12 | 12 | 2025

What is Marketing Analytics? Examples and Key Tools

Author

Lucas Suarez

Index

4 - 5 min.

How does data analysis work in marketing?

Marketing Analytics, or marketing analysis, is the discipline that enables the measurement, interpretation, and application of data generated by marketing strategies to optimize their performance. In the information age, where big data dominates the business landscape, understanding how our campaigns work and what real impact they generate has become a fundamental part of marketing teams activities.

Thanks to data analytics and data science tools, brands can go beyond simply tracking metrics and enter a new dimension of marketing intelligence. This approach ensures that decisions are backed by solid data, resulting in greater efficiency, accuracy, and sustained growth.

IN THIS ARTICLE

Where does big data in marketing come from?

Social Media: Data from interactions, behavior, and engagement on social media platforms such as Facebook, Instagram, X (Twitter), TikTok, and LinkedIn.
Paid Media: Platforms such as Google Ads, Meta Ads, LinkedIn Ads, and programmatic advertising provide data on impressions, clicks, conversions, audiences, and more.
Traditional media: TV, radio, print, and out-of-home, especially when integrated with tools that allow measuring their digital or physical impact. TV, radio, print, and out-of-home advertising, especially when integrated with tools that allow measuring their digital or physical impact.
CRM and databases: Information on customer history, post-sales behavior, email marketing, campaign responses, and journeys within the customer journey.
Web and apps: User behavior on websites, e-commerce sites, and mobile apps, including page views, scrolls, conversions, and abandonment.

How does data analysis work in marketing?

The data analysis process in marketing begins with collecting information from various touchpoints. Once collected, this data is normalized and integrated into centralized platforms, known as data lakes or data warehouses, which allow variables to be cross-referenced, patterns to be detected, and trends to be visualized.

Subsequently, data visualization techniques are applied, using dashboards that allow information to be understood quickly and intuitively. In addition, deeper analysis is carried out using predictive models, audience segmentation, multichannel attribution, and cohort analysis. This entire process is based on data science principles applied specifically to marketing.

Finally, with all this information organized and understood, the most valuable moment arrives: decision-making. This is where KPIs come into play, such as ROI, ROAS, LTV, CAC, and other key indicators, which guide future actions with greater precision and a smaller margin of error.

Key stages of data analysis applied to marketing

Data collection

Data is obtained from multiple sources such as social media, CRM, websites, campaigns, and apps. Both quantitative metrics and qualitative data are collected to better understand user behavior.

Normalization and integration

The data is cleaned, sorted, and transformed to ensure its consistency. Then, they are integrated into platforms such as data lakes or data warehouses, which enable the unification of information and facilitate cross-functional analysis.

Data visualization

Dashboards and reports are created that display information clearly and visually. This makes it easier to interpret results, identify trends, and communicate with the entire team, without needing to be technical.

Advanced analysis

By applying data science techniques such as predictive modeling, segmentation, and multichannel attribution, strategic insights are gained that enable anticipation of consumer behavior and personalization of actions.

Data-driven decision-making

Based on the analysis conducted, KPIs such as ROI, ROAS, LTV, and CAC are used to guide decisions. This approach enables the optimization of resources, reduces errors, and improves the impact of each action on the marketing strategy.

Most commonly used marketing analytics tools

In the market, there are dozens of specialized tools that allow you to analyze marketing data, from well-known platforms like Google Analytics 4, BunkerDB, Adobe Analytics, and Tableau, to simpler solutions like Looker, which unify information in one place with a strategic view of the business.

Unlike other platforms, Bunker DB has been designed for marketing teams that need speed, clarity, and action. It not only allows you to visualize data, but also to make decisions based on it, integrating multiple sources such as social media, paid media, CRM, and more.

This ability to centralize marketing intelligence on a single platform is key to accelerating decision-making and improving overall campaign performance.

Do you want to see how it works in action?

Schedule a free demo and discover it for yourself.

Why is Marketing Analytics important?

The importance of Marketing Analytics lies in its ability to reduce uncertainty and increase effectiveness. When decisions are based on real data rather than assumptions, the results become more predictable and measurable.

Additionally, this approach enables the optimization of media investment by identifying which channels perform best, improving audience segmentation, and uncovering hidden opportunities in consumer behavior. Brands that regularly implement marketing analytics have a much clearer view of which tactics work and which need to be adjusted or eliminated.

It is also essential for monitoring the real-time behavior of the business’s most relevant KPIs, such as conversion rate, engagement, or cost per acquisition. This enables a continuous improvement process in which campaigns are dynamically adjusted based on performance.

What KPIs can be measured with Marketing Analytics?

Which channel has the best return on investment (ROI)?
Which campaigns generate the highest ROAS (Return on Ad Spend)?
What content generates the most engagement?
Which audiences have the best conversion rate?
At what stage of the funnel are the most leads lost?
What is the average lifetime value (LTV) of a customer?
Which products or services perform best by segment?
How do the results vary by channel, region, or device?
Which media combinations generate the best results?
Where are the hidden opportunities to optimize the budget or personalize the experience?

Are you ready to turn your data into decisions?

With Bunker DB, you can integrate and visualize all the information your marketing team needs to act quickly and intelligently. Our Marketing Analytics platform is designed to help you turn complexity into clarity.

📊 Click here to request a live demo and discover how to turn your data into action.