Generative Artificial Intelligence (GenAI) is one of the most discussed topics in digital commerce. But between technical buzzwords, overloaded promises and pure visions of the future, many e-commerce managers lack a clear understanding: What exactly can GenAI actually achieve? And more importantly, what does it mean for my online shop?
This article demystifies GenAI in the context of e-commerce. Without buzzwords, but with realistic assessments and concrete application examples.
While traditional AI (e.g. recommendation engines or rule-based filter systems) is usually based on fixed rules and predictable patterns, generative AI is characterised by creative, new content. GenAI generates language, images or data on the basis of training data, but without fixed rules. Instead of "If A, then B", the principle is: "Based on thousands of comparable patterns, this is the most likely solution."
A classic filter system shows products with the properties "green", "under 50 euros" and "available". A GenAI system, on the other hand, could generate a suitable product offer including an explanation in response to the input "I am looking for a sustainable gift for my girlfriend".
Many applications are less futuristic than expected - they solve real problems:
Virtual advice (conversational commerce): Customers receive interactive advice via chat or voice, comparable to a conversation in a bricks-and-mortar store.
Product data enrichment: GenAI helps to automatically improve incomplete or unstructured product information.
Personalised product recommendations: Instead of standardised "customers also bought" lists, GenAI provides individual suggestions based on context and language.
Content generation: Creation of product descriptions, meta titles or landing pages - automated and scalable.
"It's just a nice gimmick."
Wrong. GenAI solutions measurably increase the conversion rate, reduce support costs and improve data quality.
"It's too complex for us."
Also wrong. Modern enterprise solutions such as Frontnow Advisor or Enhance have a modular structure and integrate seamlessly into existing shop architectures.
"Our data is not sufficient for that."
Partly true, but not a showstopper. Many GenAI applications do not require huge internal data sets, but utilise existing product data and combine it with pre-trained models.
The use of GenAI does not mean that human intelligence becomes superfluous. On the contrary: GenAI enhances what humans are already good at.
Advisors become curators: Instead of recommending each product themselves, they review the AI's suggestions and optimise the experience.
Marketing teams become more productive: instead of creating content from scratch, they receive high-quality drafts that they only need to adapt.
These "human-in-the-loop" models ensure high quality and acceptance among customers.
A typical application scenario from the DIY sector:
Initial situation:
An online shop for DIY products has a conversion rate of less than 1.5%, high bounce rates and many support enquiries such as: "Which drill fits my cordless screwdriver?"
With GenAI:
customers ask this question in the Conversational Advisor.
The AI understands the context, recognises product types and offers 3 suitable options with an explanation.
The conversion rate increases to over 2.5%, support requests fall by 30%.
GenAI is no longer a vision, but a real competitive advantage. If you start now, you can increase customer loyalty, efficiency and sales at the same time. It is important not to over-academicise the topic. It's not about "AGI", but about clear, simple applications that solve problems.
In our article, “5 Concrete Use Cases for GenAI in Online Shops - and What They Achieve” we show 5 concrete use cases for GenAI in the online shop.
If you want to go deeper:
Request a demo of the Frontnow Advisor or Enhance now.
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