Neural networks are a powerful tool that helps create high-converting product cards for marketplaces. They save time, reduce costs, and make your content more appealing to buyers. This article is for those who are just starting with AI and want to boost sales on platforms like Uzum, Ozon, or Wildberries.

Contents

1. What are neural networks and how they help on marketplaces
2. Advantages of using neural networks for product cards
3. Overview of available tools
4. Step-by-step guide to creating a product card
5. Tips for effective use
6. Risks and limitations

What are neural networks and how they help on marketplaces

Neural networks are AI-based programs trained on large datasets to generate content according to specified parameters. In the context of marketplaces, they automate product card creation—a trend in digital marketing since the 2020s. According to McKinsey, companies using AI for content creation improve efficiency by 20–30%.

For product cards, neural networks can generate:

• Titles: catchy and keyword-rich, e.g., “Women’s eco-leather handbag for a stylish look.”
• Descriptions: persuasive text highlighting product benefits.
• Specifications: structured lists (size, material, color).
• Infographic copy: short phrases for visuals.
• Photo ideas: scene descriptions to showcase products effectively.

Machine learning theory explains how they work: neural networks analyze successful examples (e.g., top cards on Ozon) and adapt them to your data. You input product information (name, specs, target audience), and the AI generates content that meets marketplace requirements and attracts buyers. In 2025, this process is even more accessible thanks to open APIs and local CIS services.

Advantages of neural networks for product cards

Neural networks offer many benefits, especially for small businesses and beginners. According to Gartner, AI implementation in marketing reduces content costs by 40% and speeds up production threefold. Key advantages:

• Speed: content or idea generation takes minutes instead of hours.
• Cost-efficiency: no need to hire copywriters or designers for drafts.
• Quality: AI uses proven examples to create compelling text.
• SEO optimization: automatically adds keywords to boost search visibility.
• Idea generation: inspires new approaches, even if text isn’t used fully.
• Scalability: easily create content for dozens of products simultaneously.

Marketing automation theory emphasizes that AI allows small businesses to compete with larger brands, especially in emerging e-commerce regions like the CIS. For example, using tools like shamCRM, neural networks can generate automated product descriptions, speeding up new product launches.

Overview of available tools

The CIS AI tool market is rapidly growing. According to Statista, in 2025 AI tools will make up 35% of marketing technology in Eastern Europe and the CIS. Key categories include:

Text generators

• Grok (xAI): creates descriptions, titles, and posts; free tier available.
• CopyMonkey: marketplace-focused, supports Russian.
• Jasper AI: suitable for high-converting text; free trial available.

Image generators

• MidJourney: realistic images from prompts (subscription required).
• DALL·E: image generation, accessible via platforms like ChatGPT.
• Stable Diffusion: free alternative, runs locally.

All-in-one platforms

• Canva: built-in AI tools for text and infographic design.
• Figma + AI plugins: good for infographic layouts and mockups.

Tool selection theory: optimize for budget and goals. Tip: start with free versions (Grok, Canva) and test on one product. Check for Russian language support and marketplace integration.

Step-by-step guide to creating a product card

Design thinking theory suggests an iterative approach to content creation. Steps to create a high-converting product card:

1. Collect product data: name, specs (size, material, color), benefits, target audience, and usage scenarios.
   Example: “Ceramic plate, 20 cm, everyday use, target audience — families.”

2. Choose a tool: text—Grok or CopyMonkey; images—Canva or MidJourney.

3. Write a prompt: clearly describe your task.

   • Text: “Create a 300-character description of a ceramic plate with keywords ‘ceramic tableware,’ ‘home use,’ ‘stylish design.’ Audience: young families, tone: friendly.”

   • Image: “Plate on a wooden table with fruit, warm lighting.”

4. Generate content: enter the prompt and receive the output.

5. Check quality: ensure accuracy and that images match the product.

6. Optimize for marketplace: add keywords, check character limits (e.g., 200 for Ozon titles), create infographics from AI-generated points.

7. Test and analyze: upload the card and monitor views and conversions.

Example: “Stylish ceramic plate for home, 20 cm. Perfect for family dinners. Durable, easy to clean, modern design.”

Tips for effective use

• Write precise prompts: include style, audience, and keywords.

• Fact-check: AI may invent features.

• Add uniqueness: incorporate your brand’s story or regional context.

• Analyze competitors: see what works on Uzum or Ozon.

• Test variations: compare descriptions and track conversions.

• Use Telegram: share drafts for peer feedback.

Risks and limitations

AI error theory: neural networks can inherit data biases. Risks include:

• Generic content: texts may look similar to competitors’.
• Errors: AI can invent product features.
• Image mismatch: generated visuals may misrepresent colors or details.
• SEO issues: missing keywords reduce visibility.
• Lack of emotional tone: AI doesn’t know your audience; add emotion manually.

How to minimize risks

• Review every text and image manually.
• Add unique details, e.g., brand story.
• Use marketplace analytics to track effectiveness.

Neural networks are an assistant, not a replacement. They speed up content creation, but success depends on your review and refinement. Start small: test AI on one card and improve it based on results.