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.
Big Data in practice for customer acquisition
Big Data is a set of large data volumes with high variety and speed, coming from different sources, that traditional software cannot process and store efficiently. This data usually lives on companies’ online servers, with connections between datasets and remote access, enabling faster and more reliable analysis. By structuring and interpreting these data, teams can better understand the consumer market, identify behavior patterns, and make decisions less based on guesswork — supporting marketing strategies and lead acquisition in a more assertive way.
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Understand the 5 Vs: volume, velocity, variety, value, and veracity;
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Use structured data for faster, more confident decisions;
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Merge data from different channels to segment customers and leads;
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Analyze behavior to improve user experience and retention;
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Find similar audiences and apply recommendations in acquisition.
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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 you will see in today’s content
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What Big Data is and why it involves volume, speed, and variety;
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The 5 Vs of Big Data and how they help evaluate data;
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How the concept evolved with social networks, IoT, cloud, and machine learning;
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Why Big Data strengthens faster decisions with less guesswork;
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The difference between Big Data and BI, and how each supports decision-making;
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How to apply Big Data to acquisition: segmentation, experience, and recommendations;
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Privacy and transparency care points when using data in digital marketing.
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.
for a wake word. Only after activation (or a button press) can audio be sent for processing, and you can manage privacy controls. Learn more in Alexa privacy.
Read too:
- Artificial Intelligence in education: future becomes reality
- What is Big Data and why you should apply
- Which are the best tools to optimize a website
- How can we map the customer's purchase journey
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".
You may find “lead lists for sale,” but be careful: processing or sharing personal data requires a lawful basis and transparency. In practice, lists with unclear origin, missing consent, or vague purpose can create legal and reputational risk. A solid baseline is Brazil’s General Data Protection Law (LGPD).
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 refers to datasets whose characteristics (like volume, velocity, and variety) often require scalable architectures and processing. BI (Business Intelligence) is the set of practices and tools that turn data into reports, dashboards, and decisions—whether or not Big Data is involved. For a technical baseline, see the NIST definition of Big Data.
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?
When applying Big Data to acquisition, also review how data is collected on digital channels (websites, landing pages, paid media). Best practices include transparency, preference management, and careful use of cookies/trackers—especially in education, where trust is central. A helpful reference is the ANPD cookies guidance.
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.
Image: Data volume and speed help explain why Big Data requires the right technology and analytics.
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.
Frequently asked questions about Big Data and customer acquisition
What is Big Data?
Big Data is a set of large data volumes that arrive with high variety and speed, often coming from different sources. It gets this name because traditional tools may not have enough processing and storage capacity to handle such scenarios. In general, this data is available on companies’ online servers, with links between datasets and the ability to be accessed remotely. The core idea is not only “having a lot of data,” but organizing it and making it useful for analyses that support decisions and strategies.
What do the 5 Vs of Big Data mean?
The 5 Vs describe the main characteristics of Big Data. Volume is the amount of data. Velocity is receiving and managing data quickly and directly. Variety refers to different types of data coming from different sources. Value highlights that every piece of data has value beyond the number itself, depending on context. Veracity is about the level of trust you can and should have in your data, reinforcing the need for quality and consistency.
How did Big Data emerge?
The concept of Big Data is relatively new, but large-scale databases have existed for decades. Large-scale datasets appeared around the 1960s and 1970s. As technology evolved, it became clear that massive amounts of data were generated daily, something that stood out with platforms like Facebook and YouTube around 2005. From there, storage and processing structures kept improving, gaining even more relevance with IoT—when “everything generated data”—and with the growth of cloud activities and machine learning.
Why is Big Data important for companies?
Big Data goes beyond simply combining information: it enables data analytics specialists to obtain faster and more complete answers, with more confidence in data than in assumptions. This helps companies get closer to consumers and identify, in real time, opportunities and gaps throughout the buying journey. With structured data and market insights, it becomes more feasible to make decisions quickly, especially in fast-changing environments, reducing reliance on guesswork in planning and execution.
What is the difference between Big Data and BI?
Big Data describes datasets with characteristics such as volume, velocity, and variety, which often require scalable architectures and processing. BI (Business Intelligence) is a set of practices and tools that turn data into reports, dashboards, and decisions—with or without Big Data. Big Data can include analysis (Big Data Analytics), but it does not always work together with BI, and the reverse is also true. One example mentioned is that BI can be done with a small dataset, while Big Data may serve “only” to provide data efficiently to an API.
How does Big Data help with customer segmentation and lead acquisition?
Data can come from marketing and sales actions, social networks, apps, and other channels, often stored in different places. One Big Data possibility is merging market data with data collected by your company, allowing you to segment customer bases and create more specific target profiles. This is valuable for lead acquisition because deeply understanding the potential customer profile is the first step to building more assertive strategies. In addition, more targeted actions tend to optimize resources, lowering investment costs and increasing return.
How can Big Data improve user experience and retention?
Economic survival depends not only on acquiring customers but also on retaining them. To do that, it’s important to analyze user behavior after actions such as visiting a site, buying a product, or logging into a monitored area. Understanding behavior patterns supports more personalized experiences for existing customers. Those same patterns also generate insights that can be used during acquisition, because they help reveal what works in the journey, where friction appears, and which signals indicate needs or preferences.
What are “referrals” and how does Big Data support recommendations?
In acquisition, it’s common to look for audiences similar to the leads you want to attract. With information collected from those leads, data analysis can support recommendations and point to similar audiences based on behavior habits. The text notes that this approach is common in streaming services, which suggest content based on observed patterns. In marketing, the idea is to leverage existing data to understand similarities and direct acquisition efforts more efficiently, rather than treating the audience as a single, loosely defined group.
Are Big Data, Machine Learning, and Artificial Intelligence the same thing?
No. Big Data, Machine Learning, and Artificial Intelligence (AI) can work together, but they are different. Big Data provides the input: the data used for analysis and to “teach” machines. Machine Learning is the ability of software to change its behavior based on learning from interactions, and it is part of AI. AI refers to machines performing tasks with different levels of complexity that simulate human capacity to think and solve problems. The text emphasizes that Machine Learning actively depends on AI, but AI does not depend on Machine Learning.
Are there privacy considerations when using data for acquisition?
Yes. The text warns about “lead lists for sale,” highlighting that personal data can only be processed and shared with a legal basis and transparency, and that lists without clear origin, consent, or purpose tend to create legal and reputational risk. It also mentions care points in digital collection (site, landing pages, and media), reinforcing best practices such as transparency, preference management, and attention to cookies and trackers—especially in contexts where trust matters a lot. The practical message is aligning acquisition and data use with controls and clarity for the user.
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