How Big Data Can Bring More Customers?

Everything that surrounds us consists of data. We receive and give away pieces of various information daily. And, being put together, these pieces turn into zettabytes. Apparently, humanity has produced about 44 zettabytes of information worldwide. Of course, the number is not final and by 2025 it can amount up to 180 zettabytes, as Statista projects. Quite a bold prediction. But, as the tendency shows, we shouldn’t underestimate it. 

Major growth in the Big Data sector is expected in the nearest future. It may reach the predicted $68.09 billion by 2025. Hence, we may speak of technological progress in the data processing. And, the software segment will account for $103 billion by 2027. So, what is this Big Data everyone is talking about?

When all the written, electronic, audio, video, and other data accumulates enough to be uncountable and too complex to be processed, it becomes Big Data. 90% of the world’s data is replicated, leaving the rest 10% the unique-created status. Here, you might ask, why do we need to process these large piles of reposited files? The answer is simple - to extract value from them. Using advanced and predictive analytics, businesses can obtain great insights about their customers. And, rely on data-driven decision-making regarding these customers. But, how can you get the user data that is worthy? Let’s explore the possibilities!

How can websites get user data?

A website is a set of different web pages. It has related content and a single domain name. And, usually, is located on one web server. But, it depends on the website's complexity. Any site you access on the web is a website. Some of them are informative, social communication platforms, and some belong to products and services, so they produce stuff from scratch. The others sell and buy the produced goods. These are Google, Amazon, Wikipedia, Apple, Meta, Inoxoft, etc. 

Cookies

There are over 1.7 billion websites on the internet these days. Not all of them are active, but the numbers astonish. How do you think they find out more about their potential users? There’s one trick you have probably noticed on any website. It is called “cookies” - an essential and most common tracking tool.  

Cookies are small pieces of data sent from the webserver to the user’s browser. A website sends cookies to understand how its potential users navigate between pages.  Also, the mission of cookies is to remember what information each user has entered, searched for, etc. That’s because web pages don’t have a memory. And, there’s a need to get user data to understand their actions and remember these users at once.  

Types of users data websites collect may be broken down into the following categories: 

• Personal data includes identifiable information like  Social Security numbersbut also nonpersonally identifiable information,such as  IP address, web browser, etc.

• Engagement data details how exactly user interacts with a website: mobile apps,  text messages, social media, paid ads, etc.

• Behavioral data covers details like purchase histories, product usage information, repeated actions, and qualitative data

• Attitudinal data encompasses metrics on clients satisfaction, purchase criteria, product desirability, etc. 

To get even more insights and deeper knowledge of user preferences on your website, businesses use Big Data technologies. One of these technologies is Artificial Intelligence.  

Artificial Intelligence (AI)

It is hard not to know about AI today. But still, it is a smart machine that can carry out human-like tasks. For example, SIRI is a smart machine trained to provide iOS users with support, offer help and solutions, and many more. It was trained to do so, but now SIRI thinks on her own and produces great output. A similar smart machine is Alexa. 

AI has two approaches to analyzing big data. These are via machine learning and deep learning. With Machine Learning it is possible to train algorithms to collect and analyze user data. What this data might look like? User location, weather conditions, age, gender, user preferences, and so on. Deep learning, in its turn, is a neural network that has layers (usually, 3 of them or more). Neural networks replicate the behavior of the human brain and learn from piles of accumulated data. Therefore, it can make quite accurate predictions. 

How can you operate with the obtained user data?

Let’s discover  a few interesting cases that enable stakeholders to:

Get valuable insights

Let’s imagine a project that aimed to help people find jobs that would meet their skills. After some time a platform that worked on real-time data was delivered. It was trained to gather career options and match the user and the option on multi-levels. Also, it could predict and match job hunters with potential careers, and decode the required skills or expected activities. Besides the accurate match, the platform offers assistance via video chat.

Predict future user satisfaction

The other project worth mentioningm revolved around the Retail industry. The client’s business did not interact with its clients directly. Only, through catalogs with clothes. And, the sales dropped significantly as it was unaware of his potential buyers and their needs. So, to ensure the business stayed strong and obtained user satisfaction as well as new purchasing opportunities, lots of customer information had to be analyzed. Thus,  an ML model to perform categorization was created. This model categorized every word and analyze websites and predict consumers’ preferences further. The model was developed as a multilingual classifier. It gave an essential possibility to keyword websites written in any other language.

Offer the right advertisements to the right users

To offer the right ads to people, who might enjoy them, one company wanted to analyze complex websites. Innovative ML model managed to  perform categorization, work with the big data and analyze it.  Now making the right advertisement is easier.  According to the model, each word was given an IAB category to analyze websites and predict clients further preferences. ML model was created as a multilingual classifier enabling keyword websites written in any other language. 

Conclusion

Big Data is a powerful tool that can turn the pieces of various information into valuable insights. The role of business is to understand the concept of it and employ advanced and predictive analytics to obtain great insights about their clients and make data-driven business decisions that always bring more profit.