11 February, 2025 POR Jaume Riera

Transformation of digital commerce due to generative AI

The way we interact with websites and search for products has undergone a radical transformation in recent years. Thanks to advancements in generative artificial intelligence (AI) and conversational search, the online shopping process has become more intuitive and efficient. In this article, we explore how these advancements can optimize the shopping experience on websites, present use cases in both B2B and B2C sectors, and discuss how large companies can integrate these technologies to stay competitive.

The Transformation of Online Search

With the rise of generative AI, the way users conduct online searches has changed significantly. AI-powered tools can analyze and understand natural language in ways that traditional search engines cannot. This allows users to make more complex queries and receive more relevant results. Additionally, conversational search makes interactions with search engines feel more natural, as if speaking with a personal assistant.

Benefits for Consumers and Businesses

For consumers, these advancements mean a more personalized and efficient shopping experience. AI-powered data analysis improves product recommendations, aligning them better with individual preferences. For businesses, this translates into higher conversion rates and increased customer loyalty. Companies can optimize their product catalogs and better understand their target audience’s needs, driving sales and reducing abandonment rates.

Use Cases in B2B and B2C Sectors

B2B: Personalization and Supply Chain Efficiency

In the B2B space, generative AI can provide customized solutions that simplify complex processes. For instance, a manufacturing company can use AI to analyze large data volumes and predict product demand. This enables better supply chain management, ensuring optimal stock levels without overloading warehouses. Additionally, conversational search can enhance interactions between businesses and suppliers, improving communication and operational efficiency.

B2C: A Richer User Experience

For the B2C sector, these technologies can revolutionize the online shopping experience. Imagine a fashion retailer using AI to recommend products based on a customer’s purchase history and recent searches. A conversational search could allow the customer to say, “Show me summer dresses that match these shoes,” instantly generating personalized suggestions. This level of customization not only enhances the user experience but also significantly increases sales potential.

Implementation in Large Enterprises

Integrating generative AI and conversational search into large enterprises requires a well-defined strategy. Here are the key steps to achieve this:

1. Assessing Needs and Objectives

Before implementing any technology, companies must evaluate their specific needs and establish clear objectives. Identifying areas where AI can have the greatest impact is essential to maximizing benefits.

2. Integrating Existing Technologies

It’s crucial to integrate generative AI and conversational search with existing technologies, including customer relationship management (CRM) systems, e-commerce platforms, and product databases. Proper integration ensures seamless data flow between systems, improving recommendation accuracy and analytics.

3. Employee Training

To maximize the benefits of these tools, companies must invest in employee training. Staff should understand how these technologies work and how to leverage them for better performance.

4. Continuous Measurement and Optimization

Finally, setting metrics to measure the success of these technologies and implementing a continuous optimization plan is essential. Companies should be prepared to adjust strategies based on data and feedback, ensuring that generative AI and conversational search continue to provide value.

Conclusion

Generative AI and conversational search represent a paradigm shift in digital commerce, benefiting both consumers and businesses. Companies aiming to remain competitive should seriously consider integrating these technologies into their operations. Doing so not only enhances the user experience but also positions businesses for long-term success in an increasingly competitive market. With a well-defined strategy, these technological advancements can transform how we buy, sell, and interact in the digital world.
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