7 February, 2025 BY Sara Fernández

Understanding Code Through AI

In a world where digital transformation is revolutionizing every sector, artificial intelligence (AI) has become a key tool for improving operational efficiency and decision-making in businesses. The automation of processes, the visualization of code execution flow, and the analysis of large volumes of data are taking digital analytics and CRM to new heights. In this article, we explore how AI tools, such as LLaMA, are impacting digital analytics and how these technologies are effectively integrated into businesses, both in the B2B and B2C spheres.

AI as an Engine of Transformation in Digital Analytics and CRM

The automation of data analysis and the improvement of CRM systems are not a luxury, but a necessity in today's competitive environment. Companies that want to optimize their ability to understand and act on their customer information must take advantage of the most advanced tools available, such as LLaMA, a cutting-edge AI model. These types of tools allow for a quick and accurate understanding of data, which improves both strategic decision-making and daily operations.

LLaMA and Graph Analysis: The Future of Digital Analytics

LLaMA is not just a tool for software developers; its capabilities go beyond the realm of code. By integrating graph analysis into its core, LLaMA enables the clear visualization of the execution flow of the processes that support data analysis. This automation of complex tasks makes it easier for companies to:

  • Optimize data integration: B2B companies that integrate multiple data analysis platforms can do so more quickly and accurately, avoiding human errors.
  • Improve decision-making: By having real-time visibility into the data flow and its interpretation, decisions can be based on more accurate and up-to-date information.

Benefits of AI for B2B Companies

In the B2B sector, digital analytics plays a crucial role in informed decision-making. The automation of large data volume analysis, platform integration, and efficient CRM management are fundamental to improving operational efficiency.

Optimization of Operational Processes and CRM

Companies that manage large volumes of data or customer interactions can benefit greatly from automated data flow visualization. Tools like LLaMA allow analysis teams to understand the available data more quickly, leading to more agile and less error-prone processes. In addition, in the case of CRMs, automation allows for improved customer segmentation and more effective personalization of the user experience.

Identification of Inefficiencies and Opportunities

The analysis of the execution flow can help companies identify bottlenecks in their analytics systems or in customer service processes. With AI, it is possible to automate the detection of inefficiencies, which improves both the customer experience and the company's operating results.

Impact on the B2C Sector

In the B2C environment, the customer experience is a determining factor in competitiveness. Advanced tools such as LLaMA not only allow optimizing the performance of applications or platforms used by consumers, but also help to ensure the security and reliability of data, which generates trust in end users.

Improvement in Customer Personalization

AI allows companies to personalize the user experience in a much more precise and effective way, managing the data flow and ensuring that customer information is used strategically to optimize interactions. Powerful CRM systems can segment customers more efficiently, leading to more effective marketing campaigns and increased satisfaction.

Optimization of the Shopping Experience

For example, in the e-commerce sector, automation in analytics allows companies to detect patterns in shopping behavior and improve the user experience in real time. Developers, with the help of AI, can quickly identify technical problems, such as slow loading times, and solve them, ensuring a smooth and uninterrupted experience.

Effective Implementation of AI in Large Companies

Adopting tools such as LLaMA and other AI solutions in data analysis and CRM is not just a matter of incorporating technology; it requires a strategic approach to ensure successful implementation. For large companies to effectively integrate the automation of data flow visualization, they must follow some essential steps:

  • Training and development of internal talent: Companies must ensure that their staff is trained to work with advanced AI tools. Technical training and education on how to implement these technologies effectively will be key to success.
  • Interdepartmental collaboration: The implementation of AI in digital analytics and CRM is not just a task for the IT department. It is crucial that the marketing, sales, and customer service teams work together to maximize the value that these technologies can bring to all areas of the company.
  • Continuous evaluation and improvements: Implementing AI and analyzing the data flow is an ongoing process. Companies must establish mechanisms for constant evaluation to measure the impact of these tools on their analytics and CRM processes, and be prepared to make adjustments as necessary.

Adaptation to the Digital Analytics Sector

At Hike & Foxter, as a consulting firm specializing in digital analytics, CRM, and AI, we understand the importance of adapting the latest technological innovations to the analysis of large volumes of data and the improvement of customer management systems. The automation and visualization of code execution flow through AI are powerful tools that allow optimizing digital analytics processes accurately and efficiently.

By integrating these technologies, companies can improve the performance of tools such as Google Analytics, Adobe Analytics, and other CRM systems, facilitating decision-making based on more solid and up-to-date data. Automated data visualization allows for the rapid identification of patterns and behaviors within customer data, improving segmentation and personalization of marketing campaigns. In addition, automation reduces the time and effort required to process data, allowing analytics and marketing teams to focus on generating more effective strategies.

Conclusion

The automation of data analysis and the visualization of the execution flow through AI is transforming the way companies manage their operations and customer relationships. Tools such as LLaMA allow not only greater efficiency in software development, but also a significant improvement in the effectiveness of digital analytics and CRM strategies. For companies seeking to remain competitive in a digital world, adopting these technologies is essential. As technology evolves, the effective integration of AI will be key to offering innovative solutions that continue to meet market demands and customer satisfaction.
PREVIOUS
NEXT

EXPERT TIPS

Subscribe to boost your business.

LATESTS ARTICLES

16 July, 2025 POR Alba Fiorella Aviles
crm Data management AI Automation

The seventh wave of AI is redefining CRM and data strategy

Artificial intelligence is not just another improvement: it is, in the words of George Colony, CEO of Forrester, the seventh wave of transformation that will redefine the technology sector. This change directly affects CRM, analytics, and marketing automation, forcing companies to adapt or be left behind.File:George Colony in 2011.jpg
SEGUIR LEYENDO
14 July, 2025 POR Luz Castillejo Flor
Marketing strategy Tracking data stratergy third-party cookies

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.

Schedule a meeting

SEGUIR LEYENDO
9 July, 2025 POR Luz Castillejo Flor
data base data architecture data quality

No solid base, no AI performance: the challenge of the Data Foundation

In a business context where AI has become the new standard for efficiency and scalability, many organizations face a paradox: they have advanced technology, but they fail to achieve consistent results. The issue usually isn’t the algorithm—it’s the foundation. The Data Foundation is the true determinant of success or failure for any AI, automation, or CRM strategy.

This is confirmed by the latest TDWI (Transforming Data With Intelligence) study, published in June 2025, which warns that more than 49% of companies still lack a database ready to scale artificial intelligence projects.

The Data Foundation: more than just infrastructure

Having a modern data platform doesn’t mean having a solid foundation. The TDWI study emphasizes that an effective Data Foundation must meet three conditions:

  • Data quality and governance from the source
  • Scalable and connected architecture
  • Real-time activation capability

When a company fails in any of these three areas, AI becomes more of a promise than a real business lever.

Key findings from the study

Here are some of the main conclusions of the report:

Only 10% of companies claim to have a fully operational Data Foundation.
40% report severe limitations due to poor data quality, silos, or outdated processes.
Most organizations suffer from fragmentation across data sources, preventing a 360-degree view of the customer.
55% of companies already using AI operationally do so despite their technical limitations, not because of their strengths.

In other words, many companies are running with a backpack full of ballast. And that limits the performance of their AI, automation, or CRM tools.

Why does this matter for your CRM or marketing?

At Hike&Foxter, we see it frequently: companies investing in advanced CRMs, analytics platforms, or generative AI engines… without first securing the technical and structural foundation of their data.

The result:

  • AI models that fail in production.
  • Automations triggered incorrectly.
  • Unreliable analytics reports.
  • Inconsistent customer segmentations.

All of this can be avoided with a well-designed Data Foundation, connected to key processes and with controlled data flows.

How to build a real Data Foundation

These are the phases we recommend implementing if you want to turn your data architecture into a competitive advantage:

1. Technical and functional audit

Before incorporating AI, it's important to review:

What data sources exist and how they are integrated
The degree of duplication, obsolescence, or noise they contain
Where the main bottlenecks are (latency, format, access)

2. Standardization and governance

Without a common taxonomy and control rules, any automation attempt will be fragile. This involves:

Defining unified structures (customers, products, interactions…)
Establishing automatic validation rules
Creating clear roles: who creates, modifies, or validates data?

3. Connected and flexible architecture

A data warehouse alone is no longer enough. You need to:

Connect CRM with analytics, automation, and digital channels
Use scalable environments (Snowflake, BigQuery, Azure Fabric)
Consider data mesh or federated architecture if there are multiple business units

4. Real-time activation

The value of AI lies not just in predictive analysis but in its ability to act.

Therefore:

Connect your Data Foundation with activation tools (such as Customer Data Platforms, personalization engines, RPA)
Ensure data flows in real time
Prioritize use cases with direct business impact (retention, up-selling, lead scoring…)

Conclusion

Investing in AI, automation, or CRM platforms without a solid Data Foundation is like building a house on sand.
Before thinking about “which model to use,” you should ask yourself “what data feeds it and how is it governed?”

A robust and well-connected infrastructure not only improves your current projects but also prepares you for what’s next: autonomous agents, contextual decisions, predictive personalization, and end-to-end automation.

Want to strengthen your Data & Tech Foundation?

At Hike&Foxter, we help you build the digital foundations your business needs to grow with confidence.

Schedule a meeting

SEGUIR LEYENDO
9 July, 2025 POR Luz Castillejo Flor
Artificial Intelligence digital transformation Google I/O search engine

Google transforms its search engine with Artificial Intelligence.

In May 2025, Google took a decisive step toward transforming the world’s leading search engine.
At its highly anticipated annual developer event, Google I/O, the Mountain View-based company unveiled a host of innovations powered by artificial intelligence (AI) that not only enhance user experience but are set to redefine how we interact with digital information.
SEGUIR LEYENDO
VIEW MORE
data
Mallorca 184, 08036
Barcelona, Spain