7 February, 2025 BY Arnau Paniagua

Introduction to Validation in the CRM Import Process

CRM (Customer Relationship Management) tools are essential for effectively managing customer relationships, both in B2B and B2C environments. One of the most important functionalities in any CRM platform is the ability to import data in a structured and efficient manner.

In this article, we will analyze how the CRM Import API is improving its process by incorporating a validation that ensures that the required records contain the necessary properties to create new object entries. This update is especially significant for large companies looking to optimize their internal processes.

Importance of Required Properties in CRM

When working with objects in a CRM system, each type of object (such as deals, contacts, or companies) has specific properties that are essential for creating a valid record.

For example, in the case of Deals, it is essential that each record has a "Deal Name." These properties function as the DNA of the records, ensuring the consistency and integrity of the stored data.

When required properties are absent or incorrect, errors are generated that can negatively impact business operations. This is especially critical in large companies, where the scale and complexity of the data can amplify problems. Therefore, the validation function during the import process is a welcome improvement.

Validation in the Import Process with the CRM Import API

The new validation functionality in the CRM Import API marks a milestone in the way data is ingested by customer relationship management systems. This functionality is activated when the import options map (importOptions) has a value of "CREATE" in the import request (importRequest).

Benefits of Validation in the Import Process

Data Quality Guarantee

By ensuring that all imported records contain the necessary properties, the quality of the data in the system is significantly improved. This is crucial for generating accurate reports and making informed decisions.

Error Reduction

Validation helps identify and correct errors before the data enters the system, which saves time and resources in data cleaning later.

Efficiency Improvement

By having a cleaner data process from the start, the time that teams spend on reviewing and manually correcting errors is reduced. This allows them to concentrate on higher value-added activities.

Use Cases in B2B and B2C Environments

Implementation in B2B Services

In a B2B environment, a company that sells software to other organizations may need to import large amounts of data from potential customers or existing accounts.

By validating each record during import, it is ensured that all necessary records (such as customer accounts and relevant contacts) are complete. This level of detail allows sales teams to better prioritize leads and, at the same time, improves communication by having all the necessary information from the start.

Example in the B2C Environment

Consider a retail company that wishes to import the purchase data and customer profiles from its point-of-sale system to its CRM.

Validation ensures that for each transaction, the correct product, payment method, and customer information are recorded. This allows for better handling of consumer behavior and offering personalized loyalty programs.

How to Implement These Improvements in a Large Company

For large companies, implementing this validation functionality during the import process is a strategic move towards more robust data management. Here are some key steps to achieve this:

Audit of Existing Data

Before starting, it is vital to conduct an audit of the existing data to identify missing items in terms of required properties. This facilitates data correction before future imports.

Training of Internal Teams

Allocate time to train relevant team members on the new validation process. A clear understanding of the requirements will avoid errors in the long term.

Integration with Other Systems

Validation should be integrated not only with the CRM, but with other data systems to ensure that information flows correctly and without interruption between platforms.

Constant Monitoring

Use monitoring tools to examine data quality regularly. This allows for the detection of potential problems in time and adjusts the processes accordingly.

Feedback and Continuous Improvements

Request feedback from system users to identify areas for improvement. In a corporate environment, continuous improvement ensures rapid adaptations to market changes.

Conclusion

The new validation functionality of the CRM Import API is a positive change for companies looking to optimize the quality of their data and the efficiency of their operations.

Implementing these improvements not only helps prevent errors before they occur, but also allows companies to consolidate a cleaner and more effective data repository, which is crucial in a competitive market.

In both B2B and B2C environments, these practices not only facilitate smoother operations, but also offer a better customer experience.
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