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Stack ranking is natural human behavior 

I have a buddy from high school named Murph. His real name is Ben Felt but every legit teenage crew in Pitsford, New York in the 90s needed a Murph so Ben took one for the team.

He’s a quant. Like Taylor Mason from the show Billions.

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Attention is the new gold; brands are in a constant competition for our attention.

A big portion of our time we spend online, where we are bombarded with insane amounts of information and advertisements. It’s hard not to become overwhelmed in this world of consumerism. We have had to become good at quickly evaluating which information is important, especially on the internet.

Good marketing specialists know that they have mere seconds to turn a potential customer into a lead. People are not going to spend a lot of time examining your advertisement or landing page, either it clicks or not. Moreover, most users do not read the articles, they scan them. First impression plays a huge role in the success of your business, so do not leave that to a chance.

You really don’t want your customer to ignore that special sale, subscription option, or another call to action on your webpage. That is why you need to know where that gold-worthy attention goes when a user opens your landing page. Here’s where technology can come in handy.

Eye-Tracking in Web Design

It is very important to know where your website visitor’s attention goes first. How to get that info? Eye-tracking is the answer.

Eye-tracking technology can be used to optimize your website conversions. By tracking eye movements, technology will recognize which content is most intriguing for the users. It will reveal whether people pay most attention where you want them to, which elements are distracting or not visible enough, and where sales are lost. This information is invaluable if you want to succeed in the current market.

This information is invaluable if you want to succeed in the current market

How does it work? An eye tracker, such as webcam or goggles, measures movement of an eye. Collected data is analyzed and presented as a heatmap, highlighting which elements of your design attract most attention. Having in mind that browsing time rarely exceeds a few seconds, this information is very valuable when you try to understand your audience.

You wouldn’t want to spend much time on your website design just to discover it does not generate desired conversion rate. By employing this technology you can make changes based on reliable data rather than intuition and guarantee your business future success.

By now you may think that you definitely need to carry out this eye-tracking study, but there is a catch. A high-quality behavioral observation or eye-tracking is a time-consuming, budget eating complicated process.

If you want to draw conclusions from heatmaps, you would need to include at least 39 participants in a study. One individual test may last from 20 minutes to an hour. Time quickly adds up when you include preparation and analysis of the results. The average eye tracker price is around $17,500 and it may vary between several thousand dollars and $50 000. Of course you can hire a company to carry out this research for you but it may cost you several hundred dollars a month. Luckily, technological innovations allow us to acquire the same insights about users’ attention flow much cheaper and faster than conducting or buying an actual eye-tracking study.

Technological innovations replace real eye-tracking study

AI-Powered Automatization of Eye-Tracking

In this task of understanding how internet users are interacting with your website, Artificial Intelligence (AI) seems to be an answer. AI-based technologies already have become prevalent in various services we use on a daily basis. For example, Netflix’s highly predictive algorithm offers viewers personalized movie recommendations. Medical researchers utilize eye tracking to diagnose conditions like Alzheimer’s disease or Autism. As these algorithms become better every year, AI also becomes an irreplaceable tool in business.

Over the years researchers have collected so much data that human behavior becomes really predictable

How can AI help you to understand your customer’s attention? The main feature of AI is that it can mimic human intelligence and constantly improve itself by learning from data. Predictive eye-tracking is based on deep learning and trained with previous eye tracking study data. Over the years researchers have collected so much data that human behavior becomes really predictable. Technology predicts which specific areas of your website attract most interest. In this way, AI enables you to speed up the UX research process and get insights about your design in a matter of seconds.

Too good to be true? There are already several available tools on the market, such as Attention Insight or EyeQuant. These predictive design tools are based on deep learning and trained with previous eye-tracking studies data. Up to date, they have achieved an 84-90% accuracy.

AI-powered attention heatmap

AI solutions for designers and marketers have already become major competitors to traditional eye-tracking studies. Due to active competition, predictive eye-tracking tools are constantly innovating and recently started generating heatmaps for videos. Another useful feature that provides decision-makers with quantitative data is a percentage of attention. Users can define an object that they want to test and get an exact percentage of attention that the object receives.

Conclusion

Since all digital products are competing for user’s limited attention, it has become one of the most valuable resources. Due to fierce competition, it is not enough to rely on your intuition and gut instinct while making important decisions anymore. Designers have a choice in this economy of attention, though.

Yes, there are eye-tracking studies that require a significant amount of time and financial resources.

However, you can make user-centric, data-driven decisions in a quick, scalable, and private way while your product is still under development. AI-powered predictive eye-tracking tools might be an answer. Attention is a new currency, and you must measure it.

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Machine learning and artificial intelligence, in general, have been on everyone’s lips for some time now. While the topic of AI is in the foreground in the media, most people (especially the management) still don’t know how machine learning is best applied.

Ultimately, machine learning can be described as a synergetic relationship between man and machine. Machine learning in practice requires the application of the scientific method and human communication skills. Successful companies have the analytical infrastructure, know-how, and close collaboration between analysts and business professionals to translate these synergies into ROI.

Source de l’article sur DZONE

To understand the current and future state of IoT, we spoke to more than a dozen IT executives active in the space. Here’s what they told us when we asked, "How can organizations get more from IoT?"

Use Case

  • We’ve seen a lot of B2B implementations with high novelty, smart devices of every kind. There needs to be a legitimate use case. With the continued miniaturization of sensors and devices and the proliferation of 5G, there will be plenty of compelling business problems for IoT to solve. 
  • We are making the transformation from just connecting things and getting data to now figuring out the problem we’re trying to solve. There’s a data explosion. We need to determine how to manage and get value from the data. You need to think about business first. What business problem are you trying to solve? Play the “what if?” Dream ask the big question and put in business metrics. Align people from the technology with the business needs and the business partners. Quantify the business value. Identify the vision, strategies, goals, and hypotheses to validate. Have a clear destination. 
  • I think companies can get the most out of IoT if they start by looking for problems or opportunities that can be addressed by IoT technologies like a temperature monitoring system with alerts or remote control capabilities. Too often, companies look at the products or solutions on the market first and then try to think of ways they could apply them. It’s generally just faster and more productive to go after solving a well-defined issue first — and besides, building some expertise and practice in implementing IoT, it often leads to faster wins and a sense of momentum. 
  • In order to get the most out of the IoT, companies should focus on two things: leveraging most of your technology and building a true revenue model. For example, are you leveraging your data in the best ways? If not, what can you adjust in your business model to ensure you are properly leveraging that data. Companies also need to ask themselves: can I create a business model around connectivity that justifies the recurring cost incurred by connected devices? Many companies work backward by imagining the connected product first, and then the value proposition. These IoT projects are hardly ever successful because the company never took the time to fully understand the problem they were trying to solve. Simply adding an Internet connection to your widget doesn’t mean your business will make immediate profits. IoT products come with significant ongoing costs — web infrastructure, networking, and other connectivity and data-related costs. If you can’t justify the added value to your customers, those costs will eat away at your margins. The most successful IoT products are those that deliver recurring, continuous value for your customers (and recurring revenue for you). Companies are finding ways to deliver this recurring, continuous value by using IoT for preventative maintenance, asset tracking, and environmental monitoring. These are business models that not only contribute back to the business but help the customer as well.

Value of Data

  • One thing IoT companies don’t realize is how valuable their data really is. Take a home automation company. The amount of data streaming through their service is staggering — temperatures, energy usage, humidity, the list goes on. They can take that data, turn it into a data firehose, and make it consumable as a business in itself.
  • 1) Think about short-term design to feed into the long-term. Value comes from applications and services that make use of the data. You need to address a real business need and be able to generate the real value, which is to sell and make money.
    2) In the long-term, you need to be able to support and scale. That’s where standards and open source come in. Devices needed to get smart and detected aren’t real expensive. Make sure you have a support structure to manage the cost so as not to eat up benefits. Right now, every time you get a new IoT device, you get a new app for your smartphone. That doesn’t scale; hence, we have a need for standards. You need to be able to bridge to other ecosystems.
  • Connect the data to the problems people have. Five years ago, we focused on the data but didn’t gain traction until realize how data impacts the people in the industry. Business improvement and optimization software — how it impacts the people in their day-to-day life. If you don’t make the connection, you won’t get the adoption.

Other

  • The main benefit that APIs bring is the ability to stitch together IoT deployments within a wider ecosystem of other applications and capabilities across the business. When IoT assets are exposed internally as APIs, they form part of an application network, which provides a way of connecting IoT capabilities with other applications, data, and devices. In this model, these assets are reusable across the business, removing the need for IT to create point-to-point connections for every IoT deployment. As such, APIs become the ‘digital glue,’ providing a future-proof way of combining IoT with other business systems to create a rich ecosystem that gets the most benefit from IoT deployments, all within a secure-by-design approach.

    Source de l’article sur DZONE