 

 

They will know in advance, before anyone sees or test the interface, the effectiveness at creating Instant Brand Attention and Lasting Brand Recall. Deliver design impact results in advance; addresses the shortcomings of traditional heatmaps; and makes them more relevant for business, faster and more accurate

Original Image

The Neuro-AI accuracy and speed advantage

Where consumers will look and what they will remember is two different brain processes. Neuroscience is an infinitely more suitable method of measuring these two critical brain functions (instant attention and lasting memory recall) which directly impact the user experience. 

This cutting edge technology predicts the information the audience will look at and what it will remember (in advance, in seconds and with accuracy), and for all types of visual content (website, interface, image, blog, etc.). 

Neuro-AI reinvents the heatmap!

 

Using neuroscience research data from more than 300,000 people and 15 million images; combined with artificial intelligence models trained with deep learning methods, Hippoc created Neuro-AI heatmaps. 

By analysing this very large sample, Hippoc Algorithms have prediction accuracy of 94% for Instant Attention and 96% for Lasting Recall based on the MIT Dataset. This assessment has been made using Area Under the Curve (AUCJudd) metric which is currently the main metric in MIT saliency benchmark.

In short, Hippoc’s Neuro-AI software, reimagines the heatmap world, offering the best speed, the best accuracy and the best validity, to evaluate the impact of all your visual content on your audience’ mind in advance, for both digital and traditional channels.

Hippoc website fog map
Hippoc website heat map

With traditional heatmaps, bad data was more acceptable than no data. 

There are multiple shortcomings  with the actual user experience heat maps. 

  • They  require a large traffic data set to generate relevant stats; 

  • Many of the metrics provided are based on assumed consumer behaviours, resulting in  inaccurate and misleading data analysis; 

  • They are reactive; 

  • They are time consuming;

  • Eye Tracking requires highly trained ressources.

 

In summary, we believe the future of heatmaps for measuring and predicting consumer digital experiences, will be driven by advances in Neuroscience and Artificial Intelligence (deep learning). Traditional heatmap approaches will provide limited value in very specific applications of usability testing. Finally, the increasing restrictions and laws surrounding consumer data privacy will make the scope of much of the  data collection in traditional heatmaps increasingly limited and therefore dramatically less effective. 



Below for your convenience we have provided a short description of the most common heat map tools along with their strengths and limitations.

Hover maps (mouse-movement tracking)

Hover maps is the collection by software of the mouse movements of visitors to a website. It’s sold as a simple way to do classic usability testing (instead of using eye tracking) and it has been widely democratized by Hotjar. However, unlike eye tracking, which captures what really appeals to the customer’s attention, hover maps provide an indirect and therefore imperfect measure of what catches the customer’s attention. 

 

A research by Dr Anne Aula, a Senior UE researcher at Google presented disappointing findings on hover maps. There is a serious validity problem with hover maps, making them misleading and irrelevant for businessIn fact.

Using hover maps to predict the audience’s attention in your visual content is like trying to predict the weather by looking in the basement.

Click maps

Similar to hover map, click maps display where users click the mouse on a desktop device or top on a mobile screen. They only have an educational value. They demonstrate the importance of optimization. 

Still, the only thing you need to remember about click maps is that when a picture or something else looks like a link and it’s not, it’s an important usability issue, you may want to add the link or change the item.

Scroll maps

Scroll maps help show how far people scroll down the page and help you understand where the users drop off. They are especially pertinent when designing long-form landing pages.

The only takeaway is that the longer the page is, the more the people will drop off before the end of it. Keep this in mind and prioritize the content that is more important first.

Eye tracking maps

It’s not because the audience is exposed to the content that everything catches their attention. Eye tracking is a good way to conduct user experience testing and understand the audience’s attention for the content (interfaces, packaging, ads, etc.) . It’s powerful in the attention economy, where the audience is overwhelmed by the volume of content on a daily basis.

The main limits of eye tracking is that it’s time consuming, costly, one time use, and require neuroscience expertise to interpret the results. 

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