Product returns are complicated for online retailers to manage. They not only affect their revenue but also damage the reputation and eventually upset the business growth. There could be more than one reason for product returns and ‘inaccurate product descriptions’ is an important and unavoidable one.
With Product Information Management (PIM), retailers can reduce product returns caused by wrong product depiction. PIM system has various features that help eCommerce brands provide detailed and accurate product data that enable online shoppers to make well-informed decisions before purchase. Let’s explore them, one by one, here.
Automated enrichment rules
Maintaining consistent product information is tough, especially for distributors and retailers dealing with various goods.
Automated enrichment rules in PIM eCommerce are set to standardize and consistently enrich product information. It helps the product and marketing team focus on creating compelling product descriptions and group matching products for ease of search, even when the product data is in bulk (e.g., 1 million products). Overall, automated enrichment rules not only removes human errors but also improves quality and data accuracy.
The screenshot below is the Akeneo PIM dashboard showcasing the various rules set for automatic enrichment. The table consists of four columns: ‘Code,’ ‘Condition,’ ‘Action,’ and ‘Affected products.’ The code refers to the unique name for each rule (eg: camera_set_canon_brand), the condition refers to the criteria to be checked (eg; if the name contains canon) and the action to be taken (eg: then set it to ‘camera_brand’). Finally, there’s a status of affected products, which lists the products that satisfy the criteria set by the enrichment rule.
Online customers rely on the product details page, especially when trying new products. But, when they don’t have sufficient product information such as incomplete product descriptions, lack of sufficient images, no video content about product features and other details that help in their purchase decisions, they may likely return the product after they receive it. PIM’s product completeness features ensure that every product has complete information thereby enriching all product attributes before it goes live across all online channels.
The other advantage of product completeness features in PIM eCommerce is their role in building data governance policies. These policies check if high-quality product descriptions are used and that future product data imports are correct.
In the image, Akeneo PIM dashboard showcases the completeness aspect of a product across every channel. For example, in the eCommerce channel, we have three locales, Germany, English and French and all the three are 100% complete. The rest of the channels, mobile and print are 75% and 92% complete.
Data accuracy is an issue that could bother even after enriching product data and ensuring its completeness. Effects of data accuracy can’t be ignored because it may mislead customers who will more likely return the product costing more to the business while driving customers away.
To avoid data inaccuracy, validation features of a PIM system allow you to check the accuracy of product data compiled from each data source and then the overall data completeness before they get published.
The screenshot shows one of the steps to set validation rule for a particular attribute under settings. For example, if the attribute is contact number, the validation rule should check if all the characters are digits.
Using automated enrichment rules, product completeness and validation, you can enhance the product information in your eCommerce store and avoid wrong or incomplete information that leads to product returns.
Are you looking for an agency to perform PIM eCommerce integration? Ziffity can help. Being an official Akeneo partner, you can bank on our expertise in PIM implementation and reduce product returns significantly.