7 February, 2025 POR Luz Castillejo Flor

New HubSpot CRM Update

In the ever-evolving business world, staying up-to-date with new tools and features is essential for success in digital marketing. One such change is coming on April 16, 2025, with the HubSpot CRM update, designed to revolutionize email marketing personalization by including both the date and time stored within the CRM.

In this article, we’ll explore the benefits of this update, its application in both B2B and B2C companies, and how large corporations can make the most of this enhancement.

The Importance of Personalization in Marketing

Personalization is one of the most effective strategies in marketing, as it creates more relevant experiences for customers and significantly improves open rates, click rates, and conversions in email marketing campaigns.

With HubSpot's new functionality, allowing the use of both date and time in personalization tokens, businesses can take their strategies to the next level.

Advantages of Including Date and Time in Emails

  • Greater Relevance: Including specific details such as the date and time in emails enhances the relevance of the message. For example, an email that includes the exact date and time of an offer creates a greater sense of urgency.
  • More Precise Segmentation: The ability to pinpoint the exact time of previous interactions allows marketing teams to send messages at strategic moments.
  • Increased Conversion Rates: The combination of relevance and accurate segmentation makes it more likely that recipients will respond to the call to action, whether it's making a purchase or registering for an event.

Applications in B2B and B2C Sectors

  • Usage in B2B Companies

In the B2B sector, where relationships and sales cycles are usually longer, precision in communication is crucial.

  • Meeting Reminders: Businesses can schedule personalized emails with the exact date and time of upcoming meetings or demos.

  • Post-Meeting Follow-ups: A scheduled message outlining next steps or including specific documentation strengthens relationships with potential clients.

  • Usage in B2C Companies

B2C companies can also greatly benefit from this feature in their promotional campaigns.

  • Flash Sales and Promotions: A retailer can send emails not only informing customers of a special sale but also detailing the exact time when the offers will be available.
  • Automated Communications: Subscription services can send reminders with the date and time of the next delivery, enhancing the customer experience.

Implementation in Large Companies

Large corporations face both challenges and opportunities when implementing new features. A well-planned strategy can ensure the success of this update.

Strategies for Successful Implementation

  • Integration with Existing Systems: It's essential to synchronize HubSpot CRM with other management tools to ensure smooth operations.
  • Staff Training: Investing in team training through workshops and reference materials will help maximize the use of the new feature.
  • Measurement and Optimization: Setting clear metrics to evaluate the impact of this improvement on marketing campaigns will allow for real-time adjustments and optimizations.

Conclusion

The HubSpot CRM update to include date and time in personalization tokens marks a significant advancement for businesses looking to optimize their communication with clients.

In both B2B and B2C environments, this feature improves message relevance, enables more precise segmentation, and boosts conversion rates. Large companies that adopt this tool and integrate it strategically will be better positioned to stand out in a competitive market.

Detailed and strategic email marketing personalization is not just a trend, but a proven path to business success.
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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

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  • 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.

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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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