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What is Big Data and How to Use It in Customer Acquisition - Mkt4edu

Gustavo Goncalves
Gustavo Goncalves

Aug 8, 2023

Big data

Most likely you have heard the word Big Data in some data-related conversation, right?

Big data is not an extremely new concept in the market, but it is still quite common to have divergent information about what exactly it is and how to use it in our favor, especially when we talk about attracting customers. 

In today's post, we will explain better what big data is and what it is for, in addition, we will explain how to use it to make decisions based on big data and, consequently, improve your customer acquisition process. 

What we will see in the post:


Come on? 

What is Big Data? 

Big Data is a collection of large volumes of data. When we talk about Big Data, we are talking about an immense variety of data that arrive in large volumes, often from different sources and with high velocity.

We call this data set Big Data, as traditional software does not have enough processing and storage capacity. This set of data is generally available on the companies' online servers and, in addition to being interconnected, can be accessed remotely.

Within Big Data, there are the so-called 5 Vs:

  • Volume: amount of data;
  • Speed: receiving and administering data quickly and directly; 
  • Variety: different types of data that are available from different sources;
  • Value: every data has a value (which goes beyond the number);
  • Veracity: the confidence you can and should have in your data. 

If you are reading this text and remembering Marketing 5.0, where we need to have a "ready to change" culture, know that this association is perfectly correct. 

With the speed at which things change, it is extremely important to have structured data and information at our fingertips that offer insights into the consumer market and that can serve as a basis for quick decision making. 

How did Big Data come about?

The concept of Big Data is relatively new, but the same cannot be said for these large databases. The first large-scale datasets appeared, more or less, between the 1960s and 1970s. 

After that, as technology developed more and more, it became easy to see that massive amounts of data were being generated day after day, and this perception became very clear with the beginning of Facebook and YouTube, more or less in 2005. 

At that time, code structures were already being created to store these data sets (and, from then on, this only got better). These structures that made it easier and cheaper to store data began to gain greater repercussions with the advent of the IoT (internet of things), so data didn't just come from a desktop, everything generated data!  

This data began to provide even more information about behavior patterns, product performance, habits, etc.

As if all this data generation was not enough, machine learning and cloud activities have arrived to generate even more databases and understand the consumer even more. 

Well, that’s where a lot of people end up asking that classic question: “does my cell phone hear me?” or "Does Alexa hear me?". 

The answer is yes to all of them. From the moment you connect to a mobile device, you are generating data. 

Read too:

How important is Big Data?

Big Data goes far beyond just gathering information. Through this vast amount of data, data analytics experts are able to get faster and more complete answers, with more confidence in data than "guesses". 

This is so serious and so important that, even today, it is possible to find people and companies trying to sell customer databases, and this is very serious. Be aware that this practice is not legal and does not offer any benefit! It's no use having a huge amount of data without knowing who they are, where they come from, etc.

Technology, today, allows us to be closer to our consumers and identify, in real time, gaps that allow us to more actively monitor the purchase journey and act in a predictive way to meet consumer needs. 

At this point in the 21st century, you certainly already know that, although feeling is important, there is no such thing as making decisions based on guesswork, right? 

What is the difference between Big Data and BI?

As we are talking about data, it is quite common to confuse these two technologies. So, here we go: 

Big Data is the set of data that is processed and transformed into information.the Business Intelligence process made with Big Data through technology to extract useful information. 

An important thing is that Big Data can also contain a level of analysis (this process is inside Big Data Analytics) and this process does not always work together with BI and vice versa. In this scenario, BI can be done with a base of 10 lines and Big Data can only have the objective of providing data efficiently to an API (Application Programming Interface), for example.

We can say that BI is the intelligence behind data analysis. As we have already said here, data are just numbers, and it is necessary to interpret and cross these data so that they become information, that is, big data and data analytics must be related!   

Now, how to use all this information within your marketing strategies?

How to use Big Data? 

The application of Big Data is quite diverse, after all, data analysis can be applied in practically every type of business. But here we separate some specific situations related to customer acquisition. Check it out: 

Big Data applied to customer segmentation

Having data is good, but having segmented data is amazing! 

Data comes from different places: marketing and sales actions, social networks, apps, etc. It turns out that this data ends up being available in different places, and one of the many possibilities that Big Data offers is to merge market data with the data collected by your company. 

This allows you to segment customer bases and draw more specific profiles of people your company wants to reach. This data segmentation is practically gold for the lead capture process. 

Knowing the profile of your potential customer in depth is the first step to create assertive strategies for capturing (and, later, retention). In fact, if your company offers different types of products and services, you can have separate bases for each profile and define different strategies according to the needs of each audience to be reached. 

In addition, this segmentation will help you optimize resources, as more targeted actions tend to have lower investment costs and higher returns. 

Big Data Applied to User Experience 

The economic survival of a company is not just about attracting customers, but also about retention. 

And how to do it? Analyzing the behavior of the user after he has already performed the desired action (entered a website, purchased a product, logged into a monitored area, etc.). 

Understanding the behavior of this user is essential to offer more personalized experiences to those who are already your customers.

The analysis of these behavior patterns will also generate powerful insights to use when capturing.

Big Data applied to referrals

When we talk about attracting, we work very actively with the search for similar audiences, and Big Data is an essential key to that. 

When you already have information collected from the leads you want to attract, you can work with recommendations. The data analysis process can indicate lookalike audiences according to their behavior habits. 

This is a very common practice of streaming services (such as YouTube, Netflix and others). 

Big Data, Machine Learning and Artificial Intelligence

Before finishing this post, you need to understand that Big Data and Machine Learning are different things. 

Although these two things + Artificial Intelligence work together, they are different. 

Big Data provides the input to teach machines (Machine Learning). 

Machine Learning is the ability of software and robots to modify their own behavior by learning from interactions. This learning process happens in many ways and is just a little piece of the Artificial Intelligence (AI) universe. 

When we talk about AI, we are talking about the ability of machines to perform the most diverse tasks with different levels of complexity that simulate the human ability to think and solve problems. 

That is, Machine Learning actively relies on AI, but AI does not rely on Machine Learning. 

These three technologies are directly related, since Big Data provides the data, Artificial Intelligence consults this database and Machine Learning, in turn, learns patterns to be applied in the most diverse ways. 

So, were you able to understand the importance of Big Data in the customer acquisition process? 

This topic is quite extensive and we could spend hours talking about it, but if you want to continue learning about the universe of data, take the opportunity to check out this post that we recently put here on the blog: Data Analysis: really know your customer and make less mistakes. 

Really know your customer 

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