The Role of Big Data in Personalizing Online Experiences

Big data is about the analysis of huge numbers, like millions of sources of information, and making sense of it. All social media and gaming platforms use it. Once you get past a page, like the Vulkanbet login, everything you do is pretty much recorded for good purposes, of course.

What is big data and how do companies use this to personalize the experience of the user? In this article, we will explore this phenomenon, its uses, and how it affects what users consume online.

What Is Big Data?

It refers to a branch of statistics that analyzes and mines information in huge volumes. After mining the information, the machines calculate behavioral statistics, and they, the machines, learn from what’s been discovered. The output is what we call predictive modeling.

The level of analysis is complex. Although humans can do the calculations, it will take them some time. Think of it in the same way one human can calculate 3,467.86 multiplied by 2.456. A person can do it, but a calculator is much faster.

One good example we can use here is YouTube. Billions of people watch videos, and below are some of the data types that big data can look for: 

• Type of videos people watch;
• Words people use to search;
• The time when they watch videos;
• Country, age gender, etc.

Once the machine has made conclusions, it can predict what a specific user wants. This is why the feed users see in their YouTube apps varies from one user to another. It is the algorithm or the set of programmatic instructions, that makes this data analysis and decisions.

The same principle goes for advertising and gaming. The machines that process big data can tell a business establishment what people prefer, how they behave, and what things make them act. From this information, and its analysis, the business can make offers relevant to a specific user, giving way to a much more personalized experience.

What Is the Role of Big Data? 

It plays a critical role in personalizing customer or user experience. The aim is not only to analyze billions of information but to make use of it at a personal level. In this section, let us take a look at each fundamental function of this phenomenon. 

• Customer Segmentation – the analysis will result in segregating customers into buckets. The most common type of bucketing is demographics. Based on user characteristics, the machine can segregate users based on location or gender. This should help marketers and businesspeople make decisions about how they want to approach a specific business endeavor. 
• Customer Insights – insights are based on customer behavior. If people are dropping off the first 30 seconds of a 10-minute video, the algorithm can say, with statistical accuracy, that it is not interesting. As such, I will not recommend this video anymore. YouTube, for example, has no reason to show this bad video on users’ feeds, knowing that the previous viewers did not like it. 
• Customer Journey Mapping – this mapping has something to do with how a user finds themselves on a specific page. This usually applies to users of websites. Big data algorithms track how they found out about the product, what links they clicked, and whether or not they made the purchase. 
• Predictive Analysis – this process allows machines to “think” how consumers would react to particular content. Based on this prediction, the machine can now make recommendations.

What Exactly It Is Used For?

At the end of it all, the machine will now recommend content to the user. For example, if you keep watching cooking videos, the machine knows you are interested in cooking. It will look for the statistical findings in its database about other users who love watching cooking videos. 

Now, the machine may find out that other users spent time watching videos about how to take steak. In this case, the machine would predict that you may be interested in watching a video of how to cook steak. This is why you are likely to see this video recommendation in your feed.

Advertising agencies or platforms use the same concept. With machine learning and AI at the helm of their programs, they are not likely to show an ad to a user who, based on their browsing history and behavior, is not likely to be interested in the ad.

The era of a one-size-fits-all content and marketing strategy is over. Today, companies use big data to analyze how users behave and use that information to make an offer. The same concept applies to companies that operate social media sites and games. Ultimately, the benefit of this approach is that the effort can produce better results, not only for the company but also for the user.