The Importance of Validating Properties in CRM

n today's business world, Customer Relationship Management (CRM) systems are essential for companies of all sizes. The ability to store and analyze detailed customer information can make or break a business. However, the value of a CRM is entirely dependent on the quality of the data it holds.

Why Validate Properties in CRM?

One of the most common mistakes businesses make is failing to ensure that all necessary data is present and correct when creating new CRM records. Inaccurate or incomplete data can lead to poor business decisions and lost opportunities.

Validating properties in a CRM ensures data consistency, which in turn improves the reliability of information used for strategic decision-making.

Key Benefits of Data Validation in CRM

  1. Improved Data Quality: By ensuring that all required fields are complete before an import, you guarantee that the information entered is accurate and relevant. This is crucial for large companies that rely on reliable data for their marketing and sales strategies.
  2. Reduced Errors and Duplication: Automating property validation minimizes the possibility of human error, duplicate records, and incorrect data. This improves information integrity and prevents operational problems.
  3. Increased Operational Efficiency: Companies can save time and resources by avoiding manual entry of incorrect data and the need for later corrections. Automated validation streamlines internal workflows and improves team productivity.
  4. Marketing and Sales Optimization: In both B2B and B2C sectors, having well-structured and validated customer data allows for more accurate segmentation, personalized communication, and improved effectiveness of marketing campaigns.

Use Cases in B2B and B2C Companies

B2B Use Cases:

  • Key Client Management: Ensuring that all critical data of business clients is up-to-date to maintain effective commercial relationships.
  • Sales Process Optimization: Sales teams can access accurate and complete information to personalize their approach and maximize conversion opportunities.

B2C Use Cases:

  • Marketing Personalization: Data validation ensures the collection of relevant information to design marketing strategies based on customer behavior and preferences.
  • Improved Customer Service: Support teams can offer quick and accurate answers by having well-structured data.

Effective Implementation: Using the CRM Import API

To ensure efficient validation, companies can use the CRM Import API, which allows automating property verification before adding new records. Some key steps include:

  • Configure validation rules for critical fields.
  • Train teams on the importance of data validation.
  • Continuously monitor and adjust validation processes.

Adaptation to the Digital Analytics Sector

Property validation in CRM is directly related to digital analytics. A CRM with structured and accurate data allows:

  • Improve segmentation and audience analysis.
  • Enhance the effectiveness of tools such as Adobe Analytics, Google Analytics 4, and HubSpot.
  • Optimize the use of CRM in marketing and sales strategies with actionable data.

Conclusion

Validating properties in CRM is an essential strategy for any company looking to maximize the potential of its data. It ensures information quality, improves operational efficiency, and enhances commercial strategies. At Hike & Foxter, we help companies like yours optimize their processes and make decisions based on reliable data

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Group your data like a pro: clustering with K-Means and BigQuery ML

Working with large volumes of marketing data—whether it’s web traffic, keywords, users, or campaigns—can feel overwhelming. These data sets often aren’t organized or categorized in a useful way, and facing them can feel like trying to understand a conversation in an unfamiliar language.

But what if you could automatically discover patterns and create data groups—without manual rules, endless scripts, or leaving your BigQuery analysis environment?

That’s exactly what K-Means with BigQuery ML allows you to do.

What is K-Means and why should you care?

K-Means is a clustering algorithm—a technique for grouping similar items. Imagine you have a table with thousands of URLs, users, or products. Instead of going through each one manually, K-Means can automatically find groups with common patterns: pages with similar performance, campaigns with similar outcomes, or users with shared behaviors.

And the best part? With BigQuery ML, you can apply K-Means using plain SQL—no need for Python scripts or external tools.

How does it actually work?

The process behind K-Means is surprisingly simple:

  1. You choose how many groups you want (the well-known “K”).

  2. The algorithm picks initial points called centroids.

  3. Each row in your data is assigned to the nearest centroid.

  4. The centroids are recalculated using the assigned data.

  5. This process repeats until the groups stabilize.

The result? Every row in your table is tagged with the cluster it belongs to. Now you can analyze the patterns of each group and make better-informed decisions.

How to apply it in BigQuery ML

BigQuery ML simplifies the entire process. With just a few lines of SQL, you can:

  • Train a K-Means model on your data

  • Retrieve the generated centroids

  • Classify each row with its corresponding cluster

This opens up a wide range of possibilities to enrich your dashboards and marketing analysis:

  • Group pages by performance (visits, conversions, revenue)

  • Detect behaviors of returning, new, or inactive users

  • Identify products often bought together or with similar buyer profiles

  • Spot keywords with unusual performance

How many clusters do I need?

Choosing the right number of clusters (“K”) is critical. Here are a few strategies:

  • Business knowledge: If you already know you have 3 customer types or 4 product categories, start there.

  • Elbow Method: Run models with different K values and watch for the point where segmentation no longer improves significantly.

  • Iterate thoughtfully: Test, review, and adjust based on how your data behaves.

Real-world examples

With K-Means in BigQuery, you can answer questions like:

  • What types of users visit my site, and how do they differ?

  • Which pages show similar performance trends?

  • Which campaigns are generating outlier results?

Grouping data this way not only saves time—it reveals opportunities and issues that might otherwise go unnoticed.

Conclusion

If you're handling large data sets and need to identify patterns fast, clustering with K-Means and BigQuery ML can be a game-changer. You don’t need to be a data scientist or build complex solutions from scratch. You just need to understand your business and ask the right questions—BigQuery can handle the rest.

Start simple: take your top-performing pages, group them by sessions and conversions, and see what patterns emerge. You might uncover insights that completely shift how you approach your digital strategy.

 

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