Today, we want to start this post in a different way, inviting you to think about your marketing tactics. If you invest in your company's strategies, we want to ask you a question: do you know the importance of data analysis for capturing leads?
If your answer was no, it's time to worry, because when it comes to marketing digital data analysis must go hand in hand with your sales tactics for an effective strategy.
At this point in the post, you must be thinking about all your strategies so far, right? And if the results have not been what you expected, it may be a consequence of a lack of good data collection and analysis.
Data analysis in practice for lead generation
Data analysis is the process of organizing and analyzing raw data — often using software — to understand audience behavior, identify patterns, and support marketing decisions. By tracking the lead’s journey, your company can connect better with real concerns and goals, fix strategic gaps, and personalize communication. It also helps prioritize leads with higher conversion potential, optimizing spend and enabling smarter business management. To use data safely, it’s important to map purposes, legal basis, and retention, following Brazil’s LGPD and transparency best practices.
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Organize and interpret target-audience data to guide decisions;
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Review strategies, spot gaps, and avoid repeating mistakes;
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Personalize communication to build stronger lead relationships;
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Focus budget on leads with higher conversion potential;
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Apply privacy and compliance care (LGPD and transparency).
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What you will see in today’s content
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What Data Analysis is and how it helps understand audience behavior;
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Qualitative and quantitative approaches and how they differ in analysis;
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Why data collection is key to optimizing lead generation;
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Essential care to use data safely (purpose, legal basis, and retention) in the LGPD context;
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How data analysis works in practice through a sequence of steps
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A data-driven methodology reference and why the cycle is iterative;
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The four types of analysis: descriptive, predictive, prescriptive, and diagnostic;
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How to turn knowledge into action to improve marketing results.
If you want to know more about data analysis, read on and discover the types of analysis and the importance of data collection for digital marketing.
What is Data Analysis?
Every day your company is fed with a lot of data with great speed and you should know that well. But the big question is: what should you do with all this? After all, how to organize this data and use it in the best way in your strategies? If this is one of your questions, we have the answer!
Data Analysis is the process of analyzing raw data, often done through software with a focus on knowing your customer's behavior and identifying patterns. That is, data analysis will interpret information from your target audience and contribute to your brand's next decision-making.
In this analysis, it is important to highlight that there are two methodologies used in the process: the qualitative, based on the responses of each client and discursive information, and the quantitative, focused on numbers and statistics.
But what does Data Analysis have to do with your marketing strategies? If this is one of your questions, the answer is now in the next topic.
How important is data collection for marketing strategies?
When it comes to marketing strategies, all companies have a common challenge: capturing leads. After all, how to attract him and improve his purchase journey so that he becomes a possible customer?
Well, you already know that, with data analysis, you can analyze information about your target audience, right? By following your future customer's journey and obtaining this data, it is possible to understand more about your lead, their concerns and dreams and connect with your potential customer.
With the analysis in hand, you will also be able to review your strategies and identify your main flaws. That way, your marketing team can fine-tune processes and better control operations. In other words, don't make the same mistakes!
It's time to develop a solid relationship between your brand and the lead through a more personalized and assertive communication strategy, you know? All this, of course, based on the data collected so far.
One key note: collecting and using data responsibly requires clear purpose, legal basis, and retention rules — especially with forms, CRM, and paid media. In Brazil, make sure you follow the Brazilian General Data Protection Law (LGPD) and cookie transparency best practices aligned with the ANPD cookie guidance.
In addition to helping you get to know your customer better and correcting your strategies, data analysis can contribute to smarter business management and assist in your brand's decision making.
Not to mention that, by identifying and focusing your investment on leads that are most likely to convert, your company can still save money. After all, you will be directing your efforts to those who are most likely to become your customers. Wonderful, isn't it?
With data analysis and a broader view of consumer behavior, your company still has the opportunity to invest in innovations and promote a unique experience for the lead.
Ufa! How many advantages, isn't it? If you arrived here wondering what the purpose of data analysis is, in this topic, you have already managed to understand in depth that this process is fundamental when it comes to capturing leads and identifying which of these leads have the greatest potential for conversion. And if you still haven't invested in professionals and software with the necessary skills, it's time to start rethinking that decision.
Okay, but now that you understand the importance of parsing. It's time to find out how this process actually works, shall we?
Image: With data analysis, you can track metrics, spot patterns, and improve lead generation with more predictable results.
After all, how does data analysis work?
You already know that Data Analysis, in general, is done through highly modern software and deployed in companies for information organization and analysis. But in practice, how does all this work? In order to do data mining, the analysis usually follows a sequence of eight steps, which we will cover now.
Note: this “step-by-step” can vary depending on the goal (BI, campaign performance, churn prediction, etc.). If you want a proven project structure, CRISP-DM is a classic methodology covering business understanding, data preparation, modeling, evaluation, and deployment—and it emphasizes the process is iterative.
- Data entry: The first step in starting data analysis is defining requirements and collections. This is where you will determine the information you need and how you will be able to gain access to that data.
- Data preparation: are we going to the second stage? It's time to prepare the data to produce insights. It is in this phase that the raw information is filtered and the data structured for the next phase.
- Data Exploration: As the name implies, it is at this stage that there is a research and study of a large set of data. At this point, the focus is on understanding this information in depth, taking into account profiles, sampling, statistical analysis, patterns, among others.
- Data enrichment: After collecting and filtering the information, it's time to enrich the data analysis with additional information. It is at this stage that it is possible to see the process from a new perspective and gain new insights.
- Data science: in this phase, a new method is added to extract deeper and more detailed information. This process encompasses algorithms, Artificial Intelligence (AI) and machine learning.
- Business Intelligence: One of the last steps in data analysis is Business Intelligence. In this phase, it is possible to have access to reports made through the collected results, in addition to obtaining dashboards and studying the business process.
- Report Generation: In the penultimate phase, reports are generated and shared as quickly and effectively as possible.
- Optimization: although this is the last phase and all the data has been analyzed so far, it does not mean that your company has stopped receiving information, on the contrary, new data arrives all the time, supplying the created base, so it is necessary to elaborate variables, improve them and optimize the ones already created.
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What types of analysis?
We can say that, at this point, you are already mastering Data Analysis much better than when you started reading this post. Just to recap, you already know what data analysis is, its impact on marketing strategies and how each step of this process works. But now that you've had most of your questions answered, it's time to get to know the four types of analysis. Prepared?
1. Descriptive Analysis
Based on facts, the descriptive analysis model takes real data as a reference. The purpose of this analysis is to guide digital marketing strategies. It is important to emphasize that descriptive analysis is directly linked to descriptive statistics, such as frequency, mean, median and percentages.
By focusing on real data, this data analysis model is widely used, especially in the educational and business sectors. Some examples of descriptive analysis in use are: reports, segmentation, application of metrics, among others.
2. Predictive analytics
With predictive analytics, your business will always be one step ahead. Now, you must be asking yourself "how so?". The focus of predictive analytics is to identify patterns and predict situations that may occur.
With its prediction of the future based on data collection, predictive analytics is one of the most popular models among companies. After all, who wouldn't want to predict effects or have a formula to anticipate their results?
In prescriptive analytics, the goal is to recommend actions — what to do next — to optimize outcomes based on predicted scenarios. Teams often combine simulation, business rules, and optimization techniques, and add AI only when it helps. A solid overview is prescriptive analytics.
3. Prescriptive
analysis If in the predictive analysis the company has the possibility to foresee scenarios, in the prescriptive one, the objective is to define possible decisions and solutions for the foreseen situations.
It is from the possibilities that, in the prescriptive analysis, you will find recommendations and simulations to obtain a better result. In short, prescriptive analysis will show you where you should put your efforts to get around the future situation. In this model, artificial intelligence (AI) is used as machine learning and algorithm. AI can support prescriptive analytics data.
4. Diagnostic Analysis
In the process of diagnostic analysis, the investigation is focused on something that has already happened. Far from predicting the future, as in the previous analysis, in this model, the central objective is cause and effect.
In diagnostic analytics, you start from what already happened to answer: “why did it happen?”. It focuses on root causes using techniques like drill-down analysis, segmentation, correlation, and hypothesis testing—so teams can correct funnel issues with confidence.
After understanding the types of analysis and everything about data analysis, the question is: what are you going to do with all this knowledge? You can't just stay in theory!
If you want to turn this into action, keep exploring our blog content on data, funnels, and campaign optimization. Visit the mkt4edu / 4RevOps Blog and pick the topic that matches your current priorities (acquisition, CRM, BI, or automation).
Access now our last post: How to retain and attract customers through audiovisual content.
Frequently asked questions about Data Analysis for lead generation
What is Data Analysis?
Data Analysis is the process of analyzing raw data, often with the support of software, to understand customer behavior and identify patterns. The goal is to interpret target-audience information to support the brand’s next decisions. Since companies receive data in high volume and at high speed, analysis helps organize what has been collected and turn it into useful insights for strategy. In marketing, this connects directly to the ability to track the lead journey and adjust actions based on what the data shows.
What is Data Analysis used for in digital marketing?
In digital marketing, Data Analysis is used to guide strategies based on evidence of audience behavior. By analyzing lead-journey data, a company can better understand concerns, desires, and interests, making it easier to create relevant connections and improve communication. Analysis also helps review processes, identify gaps, and control operations more effectively, reducing the chance of repeating mistakes. As a result, decision-making becomes more consistent and aligned with what actually happens in the funnel.
What is the difference between qualitative and quantitative analysis?
Qualitative analysis is based on customer answers and discursive information, helping interpret perceptions, opinions, and context. Quantitative analysis focuses on numbers and statistics, supporting the identification of measurable patterns and scalable results. In Data Analysis, both approaches can be used to better understand the audience and support decisions, combining the “why” (qualitative) with the “how much” (quantitative), depending on the objective of the analysis.
Why is data collection important for lead generation?
Data collection is important because it allows you to track the future customer’s journey and better understand who the lead is, along with their concerns and goals. With this information, the team can review strategies, identify gaps, and adjust processes to improve performance. This enables more personalized and assertive communication based on what was observed throughout the funnel. In addition, analysis can help direct efforts toward leads with higher conversion potential.
What essential care is needed to collect and use data safely?
To collect and use data safely, it is essential to map purposes, legal basis, and retention, especially when forms, CRM, and paid media are involved. It is also important to adopt transparency best practices for tracking. In Brazil, the main reference is the General Data Protection Law (LGPD), paying attention to guidelines about cookies and transparent practices. These measures help reduce risk and provide more predictability for responsible data use.
How does data analysis work in practice?
In practice, data analysis typically follows a sequence of steps to organize and turn data into insights. The process starts with defining requirements and collecting data, then moves into preparation and exploration, and may include enrichment with additional information. It can then incorporate Data Science methods such as algorithms, AI, and machine learning, and evolve into Business Intelligence, with reports and dashboards. Finally, optimization keeps the cycle active, since new data keeps arriving.
What are common steps in the data analysis process?
One sequence presented includes eight steps: data entry (requirements and collection), data preparation (filtering and structuring), data exploration (study and statistical analyses and patterns), data enrichment (additional information), data science (algorithms, AI, and machine learning), business intelligence (reports and dashboards), report generation (fast and effective sharing), and optimization (continuously creating and improving variables). The order can vary depending on the objective, and the cycle tends to be iterative.
What is descriptive analysis and when is it used?
Descriptive analysis uses real data to guide strategies and is tied to descriptive statistics such as frequency, mean, median, and percentages. Because it focuses on observed facts, it is widely used in educational and business contexts. Examples mentioned include reports, segmentation, and applying metrics. In digital marketing, it helps clarify what happened and how results are distributed, supporting initial assessments and course corrections.
What is predictive analysis and why is it popular?
Predictive analysis focuses on identifying patterns to forecast situations that may occur. By using collected data to anticipate outcomes and results, it helps the company stay “one step ahead,” estimating future scenarios based on history and identified trends. This helps explain why it is one of the most popular models among companies: it supports planning and action prioritization, reducing decisions based solely on intuition.
What is prescriptive analysis and what does it recommend?
Prescriptive analysis aims to define possible decisions and solutions for predicted situations—meaning it recommends actions. It seeks to answer “what to do now” to optimize results based on the forecasted scenarios. To do so, it commonly combines simulations, business rules, and optimization techniques and, when it makes sense, uses AI models as support. Together with predictive analysis, it helps turn forecasts into practical operational guidance.
What is diagnostic analysis and what question does it answer?
Diagnostic analysis investigates something that has already happened and focuses on cause and effect. Instead of predicting the future, it starts from the outcome to answer “why did this happen?”. The approach can involve correlations, segmentation, drill-down, and hypothesis tests to explain performance drops or spikes. In marketing, this is useful for understanding funnel changes and enabling quick fixes based on evidence.
How can Data Analysis help save money and improve conversions?
By identifying which leads have a higher probability of conversion, a company can focus investments and efforts on audiences more likely to become customers. This can reduce waste and improve acquisition efficiency. Additionally, with a broader view of consumer behavior, the brand can adjust strategies, invest in innovation, and promote an experience that better matches the lead, increasing the likelihood of progress in the buying journey through more personalized communication.
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