From idea to impact, GenAI is not an end in itself - the technology only realises its full value when it meets real business processes.

In this article, we show five typical use cases of how GenAI has a concrete impact in e-commerce environments: from intelligent customer advice to automated data maintenance.

Also included: practical examples, measurable effects and tips for implementation.

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1. Conversational Product Advisor: Advice at a Human Level

Problem:

Online customers cannot find their way around the product range. They cancel purchase processes or contact overloaded support teams.

Solution:

A GenAI-supported advisor provides interactive advice via chat - including voice if required. It understands free speech input (e.g. ‘I'm looking for a vegan protein shake to build muscle’), analyses the query semantically and recommends specific products with reasons.

Effects:

Conversion rate +40-70 %

Bounce rate -30 %

Support relief of up to -40 %

Example: Pflanzen Kölle, Miele or a large DIY retailer

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2. Automated Content Creation for Product Pages

Problem:

Many retailers stock thousands of products with incomplete, outdated or generic texts.

Solution:

GenAI models generate scalable, SEO-optimised texts based on structured or semi-structured data.

What is created:

Product descriptions

SEO texts (meta tags, H1, rich snippets)

Feature bullet points

Advantages:

Better findability (SEO)

Shorter time-to-market

Consistent brand communication

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3. Dynamic Filter and Sorting Logic in the Shop

Problem:

Customers encounter rigid filter logic (‘only by price, colour, size’) that does not reflect their actual search intention.

Solution:

GenAI recognises implicit needs and dynamically suggests relevant filters or sorting (e.g. ‘skin-friendly’, ‘suitable for winter’, ‘available with spare parts’).

Benefit:

More intuitive shopping experience

Higher satisfaction and dwell time

Cross-selling potential increases

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4. Support Automation in Self-Service

Problem:

Support teams are overloaded, FAQs are rarely used.

Solution:

GenAI-based FAQ bots interpret natural language, understand product references and answer questions contextually.

Difference to classic chatbots:

The AI interprets intent, context and synonyms - even in long entries.

Example:

‘My barbecue doesn't work with gas cylinder XY - what should I do?’ → Answer with instructions & product recommendation

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5. Optimisation of Product Data Quality through GenAI (Enhance)

Problem:

Product data from different sources is inconsistent, incomplete or incorrect. This leads to incorrect listings and low visibility.

Solution:

Frontnow Enhance recognises patterns, suggests correct values or automatically completes missing data. Ideal for extensive product ranges or data migrations (e.g. PIM change).

Advantages:

Higher data quality without manual maintenance

Better findability & UX

Fewer returns thanks to correct information

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Conclusion: GenAI Works Where it Counts

These five use cases show that GenAI is not hype, but a tool for concrete impact - along the entire customer journey. The key is to get started in a targeted and pragmatic way - not with the aim of automating everything at once, but with well-chosen pilot projects.

Read more: In the article “How GenAI Increases Conversion Rates - Figures and Examples”, we show how GenAI measurably increases the conversion rate - with figures, benchmarks and practical examples.

‍Discover now: How the Frontnow Advisor and Enhance implement these use cases - book a demo or talk to our team.