No-code tools or AI-as-a-Service: Which approach fits your ambitions best?

The way businesses automate processes has drastically changed in recent years, largely due to the rise of no-code solutions. No-code tools enable businesses to quickly test new ideas without writing a single line of code. But what types of automation are these tools best suited for? And when should you opt for specialized solutions without immediately investing significant time or money? In this article, we break it down for you.

The Rise of No-Code in E-Commerce and Marketing

Coding without technical knowledge - it almost sounds unreal. Yet, tools like Zapier, Make.com (formerly Integromat), and Shopify apps make it possible. They have become increasingly popular among - for example - marketers and e-commerce managers in recent years. And not only because of their functionalities but also because of their accessibility, quick implementation, and low learning curve.

No-code platforms enable teams to:

• Automate processes without extensive technical knowledge

• Quickly test new ideas without relying on IT resources

• Set up marketing and sales workflows without development costs

What no-code tools are great for

No-code platforms are particularly suitable for ad hoc testing of new ideas and setting up so-called “linear workflows” (if-this-then-that). For example, within a few clicks, you can set up an automated email flow for a cart abandonment campaign or a confirmation email after a purchase. But setting up a follow-up with an enticing discount upon exit is also a breeze. Or sending a survey after a purchase to collect feedback about the ordering process.

No-code platforms give marketing teams more flexibility and autonomy, allowing them to quickly respond to opportunities and experiment with new use cases. And all this without investing in extra IT capacity.

Real-world example: A small webshop selling handmade jewelry automates its customer service entirely using Shopify apps and Zapier. When a customer places an order, they automatically receive a confirmation email. After delivery, a follow-up email is sent after 5 days asking for a review. If the customer responds, a discount code for the next purchase is automatically sent. This entire process was set up without any technical knowledge.

Where no-code tools fall short

No-code tools are ideal for simple automation but quickly reach their limits as complexity increases. With high volumes of data or complex data flows, you will soon experience performance issues. These platforms lack deep learning algorithms that can recognize patterns, and in advanced personalization, the chance of delays in real-time applications increases rapidly. Moreover, such tools execute automation for individual events (new order, product updated) and cannot efficiently analyze multiple data points - let alone across different channels. Lastly, the possibilities for simultaneous processes are also limited.

Real-world examples
A cosmetics company wanted to offer product recommendations based on combined behavior: website interactions, purchase history, product reviews, and even seasonal preferences. Their no-code tool could only process one behavioral variable at a time, which resulted in superficial personalization.

A fashion marketplace with over 100,000 products and millions of monthly visitors tried implementing real-time product recommendations via a no-code solution. The result: slow loading times, inaccurate recommendations, and ultimately a negative impact on user experience and conversion rates.

The alternative: AI-as-a-Service

When you want to automate these more complex processes, you quickly turn to custom solutions. The problem, however, is that many organizations do not have the expertise or resources internally to develop such solutions. And having custom software developed externally is often very expensive. In such cases, AI-as-a-Service provides a solution. With this approach, the development and maintenance of complex AI-driven solutions are outsourced to a specialized provider for a fixed monthly fee.

When should you consider embracing AI-as-a-Service? Below are four concrete examples of situations where AI-as-a-Service is the recommended approach:

Advanced product recommendations
When you want to offer hyper-personalized recommendations based on real-time behavior, historical purchases, and contextual data. Ideal for large e-commerce platforms with thousands of products and dynamic customer preferences and for optimizing cross-selling and upselling with advanced algorithms.

Predicting and preventing churn
Essential for subscription-based e-commerce or loyalty programs where retention is crucial, for products with high customer acquisition costs, and for markets with intense competition where customer retention provides a competitive advantage.

Dynamic customer segmentation & behavioral analysis
For data-driven marketing campaigns and hyper-personalized customer journeys, when combining behavioral data from multiple channels, and for targeting niche audiences within your larger customer base.

A/B testing and optimization "on steroids"
For large-scale e-commerce campaigns with complex variables, when optimizing product pages or checkout flows, and when you want to apply personalization at scale with thousands of possible variations.

The added value of custom AI over no-code solutions lies in the dynamic, context-aware approach. Where standard tools often work with static rules and simple segmentation, AI offers advanced machine learning models that learn and adapt in real-time, enable multi-variable experiments, and uncover hidden patterns that human analyses miss. The result: potentially higher conversions, better retention, and more effective marketing efforts.

Real-world examples
A sporting goods store implemented an AI-driven recommendation engine that considers local weather, seasonal sports, and personal preferences. When a customer who previously bought running shoes visits the website during rainy weather, the system automatically suggests waterproof running accessories. In the summer, the same customer receives recommendations for summer sportswear based on local temperatures. This resulted in an increase in cross-selling conversion.

A food box subscription service implemented an AI churn prediction model that analyzes customer behavior: meal choices, modification frequency, customer service contacts, and even changes in delivery times. The system identifies customers with a high churn risk two weeks before they are likely to cancel and automatically triggers a personalized retention program: a personalized email with recipes that match their preferences, possibly a phone call from customer service, or a special discount on their favorite meal box.

In summary: no-code tools vs. outsourcing to AI-as-a-Service

No-code tools are ideal for:

• Small to medium-sized businesses with simple automation needs

• Teams that want to experiment quickly

• Standard marketing workflows such as simple email campaigns

• Static segmentation and linear customer journeys (if-this-then-that)

• Companies with limited technical resources or budget

Outsourcing to AI-as-a-Service is a better solution for:

• Companies looking to compete based on superior customer experience

• E-commerce platforms with large product catalogs and high traffic

• Marketers seeking a competitive advantage through advanced personalization

• Businesses with complex customer segments and diverse audiences

• Subscription-based models where churn prediction is crucial

• Organizations that want to optimize and personalize at scale

• E-commerce and marketing teams that want to make truly data-driven decisions

Ultimately, the choice between no-code and outsourcing depends on your specific goals, scalability, complexity, and desired outcomes. For simple use cases, no-code tools are often sufficient, but for companies working with large amounts of data or requiring complex analyses or predictions, custom AI-as-a-Service offers the best route to success.

How Entropical Supports Your E-Commerce and Marketing with AI-as-a-Service

Entropical takes care of the development and maintenance of complex, integrated AI solutions, starting from €149 per month.

• No standard solution: AI automation is specifically built around your existing platforms and processes. Unlike standard tools that force you to work within their frameworks, an AI-as-a-Service solution is fully tailored to your specific needs.

• Fully managed: Your team doesn’t have to worry about technical complexity or maintenance. AI experts handle development, implementation, and continuous optimization, allowing your marketing team to focus on strategy and results, not technology.

• Scalable and secure: Professional AI solutions are designed to scale seamlessly with your business, with real-time processing of large amounts of data without performance loss. They also comply with the highest security standards for data protection.

• Strategic consultancy: In addition to technical implementation, Entropical offers strategic consulting to help you achieve maximum ROI and continuously refine your solutions based on results and changing market conditions.

Would you like to learn more about how custom AI solutions can enhance your e-commerce or marketing strategy? Contact us for a free consultation.