Creativity and AI: Strategic Allies

Creativity has been for centuries one of the most unique and valuable characteristics of human beings. It is the result of imagination, emotion and a personal touch that allows us to develop innovative and artistic ideas. However, with the rise of technology and artificial intelligence (AI), we are faced with a question that has generated debate: is AI a friend or a competitor in the creative industries? In this article, we will focus on how AI can function as a strategic ally, enhancing creativity in both B2B and B2C environments.

Artificial intelligence as a collaborative tool

Transforming traditional creative processes

In recent decades, AI has emerged as a technology capable of transforming traditional processes, including those in the creative industries. Rather than being a replacement, AI acts as a collaborative tool that assists artists, designers and creatives in the development of their projects. Instead of seeing AI as a threat, we must harness its potential to enhance human creativity.

For example, in graphic design, tools that integrate AI can analyze complex datasets to generate patterns or design suggestions that may not have been considered by humans. This allows designers to explore new possibilities and enrich their creative process.

Applications in marketing and advertising

Another prominent application of AI in the creative industries is in marketing and advertising. In B2C environments, companies can use AI algorithms to create more personalized and effective campaigns. By analyzing consumer data, AI can help identify trends and preferences, allowing for the development of advertising messages that resonate better with the target audience.

For example, large companies can implement automated marketing platforms that optimize advertising campaigns in real time, adapting to consumer behavior and thus increasing the effectiveness of their marketing efforts.

Use cases in B2B and B2C

Innovation in content production

Content production is an area where AI can have a significant impact. In a B2B environment, companies, especially marketing agencies, can use AI tools to generate informative or creative content on a large scale. This not only reduces costs but also allows for the creation of content pieces in different languages and formats, adapting to the specific needs of clients.

A practical case would be the use of AI for the creation of blogs or technical articles that meet SEO parameters, optimizing visibility in search engines and increasing traffic to the client's website.

Personalization of experiences in the retail sector

In the B2C sector, AI can be used to personalize the customer experience in online stores. Large companies in the retail sector already use algorithms that study the purchasing behavior of users to offer personalized product recommendations. This not only improves the user experience but also increases conversion rates and customer loyalty.

For example, e-commerce platforms can integrate AI to analyze the purchasing and interaction habits of their customers, allowing for better product recommendations and personalized offers that increase sales and customer satisfaction.

Implementation in large companies

Data analysis for decision making

Large companies have a large amount of data that, if analyzed properly, can provide valuable insights. AI can process this data efficiently, helping companies to better understand their market and make more informed decisions. This applies to both B2B and B2C, where understanding the customer and the market is crucial for success.

For example, sentiment analysis using AI can help companies to better understand customer opinions on social media, allowing for product or communication strategies to be adjusted quickly and effectively.

Optimization of internal processes

In addition to improving the customer experience, AI can also be used to optimize internal processes within companies. In a B2B context, companies can use machine learning to improve operational efficiency, from supply chain management to automated customer service.

For example, AI-powered chatbots can handle basic customer inquiries effectively, allowing human employees to focus on more complex cases.

Conclusion

Artificial intelligence offers endless possibilities to improve and enhance creativity in the creative industries. Far from being a competition, it acts as a powerful ally that complements and amplifies human capabilities. In both B2B and B2C environments, the implementation of AI can generate efficiencies, personalization and new business opportunities.

The important thing is to adopt an open and collaborative mindset, allowing AI to be a strategic complement on the path to innovation and business success. At the end of the day, it is we, humans, who control the narrative of how technology can be used to improve our lives and creative environments.

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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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