Last call for humans

As technologies advance and AI becomes more sophisticated, the question of whether artificial intelligence (AI) will replace humans in different areas is becoming more and more recurrent. But can technology "humanize" the "non-human," and will we do without human interaction altogether?"

Will we do without human interaction altogether.

We list below situations that are already solved by AI and others that still require human intervention in Marketing, Sales, and Customer Care (CTA) with the goal of leading this convergence from the point of view of a brand with CRM technology.

 


Screenshot 2024-10-07 at 9_25_05

 

Situations where human assistance is dispensable

Basic customer service

Automation through Chatbots: For customer service queries such as order tracking, returns, issue opening, FAQs or account updates, AI-powered Chatbots can quickly and efficiently handle these interactions, or redirect these queries to the most appropriate agents. These systems can be available 24/7, providing instant responses that previously required human intervention, thus leaving humans to focus on high-value tasks.

CRM and Chatbots: A CRM system can feed Chatbots with customer data to personalize each interaction, ensuring that the customer feels that their specific needs have been met in real time and in the absence of a human agent.

CRM and Chatbots: A CRM system can feed Chatbots with customer data to personalize each interaction, ensuring that the customer feels that their specific needs have been met in real time and in the absence of a human agent.

Product/service recommendations

Automated personalization/hyperpersonalization: advanced analytics tools and machine learning algorithms that enable the analysis of large data sets, such as the buying behavior and preferences of each user, to provide recommendations, offers and personalized content, something that until now was in the hands of the intuition or experience of marketers.

CRM and Personalization: Integrating these tools into a CRM system allows recommendations to be based not only on the purchase history of each customer, but also on their multichannel interactions, increasing the relevance and effectiveness of automatic suggestions.

 

Situations where human assistance is essential

Specific customer service

Necessary human interaction: In situations that require empathy, contextual judgment, creativity and flexibility, such as managing a service crisis, logistical challenges or very specific technical incidents; customers prioritize human assistance over AI, which by its structured nature, will struggle to find solutions not contemplated in its programming.

CRM and human escalation: A CRM system can be configured to identify these critical situations through keywords or by recognizing the customer's tone of voice/typing to automatically escalate the case to a human agent to ensure more responsive and appropriate handling. This not only improves the customer experience, but will also strengthen the relationship with the customer.

Strategic business negotiations

B2B and large accounts: In B2B environments and in high-value sales, AI can become a powerful tool in the negotiation process. However, the complexity of human interaction and the need for emotional and cultural intelligence will remain the prerogative of human beings.

CRM and sales support:

A CRM can provide sales reps with valuable information about the customer's negotiation and purchase history, preferences and previous behaviors to inform their negotiation strategies and close deals more effectively.

Synergies between AI and humans for a brand with CRM technology


Integrating AI into CRM: Incorporate AI within CRM platforms to automate repetitive tasks, allowing teams to focus on high-value tasks where human interaction is necessary and irreplaceable. 

Integrating AI into CRM: Incorporate AI within CRM platforms to automate repetitive tasks, allowing teams to focus on high-value tasks where human interaction is necessary and irreplaceable. 

Smart Escalation Routes: Use CRM to create AI-based escalation routes that determine when a customer needs to speak to a human, based on sentiment analysis, query complexity and customer history.

Smart Escalation Routes: Use CRM to create AI-based escalation routes that determine when a customer needs to speak to a human, based on sentiment analysis, query complexity and customer history.

Continuous training and support: Ensure reps are equipped with CRM tools that provide instant access to up-to-date customer information, enabling more personalized interactions.

Feedback loop: Use customer feedback captured through CRM to continuously improve both human and technology interactions, ensuring that technology is used where it adds value and that humans step in where they are most needed.

Conclusion

The challenge for brands will be not only to implement advanced CRM technology, but also to understand where and how to integrate human interactions in a way that complements technological capabilities, thereby maximizing customer satisfaction and operational efficiency.

 

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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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