Strategies for Effective Product Data Enrichment in Distribution Channels

A recent study by Baymard Institute, an ‘independent web UX research establishment’ indicates that the average online shopping cart abandonment rate is a staggering 70.19%. This phenomenon is closely linked to the quality and relevance of product information presented to customers.

Consequently, product data enrichment emerges as an essential strategy for businesses to thrive (and survive). First, it enhances product data quality through ingestion, cleansing, and standardization. Secondly, it converts this data into accurate, relevant, and fully machine-readable content with rich attributes like product descriptions, pricing details, and digital assets.

Challenges in Product Data Enrichment

However, product data enrichment faces several challenges that enterprises must navigate to ensure customer satisfaction and successful business outcomes. Here are some of the key challenges:

· Poor Quality: Data accuracy and integrity across diverse sources and platforms become hurdles as product data moves from suppliers to partners, employees, and other external entities.

· Inconsistent Data: Maintaining uniformity and eliminating obsolete data in product information across multiple channels can pose problems when various individuals across the value chain alter the information.

· Fragmentation and Silos: As businesses grow, managing many products with different SKUs across geographies and marketplaces and integrating data from disparate systems and sources becomes cumbersome.

· Compliance Issues: Industry-specific guidelines dictate best practices for eCommerce operations, such as product labeling requirements, shipping regulations, and ethical business practices. Inconsistencies can pose legal issues and further hamper the business.

Critical Strategies for Effective Product Data Enrichment

Product data enrichment is beneficial in identifying profitable products and SKUs, positioning them strategically within product catalogs, streamlining product content, and optimizing taxonomies to optimize the supply chain. This efficiency allows for more favorable discounts and deals while empowering product teams to focus on creating better strategies.

Here are the top strategies that one must employ to maximize business outcomes.

Middleware Integration: Implementing a middleware layer integrated with PIM bridges internal systems like inventory management, payment gateways, customer relationship management (CRM), and external platforms like eCommerce websites and marketplaces. By streamlining data exchange, businesses can ensure that product information remains consistent and up-to-date across all channels. When coupled with PIM, middleware integration enables omnichannel publishing, streamlines workflows, and enhances operational efficiency, ultimately leading to enhanced customer experiences and improved sales. For instance, a retail store with an online and offline presence implements a middleware integration solution to seamlessly connect its internal systems, external platforms, and sales channels. It is a central hub facilitating data exchange and synchronization across the value chain.

Automation Tools: Leveraging automation tools via a PIM solution can efficiently manage product data from various sources. These tools, powered by artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), automates processes such as data retrieval, validation, updating, and enrichment. By eliminating manual tasks, eCommerce businesses can save time and resources while improving data accuracy and relevance. Automation also enables real-time updates, ensuring customer access to the most current product information, enhancing the shopping experience, and boosting conversions. This is especially helpful when managing vast catalogs, employing automated email-marketing platforms, introducing chatbots and virtual assistants, enabling dynamic pricing software, etc.

Enhanced Content: PIM implementation enables enriched product descriptions with rich media, such as high-quality images, videos, and interactive elements, and enhances product visibility and engagement. Additionally, compelling marketing text managed through PIM highlights vital selling points and benefits, capturing the attention of potential buyers and guiding them toward a purchase. Enhanced content improves the customer experience, differentiates products from competitors, and drives sales. These can range from marketing texts, size charts, and virtual try-ons to a 360-degree view that allows customers to examine products from all angles. At the same time, detailed specifications offer crucial information about features and dimensions. 360-degree views come in handy when purchasing furniture and other home decor items.

User Behavior Analysis: Analyzing user behavior and feedback provides valuable insights into customer preferences and purchasing patterns. By monitoring how customers interact with product listings, businesses can identify which attributes and content resonate most with their target audience. When combined with PIM, eCommerce players can use this insight to tailor product data accordingly, ensuring that offerings meet customer needs and expectations. They can continuously adapt and optimize product data based on user behavior, enhance relevance, drive engagement, and foster customer loyalty. For example, tools like heatmap monitoring and shopping cart abandonment analysis can tap into website interactions and shoppers’ behavior across different channels.

Data Enrichment Tools: Data enrichment tools and services help augment and verify product data with additional information from third-party sources. A PIM solution provides these tools to enhance data accuracy and completeness by providing access to external databases, market research, and industry-specific information. Moreover, data enrichment tools enable businesses to stay updated on industry trends and regulatory changes, ensuring compliance and mitigating risks. These tools help with different features like data quality and validation, image recognition and tagging, attribute extraction, social media listening, and more – combining technology such as AI and AR to derive business insights and increase conversions.

AI-Assisted Optimization: Applying AI algorithms for rich content optimization enables businesses to automatically generate persuasive product descriptions and prompts. PIM uses these algorithms to analyze data trends, customer preferences, and market dynamics to create personalized and compelling content. By leveraging AI-assisted optimization, eCommerce businesses can efficiently scale content creation efforts, reduce manual workload, and deliver tailored customer experiences. This integrated approach enhances product discoverability and improves search rankings. For example, Amazon’s recommendation system uses collaborative filtering and natural language processing (NLP) algorithms to suggest products based on past browsing history, past purchases, and similar customers’ behavior. Similarly, tools like search relevance, ranking, and ad targeting also help navigate seamlessly through the technology-first world of eCommerce.

Elevating eCommerce Through Data Enrichment

Effective product data enrichment strategies are business-critical for eCommerce success. As customer expectations evolve, businesses must prioritize optimizing product data accuracy, relevance, and engagement across all channels. A robust PIM system that serves a single source of truth can manage and enrich product data, ensuring interoperability and adaptability across channels and geographies.

Learn how our Pimcore Solutions can help your business in product data enrichment.

Source: Happiest Minds Technologies