Five ways in which Generative AI is transforming E-commerce Product Content Management
Generative AI in the e-commerce market stood at $721.31 million in 2023 and is expected to reach around $3,519.84 million by 2034, growing at a CAGR of 15.5% over the forecasted period. Truly, Generative AI is transforming e-commerce in more ways than one.
To begin with, Generative AI enables innovative and personalized customer experiences that span multiple business functions, including personalized product recommendations, advanced segmentation, dynamic content creation, and enhanced customer support. But that’s not all; Generative AI in e-commerce goes deeper in areas like product content management.
In this post, you will learn about five use cases for Generative AI, focusing specifically on product content management for e-commerce catalogs. These include-
- Generating multilingual product descriptions from a set of product attributes
- Fill out missing attributes using product images
- Standardizing product attribute descriptions across digital platforms
- Dynamic content personalization
- Generating product images based on product images based on product descriptions
Use case 1-Generate Multilingual Product Descriptions From a Set of Product Attributes
Using generative AI in e-commerce to generate product descriptions from product attributes in multiple languages involves several steps, from leveraging the capabilities of natural language processing (NLP) to machine translation models.
Here is a five-step process to implement the workflow:
1. Attribute Identification
Start by gathering detailed product attributes such as size, color, material, features, and specifications. These attributes will serve as the foundation for the product descriptions.
2. Description Generation
Use a generative AI model trained on large datasets of product descriptions to generate coherent and contextually appropriate descriptions from the provided attributes. The AI can create descriptions that are accurate, engaging, and aligned with the brand voice.
3. Multi-Language Translation
Once the product description is generated in the primary language, use advanced AI-based translation models to convert the description into multiple languages. Modern AI models like those from OpenAI or Google Translate’s neural network can produce translations that maintain the original description’s context and tone.
4. Language Localization
Beyond direct translation, AI can adapt descriptions to local cultures and languages, ensuring that the wording, idioms, and references are relevant to each target market. This step may involve adjusting the tone or format to match local preferences.
5. Quality Assurance
Implement AI-driven quality checks to ensure that the translations are accurate, contextually relevant, and free from errors. The AI can compare the translated descriptions against known benchmarks or human-generated descriptions to verify quality.
Example: Product Attributes:
- Size: Medium
- Color: Red
- Material: Cotton
- Feature: Breathable fabric, Machine washable
Generated English Description:
“This medium-sized red cotton shirt is perfect for everyday wear. Crafted from breathable fabric, it ensures comfort throughout the day. Plus, it’s machine washable for easy care.”
Translated Description (Spanish):
“Esta camiseta de algodón roja de tamaño mediano es perfecta para el uso diario. Confeccionada con tejido transpirable, garantiza comodidad durante todo el día. Además, es lavable a máquina para un cuidado fácil.”
For example,
Amarra, a New Jersey-based global distributor of special-occasion gowns, integrated ChatGPT into their operations to enhance efficiency and improve customer experience. The Amarra team inputs product features and unique selling points into ChatGPT, which then generates initial drafts of product descriptions. These drafts are subsequently refined to align with Amarra’s brand voice.
This approach has reduced content creation time by 60%, enabling the team to focus more on strategic tasks.
Use case 2-Fill Out Missing Attributes Using Product Images via Gen AI
Manually entering all the product attributes across all variants in a large, multi-lingual product catalog is a time-consuming process, and the mere thought of it is daunting. It also leads to consistency and data entry errors that can cause significant confusion among customers. In such cases, AI can be used to fill out missing product attributes based on images. Technically, the process involves leveraging advanced computer vision and machine learning techniques and could conceptually look as follows:
Image recognition
Use AI models trained in computer vision to analyze product images. These models can identify and extract key visual features from the images, such as color, shape, size, material, and brand logos.
Attribute prediction
The AI can use the extracted visual features to predict missing product attributes. For instance, if an image shows a pair of shoes, the AI might predict attributes like “color,” “shoe type,” “material,” or “heel height” based on its analysis of the image. Any feature not already present in the listing or expressed inconsistently can then be easily added.
Natural Language Processing (NLP) Integration
If the image is associated with some text (like partial product descriptions), NLP models can be used in conjunction with image recognition to enhance attribute prediction by providing context. For example, if an image shows a shirt and the text mentions “summer wear,” the AI might infer that the material is likely to be lightweight.
Automation and Scalability
Implement the AI system to automatically scan product images and fill in missing attributes across large inventories. This automation method reduces manual work and ensures that all products have complete and accurate attribute information.
A case in point
Rakuten enhanced its fashion product catalog by implementing an AI-powered image analysis framework to automatically extract and fill missing product attributes such as color, sleeve type, pattern, and neck style. Using deep learning models like EfficientNet-B0, the system processed thousands of images to generate accurate, fine-grained attribute data. This led to improved catalog completeness, better product discoverability, and an enriched shopping experience for customers—showcasing the power of generative AI in large-scale catalog management.
Use case 3-Standardize product attribute descriptions across digital platforms
Standardizing product attribute descriptions with generative AI involves using AI models to create consistent, accurate, and clear product descriptions across digital platforms.
For example, say you manage an e-commerce store that sells electronics, and you want to standardize the attribute descriptions for smartphones across your platform. Currently, descriptions for attributes like “battery life,” vary widely across different smartphone models which often leads to confusion among customers.
Without Standardization:
- Battery Life: “Up to 20 hours of talk time.”
- Battery Life: “Long-lasting battery.”
- Battery Life: “5000 mAh battery.”
After Standardization:
- Battery Life: “Battery Life: 20 hours of talk time, powered by a 5000 mAh battery.”
- Battery Life: “Battery Life: 18 hours of extended talk time, supported by a 4500 mAh battery, ideal for heavy users.”
To create this standardized description, the AI model analyses your existing smartphone product descriptions, understanding the different ways in which “battery life” has been described. It then generates a standardized template, such as: “Battery Life: [X] hours of talk time, powered by a [Y] mAh battery.”
The AI then ensures that if a specific brand emphasizes battery longevity, the description might include: “Battery Life: [X] hours of extended talk time, supported by an efficient [Y] mAh battery, ideal for heavy users.”
The template is then applied across all smartphone listings, updating every product’s battery description to match the new standardized format. As new smartphone models are added, the AI continuously updates its template to include new technologies or industry terms, keeping the descriptions both standardized and up-to-date.
Example
Hexaware partnered with a leading global home improvement wholesaler to enhance product descriptions across 19 departments and over 3,000 subclasses. By implementing a cloud-based Generative AI solution leveraging Google’s Palm2 model, they automated the creation of detailed, tailored descriptions optimized for search and discovery. This approach enriched product data with metadata, derived attributes like features and specifications, and reduced manual effort. The result was improved description quality, consistency across platforms, better customer engagement, and increased operational efficiency.
Use case 4-Dynamic content personalization
The older style of content personalization, which is an integral part of content production, relied on showing pre-configured content to a rules-based segment of the audience. These rules would have to be pre-defined and then a matching content description created for it.
Generative AI technologies make this completely dynamic. Instead of using rules to identify segments, the tool can automatically infer a user’s choice or preference based on a combination of real-time browsing data, demographics, past purchase history, and many other such parameters.
Example: Customer Profile:
- Demographics: A 30-year-old male, living in a cold climate region.
- Behavior: Frequently purchases outdoor gear, with a focus on hiking and winter sports. Recently browsed through the store’s collection of winter jackets.
Generic Product Description (for a winter jacket):
This high-quality winter jacket is made from durable materials, featuring a waterproof shell and insulated lining for warmth. Available in multiple colors and sizes.
Personalized product description
Stay warm and adventurous this winter with our rugged, all-weather jacket designed specifically for outdoor enthusiasts like you. Perfect for tackling harsh winter hikes, this jacket features a waterproof shell that keeps you dry in snowy conditions and a premium insulated lining that locks in warmth even in the coldest temperatures.The deep pockets are ideal for storing your hiking essentials, and the adjustable hood provides extra protection against the wind. Choose the classic forest green to blend seamlessly with your natural surroundings on your next trail.
So how did this work? The answer lies in how the behavioral segment is identified in a Generative AI vs. a rules-based environment. In the latter, we would have to manually create a rule that included all pages for outdoor gear and in particular, the specific pages for hiking and winter sports.
On the other hand, in the Generative AI approach, the tool automatically identifies pages that relate to hiking and winter sports. We simply need to feed all the pages that the visitor has seen and then the tool automatically draws inferences.
Not only can this greatly simplify tagging complexity, but this approach is also far more dynamic and can create personalized content for all sorts of behavioral segments without any prior configurations.
Example
Hexaware partnered with a leading global home improvement wholesaler to enhance product descriptions across 19 departments and over 3,000 subclasses. By implementing a cloud-based Generative AI solution leveraging Google’s Palm2 model, they automated the creation of detailed, tailored descriptions optimized for search and discovery. This approach enriched product data with metadata, derived attributes like features and specifications, and reduced manual effort. The result was improved description quality, consistency across platforms, better customer engagement, and increased operational efficiency.
Use Case 5-Create product images based on product descriptions
Generative AI models such as Stable diffusion, Midjourney, and DALL-E 3, which are trained in text-to-image generation, can be used to generate images and videos across multiple languages. Here is how this works:
1. Input Processing
- Text Analysis: Start with a detailed product description that includes attributes like color, size, shape, material, and any distinctive features (e.g., “a five-seater budget sofa set made of fabric and available for express delivery”).
- Key Features Extraction: Use natural language processing (NLP) to extract key features and attributes from the description. This helps the AI understand what elements need to be represented in the image.
2. Text-to-Image Generation
- Generative AI Model: Utilize a generative AI model, such as DALL-E or similar, that has been trained to create images from text descriptions. The model uses the extracted features to generate an image that matches the product description.
- Image Composition: The AI combines visual elements, such as color, texture, and shape, to create a coherent image that visually represents the product. For example, the AI would generate an image of a blue t-shirt with the correct fabric texture and design.
3. Customization and Refinement
- User Feedback: Allow for feedback loops where users or designers can tweak the image generation by providing additional details or corrections. For instance, if the shirt color is slightly off, the user can adjust the description to refine the output.
- Style Adaptation: The AI can adapt the generated image to different styles (e.g., realistic, cartoonish) depending on the intended use, such as for e-commerce listings or marketing materials.
4. Quality Assurance
- Validation: Implement automated checks to ensure that the generated image accurately reflects the product description. AI models can compare the generated image against the description to verify consistency.
- Human Oversight: In some cases, especially for complex products, human designers might review the generated images to ensure they meet quality standards before publication.
5. Deployment and Use
- E-Commerce Integration: The generated images can be automatically uploaded to e-commerce platforms, ensuring that even products without existing photos have a visual representation.
- Marketing and Advertising: Use these images in ads, social media posts, or promotional materials to visually showcase products.
Example:
| If you input the description: “A modern, sleek black office chair with a high back, padded armrests, and chrome legs,” the AI will generate an image of an office chair matching this description, complete with the specified features and design elements. |
Benefits:
- Time Efficiency: Quickly create product images without the need for photoshoots, especially useful for new or prototype products.
- Cost Savings: Reduces the costs associated with photography, especially for large inventories.
- Flexibility: Easily update images as product descriptions or features change.
This approach allows businesses to maintain a visually consistent and attractive product catalog, even when images are unavailable.
Example
Ghawali, a modern oriental perfume brand, successfully transformed their product imagery using ProductAI. Facing challenges with traditional photo shoots that were time-consuming and costly, Ghawali partnered with ProductAI to generate modern, minimalistic images reflecting their brand’s essence. By uploading their product catalog to ProductAI, they swiftly produced new images in various sizes and aspect ratios tailored for their webstore and social media platforms. This approach led to a tenfold reduction in content production costs and refreshed their entire product catalog in under a week.
Final Words
In conclusion, the integration of generative AI in product content production and management is not just a technological upgrade but a strategic transformation to make things easier, faster, and simpler.
The use cases outlined above require a fair degree of data maturity and a focused generative AI strategy with clear prioritizations and defined business outcomes. From a pure technology perspective though, the future of product content management is here, it is intelligent, dynamic, and endlessly creative.
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