15 January, 2025 POR Alberto Company

How AI affects the role of the salesperson

In a world where digital transformation is our daily bread, artificial intelligence (AI) has become a fundamental pillar for sales strategies. As a specialist sales consultant, I am experiencing firsthand how AI is redefining the role of account executives, opening up a range of opportunities and challenges.

In this brief post, we'll explore how AI is impacting sales strategies and what this means for sales professionals.

In this brief post, we'll explore how AI is impacting sales strategies and what this means for sales professionals.

OPPORTUNITIES: The #IA your new "partner" in sales

AI is not only a tool, it is a strategic assistant in the sales process. Its ability to analyze large volumes of data and extract insightsvaluable is unmatched. This means that marketers can now better understand their customers, anticipate their needs and offer more accurate solutions. AI also automates repetitive tasks, allowing salespeople to focus on what really matters: building meaningful relationships with customers.

Improved decision making: Effectiveness.

Thanks to AI, salespeople can make data-driven decisions, which increases accuracy and efficiency in their sales strategies.

Increased Productivity: Efficiency.

Automating routine tasks frees up valuable time, time that can be invested in higher value-added activities, "close to the relationships, far from the routine."

Technological dependence: An unavoidable challenge

While AI offers numerous advantages, it also comes with increasing technological dependence. Marketers must be willing to adapt and constantly learn about new tools and platforms. This dependency, which is not new since we started talking about digital transformation, should not be seen as a weakness, but as an opportunity to grow and evolve in an increasingly technological environment.

Resistance to change: Overcoming the obstacles

Implementing AI into sales strategies can face resistance, especially from those accustomed to traditional methods. It is crucial to approach these challenges with an open mindset and a focus on continuous learning. Adaptation is the key to not being left behind in the digital race.

Advanced Personalization: The New Frontier

One of the biggest impacts of AI on sales is the ability to deliver advanced personalization. AI systems can analyze customer behavior and preferences to create highly personalized offers and messages. This not only improves the customer experience, but also increases the chances of closing sales.

Knowing the customer inside out

AI enables in-depth customer insight, which facilitates more effective and personalized communication.

Tailored offerings

The ability to generate customized offers in real time is a significant competitive advantage in today's marketplace.

Considerations to keep in mind:

Integrating AI into your strategy

Incorporating AI into your sales strategy is not just an option, it's a necessity to stay relevant in today's marketplace. The key is to understand how AI can complement and leverage your existing skills and resources.

Effective strategies with A

Adopting AI in your sales strategies can mean a radical change in the way you approach your customers and in the efficiency of your processes.

Continuous training

Education and training on AI tools are essential to take full advantage of their benefits.

Education and training on AI tools are essential to take full advantage of their benefits.

So far, some of my thoughts on this exciting era we are living in and how to incorporate all of this into our day-to-day lives.
ANTERIOR
SIGUIENTE

TIPS DE EXPERTOS

Suscríbete para impulsar tu negocio.

ÚLTIMOS ARTÍCULOS

2 June, 2025 POR Luz Castillejo Flor
Big Query clustering SQL GA4

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.

 
SEGUIR LEYENDO
26 May, 2025 POR Alba Fiorella Aviles
Artificial Intelligence Automatisation AI tools Claude 4.0

Claude 4.0: Advances and Challenges in Conversational AI

Artificial Intelligence (AI) continues to progress at an accelerated pace, and Claude 4.0, developed by Anthropic, marks a major milestone in this journey. This next-generation language model stands out for its ability to comprehend complex contexts, deliver accurate responses, and adapt to a wide range of business needs.
SEGUIR LEYENDO
20 May, 2025 POR Luz Castillejo Flor
automatización Artificial Intelligence gemini Alpha Evolve

AlphaEvolve: The new coding agent powered by Gemini

In a world where technology advances at unprecedented speed, artificial intelligence has emerged as a key driver of transformation. Among the most promising innovations today is AlphaEvolve, an evolutionary coding agent that combines the creative power of large language models (LLMs) with automated evaluators, opening new frontiers in software development, algorithm optimization, and solving complex problems in mathematics and computing.
SEGUIR LEYENDO
13 May, 2025 POR Luz Castillejo Flor
Artificial Intelligence digital transformation Design Figma

How AI Is Revolutionizing Design and Development

At its Config 2025 event, Figma made it clear: the future of digital design will be deeply shaped by artificial intelligence. Beyond announcing new features, the company highlighted a paradigm shift — design is no longer a standalone process, but the core that connects creativity, technology, and product development.
SEGUIR LEYENDO
VER MÁS
data
Mallorca 184, 08036
Barcelona, Spain