For Shopify Plus stores, managing tens of thousands of SKUs and product catalogs can be a huge challenge.
For instance, inconsistent tagging, incomplete product descriptions, and fragmented taxonomies can lead to poor product discoverability, increased bounce rates, and low sales.
Is there a solution?
AI-driven product tagging and Product Detail Pages (PDP) enrichment can transform product catalog management.
In this blog, we deep dive into AI product tagging for Shopify, the problems it solves, why it is critical for the growth of Shopify stores, and how to implement it.
TL; DR
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Large Shopify Plus catalogs suffer from inconsistent product tagging, manual enrichment bottlenecks, and incomplete PDP content. This leads to poor navigation, weak search functionality, higher bounce rates, and lost revenue.
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AI-powered product tagging and taxonomy enrichment automates product classification and tagging at scale, replacing error-prone manual processes with intelligent taxonomy that adapts across thousands of SKUs in minutes instead of hours.
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AI-driven enrichment improves product discovery, strengthens SEO, saves hundreds of hours, and reduces returns.
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AI product tagging for Shopify can generate product descriptions, highlights, FAQs, and localized content while maintaining brand voice consistency plus optimizes metadata and schema for search engines across your entire catalog.
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Merchants can implement AI taxonomy through Shopify automation tools, APIs, Shopify Flow workflows, and PIM integrations with expert partners like XgenTech designing custom enrichment workflows tailored to your vertical.
Product taxonomy and PDP problems in eCommerce stores
Product taxonomy refers to the hierarchical organization of products into categories and subcategories. Ecommerce stores face a lot of challenges with taxonomies and PDP. Here are some of the common problems:
1. Inconsistent product tagging
Different team members might apply different tags to the same products across different pages. Inconsistent product tagging can create taxonomies that can be hard to sort, and can lead to poor search results.
2. Manual enrichment leading to errors
Manually writing product descriptions and optimizing SEO for thousands of products consumes time and can lead to errors. This can lead to missed optimization and search opportunities.
3. Product discovery suffers
When shoppers use filters to search products but the products are not tagged correctly, discoverability suffers. For example, customers looking for size ‘L’ might not be able to find their preferred size via search. This leads to shopper frustration.
4. Poor SEO performance
If products are entered with poor SEO, relevant products might not appear in searches. Incomplete and incorrect metadata limit visibility. Moreover, this can mean your competitors are attracting these shoppers.
What is AI product tagging in Shopify?
For tagging products using AI, the system uses machine learning (ML) models to automate classification, categorization, and tagging products. ML models use multiple data inputs from various sources, such as product titles, descriptions, images, etc., to tag products.
Unlike manual product tagging, AI analyzes product characteristics to apply consistent taxonomies across eCommerce catalogs with AI. ML systems learn to recognize relationships between product attributes. These systems also cross-reference text for better relevance.
For example, AI can automatically assign color variants, identify style attributes, such as ‘street style’ or ‘minimalist’, and determine use cases, such as ‘formal’ or ‘casual’, etc. Each product might have 15 to 20 tags across different taxonomy hierarchies.
Why is automated product enrichment important for eCommerce growth?
Here are some benefits of AI-based product tagging on Shopify stores and why you must invest in automated product enrichment:
1. Consistency
One of the biggest benefits of automated product enrichment is consistency across catalogs. When done manually by human teams, there is a high level of inconsistency and variations. AI systems ensure uniformity across SKUs, and taxonomy hierarchies.
2. Search and filtering capabilities
Proper enrichment improves search and filtering capabilities on site. When products have complete and consistent details, search results become more accurate. For instance, customers filtering products by size, color, price, etc., receive more accurate results. As product discoverability improves, so do conversions.
3. Strengthened SEO
Automated product enrichment, such as optimized meta descriptions, schema markup for rich snippets, tagged images, and unique descriptions with relevant keywords strengthen SEO. Thus, search engines understand and rank these products better.
4. Omnichannel commerce
For omnichannel commerce, consistent taxonomies and automated product enrichment are crucial. For instance, merchants selling through Shopify, Amazon, eBay, and other marketplaces need to format data in a consistent format. AI can maintain a master taxonomy for eCommerce and adapt for different platforms as needed.
How does AI improve PDP content at scale?
Product detail pages are important as they can make or break conversions. AI product tagging on Shopify and enrichment can be a gamechanger for PDP. Here’s how AI helps:
1. PDP copy enrichment
AI helps with content enrichment for PDP. For instance, it writes unique descriptions highlighting features, benefits, and use cases. Advanced AI systems have the capability to produce engaging copy that does not sound robotic and matches brand voice.
2. Auto-generating product highlights
AI has the ability to identify key product features and highlight them in the product copy. This improves scanability and discoverability of products. For instance, AI can create bullet points, comparison tables, create sizing guides, and FAQs, addressing key customer needs. This improves readability.
3. AI-based SEO optimization
SEO optimization enhances organic visibility of products. AI SEO optimization includes keyword integration based on search volume and relevance. It also includes schema markup implementation for rich snippets in search, meta titles, image optimization, etc.
4. Localized AI PDP content
Shopify Plus stores that want to expand geographically require localization of PDP. For instance, it involves cultural adaptation of product messaging, language, translations, region-specific keywords, currency and measurement adaptations, etc. Shopify AI product tagging for local users can enhance search results.
5. Brand voice consistency
Brands that want to scale often have multiple active channels and platforms. It is essential to have a consistent brand voice across all channels. With thousands of SKUs, maintaining consistency is a challenge. AI models have the intelligence to provide brand-specific tone, messaging, and terminology preference.
What are the biggest risks of manual vs. AI approaches?
Both, manual and AI, have limitations. And it’s important to understand them in order to make the most of both systems.
Risks of manual catalog management
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Accuracy issues because of human fatigue
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Inconsistent application of Shopify AI product tagging standards
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Missed tags and incomplete enrichment during deadlines
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Inconsistent PDP content quality across different categories or time periods
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Scalability limitations that prevent keeping pace with inventory growth
Risks of AI implementation
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AI requires clean data to train effectively, hence its implementation may require initial data cleanup
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Human quality check is essential, especially for regulated industries, such as health and wellness
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Hero products can benefit from human review to ensure quality standards
How can Shopify merchants implement AI taxonomy and PDP enrichment?
Implementing AI taxonomy and PDP enrichment might require different approaches based on requirements. Here are some implementation pathways:
1. Shopify automation AI tools
The Shopify App Store provides multiple solutions implementing Shopify AI tools. Some of the apps include Taggo, Tagshop AI, and various PIM solutions for AI-based tagging and enrichment. These tools require minimal setup and integrate easily with Shopify’s product data structure.
2. Shopify Flow
With Shopify Flow, you can create custom Shopify AI automation workflows. When Shopify Flow is combined with AI tools, it can tag new products, trigger automated product enrichment processes when products enter collections, update metafields, and sync taxonomy changes across products.
3. Integration with product information management systems
Product integration management systems such as Akeneo, Salsify, and Plytix manage enrichment workflows and share consistent information with sales channels. AI has the capability to handle large-scale taxonomy management and enrichment automation.
4. Custom enrichment workflows
Experienced agencies like XgenTech can design custom enrichment workflows. These implementations help improve proprietary AI models trained on specific brand catalogs and complex taxonomy.
What results can merchants expect from Shopify AI product tagging?
AI product tagging has an impact on multiple business metrics. Here are some:
1. AI PDP catalog update velocity
Performing tasks such as tag updates, new collection creation, category restructuring, etc., takes days or weeks. But AI reduces this time to hours or minutes. This enables merchants to respond to market trends faster.
2. Product discovery improvements
When catalogs are properly enriched, the product discoverability improves. Moreover, customers can filter search for more relevant results. Better product discovery leads to customer satisfaction and increased average order value.
3. Cross-sell and upsell recommendations
Adding detailed product attributes can enhance cross-sell and upsell recommendations. Instead of showing generic recommendations, customers see more relevant products. AI-powered recommendations are based on purchase patterns and use cases.
4. Improved SEO rankings
Search engines favor comprehensive and structured product information. Rich snippets generated from schema markup improve click-through rates. AI enrichment increase product page traffic.
5. Decreased return rates
When product descriptions have all details, proper sizing information, and clear images, customers are able to make better and informed purchase decisions. This reduces the return rate of products.
What to look for in an AI partner for catalog enrichment
Hiring the right AI partner for your brand can drive success for your eCommerce store. Here are some factors to consider when hiring an AI partner.
1. Relevant experience
Many AI agencies might have theoretical knowledge but the key is to understand whether they have the ability to apply the knowledge practically. Understand if the potential AI partner has experience with complex, real-world, large-scale projects. Assess if they’ve managed multi-level taxonomies, etc.
2. Shopify Plus architecture knowledge
Your AI partner should have knowledge of Shopify plus integrations and technical implementation capabilities. For instance, they should have expertise in Shopify metafield systems, API rate limits, optimization strategies, headless commerce considerations, integration patterns with common tech stack components, etc.
3. Transparency
When you know what your AI partners are working on, their progress, roadblocks, challenges, it helps build more confidence and enhances systems. Your AI partner should be able to clearly explain how AI models are trained, what is the human review process, how brand voice and compliance requirements are maintained, etc.
4. Proven results
Enrichment requirements vary by industry. It is important that your AI partners have experience and proven results in your industry. They should have tackled projects with similar complexities and goals.
Ready to transform your Shopify Plus product catalog?
Poor product catalogs and PDPs can impact customer experience and profits. AI product tagging for Shopify and PDP enrichment are key processes that every modern and ambitious brand should focus on.
If you want to design a custom AI product tagging and PDP enrichment workflow for your business, reach out to XgenTech.
