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AI, online shopping, and the importance of good product data

AI is changing just about everything, including online shopping. It's enhancing personalization, convenience, efficiency, and security, and ultimately providing a more seamless and enjoyable shopping experience for consumers, and a more efficient, secure and profitable outcome for sellers. There's now visual searches, chatbots, in-store-like experiences using virtual assistants, augmented reality with virtual try-ons, the ability to do voice commerce, dynamic pricing, stronger fraud detection and security, supply chain improvements, and streamlined product information and digital asset management.

 One point missed by almost all commentators is the importance of having great product information in the first place. Because AI systems rely heavily on data - for training and decision-making. When these systems are fed poor quality or incomplete product data, several challenges can arise:

Inaccurate Recommendations: AI-driven recommendation systems rely on product information to suggest relevant items to users. If product information is incomplete or inaccurate, recommendations may be less effective, leading to lower conversion rates and customer dissatisfaction.

Misleading Search Results: Search algorithms use product attributes and descriptions to retrieve relevant search results. Poor product information often leads to irrelevant or misleading search results, frustrating users and impeding their ability to find the products they are looking for.  This is magnified once AI generated results enter the mix.

Difficulty in Product Categorisation: E-commerce platforms often categorise products into various categories and subcategories to facilitate browsing and navigation. Poor product information can make AI-generated categorisation challenging, resulting in products being placed in incorrect categories or missing from relevant search results.

 There are other negative impacts that poor quality product data can have on your online sales, too - like, lower search rankings and reduced organic traffic, increased returns and customer enquiries, damage to brand reputation, and exposure to non-compliance and legal scrutiny.

 To address these challenges and benefit the most from AI-enabled e-commerce, merchants, manufacturers, distributors, and wholesalers should prioritise data quality management practices, including:

  • Implementing robust product information management (PIM) systems to centralise and standardise product data.

  • Conducting regular audits to identify and correct inaccuracies or inconsistencies in product information.

  • Providing tools and incentives for vendors and sellers to submit accurate and comprehensive product information.

  • Leveraging AI and machine learning algorithms to automatically validate and enhance product data quality.

  • Offering user-generated content features, such as reviews and ratings, to supplement product information and improve transparency.

 Ascend7 are the product data experts - ask us about PIM systems, AI, and e-commerce set-ups.