Reduce eCommerce Returns with Product Data Management

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Product data management: Ecommerce has experienced significant growth in recent years, and 2022 was no exception. Despite the challenging circumstances, eCommerce sales reached $1.3 trillion in the US alone. However, with the increase in online purchases, comes an increase in returns. In 2022, ecommerce companies had to contend with a staggering $212 billion worth of returned items, which equated to an average return rate of 16.5% (Shopify).

These numbers highlight the significant impact that returns can have on ecommerce companies, making it critical for them to identify methods to reduce returns organically.

And when we say organically, nothing comes closer to the heart of the matter than data. Is there a way to leverage product data to reduce returns and win over happier, more loyal customers? Read on to find out:

Automate product onboarding to boost data quality

To provide a seamless online shopping experience and support customers in making informed decisions, retailers rely on manufacturers and distributors to provide accurate and comprehensive information. This information may include specifications, images, and user manuals.

Companies can greatly enhance product information accuracy and completeness by establishing strong data cooperation with suppliers and sourcing high-quality data deeper into the supply chain. That said, according to a study by ANACONDA, data teams spend as much as 45% of their time in onboarding and enriching data.

This is where automation can be a gamechanger. Leveraging automated product onboarding, such as a product information management (PIM) system with an onboarding portal or a product data syndication portal, can be a vital link between manufacturers and retailers to ensure the quality of product data.

The appropriate integration enables the transfer of trusted data from suppliers, data pools, and content service providers to the eCommerce platforms. Additionally, it can automate data transformation to eliminate data exchange challenges across different models.

Leverage Data Governance

Brands and retailers who have effectively managed their product data typically have robust data governance practices in place to ensure streamlined and consistent processes throughout the organization. To ensure data accuracy and quality, automated data checks and pre-defined approval checkpoints must be implemented before publishing to websites and other sales channels. And this is where data governance becomes crucial.

Data governance is the building block of data management. It comes as no surprise that the data governance market is projected to grow to USD 6.04 billion by 2026. So how does data governance help? The mechanism works especially well when safeguarding product information parameters and ensuring data quality before publication.

With governance, organizations can execute data modeling, describe data processes, enforce adherence to data standards, and establish accountabilities to ensure data flows appropriately with the necessary approvals. It helps organizations improve the quality and consistency of their product information.

Unleash the Power of Analytics

Organizations need to analyze their product information in a comprehensive manner, rather than focusing on individual details, to be able to manage a vast assortment of products effectively. This allows them to gain a broader perspective of completeness across vendors, categories, and different data views.

Establishing minimum attribute completeness rules enables retailers to compare metrics against baselines at different levels of the data hierarchy, prioritizing further enrichment tasks.

Incorporating embedded analytics into a data management practice can enhance its capabilities and enable users to derive actionable insights, respond quickly to market trends, and improve collaboration through unique and rapid insights.

Companies can analyze and blend master data with other data sources, such as sales, inventory, clickstreams, IoT, and social media, to gain deeper insights into their business operations. It is recommended to merge and blend data across domains for optimal results.

AI all the Way!

Companies can use artificial intelligence (AI) to automate data enrichment and product classification processes, improving customer experiences with better product data. In fact, AI is already making big inroads in eCommerce already. AI-powered eCommerce solutions are expected to be worth USD 16.8 billion by 2030, so it’s safe to assume that AI will be a mainstay in the digital commerce landscape.

Data Intelligence powered with AI and machine learning (ML) can validate and classify items into the right categories and segments, ensuring product listings are accurately placed within a hierarchy. Automated data enrichment not only provides higher data quality but also lightens the workload for data management and product merchandising teams, enabling them to focus on more value-added projects such as creating a better customer experience, drive online sales and fewer product returns.

However, AI requires a data management system as a supporting framework. A data management solution provides the rules for which data is important in a business context. The combination of data management and AI is mutually beneficial, with AI providing speed and data management delivering data governance and accessibility, ensuring that AI delivers useful outcomes that support business goals.

Leverage Blue Meteor PCC to Reduce Returns with the Power of Data Management!

Blue Meteor’s Product Content Cloud (PCC) is a customizable and composable solution that allows organizations to take control of their product content value chain through data, technology, and strategic expertise. By incorporating data intelligence and automation, Blue Meteor provides end-to-end content management solutions to deliver engaging online product experiences at the speed of market demands.

With Blue Meteor PCC, organizations can reduce product returns by ensuring accurate and complete product information, leveraging AI to automate data enrichment and product classification processes, and utilizing embedded analytics to gain insights into product data and prioritize enrichment tasks.

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