Marketing in a new dimension: how data and AI are transforming strategy and execution.

Marcelo Firpo
Nov 20
7 min read

AI-generated summary:

The transformation of marketing is already a reality: data, machine learning, and generative AI are allowing managers to delegate operational tasks to intelligent agents and focus their energy on strategic decisions. Predictive dashboards, automatic redistribution of media budgets, generation of personalized content, and personalization at scale are just a few examples of how technology increases efficiency and relevance. Case studies from companies like Zalando, IBM, Headway, and Google show concrete gains in ROI, speed, and engagement, while the BlueMetrics case with an e-commerce platform for corporate gifts demonstrates how the use of GenAI can improve the customer journey and, at the same time, enrich the content that fuels marketing campaigns. The result is more dynamic, precise marketing capable of generating real impact at scale.

The new role of marketing

Marketing is no longer just about attracting customers or creating creative campaigns. It has become a central pillar for generating sustainable revenue, brand trust, and customer experience. With increasingly competitive markets and more demanding consumers, creativity without data or quick reflexes is no longer enough.

Today, marketing managers need to anticipate trends, automate repetitive tasks, and personalize communication on a large scale. Technologies such as machine learning, generative AI, and AI agents are no longer a differentiator, but essential tools for those who want to lead.

From operational to strategic

The routine of a marketing manager has changed radically. Where before it was necessary to review weekly reports, wait for analyses to adjust budgets, and manually monitor campaigns, today the reality is different. Intelligent dashboards offer a consolidated view and suggest automatic adjustments, transforming decisions that used to take days into almost instantaneous actions.

These gains are possible because AI agents are already directly involved in execution, performing functions that were previously entirely human, such as:

Identify campaigns with below-average ROI and automatically pause them.
Redirecting funds to ads that are performing better.
Suggest creative variations based on audience behavior.
Indicate changes in email marketing flows based on the engagement of each recipient.

With this support, there is more time for strategic decisions, such as exploring new channels, developing more consistent brand messages, and aligning marketing with growth goals.

Data integration as a starting point

One of the biggest historical challenges in marketing is the fragmentation of information. Data from analytics, CRM, social media, email campaigns, and purchase history are often isolated in different systems. This leads to incomplete views and decisions based on only part of the reality.

Data integration allows each lead or customer's journey to be tracked on a single timeline, from first contact to conversion or eventual abandonment. From there, advanced analytics become possible:

Identify patterns such as shopping cart abandonment or decreased engagement.
Predicting customers with a higher probability of purchase or churn.
Calibrate media investments in real time to increase ROI.

Not surprisingly, a SurveyMonkey survey shows that 51% of marketing teams already use AI to optimize content and 43% apply the technology to automate repetitive tasks, demonstrating that data integration is the basis for more sophisticated analyses.

Efficiency and productivity for the team.

Another clear benefit of AI is increased efficiency and productivity. The technology acts as a creative and analytical co-pilot, providing support from content production to media management.

This support translates into practical applications that are already part of the daily routine of many teams:

Creating blog drafts, product descriptions, and posts adapted for different channels.
Suggestions for more engaging email subject lines and message customization by segment.
Automatic adjustment of email sending frequency based on each recipient's history.
Continuous monitoring of media campaigns, pausing low-performing ads and redistributing budget in real time.

According to McKinsey, companies that use AI in marketing and sales achieve 10 to 20% improvements in ROI, reinforcing that the productivity gain is not just theoretical, but has already been proven in different sectors.

A more seamless customer experience

From the customer's point of view, the transformation is equally significant. Communication ceases to be generic and becomes contextualized, offering messages that make sense at each stage of the buying journey.

This impact is felt at various points of contact, such as:

Virtual assistants that answer questions, recommend products, and help with purchasing decisions.
Product descriptions automatically enriched with detailed information, comparisons, and benefits aligned with the visitor's profile.
Personalized messages that recognize where the customer is in their journey, avoiding redundancies and increasing engagement.

Recent reports confirm the relevance of these advances: email marketing remains one of the most efficient channels, with an average return of $36 for every dollar invested, and AI further enhances this performance by ensuring personalization and consistency.

Customization at scale

Historically, personalizing campaigns meant creating exclusive materials only for large clients or priority segments. Today, with structured data and generative models, personalization is possible at scale, without proportionally increasing the team's effort.

The possibilities multiply across different fronts, such as:

Dynamic segmentations automatically powered by variables such as browsing behavior or location.
Real-time tailored advertising campaigns for different audience profiles.
Product recommendations in e-commerce based on purchase history and abandoned cart alerts combined with relevant offers.

According to Deloitte, companies that apply personalization at scale can significantly multiply their ROI and increase email open and click-through rates by up to 40% compared to generic campaigns.

Real-world examples of AI applications in marketing.

The adoption of AI in marketing has gone from being an isolated experiment to becoming a consolidated practice in companies across various sectors. Several case studies have already demonstrated that the technology can directly impact efficiency, cost, and return on investment metrics.

Zalando: reducing time and cost in visual content production.

Zalando, one of Europe's largest online fashion retailers, faced pressure to create visual campaigns that kept pace with rapidly evolving fashion trends. By adopting generative AI to create digital mockups and product images, the company reduced its average production time from six to eight weeks to just three to four days.

In addition to speed, production costs fell by approximately 90%. The impact wasn't just internal: with shorter cycles, Zalando was able to react more quickly to trends on social media, which increased customer engagement. This case demonstrates how AI can balance cost reduction and market relevance.

IBM: Personalization at scale with generative creativity

IBM decided to test Adobe Firefly to expand its capacity for producing marketing materials. The goal was to create pieces with a large number of variations without compromising brand consistency. In a short time, the team generated about 200 images with over a thousand variations.

Campaigns that used this material had 26 times more engagement than benchmarks from traditional campaigns. The result demonstrates that AI is not only efficient, but can also expand creative reach, allowing for much faster testing of different approaches.

Headway: Impact on AI-powered video ads

In the field of digital education, the startup Headway sought to increase the impact of its ads without raising production costs. Using AI tools like Midjourney and HeyGen, the company began producing video ads at scale and quickly.

The return was significant: AI-powered ads delivered 40% higher ROI and reached 3.3 billion impressions in just six months. In addition to the performance gains, Headway managed to save production resources, which were redirected to strategic marketing initiatives.

Google and Nielsen: more effective AI-based advertising

A study conducted by Nielsen in partnership with Google analyzed AI-optimized digital campaigns compared to manually optimized campaigns. The results were consistent: solutions like Performance Max and Demand Gen delivered up to 17% more return on ad spend.

These numbers confirm that AI has already become a competitive differentiator in digital media management, transforming optimization processes into automated and highly effective decisions.

Case Study: BlueMetrics: Leading e-commerce company in the corporate gifts market.

Context

Our client is a well-established company in the corporate gifts segment, operating three online platforms that connect suppliers and buyers. The sector is highly competitive and demands personalized and agile service, as well as scalability to handle seasonal demand peaks, such as the end of the year.

Problem

The operation had limitations that directly affected the customer experience. Customer service was restricted to business hours, leading to delays in initial contact and user dissatisfaction. Category descriptions lacked semantic depth and did not provide sufficient context for effective recommendations.

Furthermore, there was a high reliance on the individual knowledge of the service providers, which resulted in inconsistencies, unintentional favoritism towards certain suppliers, and difficulty in scaling service without proportionally increasing costs.

Solution

BlueMetrics has developed a GenAI-based solution structured around three main pillars.

Data enrichment : the data extracted from the platforms were processed by language models, enriching the category descriptions with appropriate semantic context, purposes, and events.
Intelligent knowledge base : the information has been organized into a vector repository for semantic search, constantly updated to maintain relevance and accuracy.
Contextual virtual assistant : a conversational agent trained to understand specific requests and recommend product categories accurately, impartially, and in a personalized way, operating 24 hours a day.

Results

The implementation brought significant gains:

Service available 24/7, eliminating the limitations of business hours.
Significant reduction in initial waiting time and greater speed in recommendations.
Standardization in the category suggestion process, with less reliance on individual team knowledge.
Ability to scale service without increasing operational costs.
A more satisfying shopping experience for the customer, with quick responses and contextualized recommendations.

A strategic point was the reuse of the generated content. The enriched descriptions not only improved the e-commerce journey but also began to feed into marketing actions, such as personalized email marketing campaigns, social media posts, and ad creatives. This transformed the project into a valuable asset for the company's entire digital marketing strategy.

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Conclusion: AI-powered marketing

The examples presented make it clear that the adoption of AI in marketing is already yielding measurable results in diverse sectors, whether in fashion, technology, education, or e-commerce. The gains range from reduced costs and production time to significant increases in ROI and engagement.

For marketing managers, the message is unequivocal. Building a reliable database is the first step to ensuring that analytics and automation deliver real value. Next, well-defined pilot programs allow for verifying the return on investment and justifying the expansion of initiatives. And, above all, it's important to see AI as a strategic partner: a tool that automates operations so that human talent can focus on what matters most, such as creativity, brand positioning, and customer relationships.

The marketing of the future will not only be more digital. It will be dynamic, personalized, and driven by real-time decisions. Companies that begin structuring their AI-based strategies now will be in a privileged position to lead this new phase, in which data and intelligence do not replace humans, but enhance them.

BlueMetrics has already delivered over 200 AI and data projects to more than 90 clients in the US, Brazil, and the rest of Latin America. We can develop customized solutions for your company's context, with agile implementation and concrete results in the short term. Let's talk about it?

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