In an area as results-oriented as marketing, making decisions based on guesswork is certainly not the best way to proceed with strategic planning.
However, with the digital revolution in full swing, new technologies have been increasingly punctual and efficient, especially in the fields of business intelligence and Data Science, which seek, precisely, to use a generation methodology, collection and analysis data to provide a company with substantial information that can really make a difference in the execution of a project.
What you will see in the post:
- Business Intelligence and Data Science: What's the Difference?
- The reasons to use Data Science in marketing
- Improved customer acquisition
- Agility in monitoring results
- Better direction of actions
- Greater customer retention
- Process Improvement
- What can Data Science do for your business?
- Calculation of goals
- Conversion calculation
- Production of numerical reports
- Logic construction
- Building Custom Dashboards
- Conclusion
But before delving deeper into the importance of these areas, especially the second, let's make a brief differentiation between them.
Business Intelligence and Data Science: What's the Difference?
In theory, both areas are very similar, bringing many concepts aligned and whose main objective is to provide insights obtained through raw data and deliver them to leaders, managers and decision makers, so that they can achieve better results in their operations. positions.
The difference isn't just the time horizon. In practice, BI covers descriptive (what happened) and diagnostic (why) analytics, while Data Science expands to predictive and prescriptive (what will happen and what action to take), often with machine learning—choose the techniques based on the decision.
The subtle difference between the two fields, however, is due to the way the data is used, in which Business Intelligence makes use of tools that interpret past and current data to predict future episodes. within a short to medium term perspective, while Data Science it is a methodology of multidisciplinary processes, which applies mathematical and/or statistical models to define predictions about future results in a long-term perspective.
Like all science, Data Science formulates hypotheses and runs tests in order to seek evidence that corroborates, or not, these formulations, as well as bringing a business new ways of seeing details and scenarios that relate directly to all its stakeholders, presenting an even greater level of consistency and power to revolutionize operations.
If you can visualize Data Science as a strong ally in your marketing strategy, great, because it really can be. But if this subject is still not very clear to you, check out some of the reasons to start betting on this tool!
Image: Data Science Professional Analyzing Data for Marketing Strategies
The reasons to use Data Science in marketing
Companies have made progress in digital transformation, but they're still capturing only part of the promised value. Harvard Business Review reports that 89% of large companies already have digital transformation/AI initiatives underway, but have only realized 31% of the expected revenue gains and 25% of the cost savings. In other words, without analytical execution, data governance, and organizational change, the strategy delivers less than it could.
Therefore, it is natural that within the marketing scenario in Brazil, the two areas are still not aligned.
To change this situation and add a little more to your understanding, now learn about some of the reasons to unite Data Science with marketing:
1. Improved customer acquisition
By using Data Science, it is possible to have a better understanding of how to direct efforts to the marketing strategies thanks to the construction of intelligent algorithms that provide you with essential content and direct the best action.
Thus, having the best resources and information, customer acquisition becomes much more optimized, focusing on the identification and conversion of qualified leads for purchase within the sales funnel.
Propensity and personalization models at scale increase acquisition when fueled by quality data and orchestrated by the right architecture. McKinsey highlights that enabling near-real-time personalization sustains consistent growth and ROI.
2. Agility in monitoring results
With Data Science, it is possible to gain agility in monitoring dashboards, with results in real time, optimizing the speed in time of information and its transparency, in addition to more assertive and accurate decisions.
Beyond dashboards, marketing teams are adopting real-time decision-making (next best action, offers, routing) supported by AI. Harvard Business Review highlights the shift from "reflexive" processes to reflexive decisions, driven by data and AI, without relinquishing human control and guardrails.
3. Better direction of actions
Not having agile and reliable data sources is, basically, navigating without a compass. Without the correct assumptions, decisions become slower and more inaccurate.
Without results panels, obtaining information remains dependent on a greater number of people to reach their destination.
In April 2025, Google confirmed that it will not eliminate third-party cookies in Chrome, opting to maintain user choice. However, the market is moving toward first-party data and LGPD-compliant measurement. Prioritize explicit consent, modeling, and testing to reduce reliance on third-party cookies.
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4. Greater customer retention
Regardless of your business, having timely information about your customers and having the ability to convert them into specific actions can be a turning point for your business when it comes to keeping these customers happy. and promote their retention and loyalty.
For retention, avoid generic incentives. Evidence from the Harvard Business Review shows that churn models generate value when combined with controlled tests and offers designed for those at risk who respond to intervention, reducing discount costs and increasing margins.
5. Process Improvement
Professionals who work with Data Science, the data scientists, must have a holistic knowledge about the company's business and the scenarios that surround it. In this way, when performing tests through mathematical models, they can identify opportunities and question methods and processes then used, aiming at their optimization.
What can Data Science do for your business?
In addition to understanding some of the reasons why Data Science has become fundamental to marketing strategies, also check out everything it can do for it.
Calculation of goals
Analyzes previous data to estimate the contact goals needed to reach the established goal by measuring the maturation time between being a contact and becoming a customer making a conversion calculation between one phase and another of the purchase journey.
Conversion calculation
It brings details of the conversion of contacts into customers and aligns which standards the leads follow, optimizing the budget and directing investments to assertive campaigns.
Production of numerical reports
It translates the results through the use of graphs and images, facilitating the understanding of the numbers and helping the focal points to quickly show the results to all stakeholders.
Combine experiments, marketing mix modeling (MMM), and first-party data to attribute impact with less bias in a privacy-enhanced environment. Google itself recommends cookie-resilient practices, using AI and modeling to preserve conversion signals and guide optimization.
Logic construction
Analyze the behavior of numbers and the association with other environmental variables to optimize the use of resources and deliver better possibilities of action and reformulation of scenarios.
Building Custom Dashboards
Dashboards are customized with important information for the business. Thus, you can have, in a centralized way, analyzes gathered for a better visualization of what was extracted from the data.
Conclusion
Having said that, you can already see that using Data Science as part of a Marketing strategy Digital proves to be a valuable option for obtaining better results, isn't it? Whether to better understand a purchase journey, or to gain better insights into the user experience, this methodology promises to deliver improvements within a business's processes permanently.
Summary: Data Science applied to digital marketing expands data-driven decision-making capabilities, complementing Business Intelligence with predictive and prescriptive analytics. While BI interprets past and current information, Data Science uses statistical models, machine learning, and experiments to predict results and recommend actions with greater impact. Benefits include: attracting more qualified customers, agile real-time metrics tracking, assertive campaign targeting, increased retention and loyalty, and process optimization. With features such as goal calculation, conversion analysis, visual reports, and customized dashboards, Data Science strengthens the customer journey, increases ROI, and makes marketing strategies more efficient and sustainable.
If you are interested in relying on this technology, Mkt4edu is an expert in the implementation and execution of Data Science strategies. How about setting up a conversation about it?
Data Science and Marketing: How to Use Data for Strategic Decisions
What is the difference between Business Intelligence and Data Science?
Both fields rely on data insights, but Business Intelligence (BI) focuses on descriptive (what happened) and diagnostic (why it happened) analysis in the short and medium term. Data Science, on the other hand, applies statistical models and machine learning for predictive and prescriptive analysis (what will happen and what to do), projecting long-term outcomes.
Why apply Data Science to digital marketing?
Data Science enhances data-driven decision-making by optimizing customer acquisition, monitoring metrics in real time, directing campaigns more effectively, improving processes, and increasing retention. It strengthens the customer journey, boosts ROI, and ensures sustainable strategies.
How does Data Science improve customer acquisition?
Through intelligent algorithms, Data Science identifies qualified leads, personalizes offers, and guides actions that increase conversion rates at each stage of the sales funnel.
How does Data Science improve performance tracking?
Real-time dashboards and AI-supported analysis accelerate decision-making and improve transparency. This allows for quick adjustments in campaigns and strategies, ensuring accuracy and evidence-based actions.
How does Data Science support marketing actions?
Without reliable data, marketing loses precision. Data Science delivers fast and consistent insights, reduces reliance on third-party cookies, and strengthens the use of first-party data under privacy regulations such as GDPR and LGPD.
What is the impact of Data Science on customer retention?
With churn models and behavioral analysis, Data Science detects risks of customer loss and enables personalized actions to improve satisfaction and loyalty, lowering acquisition costs and increasing profit margins.
Which processes can be improved with Data Science?
Data scientists use mathematical models to test hypotheses, identify opportunities, and optimize workflows. This leads to improvements in goal setting, conversion rates, visual reports, personalized dashboards, and predictive logic for customer behavior.
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