14 July, 2025 BY Luz Castillejo Flor

How the end of third-party cookies impacts your marketing strategy

The announcement of the definitive elimination of third-party cookies marks a turning point in the digital ecosystem. This is not just a technical adjustment in browsers: we are talking about a structural change in the way companies collect data, activate advertising campaigns, and manage customer relationships.

And although it may seem like a distant issue or one exclusive to large corporations, the reality is that it affects any business that uses digital advertising, email marketing, retargeting strategies, or affiliate programs.
That’s why understanding its impact and knowing how to prepare is key to staying competitive.

What are third-party cookies and why are they disappearing?

Third-party cookies: the foundation of digital marketing until now

A third-party cookie is a file placed on your browser by a provider other than the website you are visiting.
For example, if you visit a blog that uses Google or Facebook ads, those systems install cookies that track your behavior—even when you browse other sites.

Thanks to those cookies, advertisers could:

  • Follow you throughout your browsing.

  • Show you ads based on your interests and behavior.

  • Measure the impact of their campaigns.

  • Build detailed profiles without requiring you to register or provide data.

In short: third-party cookies were the backbone of programmatic advertising and retargeting.

Why are they being eliminated?

The official reason is user privacy protection.
More and more users demand control over their personal data and how it’s used. Regulations like GDPR in Europe and CCPA in California have forced major players (Google, Apple, Mozilla) to move toward a more privacy-friendly model.

But there is another angle:
Google, owner of Chrome and a leader in digital advertising, is redefining the game to maintain market control and limit competition. By eliminating third-party cookies, Google ensures that only those who manage first-party data or operate within its platforms can effectively reach users.

The three major pillars changing after the elimination of cookies

1. Campaign measurement and attribution

Until now, measuring the impact of a multichannel campaign (ads, email, web visits) relied on attribution models based on cookies.
For example:

If a user saw an ad on Instagram, clicked on a Google ad, and then made a purchase on the website, cookies helped trace that path.

What happens without third-party cookies?

  • Conversions attributed to third parties will decrease.

  • The user journey will be harder to track.

  • “Last-click” or “multi-touch” measurement becomes less reliable.

How to adapt?

  • Prioritize first-party data measurement by connecting your CRM with analytics platforms.

  • Implement solutions like Google Enhanced Conversions or server-side tagging, which allow more accurate measurement without relying on cookies.

  • Explore proprietary attribution models, such as integrating sales or CRM systems with analytics tools.

2. Audience segmentation and activation

The end of retargeting as we knew it.
Without third-party cookies, platforms can no longer create audiences based on behavior across different websites. This directly affects:

  • Programmatic advertising.

  • Dynamic retargeting campaigns.

  • Affiliate campaigns based on cross-site tracking.

How to adapt?

  • Enhance your first-party data: encourage registration, subscriptions, and account creation.

  • Use activation tools like Customer Match (Google Ads) or Audiences (Meta), which let you upload your own data to reach those users on their platforms.

  • Work on lookalike strategies based on your own customer data, not third-party data.

  • Leverage contextual advertising by showing ads related to the content being consumed—without needing to know the user’s identity.

3. First-party data management and value

The direct consequence of this change is that first-party data becomes the most valuable asset of a digital company.
Without the ability to buy audiences based on cookies, you need to build your own database with real, interested users with whom you can maintain a direct relationship.

This means:

  • Developing acquisition strategies based on value: lead magnets, quality content, incentives for registration.

  • Creating automated, personalized communication flows from your CRM.

  • Focusing on the quality of the relationship, not just the quantity of impacts.

How to adapt?

  • Strengthen your lead generation strategies and improve your registration forms.

  • Implement a CDP (Customer Data Platform) if you handle large volumes, or ensure your CRM is well integrated with your marketing platforms.

  • Take care of the user experience to avoid intrusive practices like aggressive pop-ups or forced capture.

What alternatives does the market propose after the elimination of cookies?

  • FLoC and Privacy Sandbox (Google): Google proposes alternative systems based on cohorts, where users are grouped by interests without being individually identified. These proposals still generate debate over their effectiveness and privacy.

  • Data Clean Rooms: Secure environments where data from different parties (advertisers, platforms) can be matched without revealing user identities. Costly but necessary for major advertisers.

  • Contextual advertising: Making a comeback. Showing ads related to the content being visited, with no need to know who the user is.

  • Server-side models: Collecting and activating data from the server side is a technical alternative for measuring and segmenting without relying on traditional cookies.

What should companies do to adapt (and not just survive)?

  • Invest in a data strategy:
    Organize, structure, and connect your databases with your marketing tools.
    First-party data is a strategic asset—not just a list of emails.

  • Train your teams:
    Not just the marketing department. Sales, customer service, IT… everyone needs to understand the value of data and how it’s managed.

  • Strengthen customer trust:
    Transparency and good privacy management will be differentiators. Clearly explaining how you use data builds trust and, in the long term, conversion.

  • Commit to personalized omnichannel experiences:
    The CRM should be the center of a strategy where the user receives coherent impacts across all channels (web, email, app, social).

  • Prepare for new measurement methods:
    Invest in server-side solutions, predictive models, and tools that allow you to measure impact beyond cookies.

Conclusion: Threat or opportunity?

The end of third-party cookies is not the end of advertising or digital marketing.
It is the beginning of a new paradigm where companies that invest in:

  • Building their first-party data.

  • Truly integrating their systems.

  • Personalizing based on a deep understanding of the customer.

… will be the ones to take the biggest slice of the pie.

Because if one thing is clear, it’s that data remains important…
You just have to earn it now.

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Artificial intelligence (AI) and machine learning increasingly rely on high-quality data. However, obtaining large volumes of real-world information can be expensive, time-consuming, and, in many sectors, limited by privacy regulations. This is where synthetic data comes into play—a technology that allows the creation of artificial information with properties similar to real data, offering versatile solutions for training models, automating processes, and protecting privacy.

Although the concept may seem futuristic, synthetic data is already transforming sectors such as finance, healthcare, manufacturing, and automotive, enabling small and medium-sized enterprises (SMEs) to compete with resources comparable to those of large corporations.

What is synthetic data?

Synthetic data refers to artificially generated datasets designed to mimic the statistical characteristics and patterns of real data. It is created using statistical techniques, generative neural networks, or advanced AI models such as transformers and variational autoencoders.

Unlike real data, synthetic data can provide a balance between utility and privacy, as it does not contain identifiable information about real individuals. This makes it an ideal tool for training AI algorithms, testing systems, and generating insights without compromising security or individual rights.

Types of synthetic data

Synthetic data can be classified by format and level of synthesis:

Main formats

  • Tabular: Useful for relational databases and statistical analysis.

  • Text: Used in natural language processing (NLP) and automated content generation.

  • Multimedia: Includes images, videos, and unstructured data, essential for computer vision, object recognition, and image classification.

Level of synthesis

  • Fully synthetic: Generates completely new data without using identifiable real-world information. Ideal for training models in scenarios where original data is scarce, such as financial fraud detection.

  • Partially synthetic: Replaces only sensitive information from real data while preserving structure and patterns. Highly useful in clinical and medical research.

  • Hybrid: Combines real and artificial data, offering a balance between realism and anonymization, suitable for customer analysis or system testing.

Techniques for generating synthetic data

Various methodologies exist to create synthetic data, from traditional approaches to advanced AI-based techniques:

  • Statistical methods: Rely on the distribution and correlation of data, generating new samples through random sampling or interpolation/extrapolation, especially for time series or tabular data.

  • Generative Adversarial Networks (GANs): Consist of a generator producing data and a discriminator distinguishing real from artificial data. Iterative training allows the creation of images and datasets almost indistinguishable from real ones.

  • Transformer models: Process sequences of data through encoders and decoders, capturing complex patterns and relationships in text or tabular data, forming the basis of models like GPT.

  • Variational autoencoders (VAE): Compress input data into lower-dimensional representations and then reconstruct artificial variations, useful for images and time series.

  • Agent-based modeling: Simulates complex environments with autonomous entities interacting under defined rules, generating behavioral data applicable to transportation, epidemiology, or financial markets.

Key benefits of synthetic data

  • Customization and control: Enables the creation of datasets tailored to specific needs, improving analysis and data management.

  • Efficiency: Eliminates slow, costly collection of real data, and being pre-labeled, accelerates AI model training and process automation.

  • Data protection: Without containing identifiable information, it helps comply with privacy regulations and avoids intellectual property issues.

  • Richness and diversity: Allows inclusion of extreme cases, outliers, or underrepresented groups, enhancing coverage and model robustness.

Challenges and considerations

While synthetic data offers many advantages, careful implementation is necessary:

  • Bias: It can inherit biases from the original data. Integrating multiple sources and diversifying training sets can mitigate this.

  • Model collapse: Training a model repeatedly on only artificial data can degrade performance. Combining real and synthetic data prevents this issue.

  • Balance between accuracy and privacy: Adjusting the amount of personal data retained versus statistical fidelity is critical depending on the use case.

  • Verification: Testing and validation are required to ensure the quality and consistency of generated data.

Use cases by sector

  • Automotive: Enables training of autonomous driving systems, improved traffic simulations, and transportation optimization without relying on real incidents.

  • Finance: Used for fraud detection, risk assessment, and simulation of complex financial scenarios while protecting sensitive customer information.

  • Healthcare: In clinical trials and pharmaceutical development, synthetic data enables creation of artificial medical records, anonymized clinical datasets, or medical images for research without compromising privacy.

  • Manufacturing: Supports training of computer vision models for quality inspection and predictive maintenance using synthetic sensor data, anticipating failures and optimizing industrial processes.

How to start with synthetic data in your company

  1. Identify data needs: Determine what information is missing or hard to obtain due to legal or logistical constraints.

  2. Choose appropriate tools: Libraries and solutions such as Synthetic Data Vault or predefined IBM datasets simplify data generation.

  3. Pilot small projects: Start with a limited dataset to validate quality, usefulness, and reliability.

  4. Integrate and train models: Use synthetic data alongside real data to train AI systems and improve predictions or automations.

  5. Monitor results: Evaluate effectiveness, adjusting parameters and techniques as needed.

Conclusion

Synthetic data is a strategic tool that combines security, efficiency, and scalability. It allows companies to train AI models faster and more accurately, explore new scenarios without risk, and protect sensitive information.

For small and medium-sized enterprises, it represents a unique opportunity to compete in innovation and automation without relying solely on costly or limited real data. Adopting this technology can enhance efficiency, data protection, and AI quality, making it a fundamental ally in digital transformation.

 
 
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Agentic AI: How Intelligent Automation Empowers SMEs

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The term might sound technical, but the idea is simple: while traditional AI solutions work like assistants that wait for instructions, agentic AI becomes an independent collaborator that understands context, learns from experience, and makes decisions aligned with your business objectives. For a small company, this means saving time on repetitive tasks, responding better to customers, and making faster, data-driven decisions.

What agentic AI means for an SME

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Take a simple example. A traditional chatbot answers FAQs but fails when faced with off-script requests. An AI agent, on the other hand, remembers past interactions, understands the customer’s history, and can even anticipate what that person might need. If someone repeatedly checks the price of a service, the AI doesn’t just reply—it can suggest scheduling a sales call or automatically send a personalized proposal.

The impact goes far beyond sales. Agentic AI can organize invoices, filter emails, identify inventory trends, or prioritize business opportunities. For small businesses, often operating with limited resources and lean teams, having this type of support can mean the difference between stagnation and growth.

How intelligent automation transforms operations

One of the most appealing aspects of agentic AI is its ability to operate independently, without constant human supervision. This frees teams from routine tasks and allows them to focus on higher-value, strategic activities.

Some of the most relevant benefits for SMEs include:

  • Always-on customer service: digital agents available 24/7 that not only respond but learn from customer tone and questions to deliver personalized solutions.

  • Faster commercial processes: identification of potential customers based on behavior, lead prioritization, and automated follow-ups.

  • Real-time data analysis: instead of waiting for weekly or monthly reports, AI agents can instantly detect consumption patterns or stock issues.

  • Frictionless scalability: the technology grows with the business, adapting to demand peaks or an expanding client base without immediately hiring extra staff.

In other words, agentic AI doesn’t replace teams—it becomes a strategic partner that multiplies their capabilities.

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Real applications in small businesses

To better understand the value of agentic AI, let’s ground it in concrete scenarios:

  • Automated marketing: AI agents monitor campaigns, adjust budgets, and predict the best times to publish on social media—making every marketing dollar work harder.

  • Smart sales: if a client visits the pricing page multiple times, AI flags them as a hot lead and automatically books a meeting with a sales rep.

  • Customer support: a digital agent can handle basic questions about hours, policies, or products and escalate sensitive cases to a human team.

  • E-commerce shopping assistants: they remember customer preferences and suggest relevant products, improving the shopping experience and boosting average order value.

These applications aren’t futuristic—they already exist in accessible tools that let SMEs start small and scale gradually.

How to get started

The process of adopting agentic AI can be broken into simple phases:

  1. Identify pain points: start with time-consuming, low-value tasks like repetitive inquiries or database updates.

  2. Choose the right tools: you don’t need a massive investment. There are AI solutions designed for SMEs that integrate with existing CRMs or POS systems.

  3. Run a pilot project: begin with something manageable, such as a chatbot for FAQs. This validates results without major risks.

  4. Train the agent: outcomes depend on the quality of input data. Feed the system customer info, product details, and past interactions.

  5. Measure and improve continuously: track performance and adjust responses or workflows as the AI learns about your business.

The key is to start small, validate results, and expand functions strategically.

Common challenges and how to overcome them

Of course, every innovation comes with obstacles. The most common when implementing agentic AI in small businesses are:

  • Learning curve: it may feel complex at first. Rely on tutorials, vendor support, and gradual training.

  • Data security: a valid concern. The solution is choosing platforms that guarantee encryption, transparency, and regulatory compliance.

  • Fear of losing the human touch: automation doesn’t mean dehumanization. Use AI for repetitive tasks, reserving humans for emotional and strategic interactions.

  • Tight budgets: many SMEs worry about costs, but today there are affordable options built into CRM systems with no hidden fees or heavy upfront investments.

Each challenge has a solution, and the balance of effort versus benefit clearly favors adoption.

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A long-term growth tool

More than a trend, agentic AI represents a new paradigm in business management. For small companies, it can become a true growth driver, freeing time, reducing costs, and improving customer experience.

Its most compelling aspect is continuous learning: the more it interacts, the more precise it becomes. This means your automation investment doesn’t stagnate—it improves over time, something traditional processes rarely achieve.

The real question is no longer whether small businesses can afford to implement agentic AI, but whether they can afford not to—when automation and AI are setting the pace of the market.

Conclusion

Agentic AI is here to stay. Its ability to act independently, learn from every interaction, and adapt to market changes makes it one of the most powerful tools SMEs can adopt today.

Implementing it doesn’t require tech expertise or big budgets. All it takes is identifying repetitive tasks, launching a pilot, and letting automation do the rest.

If your goal is to enhance workplace efficiency, increase sales, and secure long-term business growth, agentic AI is the partner you’ve been waiting for. The next strategic decision for your company won’t be taken alone—you’ll take it with the help of artificial intelligence.

 

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