Well, if you own a business, you know that the customer journey doesn't end after the sale. In fact, one of the main objectives and challenges is to retain that customer. But what if we told you that, in this retention mission, structured data can be your great ally?
One important note: in this post, “structured data” means information organized in tables/CRMs for analytics and decision-making. That’s different from structured data markup on Google, which is code (schema) used to help search engines understand web pages—more of an SEO topic than a retention one.
If you're familiar with the world of Inbound Marketing, you've probably heard of data analysis. After all, whatever the products and services, every company receives a constant flood of data. But between you and me, the important thing is what you've been doing with this data.
When it comes to organizing this information, here at Mkt4Edu we rely on the best technologies for accurate and efficient analysis, especially in fields such as Data Science, Big Data and analytical intelligence, which can help your company make the best use of its data.
If customer retention is also one of your objectives, this information is essential for a more assertive strategy. But before we get to that, it's important that you understand what structured data is and how it differs from unstructured data.
Structured data for customer retention: how to apply it day to day
Here, structured data means information organized in tables and CRMs for analysis and decision-making — not the code markup (schema) used by search engines. In practice, it’s data with standardized fields (name, type, and format) that makes storing, querying, and comparing information faster and easier. When connected to semi-structured data (such as website events, usage logs, and JSON/XML integrations), it helps you understand behavior, identify churn risk, and create re-engagement opportunities. This moves your strategy away from guesswork, improves consistency, and strengthens retention through decisions based on real signals, metrics, and KPIs.
-
-
Standardizes fields and records for fast reading and querying.
-
Makes it easier to control, search, and segment audience information.
-
Helps understand habits, profile, objections, and customer history.
-
Supports strategic decisions with solid, consistent data.
-
Enables ongoing fixes and improvements using metrics and KPIs.
-
What you will see in today’s content
-
What structured data means (in this context) and how it works in practice.
-
The difference between structured, unstructured, and semi-structured data.
-
Common examples: CRM, spreadsheets, relational databases, documents, and social media.
-
How “schema” (fields and types) helps store and query data quickly.
-
How behavioral data and integrations (JSON/XML) connect to the CRM for retention.
-
The 3 main retention advantages: knowing customers, deciding better, and being more assertive.
-
How metrics and KPIs support course correction and new insights.
-
Why trust, transparency, and security (LGPD) affect experience and retention.
Read on and find out how data analysis can contribute to your marketing strategies.
Structured data: what is it and how does it work in practice?
If you're a regular reader of our blog, you've probably already read about data analysis. This is a fundamental process in both acquisition and retention strategies. But despite being such an important tactic, some people don't know exactly how structured data works in practice, so today we're here to explain it in an uncomplicated way.
In short, structured data is information that is in a clearly established format. Generally, this data has a predefined pattern and is easy to read.
In practice, structured vs unstructured data differs mainly by the presence of a schema: structured data follows predefined fields and data types (text, number, date, etc.). A CRM can store “Name,” “Email,” “Program of interest,” and “Lead status” in columns—and it’s still structured because it consistently follows that schema.
The main advantage of structured data is having a clear standard for storing and querying information quickly. If you need to capture a new kind of information, you update the schema concept (for example, adding a new text field like “Cancellation reason”) instead of mixing values that don’t match the defined data types.
Spreadsheets and relational databases (like the ones used in CRMs) are classic examples of structured data. Formats like XML and JSON are often treated as semi-structured data: they include tags and conventions, but allow more flexibility in fields and hierarchy — very common in integrations and product event tracking.
With this information well organized through structured data, you'll find it easier to control, search for information or even delimit the data. What's more, with the structure of this predefined data, you can analyze your target audience's responses in a practical way.
Structured and unstructured data: what's the difference?
The main difference between the two classifications of data lies in their formatting. While structured data has an established order, in unstructured data your company may not be able to identify a clear organization.
As the name implies, this type of data does not have a well-defined structure and is not limited to spreadsheets or tables. In other words, structured and unstructured data are complete opposites; while one has a pattern, the other does not.
Remember the example we gave in the previous topic? In it, you can't add any different characters, right? In unstructured data, this is not a problem, you can merge numbers and text without complications, because the information is dynamic and even flexible.
An example of unstructured data is Word documents, Powerpoint presentations or even our social networks.
In day-to-day retention work, you’ll also deal with semi-structured data: website events (clicks, visited pages), product usage logs, and API integrations (JSON/XML). It’s valuable because it captures behavior — and, when connected to CRM data, it helps predict churn risk and identify re-engagement opportunities.
Now that you've understood the main difference and how both classifications work in practice, let's get down to business. You're here to find out how they can contribute to your retention, right? And that's the subject of our next topic.
See also:
- Student retention: 6 strategies that can help improve
- Student retention: educate, engage and build loyalty
- Student retention: the 3 essential and effective techniques
3 advantages of structured data in your retention strategies
Okay, at this point you're probably wondering what the point of analyzing structured data is for your retention, right? To answer your question, we've listed the three main advantages of adding it to your marketing strategy. Let's go?
1. It helps you really get to know your customer
Do you know your customer? Even if your answer was "yes", we know that consumer behavior is not immutable; on the contrary, it changes all the time. But how do you create assertive strategies with this ever-changing behavior?
This is where data analysis is a great ally! In order to achieve customer satisfaction and keep your company up to date, structured data analysis helps you store and organize information about your consumer's behavior and use it effectively.
With structured data, your company has valuable information on consumer habits, demographics, profile, reasons for resistance and even negotiation time. And it's clear that all this helps your brand not only to get to know your consumer, but also to understand their actions in depth and obtain satisfied customers.
Image: Structured data follows a standard, making it easier to organize in databases and integrate across different systems.
2. Helps you make strategic decisions
Imagine creating your tactics using only subjective facts and "guesswork". It's like taking a shot in the dark, and it could be a huge financial loss, after all, you wouldn't be using any real data about your customer in your strategy.
Don’t worry: with the technologies we use to integrate CRM, support, and behavioral data (e.g., usage, engagement, and history), you can move past guesswork and make decisions based on real signals. The more consistent your data capture and updates are, the more reliable your retention analysis becomes.
By analyzing information, you can make decisions easily, rationally and safely, based on solid data about your customer. What's more, you can analyze the process of the customer's journey up to the present moment and demonstrate more authority and confidence when it comes to serving the customer.
3. Contributes to more assertive strategies
And while we're on the subject of retention, you're well aware that marketing strategies play a fundamental role in your mission to retain customers.
But in order to create more assertive tactics, we know that it is essential to know your customer's difficulties and needs. At this point, you've already seen how structured data can help a lot, haven't you?
With the information we mentioned earlier, your marketing team can understand your customer's actions, create strategies and achieve the main goal: loyalty. But it doesn't end there! By using metrics and KPIs, your team can still discover data that produces new insights.
Well, if you don't know what KPIs are, don't worry, we'll explain, OK?! A KPI is a kind of tool for measuring the performance of a certain process, you know? In a nutshell, a KPI or key performance indicator is a key performance indicator.
In other words, with well-structured data and added performance indicators, you can make certain necessary corrections and drive your team to develop more accurate retention strategies.
Let's face it, using structured data has only been an advantage so far, hasn't it? After all, as well as directing your team, analyzing this information contributes directly to more assertive content marketing.
If knowing your audience can contribute to your retention tactics, not knowing them can cause you to lose them to the competition. To avoid this, it's time to start implementing software in your company and use this information to your advantage within your Inbound tactics.
Retention also depends on trust: when collecting and activating data, ensure legal basis, transparency, and security under the Brazilian General Data Protection Law (LGPD). Beyond reducing risk, this improves the experience—because customers understand why you request data and how it will create value throughout the relationship.
And while we're on the subject of your marketing strategies and new technologies, be sure to check out our website and find out how the team of experts here at Mkt4Edu can transform your customer retention process.
Frequently asked questions about structured data and customer retention
What does structured data mean in this context, and why does it matter for retention?
Here, structured data refers to information organized in tables and CRMs for analysis and decision-making. It matters for retention because it creates a clear storage and querying pattern, making it easier to access customer history, profile, habits, and behavioral signals quickly. With standardized data, a company can manage and search information more practically, and analyze audience responses and interactions more efficiently. In retention work, this organization helps move away from guesswork and toward real signals, supporting more consistent actions for loyalty and re-engagement.
What’s the difference between structured data (tables/CRM) and Google’s structured data markup?
In this content, “structured data” means information organized in tables/CRM for analysis and decisions, used to understand customers and support retention. Google’s structured data markup, on the other hand, is code (schema) added to pages to help search engines understand content—more related to SEO than retention. Although they share the same name, they serve different purposes: one is about internal data organization for management and analytics, and the other is about describing page information to search engines.
How does structured data work in practice, and what is “schema” in this scenario?
In practice, structured data has a clearly established format, with a predefined pattern and easy readability. The key difference lies in the schema: each field has a name and type (text, number, date, etc.), and records follow that pattern. In a CRM, for example, fields like “Name,” “Email,” “Course of interest,” and “Lead status” are stored in columns, following the same model. If you need to collect a new type of information, the path is to adjust the schema (such as adding a “Cancellation reason” field), rather than mixing values outside the defined type.
What examples of structured, unstructured, and semi-structured data are mentioned in the text?
Structured data appears in spreadsheets and relational databases, such as those used in CRMs. Unstructured data is information without clear organization or a well-defined structure—examples mentioned include Word documents, PowerPoint presentations, and social media. Semi-structured data appears in formats like XML and JSON, which include markings and patterns but allow more flexibility in fields and hierarchy. For day-to-day retention, the text also mentions website events (clicks, pages visited), product usage logs, and API integrations as valuable sources of behavior.
Why does a structured data standard make analysis and fast querying easier?
The advantage of structured data is having a clear standard to store and query information quickly. Because fields and types are defined, it becomes easier to control, search, and segment data, while keeping consistency over time. This also improves analysis reliability: the more consistent data entry and updates are, the more reliable retention insights become. Instead of relying on mixed or hard-to-compare information, a company can organize records and generate reports, metrics, and insights in a more practical way.
How does structured data help you truly understand customers over time?
Even if you believe you know your customer, behavior changes constantly and requires ongoing monitoring. Structured data analysis helps store and organize consumer behavior information and use it effectively. The text notes that, with structured data, a company can capture habits, demographics, profile, reasons for resistance, and even negotiation time. This set of information provides a deeper view of customer actions, supporting satisfaction, relationship-building, and loyalty efforts based on concrete, well-organized information.
How does it support strategic decision-making and reduce “guesswork”?
Without real data, building tactics from subjective assumptions is like shooting in the dark and can cause financial loss. With structured data and integration technologies (CRM, customer service, and behavioral data such as access, engagement, and history), decisions can be based on real signals. Consistency in data entry and updates increases the reliability of retention analyses. The information also helps you understand the customer journey up to the present moment, enabling safer decisions and stronger authority in service interactions because actions and arguments rely on evidence.
How does structured data contribute to more accurate retention strategies?
In loyalty-building, marketing strategies can directly influence whether customers stay, and creating more accurate tactics requires understanding needs and difficulties. With structured data, the marketing team can understand customer actions and turn that into tactics aligned with the retention goal. The text also highlights that, by using metrics and KPIs, teams can uncover data that generates new insights and make necessary corrections over time. This improves action precision and increases the likelihood of keeping customers engaged in the relationship.
What are KPIs, and how do they connect to structured data in retention?
A KPI (key performance indicator) is a metric used to measure the performance of a process. In the text’s context, when data is well structured and combined with KPIs, teams can evaluate how actions are performing and identify needed corrections. This supports more accurate retention strategies because decisions and adjustments are guided by consistent measurement rather than subjective impressions. In other words, structured data provides the foundation for tracking performance, while KPIs help translate that foundation into monitoring and continuous improvement.
Why do LGPD, transparency, and security also affect retention?
The text emphasizes that retention also depends on trust. When collecting and activating data, it’s important to ensure a legal basis, transparency, and security in line with Brazil’s General Data Protection Law (Lei Geral de Proteção de Dados Pessoais – LGPD). Beyond reducing risk, this improves the experience because customers understand why data is being requested and how it will generate value in the relationship. When data use is clear and secure, it tends to strengthen the bond with the brand, support engagement, and sustain retention strategies more responsibly.
Portugal